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Health inequalities and the class system

Elise whitley, gerard mccartney, mel bartley, michaela benzeval.

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What can we learn by analysing different theories of social class?

Inequalities in health by social class have been documented for over a century. We know there are health inequalities in society, with people from less advantaged backgrounds suffering poorer health, for longer, and dying younger than their wealthier peers. Research has also shown that health inequalities can be explained by inequalities in income, wealth and power, by a country’s approach to welfare, and by specific economic and social policies .

However, there are many ways of defining social class and, if we had a better understanding of which particular characteristics were most important in explaining ill health, it could help policymakers to tackle the problems better. We wanted to add to the existing research using Understanding Society because its data are representative of the general population and allow us to look at adults of all ages.

What do we mean by ‘class’?

We examined four definitions of social class:

• French sociologist Pierre Bourdieu’s theories of ‘habitus’ and ‘distinction’ – based on the premise that we develop an approach to the world based on our experiences, with habits and skills learned from the situations we spend the most time in. These learnt behaviours give us an accent, style of dress, or set of cultural preferences similar to others with the same experiences, allowing us to bond in groups.
• German sociologist Max Weber’s theories of opportunity hoarding and social closure – social groups find ways of blocking others from opportunities such as well-paid work or education, by, for example, excluding people of colour and people with a different education, discouraging people from marrying outside their class, and giving opportunities to people in their own class through nepotism.
• Karl Marx’s theories of exploitation, domination and power relations – Marx focuses on labour markets, and suggests that people who own capital such as land, businesses or real estate can extract profits and control the activities of those without capital.
• Early years – the idea that we are shaped by the family, household and social class that we are born into, and that successive generations are shaped by the one before, meaning that inequalities persist through time.

There are mixed findings on whether social class inequalities widen or narrow over the course of one’s life. Some studies of health inequalities have suggested that inequalities increase as we age , while others seem to show that they decrease . Other work has looked at whether men and women experience class differences differently. Our research asked how much each of these different definitions of class could explain inequalities in different health outcomes, and whether the associations varied by gender and age.

Using the data

We used data from Waves 2 and 3 of Understanding Society, in which participants answered questions about their health – rating it themselves and giving answers to 12-question assessments of physical health (the SF-12 Physical Component Summary), mental health (the SF-12 Mental Component Summary), and psychological distress (the General Health Questionnaire-12, or GHQ-12). Some of our participants also gave us blood samples and had a nurse health assessment, giving us objective health data, or ‘biomarkers’. These allow us to calculate an allostatic load score – a measure of cumulative stress on the body, which captures information on five physiological systems (endocrine, inflammatory/immune, metabolic, cardiovascular and kidney function) and calculates a cumulative score based on whether people’s biomarkers for each system are in the worst quarter given their age and sex.

We measured social class with different questions from the survey.

• Bourdieu’s definition: Whether survey participants take part in cultural activities such as reading, writing, playing music, going to exhibitions, galleries or classical concerts, and volunteering or giving to charity.
• Weber’s definition: Respondent’s educational qualifications, income and what’s known as individual occupational social class – a measure that combines job security, promotion opportunities and how much control a person has over their own and other people’s work (professional and managerial occupational classes have the most favourable work conditions in these respects, while routine jobs have the least favourable).
• Marx’s definition: Does the survey participant own their own home and/or car? Do they have income from property and capital, and not just employment? We also measure their autonomy based on how much they agree with the statement “What happens in life is beyond my control”.
• Early years definition: The age at which the respondent left school combined with their parents’ job and level of education.

Over half of the participants – more than 21,000 people – gave complete data for all our social class theory measures, and 5,000 also gave allostatic load data.

Whichever definition of social class we considered, we found that people with a more advantaged social position had:

• better self-rated physical and mental health
• lower levels of psychological distress
• lower allostatic load.

The strongest associations for self-rated and physical health were with the Marxist and Weberian definitions of class, and the strongest associations with mental health were those with the Marxist definition. Associations with allostatic load were more consistent across the four definitions, although there was a suggestion of stronger associations with Marxist and Weberian theories.

When we considered gender and age, we found that links between health and class were generally stronger for women and older respondents when the Bourdieusian and Marxist definitions of class were used. Physical health links with all social class measures were stronger among those aged 50 or more.

What have we learnt?

Previous research has generated similar findings to ours. For example, twenty years ago, one paper suggested that economic inequality was more important for health than social capital . In the intervening years, autobiographies such as Didier Eribon’s Returning to Reims and Damian Barr’s Maggie and Me have also talked about the interlinked nature of social class mechanisms and structural discrimination.

Our work is further evidence that social class across the full lifecourse has a marked impact on health inequalities. When we used the early years definition of class, there were links with health outcomes even in the oldest groups of people. This research also highlights the importance of recognising the differential impact of different social class mechanisms to different groups, for example those defined by gender and age.

For policymakers interested in reducing social and health inequalities, the finding that the Marxist mechanisms of exploitation and domination have the largest impact suggests that policy should take the structure of the economy into account. Ownership of capital, inequalities in power, and the control different social class groups have over their economic life will therefore be important.

The problem of young people unable to get onto the housing ladder and facing increasing rents, for example, has led to calls for better economic democracy and community wealth-building and for inclusive or wellbeing economies. Policy can also address Weberian ideas of social closure whereby the advantages enjoyed by those who are privately educated could also contribute to health inequalities.

Read the original research

Elise Whitley is a Medical Statistician in the MRC/CSO Social & Public Health Sciences Unit at the University of Glasgow

Gerard McCartney is Professor of Wellbeing Economy in Sociology at the University of Glasgow

Mel Bartley is Professor Emerita of Medical Sociology at the Institute of Epidemiology & Health Care at University College London

Michaela Benzeval is Professor of Longitudinal Research and the Director and Principal Investigator of Understanding Society

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• Published: 28 October 2020

Theory driven analysis of social class and health outcomes using UK nationally representative longitudinal data

• Welcome Wami 1 , 2 ,
• Gerry McCartney 3 ,
• Mel Bartley 4 ,
• Duncan Buchanan 5 ,
• Ruth Dundas 1 ,
• Srinivasa Vittal Katikireddi 1 ,
• Rich Mitchell 1 &
• David Walsh   ORCID: orcid.org/0000-0002-3390-5039 6  

International Journal for Equity in Health volume  19 , Article number:  193 ( 2020 ) Cite this article

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Social class is frequently used as a means of ranking the population to expose inequalities in health, but less often as a means of understanding the social processes of causation. We explored how effectively different social class mechanisms could be measured by longitudinal cohort data and whether those measures were able to explain health outcomes.

Using a theoretically informed approach, we sought to map variables within the National Child Development Study (NCDS) to five different social class mechanisms: social background and early life circumstances; habitus and distinction; exploitation and domination; location within market relations; and power relations. Associations between the SF-36 physical, emotional and general health outcomes at age 50 years and the social class measures within NCDS were then assessed through separate multiple linear regression models. R 2 values were used to quantify the proportion of variance in outcomes explained by the independent variables.

We were able to map the NCDS variables to the each of the social class mechanisms except ‘Power relations’. However, the success of the mapping varied across mechanisms. Furthermore, although relevant associations between exposures and outcomes were observed, the mapped NCDS variables explained little of the variation in health outcomes: for example, for physical functioning, the R 2 values ranged from 0.04 to 0.10 across the four mechanisms we could map.

Conclusions

This study has demonstrated both the potential and the limitations of available cohort studies in measuring aspects of social class theory. The relatively small amount of variation explained in the outcome variables in this study suggests that these are imperfect measures of the different social class mechanisms. However, the study lays an important foundation for further research to understand the complex interactions, at various life stages, between different aspects of social class and subsequent health outcomes.

There is an extensive literature on social class and its associations with health inequality. However, often this is based on simply ranking populations (for example, by occupation-derived measures of social position) to describe differences in health. Therefore there is a clear, requirement to fully understand the complex social and economic relationships between different population groups, and how those manifest themselves in inequalities in different health outcomes [ 26 , 33 ]. This is particularly important in order that the causal processes generating and perpetuating health inequalities are understood, and, so that corrective policies can be introduced [ 29 ]. Some studies have previously considered the empirical relationship between measures of social class and health, going beyond the use of social position measures to simply expose inequalities. For example, measures of working class power were found to be a better explanation of lower infant mortality, low birthweight and nonintentional injury rates compared to social capital measures [ 28 ]. Using Scottish data, it was found that occupational social class explained more of the variation in mortality outcomes than educational attainment, which the authors described as proxies for material and cultural mechanisms respectively [ 15 ]; and a study comparing different social position measures and mechanisms using English data found independent, strong, relationships across material, occupation and cultural mechanisms with a wide range of health risks such as blood pressure and obesity [ 3 ]. Finally, some other contributions have sought to place social class relations in the context of political economy more broadly [ 36 ].

Building on the work of Wright [ 42 , 43 ], a recent paper proposed a new integrated model of such a ‘theorisation of class relations’ [ 26 ]. This proposes interacting and interdependent social class mechanisms, which represent different ways in which the class structure of societies generates differential experiences and impacts. Note that within this model social closure and opportunity hoarding, as well as the social background and early life circumstances of social groups, lead to the relative position of social groups. The five types of social class mechanism proposed within the paper can be summarised as follows:

Social background and early life circumstances: this is the intergenerational exposure to social class mechanisms and the differential opportunities this confers from birth (or even before), relating to exposures and position of people’s ancestors. It also represents the potential ‘critical period’ exposures to impacts on health after substantial lag times. As such, this mechanism encompasses the impacts of the other mechanisms detailed below in terms of the exposures individuals and groups experience when they are young.

Habitus and distinction: this theory was first described by Bourdieu [ 7 ], and is defined as the ways in which different social classes display cultural markers which differentiate each from one another. These markers are usually formed in childhood and often outlive changes in economic circumstances. Examples include accents, ways of dressing and knowledge of cultural references (sport, theatre, television, history, etc.).

Exploitation and domination: the processes through which some social classes control the lives and activities of other classes (domination) and acquire economic benefits from the labour of others (exploitation), as first articulated by Marx [ 25 , 43 ]. This social class process is therefore focused on the waged economy and how class groups are treated differently within that setting. Social groups find themselves in this position due to the power relations and legal rules to which they are exposed (e.g. the relative strength of trade unions within a workplace, and trade union legislation more generally, can have a substantial influence on the extent to which exploitation and domination occur), because of the processes of social closure and opportunity hoarding, and due to their social background and early life circumstances.

Location within market relations: describes how some social groups can maintain their advantageous economic position over others, primarily through their position within the labour market and the pay differentials this confers. This means that they have greater financial resources to use for consumption and to obtain revenue flows through interest, dividends, economic rents and profits (e.g. through savings, share ownership and housing rental). As with ‘Exploitation and domination’ above, social groups find themselves in this position due to the processes of social closure and opportunity hoarding, as well as their social background and early life circumstances [ 39 ].

Power relations: describes the ability of members of different social groups to control their own affairs and those of others – thereby incorporating all of the other social processes described above [ 20 , 24 ]. This includes the different sources of power, the form the power takes (including the social relations involved), the positions of power and the social relationships this confers, and the social spaces in which these power relations occur ([ 27 ] et al., forthcoming).

The connections between these different aspects of social class are shown in Fig.  1 .

A representation of class relations to explore the different class mechanisms which explain inequalities in health outcomes. Figure adapted from: Mccartney, G., Bartley, M., Dundas, R., Katikireddi, S. V., Mitchell, R., Popham, F., Walsh, D. & Wami, W [ 25 ]. Theorising social class and its application to the study of health inequalities. SSM - Population Health, https://doi.org/10.1016/j.ssmph.2018.10.015

As with any such model, there is a need to test the extent to which the theory is supported by empirical data. The overarching aim of this study was to assess the extent to which this could be done using an appropriate longitudinal data set. Having longitudinal data is important because the various mechanisms often involve processes such as early life social advantage and acquisition of habitus that take place over the life course.

Specifically, the research questions were:

How effectively can longitudinal cohort data be mapped to the different social class mechanisms included in the model?

To what extent are the measurable aspects of the different social class mechanisms able to explain differences in a range of health outcomes? And to what extent does their explanatory power differ in relation to different outcomes?

Study population and selection of variables

The selected data set was the National Child Development Study (NCDS), also known as the 1958 British birth cohort study [ 31 ]. It was chosen as it is broadly representative of the wider British population, contains a broad range of relevant data, and has accrued many years of follow-up. The NCDS started in 1958 with a baseline cohort of more than 17,000 babies born in Scotland, England and Wales in one week of March 1958 [ 31 ]. The initial survey at birth has been followed by ten repeated follow-ups in 1965, 1969, 1974, 1981, 1991, 2000, 2003, 2004, 2008 and 2013 aimed at monitoring the health, development and social circumstances of the cohort members Footnote 1 [ 12 ]. The study has collected detailed information on child, adolescent and adult social, economic and health-related circumstances over 50 years.

Building on the previous theoretical work regarding the links between different social class processes [ 26 ], we undertook an empirical analytical approach using indicators from the NCDS to test the potential utility of these theoretical class mechanisms in explaining health outcomes across populations. We took a pragmatic approach to provide a parsimonious best-fit of the available measures to the mechanisms presented in Fig. 1 . As a first step, the NCDS’ data dictionaries were systematically examined to identify all potentially relevant measures of class. The quality and completeness of each chosen variable was then assessed, with some variables combined across different waves to create a single measure, as shown in Table  1 . The sex variable (male/female) was included under each of the different aspects of social class theory. The data for all NCDS waves used in this study were sourced from the UK Data Service [ 12 ].

Health outcome variables

The main outcome variables in our study were based on the Short Form (SF-36) Physical functioning, Emotional well-being, and General health domains measured at age 50, analysed separately as continuous variables for each social class theory. Briefly, SF-36 is a multi-purpose health survey instrument comprised of 36 questions, yielding a profile of functional health and well-being scores [ 32 ]. Each of the scales are scored between 0 and 100, with higher scores indicating better health [ 32 , 38 ]. The three SF-36 domain scores selected as outcomes for this study were described within the NCDS as follows: Physical functioning score: - the lowest possible score indicated very limited physical activities, and the highest possible score indicated the individual was able to perform all types of physical activities without any limitations due to health; Emotional well-being score :- the lowest score indicated feelings such as nervousness and depression most of the time, and the highest score indicated good feelings such as being happy, peaceful and calm all the time; General health score: the lowest score indicated poor health and personal belief it was likely to get worse and the highest score evaluated health as excellent [ 21 ].

Analytic sample and handling of missing data

The analytic sample was composed of 8787 individuals: these are cohort members who had at least one valid response for any of the SF-36 health domains measured at age 50 (in 2008). The percentage of male and female survey participants was 48.1 and 51.9%, respectively. Of these, only a very small proportion had missing data for the outcome variables investigated in this study: 0.2% ( n  = 17) for Physical functioning, 0.3% ( n  = 26) for Emotional well-being, and none for the General health outcome. However, some data were missing (to varying degrees) in relation to some of the explanatory variables shown in Table 1 . Missing data were handled with a combination of multiple imputation and weighting [ 35 ]. Inverse probability weights were used to weight the analysis sample to the baseline sample to correct for bias and unequal sampling fractions across the different cohort survey waves [ 34 ]. The weights were derived from a logistic regression of having valid outcome data on factors associated with drop-out (gender, head of household social class at birth, maternal smoking and parity). Missing data within the analytic sample were addressed using multiple imputation. We created 20 different datasets accounting for SF-36 health outcomes, sex, and the inverse probability weights in the imputation model.

Statistical analysis

Descriptive summary statistics (sample means and standard deviations [SD]) were used to explore the distribution of the three SF-36 health outcomes. For each of the proposed social class mechanisms, a set of three separate multiple regression models were then fitted to assess the extent to which the exposure variables in Table 1 (including sex) explained subsequent health outcomes. Both complete case (results in Additional File  1 ) and multiple imputation analysis (results reported here) were conducted. The extent to which variation in the SF-36 health outcomes across the whole sample was explained by the different sets of predictor variables (i.e. for each social class theory) was assessed by means of the R -squared values ( R 2 ). As one of the aims of the study was to assess how effectively different NCDS variables could be mapped to the proposed social mechanisms, and was not seeking to find a best-fit statistical model, interactions between the explanatory variables were not considered. For each of the weighted regression models that were estimated with multiply imputed data, the combined R-squared values together with their corresponding 95% Confidence Intervals (95% CI) were determined using the Fisher’s r to z transformation technique [ 6 , 19 ]. Adjusted predicted means (least-squares means) [95% CIs] and regression coefficients [SEs], were reported for all exposure variables. The validity of the SF-36 variables within this cohort was checked by means of comparison with the self-assessed general health variable and a range of disease-specific variables. The results are not reported within this paper but are available within the online appendix (see Additional File  2 ). All modelling was carried out in SAS version 9.4 (SAS Institute Inc., Cary, NC, USA).

Mapping of variables to social class mechanisms

There are a number of exposure variables within the NCDS which are potentially relevant for exploring the class mechanisms above. However, as the data were obviously collected for purposes other than an analysis of social class processes, many aspects of class theory were not measured, and many of the available measures could legitimately be applied to multiple mechanisms. Obvious areas where there is a lack of appropriate variables include measures of social distinction (such as accent and the ability to ‘fit in’ to a wide range of social settings), good measures of the ownership of economic capital and the experience of discrimination. No NCDS variables were identified as relevant to the ‘ Power relations ’ theory. The suggested measures included in Table 1 are therefore a pragmatic application of the available data to the different social class mechanisms. We included measures of social closure and opportunity hoarding within theory 4 (location within market relations) rather than analysing separately (this particularly refers to education measures). Arguably these measures could be included in mechanisms 3 (exploitation and domination) and 4, or could have been analysed separately.

Exploring SF-36 health outcome scores

The proportion of cohort members with highest achievable SF-36 score of 100 (ceiling effect) was 36% for Physical functioning, 3% for Emotional well-being and 7% for General health. The majority of these cohort respondents had high SF-36 scores (above 60 points) (data not shown). A small proportion (< 1%) reported extreme poor health i.e. a minimum possible score of zero in the SF-36 scale (floor effect). The overall sample mean SF-36 scores were as follows - Physical functioning: mean = 86.1 (SD = 21.7); Emotional well-being: mean = 75.0 (SD = 18.1); and General health: mean = 68.3 (SD = 22.0). Men tended to have slightly higher scores than women for physical functioning (difference in means = 3.3; 95% CI: 2.4–4.2) and Emotional well-being (difference in means = 3.0; 95% CI: 2.2–3.8), but not General health, scores for which were similar for men and women (difference in means = 1.0; 95% CI: 0.1–1.9). Comparisons of the three SF-36 outcomes with cohort members’ self-assessed health showed that those who rated themselves as having at least good health or much better health (compared to a year ago) had higher mean SF-36 scores compared to those who said their health was poor or much worse (Additional File 2 , Figure S1).

Assessing measurable aspects of different social class mechanisms within the NCDS

Tables  2 , 3 , 4 and 5 shows results of the fully-adjusted regression models for each social class theory, including the corresponding parameter estimates and adjusted mean SF-36 scores predictors included in the models. F-tests of overall significance in the regression analyses are shown in Table S2 (in Additional File 2 ). Overall, there was a relatively small amount of variation explained by the predictors in all the models, as measured by the R 2 statistic. Among the different proposed measures of class theory, ‘ Social background and early life circumstances ’ explained the least variation in the three outcomes: at most only 4% of total variability in the outcome scores. ‘ Location within market relations ’ explained the most variation, although again the total amount of variability explained was low across all models: 10% for Physical functioning, 8% for General health and 7% for Emotional well-being. The following summarises the results for each theory/set of models:

‘Social background and early life circumstances’ class theory:

Results shown in Table  2 and Table S2 (in Additional File 2 ): Physical functioning , R 2   = 0.039; 95% CI: 0.032–0.048, Emotional well-being, R 2   = 0.023; 95% CI: 0.017–0.031; General health, R 2   = 0.031; 95% CI: 0.024–0.038 .

Less advantaged paternal social class (in relation to physical and general health outcomes) and experience of family financial hardships in childhood were found to be associated with poorer adult health at age 50. In addition, access to free school meals, a measure of low family income, revealed a strong relationship with adult health outcomes (Table 2 ). For example, Physical functioning scores were on average 4 points higher among those who came from households who did not qualify for access to free school meals compared with those who did. However, access to household amenities had no substantial influence on adult reported physical or emotional health.

‘Habitus and distinction’ class theory

Results shown in Table  3 and Table S2 (in Additional File 2 ): Physical functioning , R 2   = 0.052; 95% CI: 0.043–0.061, Emotional well-being, R 2   = 0.028; 95% CI: 0.021–0.035, General health, R 2   = 0.029; 95% CI: 0.022–0.036 .

Cognitive ability was positively associated with increased SF-36 scores at age 50. However, participation (voting) in general elections, religious involvement, and trade unions membership were not associated with any notable differences in the health outcomes when compared to non-participation (Table 3 ). For example, frequent attendance at religious meetings (weekly or more), had similar effects on the mean SF-36 health scores compared to those who had no religion, or attended only monthly, or rarely/never attended at all. Future career aspiration, TV watching, tabloid versus broadsheet newspaper readership, or frequency of book readership were not associated with differences in mean health outcome scores.

‘Exploitation and domination’ class theory

Results shown in Table  4 and Table S2 (in Additional File 2 ): Physical functioning , R 2   = 0.060; 95% CI: 0.051–0.070, Emotional well-being, R 2   = 0.052; 95% CI: 0.044–0.062, General health, R 2   = 0.058; 95% CI: 0.048–0.067 .

For this social class theory, the most notable effects were observed in relation to housing tenure and household size. Those who lived in larger homes (i.e. 4 or more rooms, excluding bathroom and kitchen) were associated with higher health scores compared to those in small dwellings (0 or 1 room). However, comparison of the mean scores revealed that these differences were not substantial (on average a difference in means of less than 3 points and overlapping 95% CIs). In addition, adjusting for all other factors, the analyses showed that for housing tenure, those owning property (either outright or with help of mortgage) had slightly better General health compared to those renting or squatting.

‘Location within market relations’ social theory

Results shown in Table  5 and Table S2 (in Additional File 2 ): Physical functioning , R 2   = 0.104; 95% CI: 0.092–0.117, Emotional well-being, R 2   = 0.067; 95% CI: 0.057–0.077; General health, R 2   = 0.083; 95% CI: 0.072–0.094 .

Own social class, educational attainment, and receipt of social security benefits were all associated with the three health outcomes in expected ways. For example: higher social class position (I/II) was associated with better adult health compared to the lower social class position (IV or V/other), having an educational qualification was associated with better health compared to those with no such qualification, and those frequently in receipt of means-tested state benefits had on average worse health outcomes than those who were not. With regard to the latter, particularly large differences in mean health outcomes (12 points or more) were observed between cohort members who did not receive any benefits compared to those in receipt 4 times or more times in adulthood (Table 5 ).

In this study, it was possible to map NCDS variables to different mechanisms about how social class location is determined and experienced. However, this was a pragmatic rather than necessarily accurate mapping. Generally, the mapped NCDS variables explained little variation in adult health outcomes for any of the social class mechanisms. Of the different mechanisms explored in this study, ‘ Location within market relations’ explained the most variation. Nonetheless, despite these negative results, some relevant associations were observed in the analyses, some expected (e.g. lower parental social class and worse self-reported health in later life) and some not expected (e.g. no meaningful association between health outcomes and voting participation, used as a measure of empowerment).

There are similarities between some of the findings of this study and those previously reported in the literature. The low levels of variation in the selected health outcomes that were explained have been demonstrated previously. In a study assessing childhood risk factors on adult health using the NCDS, Dibben and colleagues were only able to explain 3% of total variation in the SF-36 mental score in their linear regression analyses [ 17 ]. Similarly, analyses of other health surveys utilising the SF-36 measures also reported that a relatively small percentage of total variation in the outcomes was explained in their regressions models [ 5 , 22 , 23 , 37 , 41 ]. Potential reasons for this are discussed further below. This research found that various socio-economic disadvantages in childhood had negative effects on reported health in adulthood: this is consistent with the previous research [ 2 , 14 , 33 ].

However, other research has also suggested that participation in political or other social engagements (e.g. voting, trades unions, religious meetings) can have positive effects on adult health [ 1 , 8 ] – findings not replicated here. This may in part be because we have used these variables as markers of different kinds of social participation rather than as means of ranking or sorting the population into groups. Other factors (e.g. ethnicity, attitudes, working patterns) have been cited as having important influences on these types of participation [ 10 ] and these factors were not explored in our analyses as they were not consistently measured.

Strengths and weaknesses

This study incorporated a wide range of measures, with some variables created from a combination of information collected across different survey waves in order to maximise use of available information within the NCDS. Nonetheless, for some measures information was missing. Inverse probability weighting and multiple imputation were employed to deal with missing data and thereby minimise potential bias in our findings. In addition, to further validate the SF-36 measures (i.e. to ensure they are showing the expected association with other health measures) we included additional analyses of the three SF-36 health outcomes in relation to both disease-specific measures and general self-assessment of health. The results showed expected associations.

There were limitations to our study that should be considered when interpreting these findings. The range of measures for each of the social class mechanisms was somewhat limited in their coverage (e.g. we did not have any variable to map to ‘power relations’ and we had very limited measures of ‘discrimination’). Even for those mechanisms that we could map, these are unlikely to fully and accurately portray the true exposure to those aspects of social class. We decided to incorporate the measures we had on ‘social closure and opportunity hoarding’, particularly the education measures, into ‘location within market relations’. Arguably these could also have been incorporated into ‘domination and exploitation’ or analysed separately as their own social class theory. Second, the health outcomes were self-reported. As with other measures, self-reported data contain potential sources of bias. Recorded deaths in this cohort are still very low, hence we could not use mortality as an outcome variable, and we were restricted to self-assessed measures. Although SF-36 captures a wide range of health measures suitable for use in population-based studies [ 11 ], its validity as a long-term health assessment health tool is still questionable [ 9 , 30 ]. Consequently, it is important not to over-interpret small differences in results for the various proposed measures of social class theory reported here. In addition, the presence of ceiling scores (particularly for Physical functioning domain) could potentially distort the underlying true patterns of associations of the health experiences of this study population based on these outcomes [ 11 ]. It may be that in seeking to explain the differences in health outcomes within the population, rather than us trying to explain differences in outcomes between social groups, we encountered too much random variation that would be expected in essentially attempting to explain differences between individuals (as highlighted by [ 16 ]).

Study implications

The implications of this work are multiple. The key finding – that so little of the variation in outcomes was explained by the different sets of social class variables – suggests different potential explanations. First, it may be that we did not have adequate measures of the social class mechanisms within our dataset to explain the differences in health outcomes. Second, by focusing on explaining variation within a population rather than between social groups, our outcomes were largely determined by random variation and thereby not amenable to this approach. Third, that there is little association between the different social class mechanisms and health outcomes across populations. This is clearly not the case, given the wealth of evidence linking different aspects of social class to different health outcomes, including mortality [ 4 , 13 , 18 , 40 ]; however, clearly there is stronger evidence for some mechanisms than others. Fourth, the complex linkages between the different mechanisms (as shown in Fig. 1 ) means that a different analytical approach is required to better reflect that multiplicity of factors. Fifth, the relatively young age of cohort members may be a factor: clearer effects on health outcomes may be observed in future waves of data. Finally, the ‘disconnect’ between the age at which some exposure variables were measured and the age at which the outcomes were measured may also be relevant.

This study has therefore demonstrated both the potential and the limitations of available cohort studies in measuring aspects of social class theory. This is important when it comes to the design of future studies: we need better ways of accurately capturing different experiences of social class, and of analysing them in relation to appropriate outcomes measured at significant points across the life-course. Further research could usefully include: a follow-up study using the same dataset when there are sufficient deaths to analyse mortality outcomes; replication of the study using other cohort studies, and in different countries and contexts; the development and use of better measures for all social class mechanisms, but in particular the power relations theory and location within market relations (especially with a finer grained consideration of job strain and differentiation within the ‘skilled non-manual’ group). The implications for policy include the need to understand the complex, multifaceted, nature of social and health inequalities, and the associated need for a range of appropriate interventions at different levels and in different spheres: for example, the need for a specific focus on early years’ aspects of class (social background) alongside income-related measures (redistribution).

Our findings suggest that measures within the NCDS can be mapped to different measures of social class theory linked to health and health inequalities to a certain degree. However, the relatively small amount of variation explained in the outcome variables suggests that these are imperfect measures of the different social class mechanisms. The study lays an important foundation for further research to understand the complex interactions between different aspects of social class and subsequent health outcomes. In doing so, it also emphasises the need to develop and implement improved survey-based, and other, measures to better capture the intricacies of these social mechanisms.

Availability of data and materials

The datasets generated and/or analysed during the current study are available in the UK Data Service repository, [ https://www.ukdataservice.ac.uk ] [2000032] .

NCDS sample sizes over time: 1958 (birth), N  = 17,415; 1965 (age 7), N  = 15,425; 1969 (age 11), N  = 15,337; 1974 (age 16), N  = 14,654; 1981 (age 23), N  = 12,537; 1991 (age 33), N  = 11,469; 2000 (age 42), N  = 11,419; 2003 (age 45), N  = 9534; 2004 (age 46), N  = 9534; 2008 (age 50), N  = 9790; 2013 (age 55), N  = 9137.

Abbreviations

Advanced Level

Cohort Member

Certificate of Secondary Education

General Certificate of Secondary Education

Least-squares means

National Child Development Study

National Vocational Qualification

National Statistics Socio-Economic Classification

Ordinary Level

Perinatal Mortality Survey

R-squared, the proportion of total variation that is explained by the regression

Social Class

Standard Deviation

Standard Error

Short Form-36 Health Survey Questionnaire

95% Confidence Intervals

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Acknowledgements

We grateful to the Centre for Longitudinal Studies (CLS), UCL Institute of Education, for the use of these data and to the UK Data Service for making them available. However, neither CLS nor the UK Data Service bear any responsibility for the analysis or interpretation of these data. We would like to particularly acknowledge the help of David Bann and Brian Dodgeon of CLS for their ongoing support in extracting the NCDS variables and help in their descriptions as used in this study.

This work was supported by Glasgow Centre for Population Health.

RD, SVK and WW were supported by the Medical Research Council [MC_UU_12017/13; MC_UU_12017/15] and the Chief Scientist Office of the Scottish Government [SPHSU13; SPHSU15]. SVK also acknowledges funding from a NRS Senior Clinical fellowship (SCAF/15/02). RM’s contribution was supported by the Neighbourhoods and Communities research programme (MC_UU_12017/10) at the MRC/CSO Social & Public Health Sciences Unit.

The funding sources had no involvement in study design; in the collection, analysis and interpretation of data; in the writing of the report; and in the decision to submit the article for publication.

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This paper is based on work carried out by a collaborative research team. The team members and their individual contributions were as follows: [WW, GM, RD, DW]: Design, statistical supervision, analysis, drafting of report. [WW, GM, MB, DB, RD, RM, SVK, DW]: Design, epidemiological advice. [WW, GM, MB, DB, RD, RM, SVK, DW]: Coordination, analysis, drafting of report. [WW, GM, MB, DB, RD, RM, SVK, DW]: Design, interpretation. [WW, GM, RD, DW]: Analysis, Data preparation, statistical analysis, drafting of report. [DW]: Principal investigator, design, drafting of report. The authors read and approved the final manuscript.

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Additional file 1: table a1..

Results of complete case regression modelling of SF-36 outcomes for each social class theory. Table A2. (a): ‘ Social background and early life circumstances ’ class theory: least-squares means of SF-36 outcomes from linear regression. Table A2. (b): ‘ Habitus and distinction ’ class theory: least-squares means of SF-36 outcomes from linear regression. Table A2. (c): ‘ Exploitation and domination ’ class theory: least-squares means of SF-36 outcomes from linear regression. Table A2. (d): ‘ Location within market relations ’: least-squares means of SF-36 outcomes from linear regression.

Additional file 2: Figure S1.

Mean SF-36 scores by cohort members’ self-rated health within the NCDS measured at age 50. Error bars indicate 95% confidence intervals. Table S1. Unadjusted mean SF-36 scores by health problem or condition self-reported by cohort members within the NCDS measured at age 50. CM = Cohort Member; SD = Standard Deviation; Lower, Upper = 95% confidence intervals; MH = Mental Health; Doc = Doctor. Table S2. Results of multiply imputed and weighted regression modelling of SF-36 outcomes for each social class theory.

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Wami, W., McCartney, G., Bartley, M. et al. Theory driven analysis of social class and health outcomes using UK nationally representative longitudinal data. Int J Equity Health 19 , 193 (2020). https://doi.org/10.1186/s12939-020-01302-4

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• Health inequalities
• Self-rated health
• Social class theory
• Socio-economic position

International Journal for Equity in Health

ISSN: 1475-9276

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Sociology, Social Class, Health Inequalities, and the Avoidance of "Classism"

Affiliations.

• 1 Department of Sociology, University of Surrey, Guildford, United Kingdom.
• 2 Institute of Population Health, University College London, London, United Kingdom.
• PMID: 33869379
• PMCID: PMC8022477
• DOI: 10.3389/fsoc.2019.00056

Keywords: class/command dynamic; classism; foresight and action sociology; health inequalities; institutional taming; social class.

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Inequalities in health (e.g. by region, ethnicity, soci-economic position or gender) and in access to health care, including their causes

Source: http://www.ons.gov.uk/ons/taxonomy/index.html?nscl=Subnational+Health+Expectancies    

• Behavioural model: There are social class differences in health damaging or health promoting behaviours such as dietary choices, consumption of drugs, alcohol and tobacco, active leisure time pursuits, and use of immunisation, contraception and antenatal services. However, long-term studies (like the Whitehall study described below) have found that differences in health behaviour explain only one-third of social class differences in mortality. Furthermore, evaluations of interventions that seek to change health behaviours have rarely found clear cut improvements in health that would be predicted by the behavioural model.  
• Materialist model: Poverty exposes people to health hazards. Disadvantaged people are more likely to live in areas where they are exposed to harm such as air-pollution and damp housing.  The Black Report (see below) found materialist explanations to be the most important in explaining social class differences in health. There is some specific evidence for materialist explanations. For example, many studies have associated higher rates of childhood respiratory disease with damp housing. The full impact of living standards, however, can only be understood over the course of the life term. While most experts in public health agree that materialist explanations play a role in explaining health inequalities, many find a simple materialist model to be insufficient. In the UK, relatively disadvantaged people receive various kinds of state help (rent, school meals etc) which, some argue, makes diet or poor housing unlikely to account for all inequalities health outcomes. Furthermore, in the UK and internationally, inequalities in health tend to follow a steady gradient, rather than there being poor outcomes for the most disadvantaged and equally good outcomes for the rest of society.  
• Psycho-social model: Social inequality may affect how people feel which in turn can affect body chemistry. For example, stressful social circumstances produce emotional responses which bring about biological changes that increase risk of heart disease. Psycho-social risk factors include social support, control and autonomy at work, the balance between home and work, and the balance between efforts and rewards. There has been a plethora of research exploring associations between psycho-social factors and health. Evidence shows that people who have good relationships with family and friends, and who participate in the community, have longer life expectancies than those who are relatively isolated. Evidence of an association between stress at work and health is less clear, but most well designed studies show a higher risk of heart disease among individuals who work in jobs where demands are high and control is low. Furthermore, a number of studies have shown that an imbalance between effort and reward at work tends to be linked to high blood pressure, fibrinogen and a more adverse blood fat profile.  

A life course approach underpins the recommendations made in the Marmot Review on reducing health inequalities in England. The review states that ‘action to reduce health inequalities must begin before birth and continue through the life of the child. Only then can the close links between early disadvantage and poor outcomes throughout life be broken’. (Marmot review, 2010). Similarly, the Welsh Adverse Childhood Experiences (ACE) Study, 2015) highlights the  impact of adverse childhood experiences on individuals’ risks of developing health harming behaviours in adult life. ACEs are stressful experiences occurring during childhood that directly harm a child (e.g. sexual or physical abuse) or affect the environment in which they live (e.g. growing up in a house with domestic violence). 

• evaluating all policies likely to affect health in terms of their impact on inequalities
• giving high priority to the health of families with children
• the government should take steps to reduce income inequalities and improve living conditions in poor households.  
• Health inequalities must be addressed in the interests of fairness and social justice.
• There exists a social gradient in health: health improves as social status goes up.
• Social inequalities result in health inequalities; therefore to reduce health inequalities we must consider all the social determinants of health.
• Health inequalities cannot be properly addressed by only targeting those worst off. Reducing the steepness of the social gradient in health requires universal actions, concentrated according to levels of deprivation (‘proportionate universalism’).
• Taking action to reduce health inequalities will have a positive effect on society in many ways, such as bringing economic benefits by reducing population illness and increasing productivity.
• A country’s success is measured by more than economic growth: fair distribution of health, wellbeing and sustainability are also important. Climate change and social inequalities in health should be addressed simultaneously.
• Policy to reduce health inequalities must cover all of the following objectives: -  Give every child the best start in life -  Enable all children young people and adults to maximise their capabilities and have control over their lives -  Create fair employment and good work for all -  Ensure healthy standard of living for all -  Create and develop healthy and sustainable places and communities -  Strengthen the role and impact of ill health prevention
• These policy objectives can only be delivered through effective involvement of central and local government, the NHS, third and private sectors, individuals and communities.

Source: http://www.ons.gov.uk/ons/taxonomy/index.html?nscl=Subnational+Health+Expectancies

• Males in the most advantaged areas can expect to live 19.3 years longer in ‘Good’ health than those in the least advantaged areas as measured by the slope index of inequality (SII). For females this was 20.1 years.  
• Males in the most deprived areas have a life expectancy 9.2 years shorter (when measured by the range) than males in the least deprived areas, they also spend a smaller proportion of their shorter lives in ‘Good’ health (70.9% compared to 85.2%).
• Employment: More occupations typically followed by men involve direct risk to life (such as dangerous machinery, weather, environmental hazards, and exposure to toxic chemicals).  
• Risk taking behaviour: Men are more socialised to participate in dangerous sports like motorbike racing, rock climbing etc. Men are at higher risk of road traffic injury and tend to drive more and faster when under the influence of alcohol compared to women.  
• Smoking: In the past, men had much higher smoking rates than women. However, the gender gap between men and women in smoking has narrowed in recent years, particularly in high-income countries.  
• Alcohol: Men drink significantly more than women in all age groups and are more likely than women to exceed their recommended daily alcohol intake.
• Men and women born in the Caribbean have high rates of mortality from stroke. Men born in the Caribbean have low rates of mortality overall and low rates of mortality from coronary heart disease.  
• Individuals born in West/South Africa have high overall mortality rates, high mortality rates from stroke, but low mortality rates from coronary heart disease.  
• Individuals born in South Asia have high mortality rates form coronary heart disease and stroke.  
• Non-white migrant groups tend to have lower mortality rates from respiratory disease and lung cancer but higher mortality rates for conditions relating to diabetes.

Source: Wild and McKeigue (1997:705) in Bartly (2004)

Source: http://www.ons.gov.uk/ons/rel/health-ineq/health-inequalities/trends-in-all-cause-mortality-by-ns-sec-for-english-regions-and-wales--2001-03-to-2008-10/index.html         

• In England and Wales, there were statistically significant decreases in all-cause mortality rates for men across all socio-economic classes between 2001–03 and 2008–10.
• Across the regions, the North West had the highest mortality rates in almost all classes for both sexes for the majority of the 2001–03 to 2008–10 period.
• Conversely, the South East and East regions had the lowest mortality rates in most of the classes for both sexes for the majority of the period.
• Over the same period, the relative inequality increased for both sexes however the absolute inequality in mortality between the Higher Managerial and Professional class (most advantaged) and the Routine class (least advantaged) narrowed.
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• Level of Education .
• Travel distance to facilities is equal.
• Transport and communication services are equal.
• Waiting times are equal.
• Patients are equally informed about the availability and effectiveness of treatments.
• Charges are equal (with equal ability to pay).
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Social Class and Health: Qualitative Research Essay

Introduction, the general area of study, sense of problem, identification of theoretical conceptual framework, literature review, methodology, conclusions, works cited.

The area of public health has been one of the most significant yet underrated areas of public administrative jurisdiction in the UK. Successive governments have tried to enforce progressive Reform measures in this area to reduce anomalies, but with limited success. Despite enjoying a high GDP and income levels for the people, the standards of health care available to citizens are much below that available to people belonging to other countries of the European Union ( EU).

This study is mainly intended to throw light on the impact of social and other classes upon public health systems in the country.

This study is mainly intended to analyze aspects of the various implications of class on health, not only individual health but also public health. The class that is denoted in this study relates to the occupational or working class, or even social classes when related to non-working or young people. As is well known, the gradient of occupation is found in income, and consequently in health- related problems. It is common knowledge that people in the lower socio- economic or lower classes are more susceptible to poor health, or diseases, as compared with high income or affluent groups. Moreover, these segments of society are not always have access to high quality medical attention and care, which exacerbates their conditions and renders them unfit for gainful employment or productive work. The effects of class also affects mortality and lifespan of people in lower strata is of society, since chronic poor health and disease cuts down the life span and accelerates mortality

The right to good health and hygienic working conditions is a need that is fundamental to civilized society and could be, in a wider sense, considered a prerogative of free society to which human beings belong. Conducive working and living conditions foster a sense of well- being and vitality that is intrinsic for good health and personal welfare. The hypothesis of this study is based on the premise that occupation and social class defines the health of society, since occupation is directly linked with income generation. People who are highly educated are in a position to earn higher incomes and generate more wealth than people with little, or no education, and who consequently have to resort to menial or frugal occupations to tend to themselves and their families. Their lack of education have resulted in lower incomes and thus lesser generation of wealth for well-being. While rich people could afford to have substantial savings and higher propensity to generate wealth, this is not possible in the case of poorer and underprovided sections of society, who thus have to resort to lower jobs with lower pays.

People who are from lower classes of society have lower incomes, and are thus not able to access high quality medical treatment in private settings. Moreover, they are not also always covered by private Health Insurance Covers, unlike wealthy and privileged people, and have to resort to public health care systems.

People who are from lower classes, generally have lower wealth generation, as a result of which they are not able to afford quality treatment for their morbid health conditions, as a result of which their sickness and mortality rates may be higher than that of higher classed and wealthier strata’s of society.

This study needs to be seen in the context of reducing the differentiations between the health conditions prevalent among people in society. A person need not be marginalized or provided a lower standard of health care or treatment, just because they happen to belong to the lower strata of society, since the gravity of their health problems are more important and not their income generating capacities.

However, it is indeed sad but true, that good health care facilities and treatment in today’s scenario is mainly concentrated on the rich and privileged classes of society, to the disadvantage of poorer sections of society who have to seek lower and compromising levels of health care treatment and interventions. Moreover, the true state of present conditions can be gauged if one were to consider the fact that HIV/AIDS, coronary diseases, cancers and other serious health conditions are more prevalent among poorer and less literate sections of society.

The true sense of the problem is found in the fact that class determines the level oflving of people in today’s global environment, save under exceptional situations.

This stems from the simple fact that poorer patients cannot afford the high cost of treatment, which could be afforded by wealthier patients from higher social backgrounds.

In today’s health conscious society, medical intervention could mean long stays in health care settings with professional health care services being provided by the institution, diet regimen, exercises, physical therapy and ambient lifestyle with controlled food habits. This could not be possibility to be indulged in by a poorer patient who needs to keep himself occupied, in order to financially support himself and his dependents. Although health care insurance may be available to lower stratas of society, they may not be always adequate to cover the full course of treatment and convalescing period of the patient. Most of the treatment may be done in public health settings, which may thus deny the high quality care and treatment that could be gained in private nursing centres.

What is perceived is a circle of inadequacy stemming from poor education, which leads to lower employability, which in turns leads to lower incomes but high occupational stress leading to poor heath and disease. Under conditions when employability becomes doubtful, the patient has to be without work even after he is discharged from hospital and thus has to depend upon State Aid for livelihood.

The various methods in which health care services discrimination among lower classes of society could be seen in terms of the following:

• Medical discrimination in terms of longer waiting time.
• Lack of equal access to emergency medical care and medical intervention.
• Need for placing money deposits before treatment is commenced and also, lack of continuity in treatment for patients.
• Refusal to treat patients belonging to lower stratas of society on non-recommendation from privileged medical practitioners.

It is intended to carry out the research based on the longitudinal cohort study of around 250 men and same number of women in the age group of 25- 55 working in various capacities working in a corporate setting.

They were medically screened for various types of communicable diseases and found acceptable for the purpose of the research study. Since the purpose of the research is to establish or nullify the hypothesis through qualitative analysis, it is believed that the respondents were of moderate health conditions, the effects of which would be known after the survey had been taken up and the analysis made.

The matters that would be discussed during the course of this study would be in terms of their lifestyles, smoking, drinking and private lives and their relationships with peers, superiors and subordinates as well as family members and friends. All these aspects are believed to be significant and needs to be explored during the course of the study in order to arrive at a correct evaluation of the study and its rigour.

During the course of this research it is necessary to provide unbiased and authenticated questions to be put forth to the respondents in order to elicit correct responses from them. It is also necessary to ask open-ended question since this is one of the basis of qualitative surveys to which this survey belong. Through the use of open-ended questionnaires it is possible to gain insight into the various aspects of different classes of respondents and their perceived impact of clases on health and well being.

In a qualitative analysis as this one, it is essential that the sourcing of the data be according to the needs of the study and in consonance with their objectives. It is also necessary that the methods are patterned, standardized and follow scientific rigour.

In this qualitative survey, it is necessary that the respondents be able to source and evaluate choices of the survey and data and realize the limits of such survey methods.

It is necessary that this survey should use appropriate methods of analysis, and demonstrate an understanding of the implications of the results with reference to the existing literature in the field and how this survey contributes to the induction of new aspects into this study.

The theoretical aspects of this survey should consider the empirical implications of the results of the survey in an attempt to reduce the gap and bridge the guilt between the theoretical and practical aspects of this survey method.

The findings of this survey should be amenable to further research skills, healthy criticism and the ability to infer conclusions and offer recommendations within the particular aspect of the subject matter of this research study.

Although the subject of class interference in public health is a large and significant area for public welfare research, the literature available does not seem to be suggestive of this truth.

The Whitehall survey conducted in two parts consisted of study of 18,000 men in the Civil Service, set up in 1967.The first Whitehall study showed that men in the lowest employment grades were much more likely to die

Prematurely than men in the highest grades. The Whitehall II study was set up to determine what underlies this grade or social gradient in death and disease and to include women in the scope of its survey. (Work, Stress and Health: The Whitehall II study: 2004).

During the year 1980, the Thatcher Government in its bid to promote the cause of Britons caused the release of the Black Report. The Report stresses the need, interalia that achieving a high standard of health among its entire people represents one of the highest of society’s aspirations. Present social inequalities in health in a country with substantial resources like Britain are unacceptable, and deserve so to be declared by every section of public opinion. “ (Socialist Health Association: The Black Report).

Another significant work in this direction has been by Sarah Earthy in her book entitled social class & Health Inequalities, in which it has been explained that there are many causes for health and social class. They could be attributed to social selecting, whether direct or indirect, the cultural behaviour could be seen in terms of particular cohorts indulging in health harming conduct and the material aspects could be in terms of social class or income disparities that could lead to health differences.( Sarah Earthy : Social Class and Health inequalities: Sociology of Contemporary societies).

The UK Government had commissioned Sir Donald Acheson to study the health conditions through an independt inquiry and submit a report, which he did. In 1998.

This was entitled the Independent inquiry into inequalities in Health Care Report.

It conduced in the following crucial areas that “all policies likely to have an impact on health should be evaluated in terms of their impact on health inequalities; secondly, a high priority should be given to the health of families with children; and thirdly, further steps should be taken to reduce income inequalities and improve the living standards of poor households “(Independent inquiry into inequalities in Health Care Report 1998).

• Objective: The main objective of this study is a qualitative analysis to determine whether occupational or social class influences public health. Of people.
• Design: Cross sectional, qualitative cohort study working in various capacities – from clerk to company manager.
• Settings: Corporate setting of large industrial house in southern London, UK.
• Participants: Involving 250 men and women in the age group of 25- 55 years with more or less similar backgrounds and primary health assessments.
• Main outcome measure: The study seeks to confirm or nullify the research hypothesis whether class plays a dominant role in the determination of public health or not.
• Results: Considering the overwhelming responses to the interviews, which had a response rate of more than 76% (for men) and 67% (for women), it was seen that the hypothesis was carried unanimously

Although the survey validates the hypothesis that class cultures influences public health , there needs to be further research studies on how class divides could be reduced and a higher standards of health care in commensuration with the available resources could be made easily accessible to underprivileged and economically weaker sections of the society.

This would ensure that in future there would be more productive and better use of human resources in the country.

Work, Stress and Health: The Whitehall II study: 2004: Introduction: P 3. Web.

Socialist Health Association: The Black Report 1980: 2008. Web.

Sarah Earthy : Social Class and Health inequalities : Sociology of Contemporary societies: 2008. Web.

Independent inquiry into inequalities in Health Care Report 1998: Synopsis: 2008. Web.

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IvyPanda. (2021, October 24). Social Class and Health: Qualitative Research. https://ivypanda.com/essays/social-class-and-health-qualitative-research/

"Social Class and Health: Qualitative Research." IvyPanda , 24 Oct. 2021, ivypanda.com/essays/social-class-and-health-qualitative-research/.

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IvyPanda . 2021. "Social Class and Health: Qualitative Research." October 24, 2021. https://ivypanda.com/essays/social-class-and-health-qualitative-research/.

1. IvyPanda . "Social Class and Health: Qualitative Research." October 24, 2021. https://ivypanda.com/essays/social-class-and-health-qualitative-research/.

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IvyPanda . "Social Class and Health: Qualitative Research." October 24, 2021. https://ivypanda.com/essays/social-class-and-health-qualitative-research/.

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Social Class and Health Inequalities, Annotated Bibliography Example

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Introduction

Social determinants of health relate to the socio-economic conditions influencing distribution of health care services within a community. It exposes the individual and group disparities in health status due to apparent inequities.  Risk factors associated with social determinants of health include one’s living and working conditions, distribution of income, wealth, influence, and power.

As it pertains to this presentation the writer posits that individual biological factors do not influence social determinants of health; rather it is a person’s access to quality health care that makes the remarkable difference in determination factors. The critical question is how should Canadian government address the social determinants of health?

Why is it an issue?

• Healthcare in Canada is a mix model of public funding and private execution of services.
• There is universal insurance coverage for Canadian citizens and legal residents.
• Public sector health care is not responsible for delivery.
• Public sector health care does not monitor service accessibility to the every citizen or legal residents
• Even though health care is free it is not accessible due to private sector dominance in delivery.
• Many citizens cannot afford private delivery due to co-payment issues
• It is a relevant health care issue because many Canadians cannot access quality health care.
• Public sector negligence in assessing inequities of service delivery creates disparities among classes

Who does the issue involve?

• Children, elderly and women are mostly affected by the social determinants of health care services in Canada
• Public health administration oversees that Canadians have access to health care but does not decide on the quality nor equity.
• Private hospital management operates as a for-profit entity with public funds
• Public sector funding agencies allow private sector health care to charge according to their budget demands rather than what an individuals can afford.

Evidence to Support Argument

Buchanan, D. (1998). Beyond positivism: humanistic perspectives on theory and research in health education. Health Education Research. 13(3), 439-450.

In this article the author presented seven functions of a theory namely, prediction, explanation, making assumptions explicit, understanding, sense-making, sensitization and critique. The first two concepts he described as being associated with positivism while others were linked to humanities. Arguments advanced in this research related to alternative views of positivism, which are beneficial to health education practice. One of the issues intensifying social determinants inequities in Canada is the privilege of health education across social classes

Canada Health Act (2012). Department of Justice. Retrieved Nov 9th, 2012 from http://laws-lois.justice.gc.ca/eng/acts/C-6/

Canada Health Act (CHA) refers to a Canadian federal legislation, designed in 1984, stipulating conditions and criteria under which provincial and territorial health insurance programs must be eligible to receive transfer payments from public funds to private health care entities. These requirements embody universal coverage for every ensured person to entitle him/her for medical services such as seeing doctors, specialists; nursing home referrals, and other services deemed to be medical (Canada Health Act, 2012).  This appears to be as a social determinant of health care in Canada, but with many Canadians do not access quality health care.

Coburn, D (2004). Beyond the income inequality hypothesis: class,neo-liberalism,and health Inequalities. Social Science & Medicine 58 (2004), 41–56

Coburn contends that global and national socio-political-economic trends have increased the power of business classes and lowered working class participation in the social structure. Hence, there is a significant income inequality accompanied by unequal access to health care services. This income disparity then becomes a social determinant of healthcare. If gaps are to be narrowed or closed these disparities ought to be addressed by  Canadian social services department.

Coburn, D., & E. Coburn (2007). Health and health inequalities in a neo-liberal global world . London. Cambridge University Press.

In yet another article teaming up with his wife they advance that neoliberal doctrines influence social inequalities relevant to health inequities.  However, this should initiate action towards resolving these inequities in society. Ironically, they are not addressed in the Canadian social services arena. The question remains how government should intervene.

Raphael, D (2002). Poverty, Income Inequality,and Health in Canada. Toronto. The CSJ Foundation for Research and Education.

The author discusses the impact of poverty and income inequality on Canadian’s health care system. He advanced that poverty and income inequality are detrimental to the health of low income earners. As such, categories of Canadians are affected and it is reflected in weakening of social infrastructure and the destruction of social cohesion. If this is the actual outcome of health care delivery through the mix model intervention changes must occur to address income inequality as a social determinant of health in Canada.

Raphael, D (2000). Health inequalities in Canada: current discourses and implications for public health action. Critical Public Health , 10 (2). 194-204

This researcher explored the consequences of increasing inequality among Canadians and effects on their health status. He acknowledged that the subject has become a Canadian tradition still to be adequately addressed as a public health issue. Public health as an institution is limited in its response to the dilemma. In concluding the analyst cited misinformation or lack of it as being a major factor for silence on the issue.

Madore, O., & Tiedmann, M. (2005). Private Health Care Funding and Delivery under the Canada Health Act . Parliamentary and Information Services.

Analysts cited that many hospitals in Canada function as private not-for-profit entities run by community boards of trustees, voluntary organizations or municipalities.  Services such as pharmacies, food preparation, and facilities maintenance within hospital management are distinctly provided by a mix of private for-profit, private not-for-profit and public sectors

Muntaner, C. Ng, E., & Chung, H (2012). Better Health.  Ontario. Canadian Health Services Research Foundation and Canadian nurses association.

These authors embraced a study indicating the important role nurses play in narrowing health inequalities in their respective communities and functions. The authors conducted a scoping review to assess the empirical associations connecting social determinants and health outcomes. This was a deliberate attempt to expose public polices implicit in political activities aimed at influencing health inequality within the Canadian Health Care system.

Discussion and Implications

• Canadian Healthcare system needs to address inequities in health care delivery
• More women, elderly and children ought to be included in special health care delivery programs
• Social services department ought to make recommendation for improving accessibility to quality health care for citizens.
• Public sector health care should not only monitor private sector funding, but conduct financial assets intervention regarding the intangible health care delivery asset
• Implications are that life expectancy of Canadians will fall
• Human resource is the greatest in any nation and this will be depleted
• Social structures will collapse and the nation will crumble financially.
• Make recommendations to agencies responsible for social services development in Canada
• Organize social action
• Encourage interest group participation in peaceful protest demonstrations
• Empower minority groups by education

Individual biological factors do not influence social determinants of health; rather it is a person’s access to quality health care that makes the remarkable difference in determination factors.

Buchanan, D. (1998). Beyond positivism: humanistic perspectives on theory and research in health education . Health Education Research . 13(3), 439-450.

Coburn, D (2004). Beyond the income inequality hypothesis: class, neo-liberalism, and health Inequalities. Social Science & Medicine 58 (2004), 41–56

Coburn, D., & E. Coburn (2007). Health and health inequalities in a neo-liberal global  world . London. Cambridge University Press.

Madore, O., & Tiedmann, M. (2005). Private Health Care Funding and Deliver under the Canada Health Act . Parliamentary and Information Services.

Muntaner, C. Ng, E., & Chung, H (2012). Better Health.   Ontario. Canadian Health Services Research Foundation and Canadian nurses association

Raphael, D (2002). Poverty, Income Inequality,and Health in Canada. Toronto. The CSJ Foundation for Research and Education.

Raphael, D (2000). Health inequalities in Canada: current discourses and implications for public health action. Critical Public Health, 10 (2). 194-204

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• Published: 19 January 2022

Spatial stratification and socio-spatial inequalities: the case of Seoul and Busan in South Korea

• Seungwoo Han   ORCID: orcid.org/0000-0003-4180-6169 1  

Humanities and Social Sciences Communications volume  9 , Article number:  23 ( 2022 ) Cite this article

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• Social policy

This study approaches the spatial stratification phenomenon through a data-based social stratification approach. In addition, by applying a dissimilarity-based clustering algorithm, this study analyzes how regions cluster as well as their disparities, thereby analyzing socio-spatial inequalities. Ultimately, through map visualization, this study seeks to visually identify spatial forms of social inequality and gain insight into the social structure for policy implications. The results determine how the regions are socioeconomically structured and identify the social inequalities between the spaces.

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Introduction.

As social inequality worsens worldwide, its manifestation in complex urban environments has become a key issue in policy research. Many studies on urban inequality have attempted to measure inequality by combining the level of the economy, income, education, public service, and life expectancy (Alkire et al., 2011 ; Lee and Rodrı´guez-Pose, 2013 ; Panori and Psycharis, 2017 ; Lelo et al., 2019 ). Other studies use non-material factors such as the perception of quality and happiness of life (Senlier et al., 2008 ; Ballas, 2013 ; Okulicz-Kozaryn, 2013 ). In the wider context of overall social inequality in regard to space, however, we need to develop a better understanding of the mechanisms that shape socio-spatial inequality. In order to analyze the spatial patterns of social inequality, this study focuses on the opportunities and benefits coming from space and measures spatial stratification by analyzing the multifaceted factors that create disparities among spaces.

The objectives of this study are twofold. The first is a methodological discussion of perspectives, approaches, and data to measure spatial stratification by applying data-driven methods. The other is the application of this approach to understanding socio-spatial inequalities due to spatial stratification in South Korea. This study covers Seoul Special City (hereinafter referred to as Seoul), the capital city of South Korea, and Busan Metropolitan City (hereinafter referred to as Busan), the second-largest city in South Korea. The units of analysis are the district ( gu ) and county ( gun ) to which the two cities belong.

The inequality referred to in this study is social inequality. Social inequality refers to a state in which factors affecting human activities across various fields, such as opportunities, resources, and power, are unfairly distributed (Sen, 1992 ). Socio-spatial inequality, then, refers to a state in which significant disparities are created because they are not evenly distributed across different spaces, which means that social inequalities are manifested in spatial patterns. It proposes that socio-spatial inequalities can be identified by measuring spatial stratification.

The approach to spatial stratification in this study is based on understanding social stratification via data-driven methods. As a research method for clustering regions and analyzing disparities, this study proposes the K -means ++ clustering algorithm, which is a dissimilarity-based (distance-based) clustering method from a problem-centric perspective of spatial stratification and socio-spatial inequalities.

This study presents interpretable clustering results through a combination of a clustering algorithm and map visualization for policy implications. In the study of socio-spatial inequalities in urban spaces, the approaches using map visualization enable the spatial analysis of urban inequalities to visually identify spatial forms of inequalities, gain deeper insight into social structure and the processes that generate inequalities (de la Espriella, 2009 ; Soja, 2010 ; Siqueira-Gay et al., 2019 ; Lelo et al., 2019 ; Sohn and Oh, 2019 ; McLachlan and Norman, 2020 ; Shi and Dorling, 2020 ). The results of this analysis can be used as a foundation in policy discussions related to urban and regional inequalities and this study seeks to find implications through this approach.

In a study on data-based social stratification, it is crucial to choose which indicators meet the research objective. This study proposes that data reflecting the multifaceted characteristics of spaces have a certain pattern to measure spatial stratification. This study uses data, which reflect country-specific characteristics, provided by 15 public institutions in South Korea for its analysis. In addition, this study applies data transformations that can effectively maximize similarity and dissimilarity to optimally cluster regions based on the dissimilarity-based clustering method. As tools for analysis, this study used Python 3.7 and Scikit-learn 0.22.2.

Socio-spatial Inequality

The spatial organization of urban inequality.

Many studies heavily rely on income data to identify spatial inequality even though it is widely acknowledged that inequality is a multifaceted phenomenon affecting human activities across various fields (Sen, 1992 ). Besides income, studies to look at the socioeconomic structure of a specific space have focused on occupation, housing, and education (Jung et al., 2014 ; Kernan and Bruce, 1972 ; Henning and Liao, 2013 ; Sohn and Oh, 2019 ). They certainly help to understand the socioeconomic structure of space, but they do not show the process by which inequality is (re)produced. Urban inequality is multidimensional and highly complex. Multidimensional analysis of space provides a different perspective on its socioeconomic structure (Hacker et al., 2013 ; Lelo et al., 2019 ; Lin et al., 2015 ; Nijman and Wei, 2020 ; Spector, 1982 ; Zambon et al., 2017 ).

The central theme of this study of socio-spatial inequality is the spatial organization of urban inequality. This study argues that the spatial arrangement of economic and service facilities and classes helps us understand how space is structured socioeconomically. First, socio-spatial inequality is derived from the spatial arrangement of economic and service facilities related to the lives of residents in a city. George ( 1973 ) attempted to determine why poverty and inequality have not ended despite the progress of society. He found the answer in land. According to his claims, progress is beneficial to humanity and also increases the value of the land, so the amount of rent that the landlord can demand those who need to use the land also increases. The landlord monopolizes the fruit of growth because the price of land and the rent charged for using it increase faster than the increasing wealth to pay that rent. In other words, inequality intensifies as landowners become increasingly able to monopolize the surplus arising from economic growth.

When we speak of land, we refer not to soil and stone but to a specific location. Due to the increase in population and settlement activities following the progress of society, the scarcity of land becomes greater, so the value of land is determined by the location. To put this in terms of modern society, the primary role of land for individuals in contemporary society is the role of housing owned by individuals. Housing provides social benefits and opportunities beyond the purpose of residence. Facilities occupy discrete locations, and the friction of distance means that some people in certain places will find it easier than others to obtain opportunities, benefits, and various sources of need satisfaction (Smith, 1984 ). Individual behavior is affected by spatial structure (Horton and Reynolds, 1971 ). The residential location carries with it not only a particular quality of living environment but also a set of advantages and disadvantages arising from accessibility to sources of benefits and opportunities (Su et al., 2019 ). Disadvantages arising from lack of accessibility to sources of benefits and opportunities might affect economic performance, which might reproduce inequality (Rawls, 1971 ; Roemer, 1998 ; Lamont and Fourier, 1992 ; Thobecke and Charumilind, 2002 ; Mustard and Ostendorf, 2005 ; van Kempen, 2005 ; McDowell et al., 2006 ). In other words, the key to socio-spatial inequality in this study is the socio-spatial inequality of opportunities and benefits.

Second, the socio-spatial inequality of opportunities and benefits stems from spatial exclusivity. This exclusivity is closely related to the traits of the positional good discussed by Veblen ( 1994 ) in that the location of a particular region represents the class of the individuals residing there. People pay more for houses in locations that are coveted in terms of social status, and they are willing to bear it even if they account for more of their assets. As a result, higher barriers to entry are built up, and those who do not belong are excluded from the benefits and opportunities of the region.

In social science studies, class is an essential factor in explaining society’s various dynamics and phenomena regarding inequality, from classical discussions about the class such as Marx ( 1977 ) to Piketty ( 2014 , 2020 ), who discusses class in terms of the present time. The distribution of class shows the socioeconomic structure of society (Reich, 1991 ; Atkinson, 2006 , 2008 ; Sohn and Oh, 2019 ). Class and social strata exist in any form, regardless of age and place, and inevitably, the accompanying inequality is a byproduct of their dynamics (Pekkanen et al., 1995 ; Chan and Goldthorpe, 2007 ; Kingstone, 2000 ; Wright, 2005 ; Grusky, 2014 ; Piketty, 2020 ). If inequality exists in any form and social relations between people are perceived as non-horizontal by any standard, class and social strata can be useful tools for analyzing socio-spatial inequality.

The Korean context

In South Korea, phrases that represent specific spaces, such as the metropolitan area versus rural provinces, in-Seoul versus out-of-Seoul, and Gangnam Footnote 1 versus Gangbuk reflect individuals’ identity, social status, and economic class (Kang, 1991 ; Park and Jang, 2020 ; Yang, 2018 ). Phrases that define regions in specific ways mean that spatially, social classes are rigidly separated and the opportunities available to individuals vary depending on where they live. Whether an individual lives in a metropolitan area, in a rural area, or in Gangnam within Seoul, affects one’s life in South Korean society in many ways. Inequality can be structurally reproduced and social mobility becomes rigid if a certain group of people living in a certain area monopolizes opportunities, or if some people are spatially excluded from opportunities provided by society (Soja, 2010 ).

Previous literature on the regional inequality of South Korea mainly dealt with the economic gap between metropolitan and rural areas (Kim et al., 2003 ; Kim and Jeong, 2003 ; Noh, 2006 ; Oh, 2017 ). On the other hand, the core of structural inequality in South Korea can be captured through the analysis of Seoul and Busan in that multifaceted inequality factors are concentrated in the space of these two representative cities in South Korea. As cities increase in size, diversity also increases and reveals the overall social structure of society (Shevky and Bell, 1955 ; Duranton and Puga, 2000 ). As of 2020, Seoul’s population is about 9.8 million, accounting for about 18.8% of the total population of South Korea (about 51.84 million), and Busan’s population is ~3.5 million, accounting for about 6.6% of the total population of South Korea; Footnote 2 taken together, the two constitute more than ¼ of South Korea’s total population. Accordingly, urban inequality in Seoul and Busan is not limited to the urban space but, rather, can show the overall structure of regional inequality in South Korean society.

According to the Global Power City Index (GPCI), Seoul is ranked 8th Footnote 3 and according to the Global Cities Index (GCI), Seoul is ranked 17th among global cities in 2021. Footnote 4 In terms of container traffic per annum, Busan is ranked 6th in the world and is considered one of the key cities for port logistics in 2021. Footnote 5 Therefore, socio-spatial inequalities within the two cities, which play a key role socially and economically and are closely linked to the global economy, can be understood as a form of inequality in the global city.

Research design

Data-based social stratification approach.

The approach to spatial stratification in this study is based on measuring social stratification through data-driven methods. Data reflect human behaviors and interactions, such as how people communicate, how they form relationships, and how conflicts arise in society (Monroe et al., 2015 ). People live by building complex dynamics of life inside and outside each spatial unit. The spatial units of various lives contain each way of life and relationship. The spatial units are created by human beings as the main subject through social relations, but at the same time, society also creates spatial units through institutional or relational networks. As such, the dynamics of confrontation and rejection as well as connection and bonding are laid between each unit space. If the social stratification phenomenon reflects people’s social relations, the spatial stratification phenomenon reveals these social relations as spatial divisions. Accordingly, this study proposes that the multidimensional data reflecting social relationships have a certain pattern by which to measure spatial stratification. Table A1 of Appendix A summarizes the geographical scope, methods, indicators, and findings of past publications of applied clustering.

Dissimilarity-based clustering methods

Previous studies have generally applied one or two certain clustering algorithms for analysis (see Table A1 of Appendix A). However, there is no foundation in statistical theory or clear criteria for which clustering algorithm is preferable (Venables and Ripley, 2002 ; Ahlquist and Breunig, 2012 ; Hennig, 2015 ). There are a number of clustering algorithms, and, often, different methods produce different outcomes without sound reasons for choosing a particular method over another. Therefore, in selecting a clustering algorithm, it is difficult to clearly explain which is preferable and how many clusters are ideal. In a number of studies applying clustering algorithms, the reason for selecting a specific clustering algorithm is not clearly presented or discussed (Ahlquist and Breunig, 2012 ; von Luxburg et al., 2012 ).

This study does not aim to compare each result by applying various clustering algorithms. To determine which clustering method is preferred and suitable for clustering, the current study takes an approach in which the researcher determines the clustering algorithm to be applied in accordance with the objective and context of the research as well as the characteristics of the data (von Luxburg et al., 2012 ; Henning and Liao, 2013 ; Henning, 2015 ). Therefore, the current study is based on the data-driven approach rather than the model-driven approach.

Each region within the urban space is unique, so the regional characteristics of each region are different (Harvey, 1989 ). At the same time, however, certain regions share unique features based on specific values. Current study proposes a dissimilarity-based clustering method by focusing on this similarity and dissimilarity as reflected in data in order to measure spatial stratification. As discussed in the previous section, this study proposes that multifaceted data have a certain pattern that can be utilized to measure spatial stratification. According to this data-based social stratification approach, structural patterns can be elucidated. This study seeks to uncover them based on the similarity and dissimilarity among observations. Accordingly, this study applies the K -means++ clustering algorithm, an approach to clustering based on Euclidean distance (see Appendix B).

In addition to K -means++, there are clustering algorithms of various approaches, such as hierarchical clustering and density-based spatial clustering with noise (DBSCAN). Hierarchical clustering has the advantage that it can determine the number of clusters by searching all potential clusters through a hierarchical tree structure (Murphy, 2012 ; Johnstone et al., 2019 ; Wu et al., 2020 ). In hierarchical clustering, clusters have a tree-like structure or a parent–child relationship. Here, the two most similar clusters are joined together, and all of the clusters are continuously combined until they form a single cluster. DBSCAN is a density-based clustering method that is a non-parametric approach suitable for applications where clusters cannot be well described as distinct groups of low within-cluster dissimilarity, as, for instance, in spatial data, where clusters of points in the space may form along natural and artificial structures, such as rivers, valleys, buildings, etc. (Grubesic et al., 2014 ; Henning, 2015 ; Johnstone et al., 2019 ; Wu et al., 2020 ). The objective of this study is not to connect objects hierarchically to multiple clusters but to directly optimize certain characteristics and categorize each object into exactly one cluster. In addition, this study’s data do not require a density-based method because geographic characteristics are not included.

K -means++ is one of the clustering algorithms developed from K -means, and the principle of clustering is the same except for the initialization of the cluster center. K -means is a clustering technique that selects a cluster center called a centroid and then selects the data points closest to it (Arthur and Vassilvitskii, 2007 ; Hastie et al., 2009 ; Murphy, 2012 ) (see Appendix C).

The main disadvantage of K -means is that the initial locations of centroids are arbitrarily selected. This initial arbitrary selection of centroids often fails to form optimal clusters. K -means++ is the clustering algorithm proposed to address this drawback of K -means (Arthur and Vassilvitskii, 2007 ; Bonaccorso, 2018 ). It specifies a procedure to initialize centroids before moving forward with the standard K -means clustering algorithm.

K-means performs the clustering process by initially arranging random centroids. In contrast, K -means++ selects one of the data points as the first centroid, rather than beginning with K points in arbitrary spaces. It then selects the next centroid from the data points such that the probability of choosing a point as a centroid is directly proportional to its distance from the nearest, previously chosen centroid (Arthur and Vassilvitskii, 2007 ). Simply put, a data point placed as far as possible from the already designated centroid is designated as the next centroid. This process is repeated until K centroids have been sampled. In other words, initial centroids are placed more strategically rather than randomly selected in the centroid selection. Except for this initial procedure, the rest of the clustering process is the same as K -means. The approach of K-means++ to initial centroid selection can cluster objects more optimally and improve the algorithm’s convergence speed.

In K -means++ clustering, the number of clusters K must be specified before clustering. That is, what must be decided here is how many clusters K are optimal. Silhouette analysis can be used to evaluate the separation distance between the resulting clusters (Kaufman and Rousseeuw, 1990 ; Bonaccorso, 2018 ). Efficiently clustered means that the distances between different clusters are sufficiently far apart, and data points in the same clusters are close. The silhouette plot displays a measure of how close each data point in one cluster is to data points in the neighboring clusters and thus provides a way to assess parameters such as the number of clusters visually (see Appendix D).

Data selection

A critical question for the data-based social stratification approach is what indicators to choose. When collecting data, it is necessary to have a sufficient understanding of the society concerned, and data should be available and reliable. For the data set that is analyzed here, the focus is on economic and service facilities and socioeconomic class. Data related to the spatial arrangement of economic and service facilities include data representing the sectors of transportation, culture, safety, medical treatment, education, and economy. Class includes data related to an individual’s socioeconomic level, such as educational background, occupation, income, and wealth (Hollingshead, 1975 ; Levy and Michel, 1991 ; Sohn and Oh, 2019 ).

In modern society, public transportation plays a role in distributing opportunities to people through mobility (Social Exclusion Unit, 2003 ; Lucas, 2012 ; Chen et al., 2018 ; Pizzol et al., 2021 ). Among the various means of public transport, in South Korea, the subway is considered the most essential for urban transportation (Im and Hong, 2017 ). According to the Seoul Metropolitan Government, the average number of subway passengers per day is over 5 million, surpassing other modes of public transportation. Footnote 6 In South Korea, the area around a subway station is called a “subway station influence area”, and considering the fact that commercial areas, businesses, and public institutions are located and various social and economic activities take place near subway stations, subway stations are more important than being merely a means of transportation in various ways. In addition, considering the direct and indirect effects of the transportation infrastructure on the region and the parking problems in Korean metropolitan areas, public investment in roads and public parking spaces are also essential factors for residents (Talley, 1996 ; Yi et al., 2012 ; Ahn et al., 2014 ).

Cultural facilities such as public libraries, museums, and art galleries form cultural capital and are essential elements affecting the quality of life, vitality, and performance of individuals (Andersen and Hansen, 2012 ). In South Korea, cultural facilities have essential meanings in terms of quality of life, regional vitality, and the competitiveness of residents (Kim, 2007 ; Park et al., 2015 ). There has been continuous discussion regarding the disparities in accessibility to such facilities. In addition, access to a movie theater is one of the key factors in increasing the overall level of cultural activities in a region. In South Korea, the multiplex cinemas, which account for more than 90% of total cinemas, Footnote 7 provide the concept of a comprehensive leisure facility that can be enjoyed not only for movies but also for other leisure activities (Kang, 2016 ).

In the case of medical care, in South Korean society, there are health inequalities within and between regions (Choi et al., 2011 ; Hong and Ahn, 2011 ). In particular, tertiary hospitals occupy an important position such that the unique term “tertiary hospital influence area” was necessitated (Kang, 2014 ). There has been continuous social debate on patients’ inclination toward the top five tertiary hospitals located in Seoul. Considering the high medical service level of tertiary hospitals, residents can enjoy high levels of benefits (Yang et al., 2020 ). Safety needs are important factors for residents’ lives in modern society (Cox and Cox, 1996 ). Regarding the safety of residents in South Korea, there has been constant discussion that the utility level for people’s safety differs depending on accessibility to CCTV, police stations, and firehouses (Kim, 2014 ).

The distribution of educational opportunities as well as access to them has become an important issue in relation to educational and social equality (Coleman, 1990 ; Talen, 2001 ; Zhang and Kanbur, 2005 ). The concept of equality in educational opportunities includes the right for students to receive the benefits of a common curriculum regardless of their social background as well as the right to equal education in the community (Coleman, 1990 ). Considering the social phenomena that education is projected as a desire to increase social status in South Korean society as well as of parents’ enthusiasm for their children’s education, the meaning of education is highly significant (Seth, 2002 ; Lee, 2005 ; Kang, 2008 ).

In South Korea, disparities in educational services among regions are discussed as a serious social problem (Son, 2004 ; Choi, 2004 ; Byun and Kim, 2010 ; Byun et al., 2012 ). In particular, the disparities in the enrollment rates of elite high schools, such as specialized high schools and autonomous private high schools, which are advantageous for entering major universities, between regions are significant. In addition, according to the National Statistical Office’s announcement in 2019, 82.5% of elementary school students, 69.6% of middle school students, and 58.5% of high school students were receiving private education. Footnote 8 In this respect, the proportion of each district in the city’s total elite high school enrollment and the number of private educational institutes are included for the analysis.

Local shops are closely related to residents’ demographic characteristics (Meltzer and Schuetz, 2011 ). In Korean society, large-scale stores, such as super super market (SSM), department stores, shopping centers, multi-shopping complexes, etc., are factors that affect the residents’ quality of life (Kim and Park, 2017 ). In the case of the regional economy, the district’s gross wage and salary based on the withholding agent’s location show the region’s overall level of economic activity and job opportunities (Chapple, 2007 ). The high gross wage and salary of a district imply its economic competitiveness.

This study’s data representing class include the level of residents’ education, professional skills, income, and wealth. Educational background, occupation, income, and wealth are representative factors of socioeconomic class (Hollingshead, 1975 ; Kim et al., 2003 ; Sohn and Oh, 2019 ). In terms of educational background, university (including vocational college) graduation or above is classified as high, and high school graduation or below is classified as low. Based on the Korean Standard Classification of Occupations, professional or higher is classified as high, and others are classified as low for professional skill level. In regard to income, the fourth quartile is classified as high, and the first quartile is classified as low. The ratio of the working population of the upper tier to the lower tier in each data is measured based on national census data (KEIS, 2019 ).

This study uses the price of a condominium (called an apartment in South Korea) as data representing an individual’s wealth. According to the Korea Housing Survey of the Ministry of Land, Infrastructure and Transport, in Seoul, as of 2018, about 42% of households live in condominiums. In Busan, about 53.6% of households live in condominiums. Footnote 9 In South Korean society, the price of condominiums is heavily influenced by the region in which they are located and the surrounding living environment, and this is one of the main factors that characterize the wealth and socioeconomic status of an individual (Zchang, 1998 ; Lee et al., 2002 , Choi, 2006 ; Lee, 2009 ; Jang and Kang, 2015 ; Sohn and Oh, 2019 ). The variables are shown in Table 1 .

Data transformations

This study applies data transformations that can effectively maximize similarity and dissimilarity in order for regions to optimally cluster by applying a dissimilarity-based clustering algorithm. From a data-intuitive perspective, it may be meaningful to find a pattern from data without transformations, but this study considers that it makes more sense to cluster by ratios through log transformations rather than relying on absolute differences in variable values in that, in terms of social stratification, the interpretive difference between social groups depends on ratios rather than absolute values (Henning and Liao, 2013 ). In this study, therefore, the log transformations are applied to all variables except for the ratio variables.

Since there are 0 s in the data, the transformation log( x + c ) is appropriate. The strategic consideration in selecting c is that, rather than adding 1 to x uniformly, adding each corresponding c considering the minimum and maximum values of each variable enables more efficient clustering. For example, in the number of movie theaters in Seoul, the minimum value is 0, and the maximum value is 9. In contrast, for gross wage and salary, the minimum value is 972,996 (unit: 1 million KRW), and the maximum value is 34,245,070 (unit: 1 million KRW). Accordingly, it is logically appropriate to select c to be applied to the movie theaters variable and c to be applied to the total gross wage and salary variable differently.

The selection of c considering the values of variables is subjective, and this study takes a method of adding a multiple of 10, which is one digit greater than the maximum value. This makes the distance between small values effective while leaving the effective distance between high values less affected. Figures 1 and 2 are an example of clustering according to the difference in c values in the transformation log( x  +  c ). This shows the difference between Fig. 1 the case of applying 1 to c and Fig. 2 this study’s approach when clustering with the average price of a condominium and the number of private institutes. The approach of this study makes clustering more efficient.

Transformation log( x   +  1).

Transformation log( x  +  c ).

Clustering results and analysis

Before examining the clustering results, we can briefly analyze the socio-spatial maps of Seoul and Busan in Figs. 3 – 6 , which deliver multifaceted aspects of the socio-spatial structures in an intuitive visual manner. In Figs. 3 and 4 , we can visually confirm that the elements constituting transportation, culture, safety, education, and economy are concentrated in the south of the Han River, Seoul. Looking at the class factors, it can be seen that the corresponding factors are very high in the south of Seoul compared to other regions.

Socio-spatial map of Seoul 1.

Socio-spatial map of Seoul 2.

Socio-spatial map of Busan.

Silhouette scores ( K  = 2–6), Seoul.

In terms of accessibility to the facilities for residents, the facilities providing opportunities and benefits are concentrated in the southern area of Seoul, and highly educated, professional, high-income, and wealthy social classes reside in the area. In the following, we can look at Fig. 5 which briefly shows the socio-spatial structure of Busan.

In the case of Busan, compared to Seoul, the concentration of elements constituting transportation, culture, safety, education, and economy in a specific region is relatively weak. However, many facilities are still concentrated in the southeast region (East Busan). Gross wage and salary are higher in the west. This may be because Busan’s port facilities and related businesses are located in the west. In terms of social class, more of highly educated, professional, high-income, and wealthy social classes reside in the southeast region compared to other regions.

From the above maps, we can see the socio-spatial structures of the two cities. In the following, we can further understand their socio-spatial inequalities by analyzing the clustering results. The results of the silhouette analysis of Seoul and Busan are shown in Figs. 6 and 7 , respectively. In Seoul, when divided into two clusters ( K  = 2), the silhouette score is the highest (0.59). On the other hand, as K increases to three (0.511), four (0.445), five (0.437), and six (0.415), the silhouette score decreases gradually. In the case of Busan, when K  = 4, it has the highest silhouette score (0.364). In the case of K  = 2 and K  = 3, clustering is not efficient because there are clusters with negative values, and as K increases to five (0.3) and six (0.255), the silhouette score decreases gradually. Therefore, K  = 4 seems to be the most appropriate. First, Fig. 8 shows a map visualization of the clustering result for Seoul ( K  = 2).

Silhouette scores ( K  = 2–6), Busan.

Map visualization of clustering ( K  = 2), Seoul.

We can see the spatial shape of the result clustered into two clusters in Fig. 8 . The districts included in each of the two clusters in Seoul are shown in Table 2 . In the case of Seoul, 22 districts form Cluster 0, and three districts form Cluster 1. That is, Gangnam, Seocho, and Songpa districts form one cluster, and the rest of the districts form the other cluster.

The disparities in the mean values between the two clusters can be clearly distinguished. In all respects, Cluster 1 has overwhelming advantages. Considering subway stations, the average number in Cluster 0 is ~12.7, and the average number in Cluster 1 is 26.3. In the case of public parking spaces, Cluster 1 has about twice as many spaces on average. There is relatively little difference between the two clusters in terms of road extension, but in the case of the road extent, the difference is ~1.8 times. In the case of cultural facilities, there are about 12 cultural facilities on average in Cluster 0, but in Cluster 1, there are about 21 cultural facilities on average. In the case of theaters, there are about 2.7 theaters on average in Cluster 0, but in Cluster 1, there are about 3.5 theaters on average.

In the case of tertiary hospitals, each district of Cluster 1 has at least one, but in Cluster 0, the average number of tertiary hospitals is less than zero. For safety, Cluster 1 has at least 1.3 times more CCTVs, police stations, and firehouses on average. Regarding education, the gap between the two regions is considerable. There is a sizable gap between the two clusters in the enrollment rates of elite high schools and the number of private educational institutes. In the economy, the average number of large-scale stores in Cluster 1 is about 1.8 times higher, and the average gross wage and salary of Cluster 1 are over 4.6 times higher. In terms class, Cluster 1 significantly exceeds Cluster 0 in all areas of education, professional skill, and income. The average price of a condominium in Cluster 1 is about three times higher than that in Cluster 0. Looking at Fig. 8 and Table 3 together, we can see the spatial shape of the clusters and the disparities between them.

In Busan’s case, looking at the map visualization in Fig. 9 and Table 4 of the clustering result, we can see how the regions form clusters and take a spatial shape. When K  = 4, the regions belonging to each cluster are listed in Table 4 . From the following results, we can see that Haeundae district forms one cluster, six districts adjacent to the left of Haeundae form Cluster 1, and seven districts located on the left form Cluster 0. Gangseo district and Gijang County, located at both ends of Busan, form Cluster 3.

Map visualization of clustering ( K  = 4), Busan.

In Busan, the disparities among clusters are not relatively large compared to in Seoul. However, Cluster 2 (Haeundae district) is superior in most sectors, except for tertiary hospitals. Cluster 2 shows that the number of subway stations is two to three times higher than in other clusters, and the number of public parking spaces is greater. Regarding the number of cultural facilities, it is about two times higher than that of other clusters, and the number of movie theaters in Cluster 2 is 6–7 times higher than that of others. The number of police stations is similar to that of Cluster 1, but it is about two times higher than that of others. In the case of firehouses, the number is more than twice that of Cluster 1 (Table 5 ).

In the case of enrollment rates of elite high schools and the number of private educational institutes, Cluster 2 greatly exceeds other clusters. The number of large-scale stores in Cluster 1 is at least two to four times higher than the other clusters. Considering gross wage and salary, the gap with Cluster 3 is not significant, but it is about 1.7 times higher than that of Cluster 0. In terms of class, looking at the gaps in education, professional skill, and income, these gaps are not significant, but they clearly exceed other regions in all these areas. The price of a condominium in Cluster 2 is about twice as high as in Cluster 0.

In summation, through the analysis of the clustering results, we can identify the spatial patterns of social inequality. Certain regions, densely populated by socioeconomically upper-class people, offer residents higher levels of benefits and opportunities than other regions. In conclusion, through these findings, this study is able to determine how the regions are socioeconomically structured spatially and to identify the social inequalities between the spaces.

Conclusions and implications

This study has several main findings, based on the methodological discussion that addresses a series of views on the perspectives, approaches, and data. In Seoul, the highest average silhouette score is calculated when divided into two clusters, and in Busan, the clustering is most optimal when divided into four clusters. Seoul’s Cluster 1 has advantages over other clusters in all sectors of economic and service facility, and class. As a result, this group’s residents can enjoy higher levels of services of public transportation, safety, medical treatment, culture, education, and economic opportunities and benefits compared to other regions. In the case of Busan, Cluster 2 has advantages over other clusters in most sectors of economic and service facility, and class. Compared to Seoul, the degree of disparity among clusters is relatively small. Still, there are evident disparities in the benefits and opportunities between them. Obviously, certain regions, densely populated by socioeconomically upper-class people, offer residents higher levels of benefits and opportunities than others.

Before stressing the broader implications, it is necessary to be clear about the theoretical and empirical limitations of this analysis. The proposed causal explanation liking location, benefits and opportunities, class, and socio-spatial inequality is tentative and begs further exploration. Empirically, the findings of this study can only be suggestive. In terms of the data-driven approach, the current study acknowledges some degree of arbitrariness in the selection of data. Although the current study utilized available data reflecting multidimensional characteristics of inequality, there were missing parts that this study could not address because of the unavailability of data. If time-series data were available, we could look at the changes in socio-spatial structures. However, time-series data were not available either. Future research would greatly benefit from more extensive and reliable time-series data.

Methodologically, this study applied K -means++ in the context of the study because based on the data-driven approach it was determined that there was less need to compare and analyze the results by applying various clustering algorithms. In a follow-up study, nevertheless, it is necessary to compare various clusters to which various clustering algorithms are applied for more comprehensive interpretations.

Besides, as discussed at the beginning of this study, socio-spatial inequality refers to the state in which opportunities, resources, and power are not distributed evenly across different spaces. This study captures socio-spatial inequalities in opportunities and resources but cannot capture political inequality from a spatial aspect. In addition, although this study makes it possible to identify the social inequalities between spaces in Seoul and Busan, it is not for the whole country. The social inequalities between Seoul and other regions may be incomparably larger than those within Seoul (Kang, 1991 ; Kim and Jeong, 2003 ; Yea, 2000 ). These gaps are expected to be filled through future studies.

Nonetheless, this first attempt to uncover socio-spatial inequalities in South Korea based on data-driven methods is provocative. There are many different perspectives and positions on the analysis of inequality. Previous literature on regional inequality in South Korean society has generally focused on income inequality between provinces or metropolitan cities and provinces. On the other hand, the current study analyzed how the disparities in opportunities and classes stratify urban spaces.

We need to think about what the clustering results imply. The results of this study adequately reflect the reality of South Korean society. A Korean proverb states, “The young of a human should be sent to Seoul.” This is because people can find more opportunities and benefits in big cities such as Seoul. After belonging to the space of Seoul, people want to live in a certain area, Gangnam. Similarly, in Busan, people want to move from West Busan to East Busan, where Haeundae district is located. This social phenomenon is due to the apparent existence of socio-spatial inequalities, and many people in South Korea desire to belong to the group of people living in Seoul Gangnam and Busan Haeundae. On the other hand, these regions are a space of jealousy and frustration and are often indicated as a symbol of inequality in South Korean society due to socioeconomic polarization. In other words, these regions are a space of love and an object of desire on the one hand and space of envy and frustration on the other.

According to Soja’s ( 2010 ) conceptualization of spatial justice, if the geographic space formed by the social process is not socially just (it is not fair to all), the space formed in this way affects the society and lives of individuals in unjust ways. That is if spatial classes are formed in the historical moment and social context, the majority of human activities, except for a certain group of people, are spatially excluded from public services and investments. The results of this study, which targets two representative cities in South Korea, can be said to be an example to partly explain. It is worth noting that, in particular, the gap in the enrollment rates of elite high schools is significant between Gangnam and the rest of the region in Seoul, and between Haeundae and the rest of the region in Busan. This is a result that makes it possible to see that social classes are being reproduced through education in South Korean society.

This research has obvious implications at the local public policy level. Discussing and solving social problems arising from social inequality begins with a clear perception of reality. In this regard, through the findings of this study, we are able to identify which social inequality factors are interspersed between spaces and determine the spatial shape. Although it may not be possible to address the multifaceted inequalities presented in this study easily, geographical expansion of opportunities can be one of the solutions. This can be possible not from a non-spatial policy perspective, but by expanding the geography of opportunities to improve access to opportunities in specific living areas. This study’s policy implications include the necessity of introducing measures to reduce the gap in opportunities and benefits between regions. Social inequality is structurally reproduced if a certain social class living in a certain area monopolizes opportunities and benefits. Therefore, how to distribute these opportunities and benefits more fairly is at the heart of policy. However, the real challenge is how to decentralize economic and service facilities that have a strong centripetal tendency. The realistic plan is to develop Seoul and Busan into a multi-centric cities. Seoul, where about 9.8 million people live, should not be a simple structure that can be divided into Gangnam and the rest (Haeundae and the rest in Busan), but a multi-centric structure in which various small and medium-sized cities are connected by education, culture, transportation, and industry. These factors should not be concentrated in one place but should be spread across regions. In other words, the current mono-centric city must develop into a multi-centric city. Since social investment in the supply of these services and facilities is difficult in the short term, it should be planned and developed from a long-term perspective. Thus, follow-up studies should investigate them further.

Data availability

All data analyzed are contained in Appendix E included in the supplementary information.

There is no standard that clearly defines Gangnam, but here, Gangnam is a kind of proper noun expression referring to three districts, Gangnam-gu, Songpa-gu, and Seocho-gu, called the Gangnam 3 gu. Gangnam is defined as an area that symbolizes social classes, political behavior, wealth, consumption behavior, condominium prices, educational conditions, and public services that are different from other regions (Park and Jang, 2020 ; Yang, 2018 ).

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3 The Root Causes of Health Inequity

Health inequity, categories and examples of which were discussed in the previous chapter, arises from social, economic, environmental, and structural disparities that contribute to intergroup differences in health outcomes both within and between societies. The report identifies two main clusters of root causes of health inequity. The first is the intrapersonal, interpersonal, institutional, and systemic mechanisms that organize the distribution of power and resources differentially across lines of race, gender, class, sexual orientation, gender expression, and other dimensions of individual and group identity (see the following section on such structural inequities for examples). The second, and more fundamental root cause of health inequity, is the unequal allocation of power and resources—including goods, services, and societal attention—which manifest in unequal social, economic, and environmental conditions, also called the social determinants of health. Box 3-1 includes the definitions of structural inequities and the social determinants of health.

Definitions.

The factors that make up the root causes of health inequity are diverse, complex, evolving, and interdependent in nature. It is important to understand the underlying causes and conditions of health inequities to inform equally complex and effective interventions to promote health equity.

The fields of public health and population health science have accumulated a robust body of literature over the past few decades that elucidates how social, political, economic, and environmental conditions and context contribute to health inequities. Furthermore, there is mounting evidence that focusing programs, policies, and investments on addressing these conditions can improve the health of vulnerable populations and reduce health disparities ( Bradley et al., 2016 ; Braveman and Gottlieb, 2014 ; Thornton et al., 2016 ; Williams and Mohammed, 2013 ). This literature is discussed below in the sections on structural inequities and the social determinants of health.

• HOW STRUCTURAL INEQUITIES, SOCIAL DETERMINANTS OF HEALTH, AND HEALTH EQUITY CONNECT

Health inequities are systematic differences in the opportunities groups have to achieve optimal health, leading to unfair and avoidable differences in health outcomes ( Braveman, 2006 ; WHO, 2011 ). The dimensions of social identity and location that organize or “structure” differential access to opportunities for health include race and ethnicity, gender, employment and socioeconomic status, disability and immigration status, geography, and more. Structural inequities are the personal, interpersonal, institutional, and systemic drivers—such as, racism, sexism, classism, able-ism, xenophobia, and homophobia—that make those identities salient to the fair distribution of health opportunities and outcomes. Policies that foster inequities at all levels (from organization to community to county, state, and nation) are critical drivers of structural inequities. The social, environmental, economic, and cultural determinants of health are the terrain on which structural inequities produce health inequities. These multiple determinants are the conditions in which people live, including access to good food, water, and housing; the quality of schools, workplaces, and neighborhoods; and the composition of social networks and nature of social relations.

So, for example, the effect of interpersonal, institutional, and systemic biases in policies and practices (structural inequities) is the “sorting” of people into resource-rich or resource-poor neighborhoods and K–12 schools (education itself being a key determinant of health ( Woolf et al., 2007 ) largely on the basis of race and socioeconomic status. Because the quality of neighborhoods and schools significantly shapes the life trajectory and the health of the adults and children, race- and class-differentiated access to clean, safe, resource-rich neighborhoods and schools is an important factor in producing health inequity. Such structural inequities give rise to large and preventable differences in health metrics such as life expectancy, with research indicating that one's zip code is more important to health than one's genetic code ( RWJF, 2009 ).

The impact of structural inequities follows individuals “from womb to tomb.” For example, African American women are more likely to give birth to low-birthweight infants, and their newborns experience higher infant death rates that are not associated with any biological differences, even after accounting for socioeconomic factors ( Braveman, 2008 ; Hamilton et al., 2016 ; Mathews et al., 2015 ). Although the science is still evolving, it is hypothesized that the chronic stress associated with being treated differently by society is responsible for these persistent differential birth outcomes ( Christian, 2012 ; El-Sayed et al., 2015 ; Strutz et al., 2014 ; Witt et al., 2015 ). In elementary school there are persistent differences across racial and ethnic divisions in rates of discipline and levels of reading attainment, rates that are not associated with any differences in intelligence metrics ( Howard, 2010 ; Losen et al., 2015 ; Reardon et al., 2012 ; Skiba et al., 2011 ; Smith and Harper, 2015 ). There also are race and class differences in adverse childhood experiences and chronic stress and trauma, which are known to affect learning ability and school performance, as well as structural inequities in environmental exposures, such as lead, which ultimately can lead to differences in intelligence quotient (IQ) ( Aizer et al., 2015 ; Bethell et al., 2014 ; Jimenez et al., 2016 ; Levy et al., 2016 ). One of the strongest predictors of life expectancy is high school graduation, which varies dramatically along class and race and ethnicity divisions, as do the rates of college and vocational school participation—all of which shape employment, income, and individual and intergenerational wealth ( Olshansky et al., 2012 ). Structural inequities affect hiring policies, with both implicit and explicit biases creating differential opportunities along racial, gender, and physical ability divisions. Lending policies continue to create differences in home ownership, small business development, and other asset development ( Pager and Shepherd, 2008 ). Structural inequities create differences in the ability to participate and have a voice in policy and political decision making, and even to participate in the arguably most fundamental aspect of our democracy, voting ( Blakely et al., 2001 ; Carter and Reardon, 2014 ). And implicit biases create differential health care service offerings and delivery and affect the effectiveness of care provided, including a lack of cultural competence ( IOM and NRC, 2003 ; Sabin et al., 2009 ).

For many people, the challenges that structural inequities pose limit the scope of opportunities they have for reaching their full health potential. The health of communities is dependent on the determinants of health.

• STRUCTURAL INEQUITIES

As described above, structural inequities refers to the systematic disadvantage of one social group compared to other groups with whom they coexist that are deeply embedded in the fabric of society. In Figure 3-1 , the outermost circle and background indicate the context in which health inequities exist. Structural inequities encompass policy, law, governance, and culture and refer to race, ethnicity, gender or gender identity, class, sexual orientation, and other domains. These inequities produce systematic disadvantages, which lead to inequitable experiences of the social determinants of health (the next circle in the report model, which is discussed in detail later in this chapter) and ultimately shape health outcomes.

Report conceptual model for community solutions to promote health equity. NOTE: Structural inequities are highlighted to convey the focus of this section.

Historical Perspective and Contemporary Perceptions

Whether with respect to race, ethnicity, gender, class, or other markers of human difference, the prevailing American narrative often draws a sharp line between the United States' “past” and its “present,” with the 1960s and 1970s marking a crucial before-and-after moment in that narrative. This narrative asserts that until the 1950s, U.S. history was shaped by the impacts of past slavery, Indian removal, lack of rights for women, Jim Crow segregation, periods of nativist restrictions on immigration and waves of mass deportation of Hispanic immigrants, eugenics, the internment of Japanese Americans, the Chinese exclusion policies, the criminalization of “homosexual acts,” and more ( Gee and Ford, 2011 ; Gee et al., 2009 ). White women and people of color were effectively barred from many occupations and could not vote, serve on juries, or run for office. People with disabilities suffered widespread discrimination, institutionalization, and social exclusion.

Civil rights, women's liberation, gay rights, and disability rights movements and their aftermaths may contribute to a narrative that social, political, and cultural institutions have made progress toward equity, diversity, or inclusion. Highlights of progress include the Civil Rights Act of 1964, the Voting Rights Act of 1965, the Fair Housing Act, Title IX of the Education Amendments of 1972, the Americans with Disabilities Act, the Patient Protection and Affordable Care Act, and, most recently, the Supreme Court case 1 that legalized marriage equality in the United States. With a few notable exceptions—undocumented immigrants and Muslims, for example—these advances in law and policy have been mirrored by the liberalization of attitudes toward previously marginalized identity groups.

Today, polls and surveys indicate that most Americans believe that interpersonal and societal bias on the basis of identity no longer shapes individual or group social outcomes. For example, 6 in 10 respondents to a recent national poll said they thought the country has struck a “reasonable balance” or even gone “too far” in “accepting transgender people” ( Polling Report, n.d. ). In 2015, 72 percent of respondents, including 81 percent of whites, said they believe that “blacks have as good a chance as white people in your community to get any kind of job for which they are qualified” ( Polling Report, n.d. ). In another poll, a total of 72 percent agreed that “women and men have equal trouble finding good-paying jobs” (64 percent) or that men have more trouble (8 percent) ( Ms. Foundation for Women, 2015 ). However, when broken down by racial and ethnic categories, the polls tell a different narrative. A recent survey revealed that 70 percent of African Americans, compared with 36 percent of whites, believe that racial discrimination is a major reason that African Americans have a harder time getting ahead than whites ( Pew Research Center, 2016 ). Furthermore, African Americans (66 percent) and Hispanics (64 percent) are more likely than whites (43 percent) to say that racism is a big problem ( DiJulio et al., 2015 ). Here, perceptions among African Americans and whites have not changed substantially; however, Hispanics are much more likely to now say that racism is a big problem (46 percent in 1995 versus 64 percent in 2015) ( DiJulio et al., 2015 ).

Perceptions are confirmed by the persistence of disparities along the lines of socioeconomic position, gender, race, ethnicity, immigration status, geography, and the like has been well documented. Why? For one, historical inequities continue to ramify into the present. To understand how historical patterns continue to affect life chances for certain groups, historians and economists have attempted to calculate the amount of wealth transmitted from one generation to the next ( Margo, 1990 ). They find that the baseline inequities contribute to intergenerational transfers of disadvantage and advantage for African Americans and whites, respectively ( Chetty et al., 2014 ; Darity et al., 2001 ). The inequities also reproduce the conditions in which disparities develop ( Rodriguez et al., 2015 ).

Though inequities may occur on the basis of socioeconomic status, gender, and other factors, we illustrate these points through the lens of racism, in part because disparities based on race and ethnicity remain the most persistent and difficult to address ( Williams and Mohammed, 2009 ). Racial factors play an important role in structuring socioeconomic disparities ( Farmer and Ferraro, 2005 ); therefore, addressing socioeconomic factors without addressing racism is unlikely to remedy these inequities ( Kaufman et al., 1997 ).

Racism is an umbrella concept that encompasses specific mechanisms that operate at the intrapersonal, interpersonal, institutional, and systemic levels 2 of a socioecological framework (see Figure 3-2 ). Because it is not possible to enumerate all of the mechanisms here, several are described below to illustrate racism mechanisms at different socioecological levels. Stereotype threat, for example, is an intrapersonal mechanism. It “refers to the risk of confirming negative stereotypes about an individual's racial, ethnic, gender, or cultural group” ( Glossary of Education Reform, 2013 ). Stereotype threat manifests as self-doubt that can lead the individual to perform worse than she or he might otherwise be expected to—in the context of test-taking, for example. Implicit biases—unconscious cognitive biases that shape both attitudes and behaviors—operate interpersonally (discussed in further detail below) ( Staats et al., 2016 ). Racial profiling often operates at the institutional level, as with the well-documented institutionalization of stop-and-frisk practices on Hispanic and African American individuals by the New York City Police Department ( Gelman et al., 2007 ).

Social ecological model with examples of racism constructs. NOTES: The mechanisms by which the social determinants of health operate differ with respect to the level. For the intrapersonal level, these mechanisms are individual knowledge, attitudes/beliefs, (more...)

Finally, systemic mechanisms, which may operate at the community level or higher (e.g., through policy), are those whose effects are interactive, rather than singular, in nature. For example, racial segregation of neighborhoods might well be due in part to personal preferences and behavior of landlords, renters, buyers, and sellers. However, historically, segregation was created by legislation, which was reinforced by the policies and practices of economic institutions and housing agencies (e.g., discriminatory banking practices and redlining), as well as enforced by the judicial system and legitimized by churches and other cultural institutions ( Charles, 2003 ; Gee and Ford, 2011 ; Williams and Collins, 2001 ). In other words, segregation was, and remains, an interaction and cumulative “product,” one not easily located in any one actor or institution. Residential segregation remains a root cause of racial disparities in health today ( Williams and Collins, 2001 ).

Racism is not an attribute of minority groups; rather, it is an aspect of the social context and is linked with the differential power relations among racial and ethnic groups ( Guess, 2006 ). Consider the location of environmental hazards in or near minority communities. Placing a hazard in a minority community not only increases the risk of adverse exposures for the residents of that community, it also ensures the reduction of risk for residents of the nonminority community ( Cushing et al., 2015 ; Taylor, 2014 ). Recognizing this, the two communities could work together toward an alternative that precludes having the hazard in the first place, an alternative that disadvantages neither group.

Most studies of racism are based on African American samples; however, other populations may be at risk for manifestations of racism that differ from the African American experience. Asians, Hispanics, and, more recently, Arabs and Muslims are subject to assumptions that they are not U.S. citizens and, therefore, lack the rights and social entitlements that other U.S. residents claim ( Chou and Feagin, 2015 ; Cobas et al., 2009 ; Feldman, 2015 ; Gee et al., 2009 ; Johnson, 2002 ; Khan and Ecklund, 2013 ). The implications of this include threats or actual physical violence against members of these groups. For instance, researchers have found that in the months immediately following September 11, 2001, U.S. women with Arabic surnames who were residing in California experienced increases in both racial microaggressions (i.e., seemingly minor forms of “everyday racism”) and in poor birth outcomes compared to the 6 months preceding 9/11, while women of other U.S. ethnic groups did not ( Kulwicki et al., 2008 ; Lauderdale, 2006 ). For Native Americans, because tribes are independent nations, the issues of racism need to be considered to intersect with those of sovereignty ( Berger, 2009 ; Massie, 2016 ; Sundeen, 2016 ).

The evidence linking racism to health disparities is expanding rapidly. A variety of both general and disease-specific mechanisms have been identified; they link racism to outcomes in mental health, cardiovascular disease, birth defects, and other outcomes ( Paradies, 2006a ; Pascoe and Smart Richman, 2009 ; Shavers et al., 2012 ; Williams and Mohammed, 2009 ). Which racism mechanisms matter most depends in part on the disease and, to a lesser degree, the population. The vast majority of studies focus on the role of discrimination; that is racially disparate treatment from another individual or, in some cases, from an institution. Among the studies not focused on discrimination, the majority examine segregation. Generally, findings show that members of all groups, including whites, report experiencing racial discrimination, with levels typically, though not always, higher among African Americans and, to a lesser degree, Hispanics than among whites. Gender differences in some perceptions about and responses to racism have also been observed ( Otiniano Verissimo et al., 2014 ). Three major mechanisms by which systemic racism influences health equity—discrimination (including implicit bias), segregation, and historical trauma—are discussed in more detail in the following paragraphs.

Discrimination

The mechanisms by which discrimination operates include overt, intentional treatment as well as inadvertent, subconscious treatment of individuals in ways that systematically differ so that minorities are treated worse than nonminorities. Recent meta-analyses suggest that racial discrimination has deleterious effects on the physical and mental health of individuals ( Gee et al., 2009 ; Paradies, 2006a ; Pascoe and Smart Richman, 2009 ; Priest et al., 2013 ; Williams and Mohammed, 2009 ). Significant percentages of members of racial and ethnic minority populations report experiencing discrimination in health care and non-health care settings ( Mays et al., 2007 ). Greater proportions of African Americans than members of other groups report either experiencing discrimination personally or perceiving it as affecting African Americans in general, even if they have not experienced it personally. Hate crimes motivated by race or ethnicity bias disproportionately affect Hispanics and African Americans ( UCR, 2015 ) (see the public safety section in this chapter for more on hate crimes).

Discrimination is generally associated with worse mental health ( Berger and Sarnyai, 2015 ; Gee et al., 2009 ; Paradies, 2006b ; Williams and Mohammed, 2009 ); greater engagement in risky behaviors ( Gee et al., 2009 ; Paradies, 2006b ; Williams and Mohammed, 2009 ); decreased neurological responses ( Harrell et al., 2003 ; Mays et al., 2007 ) and other biomarkers signaling the dysregulation of allostatic load; hypertension-related outcomes ( Sims et al., 2012 ), though some evidence suggests racism does not drive these outcomes ( Roberts et al., 2008 ); reduced likelihood of some health protecting behaviors ( Pascoe and Smart Richman, 2009 ); and poorer birth-related outcomes such as preterm delivery ( Alhusen et al., 2016 ). Paradoxically, despite higher levels of exposure to discrimination, the mental health consequences may be less severe among African Americans than they are among members of other groups, especially Asian populations ( Gee et al., 2009 ; Williams and Mohammed, 2009 ). Researchers have suggested that African Americans draw on reserves of resilience in ways that temper the effects of discrimination on mental health ( Brown and Tylka, 2011 ).

Though people may experience overt forms of racism (e.g., being unfairly fired on the basis of race), the adverse health effects of racism appear to stem primarily from the stress of chronic exposure to seemingly minor forms of “everyday racism” (i.e., racial microaggressions), such as being treated with less respect by others, being stopped by police for no apparent reason, or being monitored by salespeople while shopping ( APA, 2016 ; Sue et al., 2007 ; Williams et al., 2003 ). The chronic exposure contributes to stress-related physiological effects. Thus, discrimination appears to exert its greatest effects not because of exposure to a single life traumatic incident but because people must mentally and physically contend with or be prepared to contend with seemingly minor insults and assaults on a near continual basis ( APA, 2016 ). The implications appear to be greatest for stress-related conditions such as those tied to hypertension, mental health outcomes, substance abuse behaviors, and birth-related outcomes (e.g., low birth weight and premature birth) than for other outcomes ( Williams and Mohammed, 2009 ).

Higher socioeconomic status (SES) does not protect racial and ethnic minorities from discriminatory exposures. In fact, it may increase opportunities for exposure to discrimination. The concept of “John Henryism” is used to describe an intensely active way of tackling racial and other life challenges ( James, 1994 ). Though the evidence is mixed, John Henryism may contribute to worse cardiovascular outcomes among African American males who respond to racism by working even harder to disprove racial stereotypes ( Flaskerud, 2012 ; Subramanyam et al., 2013 ).

Implicit bias John Dovidio defines implicit bias—a mechanism of unconscious discrimination—as a form of racial or other bias that operates beneath the level of consciousness ( Dovidio et al., 2002 ). Research conducted over more than four decades finds that individuals hold racial biases of which they are not aware and, importantly, that discriminatory behaviors can be predicted based on this construct ( Staats et al., 2016 ). The effects are greatest in situations marked by ambiguity, stress, and time constraints ( Bertrand et al., 2005 ; Dovidio and Gaertner, 2000 ). Implicit bias is not an arbitrary personal preference that individuals hold; for example, “I just happen to prefer pears over apples.” Rather, the nature and direction of individuals' biases are structured by the racial stratification and norms of society. As a result, they are predictable.

Much of the public health literature has focused on the implicit biases of health care providers, who with little time to devote to each patient can provide care that is systematically worse for African American patients than for white patients even though the health care provider never intended to do so ( IOM and NRC, 2003 ; van Ryn and Burke, 2000 ). The evidence is clear that unconscious racialized perceptions contribute to differences in how various individual actors, including health care providers, perceive others and treat them. Based on psychology lab experiments, functional magnetic resonance imaging (fMRI) pictures of the brain, and other tools, researchers find that white providers hold implicit biases against African Americans and that, to a lesser degree, some minority providers may also hold these biases ( Hall et al., 2015 ). Although not limited to health care professionals, the biases lead providers to link negative characteristics (e.g., bad) and emotions (e.g., fear) with people or images they perceive as being African American ( Zestcott et al., 2016 ). As a result of such implicit biases, physicians treat patients differently depending on the patient's race, ethnicity, gender, or other assumed or actual characteristics ( IOM and NRC, 2003 ; Zestcott et al., 2016 ).

Given the importance of implicit bias, researchers have considered the role of health care provider–patient racial and ethnic concordance. Even if patients have similar clinical profiles, their care may differ systematically based on their race or ethnicity and that of their health care provider ( Betancourt et al., 2014 ; van Ryn and Fu, 2003 ; Zestcott et al., 2016 ). The evidence on whether and how patient–provider concordance contributes to health disparities is mixed ( van Ryn and Fu, 2003 ). Qualitative and quantitative findings suggest that patients do not necessarily prefer providers of the same race or ethnicity; they prefer a provider who treats them with respect ( Dale et al., 2010 ; Ibrahim et al., 2004 ; Schnittker and Liang, 2006 ; Volandes et al., 2008 ). Providers appear to evaluate African American patients more negatively than they do similar white patients; seem to perceive them as more likely to participate in risky health behaviors; and may be less willing to prescribe them pain medications and narcotics medications ( van Ryn and Fu, 2003 ). In a video-based study conducted among primary care providers, the odds ratio of providers referring simulated African American patients to otherwise identical white patients for cardiac catheterization was 0.6 ( Schulman et al., 1999 ). Some evidence suggests minority providers deliver more equitable care to their diverse patients than white providers. For instance, a longitudinal study among African American and white HIV-positive patients enrolled in HIV care found that white doctors took longer to prescribe protease inhibitors (an effective HIV medication) for their African American patients than for their clinically similar white patients. Providers prescribed them on average 162 days earlier for white patients than for comparable African American patients ( King et al., 2004 ). Among African American providers, there was no difference between African American and white patients in how long before providers prescribed the medications.

Racial and ethnic minority providers play an important role in addressing disparities because they help bridge cultural gulfs ( Butler et al., 2014 ; Cooper et al., 2003 ; Lehman et al., 2012 ), and greater proportions of them serve minority and socially disadvantaged communities ( Cooper and Powe, 2004 ); however, these providers are underrepresented in the health professions, and they face challenges that may constrain their professional development and the quality of care they are able to provide ( Landrine and Corral, 2009 ). Specifically, they are more likely to serve patients in resource-poorer areas and lack professional privileges associated with academic and other resource-rich institutions. The structural inequities have implications not only for individual clinicians but also for the patients and communities they serve. Pipeline programs that grow the numbers of minority providers may help to address underrepresentation in the health professions. The available data suggest that pipeline participants are more likely to care for poor or underserved patients when they join the workforce ( McDougle et al., 2015 ). Supporting the professional development of and expanding the resources and tools available to providers working in resource-poor communities seems to be one option for improving access to and quality of care; however, the literature does not clearly elucidate the relationship between health care workforce pipeline programs (e.g., to grow the numbers of minority providers) and their impact on the social determinants of health for poor and underserved communities ( Brown et al., 2005 ; Smith et al., 2009 ). A commitment to equity is not enough to remedy the discriminatory treatment that results from implicit biases because the inadvertent discriminatory behavior co-occurs alongside deeply held personal commitments to equity. Identifying implicit biases and acknowledging them is one of the most effective steps that can be taken to address their effects ( Zestcott et al., 2016 ). Trainings can help health care providers identify their implicit biases. Well-planned allocations of resources, including time, may afford them sufficient opportunity to account for it while serving diverse persons/patients.

Segregation

Residential segregation—that is, the degree to which groups live separately from one another ( Massey and Denton, 1988 )—can exacerbate the rates of disease among minorities, and social isolation can reduce the public's sense of urgency about the need to intervene ( Acevedo-Garcia, 2000 ; Wallace and Wallace, 1997 ). The effects of racial segregation differ from those of socioeconomic segregation. Lower SES whites are more likely to live in areas with a range of SES levels, which affords even the poorest residents of these communities access to shared resources (e.g., parks, schools) that buffer against the effects of poverty ( APA Task Force on Socioeconomic Status, 2007 ; North Carolina Institute of Medicine Task Force on Prevention, 2009 ). By contrast, racial and ethnic minorities are more likely to live in areas of concentrated poverty ( Bishaw, 2011 ). Indeed, if shared resources are of poor quality, they may compound the low SES challenges an individual faces. Racial segregation contributes to disparities in a variety of ways. It limits the socioeconomic resources available to residents of minority neighborhoods as employers and higher SES individuals leave the neighborhoods; it reduces health care provider density in predominately African American communities, which affects access to health care ( Gaskin et al., 2012 ); it constrains opportunities to engage in recommended health behaviors such as walking; it may be associated with greater density of alcohol outlets, tobacco advertisements, and fast food outlets in African American and other minority neighborhoods ( Berke et al., 2010 ; Hackbarth et al., 1995 ; Kwate, 2008 ; LaVeist and Wallace, 2000 ); it increases the risk for exposure to environmental hazards ( Brulle and Pellow, 2006 ); and it contributes to the mental and physical consequences of prevalent violence, including gun violence and aggressive policing ( Landrine and Corral, 2009 ; Massey and Denton, 1989 ; Polednak, 1996 ).

Historical Trauma

Historical trauma, “a collective complex trauma inflicted on a group of people who share a specific group identity or affiliation” ( Evans-Campbell, 2008, p. 320 ), manifests from the past treatment of certain racial and ethnic groups, especially Native Americans. This is another form of structural (i.e., systemic) racism that continues to shape the opportunities, risks, and health outcomes of these populations today ( Gee and Ford, 2011 ; Gee and Payne-Sturges, 2004 ; Heart et al., 2011 ). The past consignment of Native Americans to reservations with limited resources continues to constrain physical and mental health in these communities; however, the methods to support research on this topic have not yet been fully developed ( Heart et al., 2011 ). Additional details on the health of Native Americans are presented in Chapter 2 and Appendix A .

Interventions

The literature includes a small number of tested interventions. Interventions to address the health consequences of racism need not target racism in order to address the disparities it helps to produce. Furthermore, despite the deeply rooted nature of racism, communities are taking action to address the issue. (See Box 3-2 for a brief example of a community targeting structural racism and Box 3-3 for guidance on how to start a conversation about race.) Policy interventions and multi-sectoral efforts may be necessary to address structural factors such as segregation.

Addressing Structural Racism in Everett, Massachusetts, Through Improving Community–Police Interactions.

How to Start a Conversation on Race and Health (Excerpted from Culture of Health Prize Winner, Everett, Massachusetts).

Examples of interventions that target racism include the following:

• Dismantling racism by addressing factors in organizational settings and environments that “directly and indirectly contribute to racial health care disparities” ( Griffith et al., 2010, p. 370 ); see work by Derek Griffith ( Griffith et al., 2007 , 2010 ).
• The Undoing Racism project ( Yonas et al., 2006 ), which integrates community-based participatory research with the “undoing racism” process, which is built around community organizing.
• The Praxis Project, 3 a national organization whose mission is to build healthy communities by transforming the power relationships and structures that affect lives. The organization's comprehensive strategy for change includes policy advocacy, local organizing, strategic communications, and community research.

Although there is not a robust evidence base from which to draw solutions for implicit bias and its effects, there are promising strategies. For example, there is emerging evidence that mindfulness-based interventions have the potential to reduce implicit bias ( Kang et al., 2014 ; Levesque and Brown, 2007 ; Lueke and Gibson, 2014 ). One promising avenue of research involves models of self-regulation and executive control on interracial interaction ( Richeson and Shelton, 2003 ). Mindfulness has been shown to work on the cognitive brain function attentional processes involved in executive function, which is involved in decision making ( Lueke and Gibson, 2014 ; Malinowski, 2013 ). A key component of mindfulness is paying attention with intention and without judgment.

There is also existing literature that points to the need for community-based interventions to mitigate implicit bias within the context of criminal justice and community safety ( Correll et al., 2002 , 2007 ; La Vigne et al., 2014 ; Richardson and Goff, 2013 ). According to the National Initiative for Building Community Trust and Justice, implicit bias can shape the outcomes of interactions between police and residents, which in turn result in pervasive practices that focus suspicion on specific populations ( National Initiative for Building Community Trust and Justice, 2015 ). As discussed later in this chapter, the criminal justice system is a key actor and setting in shaping health inequity (see also Chapters 6 and 7 for more on criminal justice system as policy context and as a partner, respectively). Law enforcement agencies in communities around the country have employed strategies such as “principled policing” and policy changes and trainings to strengthen police–community relations ( Gilbert et al., 2016 ; Jones, 2016 ).

The Perception Institute, 4 an organization committed to generating evidence-based solutions for bias in education, health care, media, workplace, law enforcement, and civil justice, published a report authored by Godsil et al. (2014) in which promising interventions for implicit bias are highlighted ( Godsil et al., 2014 ). Among these interventions was a multipronged approach to reducing implicit bias that Devine and colleagues (2012) found to be successful and the “first evidence that a controlled, randomized intervention can produce enduring reductions in implicit bias” ( Devine et al., 2012, p. 1271 ). The multiple strategies of the intervention tested included stereotype replacement, counter-stereotype imaging, individuation, perspective taking, and increasing opportunities for contact. As discussed above, there is an emerging body of literature that is beginning to highlight promising solutions for implicit bias; however, that research base needs to be expanded further.

Recommendation 3-1: The committee recommends that research funders 5 support research on (a) health disparities that examines the multiple effects of structural racism (e.g., segregation) and implicit and explicit bias across different categories of marginalized status on health and health care delivery; and (b) effective strategies to reduce and mitigate the effects of explicit and implicit bias.

This could include implicit and explicit bias across race, ethnicity, gender identity, disability status, age, sexual orientation, and other marginalized groups.

There have been promising developments in the search for interventions to address implicit bias, but more research is needed, and engaging community members in this and other aspects of research on health disparities is important for ethical and practical reasons ( Minkler et al., 2010 ; Mosavel et al., 2011 ; Salway et al., 2015 ). In the context of implicit bias in workplaces and business settings, including individuals with relevant expertise in informing and conducting the research could also be helpful. Therefore, teams could be composed of such nontraditional participants as community members and local business leaders, in addition to academic researchers.

Conclusion 3-1: To reduce the adverse effects and the level of implicit bias among stakeholders in the community (such as health care workers, social service workers, employers, police officers, and educators), the committee concludes, based on its judgment, that community-based programs are best suited to mitigate the adverse effects of implicit bias. Successful community programs would be tailored to the needs of the community. However, proven strategies and efficacious interventions to reduce the effects of or mitigate effects of implicit bias are lacking. Therefore: Recommendation 3-2: The committee recommends that research funders support and academic institutions convene multidisciplinary research teams that include nonacademics to (a) understand the cognitive and affective processes of implicit bias and (b) test interventions that disrupt and change these processes toward sustainable solutions.

• SOCIAL DETERMINANTS OF HEALTH

As described earlier, structural inequities are produced on the basis of social identity (e.g., race, gender, and sexual orientation), and the social determinants of health are the “terrain” on which the effects play out. Traditionally, the most well-known and cited of the factors that shape health outcomes are the individual-level behavioral factors (e.g., smoking, physical activity, nutrition habits, and alcohol and drug use) that the evidence shows are proximally associated with individual health status and outcomes. As stated in Chapter 1 , understanding the social determinants of health requires a shift toward a more upstream perspective (i.e., the conditions that provide the context within which an individual's behaviors are shaped). Again, consider the metaphor of a fish, and the role of the conditions of the fishbowl in influencing the fish's well-being, and the analogy to human beings and conditions in which people live, learn, work, play, worship, and age that affect a wide range of health, functioning, and quality-of-life outcomes and risks. These environments and settings (e.g., school, workplace, neighborhood, and church) have been referred to as “place.” In addition to the more material attributes of “place,” the patterns of social engagement, social capital, social cohesion, and sense of security and well-being are also affected by where people live ( Braveman and Gottlieb, 2014 ; Healthy People 2020, 2016 ). Although the term “social determinants of health” is widely used in the literature, the term may incorrectly suggest that such factors are immutable. It is important to note that the factors included among the social determinants of health are indeed modifiable and that they can be influenced by social, economic, and political processes and policies. In fact, there are communities throughout the United States that have prioritized addressing the social determinants of health and are demonstrating how specific upstream strategies lead to improved community conditions and health-related outcomes. (See Chapter 5 for an in depth examination of nine community examples.) Although it might be more accurate to refer to social “contributing factors” for health, the committee continues to use the widely accepted word “determinants” in this report.

For the purposes of this report, the committee has identified nine social determinants of health (see report conceptual model, Figure 3-3 ) that the literature shows fundamentally influence health outcomes at the community level. These determinants are education, income and wealth, employment, health systems and services, housing, the physical environment, transporation, the social environment, and public safety ( Table 3-1 provides a brief definition of each).

Report framework for community solutions to promote health equity. NOTE: The social (and other) determinants of health are highlighted to convey the focus of this section.

The Social (and Other) Determinants of Health .

There is a vast and growing body of literature on the social, economic, and environmental determinants of health and their impacts on health outcomes ( Braveman and Gottlieb, 2014 ; Braveman et al., 2011 ; CSDH, 2008 ; Marmot et al., 2010 ). Often, the evidence is in the form of cross-sectional analyses, and the pathways to health outcomes are not always clearly delineated, in part due to the complexity of the mechanisms and the long time periods it takes to observe outcomes ( Braveman and Gottlieb, 2014 ). Therefore, the literature is not sufficient to establish a causal relationship between each of these determinants and health, but the determinants certainly are correlated with and contribute to health outcomes. While this report focuses on the community level, it should be made clear that the social determinants of health operate at multiple levels throughout the life course ( IOM, 2006 ). This includes the individual level (knowledge, attitudes/beliefs, skills), family and community level (friends and social networks), institutional level (relationships among organizations), and systemic level (national, state, and local policies, laws, and regulations) (see Figure 3-2 , the social ecological model adapted from McLeroy et al. [1988] ). Furthermore, the various levels of influence that the social determinants of health have can occur simultaneously and interact with one another ( IOM, 2006 ). In addition to the multiple levels of influence, there is a diversity of actors, sectors, settings, and stakeholders that interact with and shape the social determinants of health. This adds an additional layer of complexity to the factors that shape health disparities.

The following sections describe each of these nine determinants and how they shape health outcomes, as well as the disparities within these social determinants of health that contribute to health inequity. To highlight the ongoing work of communities that seek to address the conditions in which members live, learn, work, and play, this section will feature brief examples of communities for each determinant of health.

Education, as it pertains to health, can be conceptualized as a process and as an outcome. The process of educational attainment takes place in many settings and levels (e.g., the home/family, school, and community), while the outcome can be described as a sum of knowledge, skills, and capacities that can influence the other social determinants of health, or health, more directly ( Davis et al., 2016 ). Within the current social determinants of health literature, the primary focus on education is on educational attainment as an outcome (i.e., years of schooling, high school completion, and number of degrees obtained) and how it relates to health outcomes.

There is an extensive body of research that consistently demonstrates a positive correlation between educational attainment and health status indicators, such as life expectancy, obesity, morbidity from acute and chronic diseases, health behaviors (e.g., smoking status, heavy drinking physical activity, preventive services or screening behavior, automobile and home safety) and more ( Baum et al., 2013 ; Cutler and Lleras-Muney, 2006 , 2010 ; Feinstein et al., 2006 ; Krueger et al., 2015 ; Rostron et al., 2010 ). Educational attainment also has an intergenerational effect, in which the education of the parents, particularly maternal education, is linked to their children's health and well-being ( Cutler and Lleras-Muney, 2006 ). For example, research suggests that babies born to mothers who have not completed high school are twice as likely to die before their first birthday as babies who are born to college graduates ( Egerter et al., 2011b ; Mathews and MacDorman, 2007 ). Death rates are declining among the most-educated Americans, accompanied by steady or increasing death rates among the least educated ( Jemal et al., 2008 ). The findings on the association between education and health are consistent with population health literature within the international context as well ( Baker et al., 2011 ; Furnee et al., 2008 ; Marmot et al., 2010 ).

Even more noteworthy about the education and health relationship is the graded association that is observed across populations with varying education levels, commonly referred to as the “education gradient.” In the United States the gradient in health outcomes by educational attainment has steepened over the last four decades in all regions of the United States ( Goldman and Smith, 2011 ; Montez and Berkman, 2014 ; Olshansky et al., 2012 ), producing a larger gap in health status between Americans with high and low education. Specifically, trends in data suggest that, over time, the disparities in mortality and life expectancy by education level have been increasing ( Meara et al., 2008 ; Olshansky et al., 2012 ). Meara et al. found that approximately 20 percent of this trend was attributable to differential trends in smoking-related diseases in the 1980s and 1990s, despite the overall population increases in life expectancy during these two decades ( Meara et al., 2008 ). Economic trends and shifting patterns of employment, in which skilled jobs linked to educational attainment are associated with increased income, also have implications for health ( NRC, 2012 ). This makes the connection between education and health, mediated by employment opportunities, even more important and worth exploring.

Data from the Behavioral Risk Factor Surveillance System reveal that across all racial groups, adults with higher levels of educational attainment are less likely to rate their own health as less than very good ( Egerter et al., 2011b ). While the education gradient is present across racial and ethnic groups, it is important to keep in mind that the rates of educational attainment vary across different racial and ethnic groups. For the 2013–2014 academic year, the high school graduation rate for white students was 87.2 percent as compared with 76.3 percent among Hispanics, 72.5 percent among African Americans, and 70 percent among Native Americans ( Kena et al., 2016 ). These rates are consistent with high school diploma and bachelor degree achievement gaps that have persisted since the late 1990s (see Figures 3-4 and 3-5 ).

Percentage of 25- to 29-year-olds who completed at least a high school diploma or its equivalent, by race and ethnicity: Selected years, 1995–2015. NOTE: Race categories exclude persons of Hispanic ethnicity. Prior to 2005, separate data on persons (more...)

Percentage of U.S.-born population ages 25 years and older with a bachelor's degree or higher by race and Hispanic origin, 1988–2015. SOURCE: Ryan and Bauman, 2016.

Although the literature linking education and health is robust, there is still some debate as to whether or not this relationship is a causal one ( Baker et al., 2011 ; Fujiwara and Kawachi, 2009 ; Grossman, 2015 ). Issues that have been raised in the course of this debate include the role of reverse causation and the potential influence of any unobserved third variables ( Grossman, 2015 ). The association between education and health is clearly bidirectional. Education outcomes are substantially affected by health ( Cutler and Lleras-Muney, 2006 ). Students living in community conditions that contribute to hunger, chronic stress, or lack of attention to visual or hearing needs are likely to have problems concentrating in class ( Evans and Schamberg, 2009 ). Unmanaged health conditions (e.g., asthma, dental pain, acute illnesses, mental health issues, etc.) give rise to chronic absenteeism, which in turn is highly correlated with underachievement ( Ginsburg et al., 2014 ). In short, health issues are much more than minor distractions in the lives of students, especially students living in low-income communities.

Disparities in Education

Educational attainment, common measures of which include high school diploma or bachelor's degree, has increased for all race groups and Hispanics since 1988, according to U.S. Census estimates ( Ryan and Bauman, 2016 ). Despite this overall progress, the gaps between these groups have remained the same for some and increased for others. For example, in 1988 African Americans and Hispanics attained bachelor's degrees at very similar rates; however, by 2015 the percentage gap between African Americans and Hispanics had reached 7 percent, with rates of completion at 22 percent and 15 percent, respectively ( Ryan and Bauman, 2016 ). Furthermore, there has been little to no progress in closing the gap of achievement between whites and African Americans ( Ryan and Bauman, 2016 ).

A recent study of school trends conducted by the U.S. Government Accountability Office (GAO) found that there has been a large increase in schools that are distinguished by the poverty and race of their student bodies ( GAO, 2016 ). The percent of K–12 schools with students who are poor and are mostly African American or Hispanic grew from 9 percent to 16 percent from 2000 to 2013. These schools were the most racially and economically concentrated among all schools, with 75 to 100 percent of the students African American or Hispanic and eligible for free or reduced-price lunch—a commonly used indicator of poverty. Moreover, compared with other schools, these schools offered disproportionately fewer math, science, and college preparatory courses and had disproportionately higher rates of students who were held back in 9th grade, suspended, or expelled ( GAO, 2016 ).

One gap in educational achievement that has successfully been narrowed over the past five decades is the gender disparity in bachelor's degree attainment, in which men historically had higher achievement rates ( Crissey et al., 2007 ). In 2015 the percentage of men ages 25 or older with a bachelor's degree or higher was not statistically different from that of women, with women leading by one percentage point ( Ryan and Bauman, 2016 ).

The evidence suggests that disparities in education are apparent early in the life course, which reflects broader societal inequities ( Garcia, 2015 ). In education, these early disparities are evidenced by wide gaps in vocabulary between children from low-income and those from middle- or upper-income families. Children from low-income families may have 600 fewer words in their vocabulary by age 3, a gap that grows to as many as 4,000 words by age 7 ( Christ and Wang, 2010 ). These word gaps directly affect literacy levels and reading achievement ( Marulis and Neuman, 2010 ). There is substantial evidence that children who do not read at grade level by 7 or 8 years of age are much more likely to struggle academically ( Chall et al., 1990 ). Both high school graduation rates and participation in postsecondary education opportunities are correlated with early literacy levels. Hence, attention to and investments in early childhood education are generally viewed as an important way to reduce disparities in education ( Barnett, 2013 ).

Although the association between education and health is clear, the mechanisms by which educational attainment might improve health are not so clearly understood. A keen understanding of the mechanisms could help to inform the most cost-effective and targeted policies or solutions that seek to improve health and, ultimately, promote health equity ( Picker, 2007 ). Egerter et al. (2011b) identified multiple interrelated pathways through which education can affect health, based on the literature (see Figure 3-6 ). The three major pathways are the following:

Pathways through which education can affect health. SOURCE: Egerter et al., 2011b. Used with permission from the Robert Wood Johnson Foundation.

• Education increases health knowledge, literacy, coping, and problem solving, thereby influencing health behaviors;
• Research indicates that each additional year of education leads to almost 11 percent more income annually ( Rouse and Barrow, 2006 ), which can secure safer working environments and benefits such as health insurance and sick leave.
• Education has also been linked to human capital, a systematic way of thinking that benefits every decision, which could positively affect health decisions ( Cutler and Lleras-Muney, 2006 ; Lundborg et al., 2012 , 2016 ).

In this framework, note that educational attainment is a predictor of health and can either improve or hinder health outcomes depending on educational attainment. This suggests that policies and practices proven to increase academic performance and reduce education disparities are important to reducing health disparities. (See Box 3-4 for an example of a community school working to improve educational outcomes.) Intervening early is generally considered a high-impact strategy ( Barnett, 2013 ). However, interventions that support academic achievement in high schools and in postsecondary settings are also important to increasing educational attainment ( Balfanz et al., 2007 ; Carnahan, 1994 ; Kirst and Venezia, 2004 ; Louie, 2007 ). One of the key factors in both high school and college completion rates has to do with how well students transition from one level of the education system to another ( Rosenbaum and Person, 2003 ).

Reagan High School: A Community School.

Income and Wealth

Income can be defined broadly as the amount of money earned in a single year from employment, government assistance, retirement and pension payments, and interest or dividends from investments or other assets ( Davis et al., 2016 ). Income can fluctuate greatly from year to year depending on life stage and employment status. Wealth, or economic assets accumulated over time, is calculated by subtracting outstanding debts and liabilities from the cash value of currently owned assets—such as houses, land, cars, savings accounts, pension plans, stocks and other financial investments, and businesses. Wealth measured at a single time period may provide a more complete picture than income of a person's economic resources. Moreover, wealth has an intergenerational component, which can have implications for who has access to wealth and who does not ( De Nardi, 2002 ).

Access to financial resources, be it income or wealth, affects health by buffering individuals against the financial threat of large medical bills while also facilitating access to health-promoting resources such as access to healthy neighborhoods, homes, land uses, and parks ( Davis et al., 2016 ). Income can predict a number of health outcomes and indicators, such as life expectancy, infant mortality, asthma, heart conditions, obesity, and many others ( Woolf et al., 2015 ).

Income Inequality and Concentration of Poverty

Income inequality is rising in the United States at a rate that is among the highest in the economically developed countries in the north ( OECD, 2015 ). The past few decades have seen dramatic rises in income inequality. In 1970, 17 percent of families lived in upper-income areas, 65 percent in middle-income areas, and 19 percent in lowest-income areas; in 2012, 30 percent of families lived in upper-income areas, 41 percent in middle-income areas, and 30 percent in lowest-income areas ( Reardon and Bischoff, 2016 ). In 2013, the top 10 percent of workers earned an average income 19 times that of the average income earned by the bottom 10 percent of workers; in the 1990s and 1980s, this ratio was 12.5 to 1 and 11 to 1, respectively ( OECD, 2015 ). Furthermore, households earning in the bottom 10 percent have not benefited from overall increases in household income over the past few decades; the average inflation-adjusted income for this population was 3.3 percent lower in 2012 than in 1985 ( OECD, 2015 ). Disparities in life expectancy gains have also increased alongside the rise in income inequality. From 2001 to 2014, life expectancy for the top 5 percent of income earners rose by about 3 years while life expectancy for the bottom 5 percent of income earners saw no increase ( Chetty et al., 2016 ).

Not only are income and wealth determinants of health, but the concentration of poverty in certain neighborhoods is important to recognize as a factor that shapes the conditions in which people live. Concentrated poverty , measured by the proportion of people in a given geographic area living in poverty, can be used to describe areas (e.g., census tracts) where a high proportion of residents are poor ( Shapiro et al., 2015 ). Concentrated poverty disproportionately affects racial and ethnic minorities across all of the social determinants of health. For example, National Equity Atlas data reveal that in about half of the largest 100 cities in the United States, most African American and Hispanic students attend schools where at least 75 percent of all students qualify as poor or low-income under federal guidelines ( Boschma, 2016 ). Given that concentrated poverty is tightly correlated with gaps in educational achievement, this has implications for educational outcomes and health ( Boschma and Brownstein, 2016 ).

Disparities Related to Income Inequality

In 2012, of the 12 million full-time low-income workers between the ages of 25 and 64, 56 percent were racial and ethnic minorities ( Ross, 2016b ). Regional percentages varied from 23 percent in Honolulu, Hawaii, to 65 percent in Brownsville, Texas ( Ross, 2016a ). Figure 3-7 shows the proportion of low-income workers of racial and ethnic minority groups across different regions of the United States. The burden faced by low-income people suggests that efforts to advance health equity through income and wealth will need to take into consideration rising income inequality as well as significant geographic variation.

The share of people of color below 200 percent of poverty ranges. SOURCE: Woolf et al., 2015. Used with permission from PolicyLink, figure from article by Angel Ross, New Data Highlights Vast and Persistent Racial Inequities in Who Experiences Poverty (more...)

Chetty and colleagues published the largest study of its kind, using 1.4 billion income tax and Social Security records to report the association between income level and life expectancy from 1999 through 2014 ( Chetty et al., 2016 ). Consistent with previous findings ( NASEM, 2015 ; Waldron, 2007 ; Woolf et al., 2015 ), they found that higher income is related to higher life expectancy and that lower income is related to lower life expectancy. The gap in life expectancy for the richest and poorest 1 percent of individuals was 14.6 years for men and 10.1 years for women. A novel contribution of the study is its examination of the income–longevity relationship across time and local areas. In certain local areas, the effect of being at the bottom of the income gradient is more pronounced than in others, with four- to five-fold differences. This strong local component reinforces the notion suggested by the literature that place matters. Trends in life expectancy also varied geographically, with some areas experiencing improvements and others declines. Others have commented on the limitations of the study ( Deaton, 2016 ; McGinnis, 2016 ; Woolf and Purnell, 2016 ).

Zonderman et al. take the findings of this study a step further by considering the role of race and gender differences in the relationship between poverty and mortality. They found that while African American men below poverty status had 2.66 times higher risk of mortality than African American men living above poverty status, white men below poverty status had approximately the same risk as white men living above poverty status ( Zonderman et al., 2016 ). Both African American women and white women living below poverty status were at an increased mortality risk relative to those living above poverty status ( Zonderman et al., 2016 ).

Infant mortality rates in the United States rank among the highest for developed nations ( NRC and IOM, 2013 ), and mortality rates for infants born to low-income mothers are even higher. Studies have shown an inverse correlation between family income and infant mortality ( Singh and Yu, 1995 ) as well as a positive correlation between income inequality (measured with the Gini coefficient) and infant mortality ( Olson et al., 2010 ). Infants born to low-income mothers have the highest rates of low birth weight ( Blumenshine et al., 2010 ; Dubay et al., 2001 ).

Chronic diseases are more prevalent among low-income people than among the overall U.S. population. Low-income adults have higher rates of heart disease, diabetes, stroke, and other diseases and conditions relative to adults earning higher levels of income ( Woolf et al., 2015 ).

Researchers have offered various hypotheses about the multiple mechanisms by which income can affect health. Woolf et al. suggest that among others, these mechanisms include more income providing the opportunity to afford health care services and health insurance; greater resources affording a healthy lifestyle and access to place-based benefits known as the social determinants of health; and economic disadvantage and hardship leading to stress and harmful physiological effects on the body ( Woolf et al., 2015 ). Evans and Kim identify “multiple risk exposure” as a potential mechanism for the socioeconomic status and health gradient. This is the convergence among populations with low socioeconomic status of multiple physical and psychosocial risk factors such as poor housing and neighborhood quality, pollutants and toxins, crowding and congestion, noise exposure, and adverse interpersonal relationships ( Evans and Kim, 2010 ).

Wealth affects health through mechanisms that are not necessarily monetary, such as power and prestige, attitudes and behavior, and social capital ( Pollack et al., 2013 ). Even in the absence of income, wealth can provide resources and a safety net that is not available to those without it. (See Box 3-5 for an example of an initiative seeking to build income and wealth in communities around the country.)

Family Independence Initiative: The Power of Information and Investment in Families Who Take Initiative.

Employment is the level or absence of adequate participation in a job or workforce, including the range of occupation, unemployment, and underemployment. Work influences health not only by exposing employees to certain physical environments but also by providing a setting where healthy activities and behaviors can be promoted ( An et al., 2011 ). For most adults, employment is the main source of income, thus providing access to homes, neighborhoods, and other conditions or services that promote health. The features of a worksite, the nature of the work, the amount of earnings or income, and how the work is organized can affect worker mental and physical health ( An et al., 2011 ; Clougherty et al., 2010 ). Many Americans also obtain health insurance through their workplace, accounting for another potential impact on health and wellbeing. While the correlation between employment and health has been well established, there appears to be a bidirectional relationship between employment and health, as health also affects one's ability to participate in and maintain stable employment ( Davis et al., 2016 ; Goodman, 2015 ). Not only that, but a healthy workforce is a prerequisite for economic success in any industry ( Doyle et al., 2005 ).

The existing literature on the social determinants of health makes it clear that there is a positive correlation between SES and health ( Adler and Stewart, 2010a ; Braveman et al., 2005 ; Conti et al., 2010 ; Dow and Rehkopf, 2010 ; Pampel et al., 2010 ; Williams et al., 2010 ). Occupational status, a composite of the power, income, and educational requirements associated with various positions in the occupational structure, is a core component of a person's SES ( Burgard and Stewart, 2003 ; Clougherty et al., 2010 ). Occupational status can be indicative of the types of tangible benefits, hazards, income, fringe benefits, degree of control over work, and level of exposure to harmful physical environments associated with a job ( Clougherty et al., 2010 ). While the mechanisms by which occupational status influences health have not clearly been delineated, there is evidence that the type of job does affect such health outcomes as hypertension risk and obesity ( An et al., 2011 ; Clougherty et al., 2010 ).

On the other end of the spectrum, unemployment is associated with poor psychological well-being ( McKee-Ryan et al., 2005 ; Paul and Moser, 2009 ). Zhang and Bhavsar (2013) examined the literature to illuminate the causality, effect size, and moderating factors of the relationship between unemployment as a risk factor and mental illness as an outcome. The authors reported that unemployment does precede mental illness, but more research is required to determine the effect size ( Zhang and Bhavsar, 2013 ). There is also evidence to suggest that emerging adults who are unemployed are three times as likely to suffer from depression as their employed counterparts ( McGee and Thompson, 2015 ). Burgard and colleagues found that even after controlling for significant social background factors (e.g., gender, race, education, maternal education, income, and more), involuntary job loss was associated with poorer overall self-rated health and more depressive symptoms ( Burgard et al., 2007 ).

Disparities in Employment

Employment data show disparities in unemployment rates across various racial and ethnic groups and geographic regions, despite the overall progress that has been made in reducing unemployment nationally ( Wilson, 2016 ). During the fourth quarter of 2015, the highest state-level unemployment rate was 13.1 percent for African Americans (Illinois), 11.9 percent for Hispanics (Massachusetts), 6.7 percent for whites (West Virginia), and 4.3 percent for Asians (New York) ( Wilson, 2016 ). Figure 3-8 shows how disparities in unemployment by race and ethnicity have persisted for more than 40 years, with the exception of whites and Asians. Disparities in employment between African Americans and whites persist even when level of education, a major predictor of employment, is held equal between the two groups ( Buffie, 2015 ).

Unemployment rates by race and Hispanic or Latino ethnicity, 1973–2013 annual averages. NOTE: People whose ethnicity is identified as Hispanic or Latino may be of any race. Data for Asians are only available since 2000. SOURCE: BLS, 2014.

Among the employed, there are systematic differences in wages and earnings by race, ethnicity, and gender. According to the U.S. Bureau of Labor Statistics, in 2013 the median usual weekly earnings 6 were $578 for Hispanics, $629 for African Americans, $802 for whites, and $942 for Asians ( BLS, 2014 ). These disparities are consistent across almost all occupational groups. The widest gap in median usual weekly earnings was found between Hispanic women and Asian men, who made $541 and $1,059, respectively ( BLS, 2014 ).

As with income, the distribution of occupations tends to differ across racial and ethnic groups (see Figure 3-9 ). Whereas half of Asians worked in management, professional, and related occupations in 2013, only 29 and 20 percent of African Americans and Hispanics, respectively, worked in those professions ( BLS, 2014 ).

Employed people by occupation, race, and Hispanic or Latino ethnicity, 2013 annual averages. NOTE: People whose ethnicity is identified as Hispanic or Latino may be of any race. Data may not sum to 100 percent due to rounding. SOURCE: BLS, 2014.

The literature suggests that there are three potential mechanisms through which employment affects health:

Physical aspects of work and the workplace

Psychosocial aspects of work and how work is organized

Work-related resources and opportunities ( An et al., 2011 ; Clougherty et al., 2010 )

The nature of work and the conditions of a workplace can increase the risk of injury or illness depending on the type of job. For employees in specific sectors (e.g., air transportation, nursing facilities, using motorized vehicles and equipment, trucking services, hospitals, grocery stores, department stores, food services), the risk of occupational injury is higher ( An et al., 2011 ). This is especially true for operators, laborers, fabricators, and laborers ( An et al., 2011 ). Occupational health can also be shaped by the physical nature of the tasks involved in a given work setting. For example, the health impact of a job that requires intense, laborious physical activity will be different than of a job in which the tasks are primarily sedentary. There is also emerging evidence suggesting that women working hourly jobs bear a larger burden due to hazardous conditions in the workplace than their male counterparts on outcomes such as hypertension, the risk of injury, injury severity, rates of absenteeism, and the time to return to work after illness ( Clougherty et al., 2010 ; Hill et al., 2008 ).

The psychosocial aspects and organization of one's job can influence both mental and physical health. The factors that make up this pathway can include work schedules, commute to work, degree of control in work, the balance between effort and rewards, organizational justice, social support at work, and gender and racial discrimination ( An et al., 2011 ). Longer commute times specifically affect low-income populations, as the cost burden of commuting for the working poor is much higher than for other workers and makes up a larger portion of their household budgets ( Roberto, 2008 ).

The resources and opportunities associated with work can have lasting implications for health. Higher-paying jobs are more likely than lower-paying jobs to provide workers with safe work environments and offer benefits such as health insurance, workplace health promotion programs, and sick leave ( An et al., 2011 ). Box 3-6 briefly describes a program that aims to increase “green” employment opportunities for underserved individuals in a community.

Green Jobs Central Oklahoma.

Health Systems and Services

Health care is arguably the most well-known determinant of health, and it is traditionally the area where efforts to improve health have been focused ( Heiman and Artiga, 2015 ). Over the past few decades there has been a paradigm shift that reflects “health” care over “sick” care. The idea is to promote access to effective and affordable care that is also culturally and linguistically appropriate. Health care spans a wide range of services, including preventative care, chronic disease management, emergency services, mental health services, dental care, and, more recently, the promotion of community services and conditions that promote health over the lifespan.

Although screening, disease management, and clinical care play an integral role in health outcomes, social and economic factors contribute to health outcomes almost twice as much as clinical care does ( Heiman and Artiga, 2015 ; Hood et al., 2016 ; McGinnis et al., 2002 ; Schroeder, 2007 ). For example, by some estimates, social and environmental factors proportionally contribute to the risk of premature death twice as much as health care does ( Heiman and Artiga, 2015 ; McGinnis et al., 2002 ; Schroeder, 2007 ). That being said, in March 2002, the Institute of Medicine released a report that demonstrated that even in the face of equal access to health care, minority groups suffer differences in quality of health. The noted differences were lumped into the categories of patient preferences and clinical appropriateness, the ecology of health systems and discrimination, bias, and stereotyping ( IOM and NRC, 2003 ). Our health systems are working to better understand and address these differences and appreciate the importance of moving beyond individualized care to care that affects families, communities, and populations ( Derose et al., 2011 ). This new focus on improving the health of populations has been accompanied by a welcome shift from siloed care to a health care structure that is interprofessional, multisectoral and considers social, economic, structural and other barriers to health ( NASEM, 2016 ).

Arriving at the place of shared understanding concerning the health care needs of individuals, families, and communities has required taking a broader look at health. The triple aim, a framework that aims to optimize health system performance, has helped conceptualize this look, bringing to the forefront the elements that matter most, considering per capita cost, improving the health care experience for patients, and focusing on population health ( Stiefel and Nolan, 2012 ). In addition to helping create new health care opportunities, the Patient Protection and Affordable Care Act (ACA) has helped mitigate the challenge of access to care. According to the U.S. Centers for Disease Control and Prevention (CDC), the proportion of people in 2015 without health insurance had dropped below 10 percent ( Cohen et al., 2016c ).

Continuing the momentum of improving access to culturally competent and linguistically appropriate care will be a crucial step to improving the health of populations. Culturally and linguistically appropriate care includes high-quality care and clear communication regardless of socioeconomic or cultural background ( Betancourt and Green, 2010 ). There is limited research studying whether there is a link between culturally appropriate care and health outcomes, but data do exist that indicate that behavioral and attitudinal elements of cultural competence facilitate higher-quality relationships between physicians and patients ( Paez et al., 2009 ). Making cultural competency training a part of the all types of providers' (e.g., physicians, nurses, medical assistants, dentists, pharmacists, social workers, psychologists) education experience, as well as making it a requirement for licensure for providers ( Like, 2011 ), may have the potential to link quality and safety. Continued work is needed to figure out how to translate increased access to care into improved health outcomes and increased health equity.

In light of the ACA's emphasis on access to improving quality, health outcomes, and population health, it makes sense to look at the environments in which patients live. 7 If the social determinants of health are not addressed in a multi-sectoral approach by educational systems, health systems, communities and others, the country will fall short of the triple aim. The Robert Wood Johnson Foundation's Culture of Health Action Framework has identified action areas meant to work together to address issues of equity, well-being, and improved population health ( RWJF, 2015b ). Social determinants of health are woven through these action areas. In fact, research shows that social determinants of health play a larger role in health outcomes than do medical advances ( Hood et al., 2016 ; Woolf et al., 2007 ).

Disparities

While some disparities in access to care have been narrowing, gaps persist among certain groups of the population. For example, the gaps in insurance that existed between poor and nonpoor households and between African Americans and whites or Hispanics and whites decreased between 2010 and 2015 ( AHRQ, 2016 ). However, systematic differences in access to care still exist and negatively affect poor households and racial and ethnic minority groups, including Hispanics and African Americans ( NCHS, 2016 ) (see Figure 3-10 ). In fact, in 2013 people living below the federal poverty level had worse access to care than people in high-income households across all access measures 8 ( NCHS, 2016 ). People living in low-income households are at an elevated risk of poor health, and access to care is vital for this vulnerable population. The ACA authorized states to expand Medicaid coverage to adults with low incomes up to 138 percent of the poverty level. From 2013 to 2014, the percent of adults who were uninsured declined in all states, with the decline in the number of uninsured being greater in the states that opted to expand their Medicaid programs ( NCHS, 2016 ).

FIGURE 3-10

Percent of adults ages 18–64 with no health insurance coverage by race and Hispanic origin: United States, 1999–June 2015. SOURCE: NCHS, 2016.

Racial and ethnic disparities in mental health services exist as well. Members of racial and ethnic minority groups are less likely than whites to receive necessary mental health care and more likely to receive poor-quality care when treated. Specifically, minority patients are less likely than whites to receive the best available treatments for depression and anxiety ( McGuire and Miranda, 2008 ). Among the barriers to access to care, the lack of culturally competent care can be a barrier for specific racial and ethnic groups who face stigma due to cultural norms ( Wahowiak, 2015 ).

The health care system has an important role to play in addressing the social determinants of health. At the community level, it can partner with community-based organizations and explore locally based interventions ( Heiman and Artiga, 2015 ), creating payment models that take into account social determinants and implementing service delivery models that lend themselves to more community engagement and intervention. Health care systems can center equity by involving the community in decision making, allocating resources to act on the determinants of health in mind, and increasing community-based spending ( Baum et al., 2009 ). Communities can be viewed as places of change for health systems, allowing for work both at micro and macro levels. (See Box 3-7 for an example of a community-based health system.) Cost-effective interventions to reduce health disparities and promote health equity should be recognized and explored, including attention to the structural barriers that affect access to health services.

Kokua Kalihi Valley Comprehensive Family Services.

Housing, as a social determinant of health, refers to the availability or lack of availability of high-quality, safe, and affordable housing for residents at varying income levels. Housing also encompasses the density within a housing unit and within a geographic area, as well as the overall level of segregation and diversity in an area based on racial and ethnic classifications or SES. Housing affects health because of the physical conditions within homes (e.g., lead, particulates, allergens), the conditions in a multi-residence structure (an apartment building or town home), the neighborhoods surrounding homes, and housing affordability, which affects financial stability and the overall ability of families to make healthy choices ( Krieger and Higgins, 2002 ). The Center for Housing Policy has outlined 10 hypotheses on how affordable housing can support health improvement ( Maqbool et al., 2015 ). These range from affordable housing freeing up resources for better nutrition and health care spending to stable housing reducing stress and the likelihood of poor health outcomes (e.g., for mental health or the management of chronic disease).

There is substantive evidence that the physical conditions in homes are important contributors to health outcomes ( Cox et al., 2011 ; WHO, 2006 ). The World Health Organization (WHO) assessed the evidence in 2005 and found that sufficient evidence was available to estimate the burden of disease for physical factors, such as temperature extremes; chemical factors, such as environmental tobacco smoke and lead; biological factors, such as mold and dust mites; and building factors associated with injuries and accidents. Since 2005 research has added to the areas where the WHO found some, but not sufficient, evidence to estimate the burden of disease, including more clarity on the relationship between rodent allergens and asthma ( Ahluwalia et al., 2013 ; American College of Allergy Asthma and Immunology, 2014 ; Sedaghat et al., 2016 ). Data from the National Health and Nutrition Examination Survey show a decrease in blood lead levels between 1976 and 2002, with a steep drop between 1978 and 1988, probably due to lead being phased out of gasoline, and later a more gradual decrease, perhaps due to a reduction in the use of lead-based paint in housing ( Jacobs et al., 2009 ). Conditions in multiunit residential buildings, including whether indoor smoking is permitted, are another dimension of housing that can affect health outcomes. Box 3-8 introduces the revitalization efforts of one multiunit apartment complex in a community in Minnesota.

Renovating the Rolling Hills Apartment Complex, St. Paul, Minnesota.

Neighborhoods matter for a number of reasons, including their influence on physical safety and access to opportunity. The U.S. Department of Housing and Urban Development's (HUD's) Moving to Opportunity program was a 10-year demonstration program, which provided grants to public housing authorities in Baltimore, Boston, Chicago, Los Angeles, and New York City to implement an experimental study—a randomized controlled trial of a housing intervention. Housing authorities

randomly selected experimental groups of households with children [to] receive housing counseling and vouchers that must be used in areas with less than 10 percent poverty. Families chosen for the experimental group receive tenant-based Section 8 rental assistance that helps pay their rent, as well as housing counseling to help them find and successfully use housing in low-poverty areas. Two control groups are included to test the effects of the program: one group already receiving Section 8 assistance and another just coming into the Section 8 program. ( HUD, n.d .)

Homeless Populations

For homeless people, a lack of stable housing contributes to disparities in the social determinants. In addition to having direct ties with lack of employment and income, a lack of housing is also associated with greater barriers to education, lower levels of food security, and reduced public safety. Compared to the overall population, homeless people have shorter life expectancies, which are attributable to higher rates of substance abuse, infectious disease, and violence ( Baggett et al., 2013 ). Infectious diseases—including HIV, tuberculosis, and heart disease—have all been linked to shorter life expectancies among homeless people ( Fazel et al., 2014 ). Other studies have found drug overdose, cancer, and heart disease to be the greatest causes of death among the homeless, with greater barriers to and lower rates of screening, diagnosis, and treatment as contributing factors ( Baggett et al., 2013 ).

The Changing American City

Neighborhoods generally change slowly, but urban neighborhoods are seeing dramatic shifts in demographics and property value and over time are becoming more segregated by income ( Zuk et al., 2015 ). Gentrification—the process of renewal and rebuilding, which precedes the influx of new, more affluent residents—is a trend that is being observed in urban centers around the country ( McKinnish et al., 2010 ; Phillips et al., 2014 ; Sturtevant, 2014 ). While the literature linking the process of gentrification to health outcomes is not definitive, there is substantial evidence that connects displacement and health outcomes ( Zuk et al., 2015 ). Displacement can occur as a direct result of a policy or program ( Freeman and Braconi, 2002 ), because of recent development and property value increases in an area, or as a result of exclusion from a property for various reasons ( Levy et al., 2006 ).

Displacement has major implications for housing, other social determinants, and the health of communities. According to the CDC, displacement exacerbates health disparities by limiting access to healthy housing, healthy food options, transportation, quality schools, bicycle and walk paths, exercise facilities, and social networks ( CDC, 2013 ). Displacement leads to poor housing conditions, including overcrowding and exposure to substandard housing with hazardous conditions (e.g., lead, mold, pests) ( Phillips et al., 2014 ). Displacement can result in financial hardship, reducing disposable income for essential goods and services. This can have a negative impact on the health of the displaced population, with income being a significant determinant of health ( CDC, 2013 ).

Physical Environment

The physical environment reflects the place, including the human-made physical components, design, permitted use of space, and the natural environment. Specific features of the physical or built environment include, but are not limited to, parks and open space, what is sold and how it is promoted, how a place looks and feels, air, water, soil, and arts and cultural expression ( Davis et al., 2016 ). All of these physical factors shape the safety, accessibility, and livability of any locale, thus providing the context in which people live, learn, work, and play. This has direct implications for health. The physical environment contributes to 10 percent of health outcomes ( Remington et al., 2015 ). Additionally, 40 percent of health outcomes depend on social and economic factors, which are intricately tied to the features of the physical environment ( Remington et al., 2015 ). Inequities observed between the different physical environments of states, towns, and neighborhoods contribute to disparate health outcomes among their populations.

Exposure to a harmful physical environment is a well-documented threat to community health. Such threats include environmental exposures such as lead, particulate matter, proximity to toxic sites, water contamination, air pollution, and more—all of which are known to increase the incidence of respiratory diseases, various types of cancer, and negative birth outcomes and to decrease life expectancy ( Wigle et al., 2007 ). Low-income communities and communities of color have an elevated risk of exposure to environmental hazards ( Evans and Kantrowitz, 2002 ). In response to these inequities, the field of environmental justice seeks to achieve the “fair treatment and meaningful involvement of all people regardless of race, color, national origin, or income, with respect to the development, implementation, and enforcement of environmental laws, regulations, and policies” ( EPA, 2016 ). Emerging considerations for low-income communities include the resulting gentrification and potential displacement of families when neighborhoods undergo revitalization that is driven by environmental clean-up efforts ( Anguelovski, 2016 ).

Built Environment: Parks and Green Space

Access to green space has been demonstrated to positively affect health in many contexts. Such green space includes both parks and observable greenery. Living in the presence of more green space is associated with a reduced risk of mortality ( Villeneuve et al., 2012 ). Nature has been shown to relieve stress and refocus the mind. Spending time in parks has been shown to improve mental health ( Cohen et al., 2016a ; Sturm and Cohen, 2014 ).

Beyond their benefits to mental health and reductions in stress, parks provide opportunities for increased physical activity. Local parks departments manage more than 108,000 outdoor public park facilities across the nation, many of them containing open space, jogging paths, and exercise equipment ( Cohen et al., 2016b ). According to Cohen et al., the average neighborhood park of 8.8 acres averaged 1,533 hours of active use per week ( Cohen et al., 2016b ). Individuals who are not as physically active face a greater risk of heart disease, diabetes, and cancer ( James et al., 2016 ). In fact, about 9 percent of premature deaths in the United States are attributable to inactivity ( Lee et al., 2012 ).

The usage of neighborhood parks and the associated health benefits are not equally distributed across communities. Research shows that recreational facilities are much less common in low-income and minority communities, though parks are more evenly distributed ( Diez Roux et al., 2007 ). Moreover, the size and quality of park facilities vary based on race and income ( Abercrombie et al., 2008 ). Accordingly, in low-income communities, residents are less likely to use parks ( Cohen et al., 2016a ). Beyond race and income, other disparities exist in park use. While seniors represent 20 percent of the population, they account for only 4 percent of park users ( Cohen et al., 2016a ). Proximity to park facilities also matters, as evidenced by a decrease in physical activity by more than half when distance between one's home and the park doubles ( Giles-Corti and Donovan, 2002 ).

Food Environment

The food environment refers to the availability of food venues such as supermarkets, grocery stores, corner stores, and farmer's markets, including food quality and affordability. In communities described as food deserts, there is limited access to affordable and quality food. When there are fewer supermarkets, fruit and vegetable intake is lower, and prices are higher ( Powell et al., 2007 ). This makes achieving a healthy diet difficult for local residents. Research indicates that a poor diet is associated with the development of cancer, diabetes, hypertension, birth defects, and heart disease ( Willett et al., 2006 ).

The distribution of supermarkets is not equitable in the United States. Neighborhoods housing residents of lower socioeconomic status often have fewer supermarkets. Discrepancies also exist between racial and ethnic groups ( Powell et al., 2007 ). Underserved communities turn to small grocery or corner stores to serve their food needs, but these businesses rarely provide the healthy selection offered by larger supermarkets. Moreover, food is most often higher priced in such stores.

Access to and the density of alcohol outlets are also associated with health outcomes in communities. In local areas where liquor store density is higher, alcohol consumption rates in the community are also higher ( Pereiram et al., 2013 ). Alcoholism has been linked to diseases such as cancer, anemia, and mental illnesses. Moreover, alcohol outlets can serve as nuisance businesses, with their clientele bothering others in the neighborhood, decreasing the sense of security, and detracting from social cohesion. There is also evidence that links high-density alcohol outlet areas with higher rates of crime and substance use. In urban environments, a higher concentration of liquor stores is found in low-income, African American, and Hispanic communities, contributing to an elevated risk of alcohol-associated disorders in these neighborhoods ( Berke et al., 2010 ).

A Changing Climate

Climate change has become a public health concern ( Wang and Horton, 2015 ). There is a growing recognition that the physical environment is undergoing changes caused by human activity, such as through the production of greenhouse gases ( IPCC, 2014 ). Human health is intricately linked to the places where we live, learn, work, and play. The air we breathe, the surrounding temperature, the availability of food, and whether there is access to clean water are all important ingredients to a healthy life, and the changing climate will affect all of these areas ( Luber et al., 2014 ).

Not only do polluting emissions make air quality worse in the short term, but climate change itself will worsen air quality. Poor air quality exacerbates previous health conditions such as asthma and chronic obstructive pulmonary disease, and air pollution is associated with cardiovascular disease and many other illnesses. The changing climate is also causing a shift in seasons, which can affect pollen production and therefore seasonal allergies. Overall, with the changing climate there will be more extreme weather events such as increasing drought, vulnerability to wildfires, floods, hurricanes, and winter storms—all with subsequent health impacts from displacement, stress, or primary physical harm. The changing temperature is even having an impact on infectious diseases. New infectious diseases that spread via a vector, such as a tick or mosquito, have the potential to emerge in previously non-affected areas. There is also a risk for an increase in food-related and waterborne illness caused by the changing temperatures and the survival of various infectious agents. Food insecurity, which is already a challenge in many locations, is at risk of worsening due to higher food prices, poorer nutritional content, and new challenges with distribution.

Although climate change will affect everyone, certain communities and groups will be more vulnerable to these effects. People with preexisting medical conditions, children, elderly populations, and low-income groups are at increased risk for poor outcomes. Existing health disparities that are due to social, economic, and environmental factors have the potential to be even more affected by climate change.

However, climate change also presents a significant opportunity. Given the existential threat to humanity, there is now a great deal of momentum to mitigate and adapt to climate change. Companies are pursuing new business opportunities, governments are forming international agreements, and policies are being implemented at the national, sub-national, state, regional, and local levels to affect change. Many of these policies to adapt to and mitigate climate change are also the key components in creating healthier, more equitable, and resilient communities. There are many co-benefits, and the policies, if implemented correctly, have the potential to significantly improve health outcomes and reduce health disparities ( Rudolph et al., 2015 ). Examples of climate change mitigation and adaptation policies with co-benefits to build healthier, more equitable places include

• Improving access to public transit;
• Promoting flexible workplace transit;
• Creating more complete streets for better pedestrian and bicycle use;
• Implementing urban greening programs;
• Reducing urban heat islands through green space, cool roofs, and cool pavements;
• Promoting sustainable food systems and improved access;
• Building more walkable, dense, affordable housing and amenities;
• Reducing greenhouse gases;
• Promoting weatherizing homes, energy efficiency, and green buildings; and
• Greening fleets and reducing emissions.

Climate change will affect the physical environment in unprecedented ways. To mitigate and adapt to climate change will require multi-sector collaboration and approaches to effect systems change. Many of the same multi-sector partners required to address the social determinants of health also are already partnering on related climate change work in their communities, creating a substantial opportunity for change (see Box 3-9 for an example of a community engaged in climate change–related work).

A Community Addressing Climate Change, Food Insecurity, and Improving Health Equity—Achieving Co-Benefits.

Transportation

In the social determinants of health literature, transportation is typically discussed as a feature of the physical (or built) environment ( TRB and IOM, 2005 ). This report highlights transportation as a separate determinant of health because of its multifaceted nature: pollution and greenhouse gas production; motor vehicle–related deaths and injuries; mobility and access to employment and vital goods and services; and active transportation. Transportation consists of the network, services, and infrastructure necessary to provide residents with the means to get from one place to another ( Davis et al., 2016 ), and it is also vital to accessing goods, services (including health and social services), social networks, and employment. If designed and maintained properly, transportation facilitates safe mobility and is accessible to all residents, regardless of geographic location, age, or disability status. However, current research suggests that transportation costs are a barrier to mobility for households in poverty, which are disproportionately represented by African Americans and Hispanics ( FHWA, 2014 ). Long commute times and high transportation costs are significant barriers to employment and financial stability ( Roberto, 2008 ). Brookings researchers have concluded, based on analyses of census data, that the suburbanization of poverty is disproportionately affecting proximity to jobs for poor and minority populations as compared with their nonpoor and white peers ( Kneebone and Holmes, 2015 ; Zimmerman et al., 2015 ).

Transportation presents unevenly distributed negative externalities, including air pollution, noise, and motor vehicle–related injuries and deaths that are more prevalent in low-income and minority communities with poor infrastructure ( Bell and Cohen, 2014 ; US DOT, 2015 ). Low-income and minority populations are more likely to live near environmental hazards, including transportation-related sources of pollution and toxic emissions such as roadways, bus depots, and ports ( McConville, 2013 ; NEJAC, 2009 ; Perez et al., 2012 ). See, for example, Shepard (2005/2006) on the high concentration of bus depots in West Harlem, which also has one of the highest rates of asthma in the nation. The Regional Asthma Management and Prevention collaborative, in Oakland, California, and the California Environmental Protection Agency's Air Resources Board, among others, have described the evidence on the relationship between asthma and exposures to diesel and other air pollution ( California EPA, 2016 ; RAMP, 2009 ).

Active transportation—the promotion of walking and cycling for transportation complemented by public transportation or any other active mode—is a form of transportation that reduces environmental barriers to physical activity and can improve health outcomes ( Besser and Dannenberg, 2005 ; Dannenberg et al., 2011 ). Since the mid-20th century, road design and transportation planning have centered on the automobile, with multiple and interconnected consequences for health and equity ( IOM, 2014 ).

The relationship between physical activity and health is well established and was summarized by the U.S. Surgeon General's 1996 report Physical Activity and Health ( HHS, 1996 ) and the U.S. Task Force on Community Preventive Services ( U.S. Task Force on Community Preventive Services, 2001 ). The evidence on the relationship among active transportation, physical activity, and health has been accumulating more recently. In a 2005 report from the Transportation Research Board and the Institute of Medicine, the authoring committee stated that “[r]esearch has not yet identified causal relationships to a point that would enable the committee to provide guidance about cost beneficial investments or state unequivocally that certain changes to the built environment would lead to more physical activity or be the most efficient ways of increasing such activity” ( TRB and IOM, 2005, p. 10 ). Since then, Pucher et al. (2010) found “statistically significant negative relationships” between active travel (walking and cycling) and self-reported obesity as well as between active travel and diabetes ( Pucher et al., 2010 ).

McCormack and Shiell conducted a systematic review of 20 cross-sectional studies and 13 quasi-experimental studies and concluded that most associations “between the built environment and physical activity were in the expected direction or null” ( McCormack and Shiell, 2011 ). They also found that physical activity was considerably influenced by “land use mix, connectivity and population density and overall neighborhood design” and that “the built environment was more likely to be associated with transportation walking compared with other types of physical activity including recreational walking” ( McCormack and Shiell, 2011 ).

CDC has developed a set of transportation recommendations that address all of the facets described above and has also developed a Transportation Health Impact Assessment Toolkit. 9 The CDC and the U.S. Department of Transportation (DOT) have also developed a Transportation and Health Tool to share indicator data on transportation and health. 10

There have been multiple national initiatives in the past two to three decades aiming to improve livability and sustainability in places across the United States, and transportation equity is a mainstay of much of this work. (See Box 3-10 for an example of a regional transportation planning agency that seeks to improve access to transportation.) Initiatives have ranged from the federal Sustainable Communities Partnership, 11 launched by the DOT, HUD, and the U.S. Environmental Protection Agency in 2009 to help U.S. communities “improve access to affordable housing, increase transportation options, and lower transportation costs while protecting the environment,” to Safe Routes to School, which aims to improve children's safety while walking and riding bicycles. 12

The Nashville Metropolitan Planning Organization.

Social Environment

How the social environment is conceptualized varies depending on the source ( Barnett and Casper, 2001 ; Healthy People 2020, 2016 ). However, there are common elements identified by the literature that collectively shape a community's social environment as a determinant of health. For the purposes of this report, the social environment can be thought of as reflecting the individuals, families, businesses, and organizations within a community; the interactions among them; and norms and culture. It can include social networks, capital, cohesion, trust, participation, and willingness to act for the common good in relation to health. Social cohesion refers to the extent of connectedness and solidarity among groups in a community, while social capital is defined as the features of social structures (e.g., interpersonal trust, norms of reciprocity, and mutual aid) that serve as resources for individuals and facilitate collective action ( Kawachi and Berkman, 2000 ).

A 2008 systematic review found associations between trust as an indicator of social cohesion and better physical health, especially with respect to self-rated health. Furthermore, it revealed a pattern in which the association between social capital and better health outcomes was especially salient in inegalitarian countries (i.e., countries with a high degree of economic inequity), such as the United States, as opposed to more egalitarian societies ( Kim et al., 2008 ).

The social environment in a community is often measured as it relates to mental health outcomes. For example, social connections between neighbors (i.e., greater social cohesion, social capital, and reciprocal exchanges between neighbors) are protective against depression ( Diez Roux and Mair, 2010 ). Factors such as exposure to violence, hazardous conditions, and residential instability are all associated with depression and depressive symptoms ( Diez Roux and Mair, 2010 ).

It is important to note that high levels of social capital and a strong presence of social networks are not necessarily guarantors of a healthy community. In fact, they can be sources of strain as well as support ( Pearce and Smith, 2003 ). Some studies explore the potential drawbacks of social capital, such as the contagion of high-risk behaviors (e.g., suicidal ideation, injection drug use, alcohol and drug use among adolescents, smoking, and obesity) ( Bearman and Moody, 2004 ; Christakis and Fowler, 2007 ; Friedman and Aral, 2001 ; Valente et al., 2004 ).

McNeill et al. (2006) postulate that the following are mechanisms by which features of the social environment influence health behaviors:

• Social support and social networks enable or constrain the adoption of health-promoting behaviors; provide access to resources and material goods; provide individual and coping responses; buffer negative health outcomes; and restrict contact to infectious diseases.
• Social cohesion and social capital shape the ability to enforce and reinforce group or social norms for positive health behaviors and the provision of tangible support (e.g., transportation).

The social environment interacts with features of the physical environment at the neighborhood level to shape health behaviors, stress, and, ultimately, health outcomes ( Diez Roux and Mair, 2010 ). For example, a built environment that is poor in quality (i.e., low walkability, fewer parks or open space, unsafe transportation) can contribute to a lack of structural opportunities for social interactions, resulting in limited social networks in a community ( Suglia et al., 2016 ). Other research points to the role of physical activity as a potential pathway by which the social environment affects health outcomes such as obesity ( Suglia et al., 2016 ).

At the community level, an important element of the social environment that can mediate health outcomes is the presence of neighborhood stressors. While the occurrence of stress is a daily facet of life that all people experience, chronic or toxic stress, in which the burden of stress accumulates, is a factor in the expression of disease ( McEwen, 2012 ). Stressful experiences are particularly critical during early stages of life, as evidenced by the adverse childhood experiences study ( Felitti et al., 1998 ), and are associated with abnormal brain development ( IOM, 2000 ; Shonkoff and Garner, 2012 ). For low-income communities, stressors are salient because of the lack of resources, the presence of environmental hazards, unemployment, and exposure to violence, among other factors ( McEwen, 2012 ; Steptoe and Feldman, 2001 ). (See Box 3-11 for an example of a community working to combat these stressors.) This applies as well to children in low-income households, who are more likely to experience multiple stressors that can harm health and development ( Evans and Kim, 2010 ), mediated by chronic stress ( Evans et al., 2011 ).

Cowlitz Community Network.

Chronic stress due to adverse neighborhood and family conditions has been linked to the academic achievement gap, in which children living in poverty fall behind those in better-resourced neighborhoods ( Evans et al., 2011 ; Zimmerman and Woolf, 2014 ). Furthermore, stress and poor health in childhood are associated with decreased cognitive development, increased tobacco and drug use, and a higher risk of cardiovascular disease, diabetes, depression, and other conditions ( County Health Rankings, 2016 ).

Public Safety

Public safety and violence are significant, intertwined social determinants of health, but they are also each significant indicators of health and community well-being in their own right. Public safety refers to the safety and protection of the public, and it is often characterized as the absence of violence in public settings ( Davis et al., 2016 ). Since the late 1960s, homicide and suicide (another form of violence) have consistently ranked among the top leading causes of death in the United States ( Dahlberg and Mercy, 2009 ).

Violent victimization affects health by causing psychological and physical injury, which can lead to disability and, in some cases, premature death. Beyond the risk of injury and death, violent victimization also has far-reaching health consequences for individuals, families, and neighborhoods. Furthermore, research shows that simply being exposed to violence can have detrimental effects on physical and psychological well-being ( Felitti et al., 1998 ; Pinderhughes et al., 2015 ). Violent victimization and exposure to violence have been linked to poor health outcomes, including chronic diseases (e.g., ischemic heart disease, cancer, stroke, chronic obstructive lung disease, diabetes, and hepatitis), asthma-related symptoms, obesity, posttraumatic stress disorder, depression, and substance abuse ( Prevention Institute, 2011 ). For youth in schools, the data suggest that there is a cumulative effect of exposure to violence, with multiple exposures to violence being associated with higher rates of youth reporting their health as “fair” or “poor” ( Egerter et al., 2011a ). There is also research that indicates a link between neighborhood crime rates and adverse birth outcomes such as preterm birth and low birth weight ( Egerter et al., 2011a ).

Violence and the fear of violence can negatively affect other social determinants that further undermine community health. Violence rates can lead to population loss, decreased property values and investments in the built environment, increased health care costs, and the disruption of the provision of social services ( Massetti and Vivolo, 2010 ; Velez et al., 2012 ). In addition, violence in communities is associated with reduced engagement in behaviors that are known to promote health, such as physical activity and park use ( Cohen et al., 2010 ).

The perception of safety is a key indicator of violence in a community that is associated with health. For example, people who describe their neighborhoods as not safe are almost three times more likely to be physically inactive than those who describe their neighborhood as extremely safe ( Prevention Institute, 2011 ). The perception of safety is also important for mental health. There is research that suggests that perceived danger and the fear of violence can influence stress, substance use, anger, anxiety, and feelings of insecurity—all of which compromise the psychological well-being of a community ( Moiduddin and Massey, 2008 ; Perkins and Taylor, 1996 ). At the community level, fear of crime and violence can undermine social organization, social cohesion, and civic participation—all key elements in a social environment that is conducive to optimal health ( Perkins and Taylor, 1996 ). Low perception of safety can also undermine the efforts of a community to improve the built environment through the availability of parks and open space to promote physical activity ( Cohen et al., 2016a ; Weiss et al., 2011 ).

Violence is not a phenomenon that affects all communities equally, nor is it distributed randomly. The widespread disparity in the occurrence of violence is a major facet of health inequity in the United States. Low-income communities are disproportionately affected by violence and by the many effects that it can have on physical and mental well-being. The conditions of low-income communities (concentrated poverty, low housing values, and high schools with low graduation rates among others), foster violence and put residents at an increased risk of death from homicide ( Prevention Institute, 2011 ). This holds true for other types of violence as well. Living in poor U.S. neighborhoods puts African American and white women at an increased risk for intimate partner violence compared with women who reside in areas that are not impoverished ( Prevention Institute, 2011 ).

Criminologists attribute the disparities in neighborhood violence not to the kinds of people living in certain neighborhoods but to the vast differences in social and economic conditions that characterize communities in the United States. Some refer to these differences as “divergent social worlds” and the “racial–spatial divide” ( Peterson and Krivo, 2010 ). This is because there are specific racial and ethnic groups, such as African Americans, Hispanics, and Native Americans, who are vastly overrepresented in communities that are at risk for violence because of the social and economic conditions. Residential segregation, which has been perpetuated by discriminatory housing and mortgage market practices, affects the quality of neighborhoods by increasing poverty, poor housing conditions, and social disorder and by limiting economic opportunity for residents ( Prevention Institute, 2011 ).

As a result of the racial–spatial divide in community conditions, the violent crime rate in majority nonwhite neighborhoods is two to five times higher than in majority white neighborhoods. This is especially true for youth of color, particularly males. Overall homicide rates among 10- to 24-year-old African American males (60.7 per 100,000) and Hispanic males (20.6 per 100,000) exceed that of white males in the same age group (3.5 per 100,000) ( Prevention Institute, 2011 ). African American males 15 to 19 years old are six times as likely to be homicide victims as their white peers ( Prevention Institute, 2011 ). More specifically, African American males ages 15 to 19 are almost four times as likely to be victims of firearm-related homicides as white males ( Prevention Institute, 2011 ). In terms of exposure to violence, African American and Hispanic youth are more likely to be exposed to shootings, riots, domestic violence, and murder than their white counterparts ( Prevention Institute, 2011 ). This has major implications for trauma in communities that are predominantly African American or Hispanic. Native American communities also suffer from a disproportionately high violent crime rate that is two to three times higher than the national average ( Prevention Institute, 2011 ). Box 3-12 briefly describes a public health–oriented model to address violence in communities.

The Cure Violence Health Model.

Child Abuse and Neglect

Child abuse and neglect are two important measures of community violence that can affect physical and mental health. The Institute of Medicine and the National Research Council published a report (2014) that cited abuse and neglect during childhood as a contributor to the following health-related outcomes: problems with growth and motor development, lower self-reported health, gastrointestinal symptoms, obesity, delinquency and violence, and alcohol abuse ( IOM and NRC, 2014 ).

In 1998, Felitti and colleagues published a pivotal study which demonstrated a link between adverse childhood experiences and the leading causes of death in adults at the time. The authors found a strong, graded association between the amount of exposure to abuse or household dysfunction and multiple risk factors (e.g., smoking, severe obesity, physical inactivity, depressed mood, and suicide attempts) for several leading causes of death ( Felitti et al., 1998 ). Child abuse and neglect not only affect health directly, they also affect outcomes within the other social determinants of health, such as education, work, and social relationships ( IOM and NRC, 2014 ). While the overall rates of child maltreatment have been declining since 2002, rates are still much higher for African American (14.3 per 1,000), Native American (11.4 per 1,000), multiracial (10.1 per 1,000), and Hispanic (8.6 per 1,000) children than for white children (7.9 per 1,000) ( IOM and NRC, 2014 ; Prevention Institute, 2011 ). Child abuse and neglect are often accompanied by family stressors and other forms of family violence ( IOM and NRC, 2014 ). As discussed above, the conditions of concentrated poverty in a neighborhood are associated with violence incidence. According to the Prevention Institute, the higher the percentage of families living below the federal poverty level in a neighborhood, the higher the rate of child maltreatment ( Prevention Institute, 2011 ).

Hate Crimes

Hate crimes, which may or may not involve physical violence, are often motivated by some bias against a perceived characteristic. 13 An FBI analysis of single-bias hate crime incidents revealed that in 2014, 48.3 percent of victims were targeted because of the offender's bias against race, and 62.7 percent of those victims were targeted because of anti-African American bias ( UCR, 2015 ). Among hate crimes motivated by bias toward a particular ethnicity in 2014, almost 48 percent of the victims were targeted because of anti-Hispanic bias ( UCR, 2015 ).

As is the case with other types of violence, exposure to hate crime violence can have pernicious effects on health. For lesbian, gay, bisexual, and transgender (LGBT) persons specifically, exposure to hate crimes at the community level has been linked to increased rates of suicide among youth, marijuana use, and all-cause mortality ( Duncan and Hatzenbuehler, 2014 ; Duncan et al., 2014 ; Hatzenbuehler et al., 2014 ). Discrimination in general, which by definition is the driving factor behind the perpetration of hate crimes, has been shown to affect the health of individuals and communities. Whether it be perceived discrimination in everyday encounters or systemic discrimination in housing policies, this type of unequal treatment has been associated with major depression, psychological distress, stress, increased pregnancy risk, mortality, hypertension, and more health-related outcomes ( Dolezsar et al., 2014 ; Galea et al., 2011 ; Kessler et al., 1999 ; Padela and Heisler, 2010 ; Sims et al., 2012 ).

Criminal Justice System

The criminal justice system is a key actor, setting, and driver of public safety as it relates to health equity. Specifically, the criminal justice system's role in the mass incarceration of racial and ethnic minorities is an important factor when examining the social determinants of health ( NRC, 2014 ). The past 40–50 years have seen a large-scale expansion of incarceration, which has had lasting effects on families and communities ( Cloud, 2014 ; Drake, 2013 ). This expansion has affected racial and ethnic minority groups, and particularly men ( Drake, 2013 ). Research suggests that disproportionately more Hispanics and African Americans are confined in jails and prisons than would be predicted by their arrest rates and that Hispanic and African American juveniles are more likely than white juveniles to be referred to adult court rather than juvenile court ( Harris, 2009 ).

When those who were formerly incarcerated are released back into their communities, successful reentry is hindered by a number of obstacles, such as stigma, limited employment and housing opportunities, and the lack of a cohesive social network ( Lyons and Pettit, 2011 ). All of these factors are vital to achieving optimal health, and for communities with high rates of incarceration, the absence of these opportunities can lead to a diminished capacity to combat crime and mobilize for resources ( Clear, 2008 ). It is important to examine the patterns and effects of mass incarceration because it not only affects the health of incarcerated populations but also has a detrimental effect on multiple determinants of health in communities. Mass incarceration has contributed to the breakdown of educational opportunities, family structures, economic mobility, housing options, and neighborhood cohesion, especially in low-income communities of color ( Cloud, 2014 ). Neal and Rick examined U.S. Census data from 1960 to 2010 and found that although great progress was made in closing the black–white education and employment gap up until the 1980s, that progress then came to a halt in large part due to rising incarceration rates ( Neal and Rick, 2014 ). In addition, communities with high levels of incarceration have higher rates of lifetime major depressive disorder and generalized anxiety disorder ( Hatzenbuehler et al., 2015 ).

Wildeman estimated the effects of incarceration on population-level infant mortality rates, and his findings suggest that if incarceration rates remained the same as they were in 1973, the infant mortality rate in 2003 would have been 7.8 percent lower and the absolute African American–white disparity in infant mortality would have been 14.8 percent lower ( Wildeman, 2012 ). A keen understanding of the precise mechanisms by which incarceration affects the health of specific populations and contributes to health inequity is needed to reduce disparities in key health outcomes such as infant mortality.

• CONCLUDING OBSERVATIONS

The root causes of health inequity begin with historical and contemporary inequities that have been shaped by institutional and societal structures, policies, and norms in the United States. As discussed in this chapter, these deeply rooted inequities have shaped inequitable experiences of the social and other determinants of health: education, income and wealth, employment, health systems and services, housing, the physical environment, transportation, the social environment, and public safety.

Conclusion 3-2: Based on its review of the evidence, the committee concludes that health inequities are the result of more than individual choice or random occurrence. They are the result of the historic and ongoing interplay of inequitable structures, policies, and norms that shape lives.

These structures, policies, and norms—such as segregation, redlining and foreclosure, and implicit bias—play out on the terrain of the social, economic, environmental, and cultural determinants of health.

What Can Academic Research Do?

The current public health interest in the role of place, including communities, stems from significant empirical epidemiological evidence. As discussed in this chapter, there are a range of factors that contribute to health and that need to be more extensively studied. These include factors beyond the individual domain, such as living and working conditions and economic policies at the local, state, and national levels that are intimately connected to health and well-being. Likewise, the American Public Health Association's (APHA's) 2014 and 2015 conference themes on the geography of health and health in all policies, respectively, reflect a growing recognition of the need for action on social and environmental factors in order to achieve the goal of becoming the healthiest nation in one generation ( APHA, 2016 ).

At a meeting of the National Academies of Sciences, Engineering, and Medicine's Roundtable on Population Health Improvement in 2013, David Williams asked, “How could we expect that the lives and health of our patients would improve if they continued to live in the same conditions that contributed to their illness?” ( IOM, 2013 ). His question points to a fundamental challenge to improving the public's health and promoting health equity. This recognition that inequities in social arrangements and community factors shape life opportunities is not new; it was asserted as early as 1906 by W. E. B. Du Bois in his address regarding the role of social status and life conditions in shaping health and inequities. Du Bois reported findings from the 11th Atlanta Conference on the Study of the Negro Problem held at Atlanta University, which in part concluded that “the present difference in mortality seems to be sufficiently explained by conditions of life” ( DuBois, 1906 ).

Despite the increasingly widespread recognition in the field, many public health efforts continue to target individuals and are most often disease specific. The existing approaches to prevention and health promotion are still “catching up” with what is known about the social determinants of health and population health. Kindig and Stoddart pointed out that “much of public health activity, in the United States at least, does not have such a broad mandate” ( Kindig and Stoddart, 2003, p. 382 ). Building the science base for how to move upstream to improve population health has begun. While our understanding of the role of the social determinants of health, including features of the physical and social environments, has greatly improved over the last several decades, the scientific progress has not been so great on how, when, and where to intervene. Progress on how to move upstream in taking action has developed much more slowly than progress in the ability to describe the role of context and community-level factors that shape the major causes of morbidity, mortality, and well-being ( Amaro, 2014 ).

Improving the science of population health interventions, place-based approaches, and strategies to improve health equity will require a workforce of scientists and practitioners equipped to develop the requisite knowledge base and practice tools. As Kindig and Stoddart noted, social epidemiology has made highly important contributions to our understanding of the social determinants of health and population health but “does not have the breadth, or imply all of the multiple interactions and pathways” involved in population health ( Kindig and Stoddart, 2003, p. 382 ). Diez Roux and Mair describe social epidemiology's most critical conceptual and methodological challenges as well as promising directions in studying neighborhood health effects ( Diez Roux and Mair, 2010 ). Specifically, models for the training of population and place-based scientists and practitioners are needed to develop the research required to guide upstream approaches—including place-based interventions—that will address the contextual factors that shape major public health problems such as obesity, interpersonal violence, infant and maternal health, cardiovascular diseases, infectious diseases, substance abuse, and mental health disorders. For example, training models such as the interdisciplinary team science McArthur Model described by Adler and Stewart could be expanded to integrate public health practitioners and community leaders alongside research leaders ( Adler and Stewart, 2010b ).

Translating knowledge on the social determinants of health into practice requires at least four essential areas of expertise:

An understanding of theories that articulate the complex mechanisms of action in the social determinants of health and how place influences health.

Expertise in the design of community-level interventions and in models of community–academic partnerships.

Expertise in the complex issues of study design, measurement, and analytic methods in assessing changes resulting from interventions focused on population-level impacts and community-level health improvement.

Expertise and understanding of various socio-demographic groups, cultures, and varied sector stakeholders and drivers that shape sustained stakeholder engagement in improving population health and community conditions.

Considering the distinct fields of expertise required for these components and theory, the approaches to intervention and measurement stem from different disciplines and have often been developed without significant interchange. Researchers face significant challenges. Thus, academic institutions involved in the training of population and place-based scientists need to integrate these diverse bodies of knowledge—including theory, methods, and tools from diverse disciplines. Models for the transdisciplinary training of researchers, practitioners, and community partners are needed. Academic institutions need to develop models for intra-professional workforce training on place-based and community-level implementation science and evaluation that target improving population health and addressing health inequities. See Chapter 7 for more on the role of academic research in community solutions to promote health equity.

The social determinants of health, while interdependent and complex, are made up of mutable factors that shape the conditions in which one lives, learns, works, plays, worships, and ages. As highlighted in the boxes throughout this chapter, communities around the country are taking it upon themselves to address these conditions. Chapter 4 will discuss why communities are powerful agents of change, along with discussing the conditions necessary for successful and sustainable outcomes. Chapter 5 will provide an in-depth overview of nine communities that are addressing the root causes of health inequities.

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In 2000 Dr. Camara Jones developed a theoretical framework for the multiple levels of racism and used an allegory of a garden to illustrate the mechanisms through which these levels operate ( Jones, 2000 ).

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Funders include government agencies, private foundations, and other sources such as academic centers of higher education.

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As access to care improves, it will be increasingly important to monitor potential disparities with respect to the nature of care that people receive. This is especially true for chronic conditions that require long-term engagement with the health care system.

Measures of access to care tracked in the 2015 National Healthcare Quality and Disparities Report include having health insurance, having a usual source of care, encountering difficulties when seeking care, and receiving care as soon as wanted.

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• Cite this Page National Academies of Sciences, Engineering, and Medicine; Health and Medicine Division; Board on Population Health and Public Health Practice; Committee on Community-Based Solutions to Promote Health Equity in the United States; Baciu A, Negussie Y, Geller A, et al., editors. Communities in Action: Pathways to Health Equity. Washington (DC): National Academies Press (US); 2017 Jan 11. 3, The Root Causes of Health Inequity.
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• Black Americans Have a Clear Vision for Reducing Racism but Little Hope It Will Happen

Many say key U.S. institutions should be rebuilt to ensure fair treatment

Table of contents.

• Black Americans see little improvement in their lives despite increased national attention to racial issues
• Few Black adults expect equality for Black people in the U.S.
• Black adults say racism and police brutality are extremely big problems for Black people in the U.S.
• Personal experiences with discrimination are widespread among Black Americans
• Black adults see voting as the most effective strategy for moving toward equality in the U.S.
• Some Black adults see Black businesses and communities as effective remedies for inequality
• Black Americans say race matters little when choosing political allies
• The legacy of slavery affects Black Americans today
• Most Black adults agree the descendants of enslaved people should be repaid
• The types of repayment Black adults think would be most helpful
• Responsibility for reparations and the likelihood repayment will occur
• Black adults say the criminal justice system needs to be completely rebuilt
• Black adults say political, economic and health care systems need major changes to ensure fair treatment
• Most Black adults say funding for police departments should stay the same or increase
• Acknowledgments
• Appendix: Supplemental tables
• The American Trends Panel survey methodology

Pew Research Center conducted this analysis to understand the nuances among Black people on issues of racial inequality and social change in the United States. This in-depth survey explores differences among Black Americans in their views on the social status of the Black population in the U.S.; their assessments of racial inequality; their visions for institutional and social change; and their outlook on the chances that these improvements will be made. The analysis is the latest in the Center’s series of in-depth surveys of public opinion among Black Americans (read the first, “ Faith Among Black Americans ” and “ Race Is Central to Identity for Black Americans and Affects How They Connect With Each Other ”).

The online survey of 3,912 Black U.S. adults was conducted Oct. 4-17, 2021. Black U.S. adults include those who are single-race, non-Hispanic Black Americans; multiracial non-Hispanic Black Americans; and adults who indicate they are Black and Hispanic. The survey includes 1,025 Black adults on Pew Research Center’s American Trends Panel (ATP) and 2,887 Black adults on Ipsos’ KnowledgePanel. Respondents on both panels are recruited through national, random sampling of residential addresses.

Recruiting panelists by phone or mail ensures that nearly all U.S. Black adults have a chance of selection. This gives us confidence that any sample can represent the whole population (see our Methods 101 explainer on random sampling). Here are the questions used for the survey of Black adults, along with its responses and methodology .

The terms “Black Americans,” “Black people” and “Black adults” are used interchangeably throughout this report to refer to U.S. adults who self-identify as Black, either alone or in combination with other races or Hispanic identity.

Throughout this report, “Black, non-Hispanic” respondents are those who identify as single-race Black and say they have no Hispanic background. “Black Hispanic” respondents are those who identify as Black and say they have Hispanic background. We use the terms “Black Hispanic” and “Hispanic Black” interchangeably. “Multiracial” respondents are those who indicate two or more racial backgrounds (one of which is Black) and say they are not Hispanic.

Respondents were asked a question about how important being Black was to how they think about themselves. In this report, we use the term “being Black” when referencing responses to this question.

In this report, “immigrant” refers to people who were not U.S. citizens at birth – in other words, those born outside the U.S., Puerto Rico or other U.S. territories to parents who were not U.S. citizens. We use the terms “immigrant,” “born abroad” and “foreign-born” interchangeably.

Throughout this report, “Democrats and Democratic leaners” and just “Democrats” both refer to respondents who identify politically with the Democratic Party or who are independent or some other party but lean toward the Democratic Party. “Republicans and Republican leaners” and just “Republicans” both refer to respondents who identify politically with the Republican Party or are independent or some other party but lean toward the Republican Party.

Respondents were asked a question about their voter registration status. In this report, respondents are considered registered to vote if they self-report being absolutely certain they are registered at their current address. Respondents are considered not registered to vote if they report not being registered or express uncertainty about their registration.

To create the upper-, middle- and lower-income tiers, respondents’ 2020 family incomes were adjusted for differences in purchasing power by geographic region and household size. Respondents were then placed into income tiers: “Middle income” is defined as two-thirds to double the median annual income for the entire survey sample. “Lower income” falls below that range, and “upper income” lies above it. For more information about how the income tiers were created, read the methodology .

More than a year after the murder of George Floyd and the national protests, debate and political promises that ensued, 65% of Black Americans say the increased national attention on racial inequality has not led to changes that improved their lives. 1 And 44% say equality for Black people in the United States is not likely to be achieved, according to newly released findings from an October 2021 survey of Black Americans by Pew Research Center.

This is somewhat of a reversal in views from September 2020, when half of Black adults said the increased national focus on issues of race would lead to major policy changes to address racial inequality in the country and 56% expected changes that would make their lives better.

At the same time, many Black Americans are concerned about racial discrimination and its impact. Roughly eight-in-ten say they have personally experienced discrimination because of their race or ethnicity (79%), and most also say discrimination is the main reason many Black people cannot get ahead (68%).  

Even so, Black Americans have a clear vision for how to achieve change when it comes to racial inequality. This includes support for significant reforms to or complete overhauls of several U.S. institutions to ensure fair treatment, particularly the criminal justice system; political engagement, primarily in the form of voting; support for Black businesses to advance Black communities; and reparations in the forms of educational, business and homeownership assistance. Yet alongside their assessments of inequality and ideas about progress exists pessimism about whether U.S. society and its institutions will change in ways that would reduce racism.

These findings emerge from an extensive Pew Research Center survey of 3,912 Black Americans conducted online Oct. 4-17, 2021. The survey explores how Black Americans assess their position in U.S. society and their ideas about social change. Overall, Black Americans are clear on what they think the problems are facing the country and how to remedy them. However, they are skeptical that meaningful changes will take place in their lifetime.

Black Americans see racism in our laws as a big problem and discrimination as a roadblock to progress

Black adults were asked in the survey to assess the current nature of racism in the United States and whether structural or individual sources of this racism are a bigger problem for Black people. About half of Black adults (52%) say racism in our laws is a bigger problem than racism by individual people, while four-in-ten (43%) say acts of racism committed by individual people is the bigger problem. Only 3% of Black adults say that Black people do not experience discrimination in the U.S. today.

In assessing the magnitude of problems that they face, the majority of Black Americans say racism (63%), police brutality (60%) and economic inequality (54%) are extremely or very big problems for Black people living in the U.S. Slightly smaller shares say the same about the affordability of health care (47%), limitations on voting (46%), and the quality of K-12 schools (40%).

Aside from their critiques of U.S. institutions, Black adults also feel the impact of racial inequality personally. Most Black adults say they occasionally or frequently experience unfair treatment because of their race or ethnicity (79%), and two-thirds (68%) cite racial discrimination as the main reason many Black people cannot get ahead today.

Black Americans’ views on reducing racial inequality

Black Americans are clear on the challenges they face because of racism. They are also clear on the solutions. These range from overhauls of policing practices and the criminal justice system to civic engagement and reparations to descendants of people enslaved in the United States.

Changing U.S. institutions such as policing, courts and prison systems

About nine-in-ten Black adults say multiple aspects of the criminal justice system need some kind of change (minor, major or a complete overhaul) to ensure fair treatment, with nearly all saying so about policing (95%), the courts and judicial process (95%), and the prison system (94%).

Roughly half of Black adults say policing (49%), the courts and judicial process (48%), and the prison system (54%) need to be completely rebuilt for Black people to be treated fairly. Smaller shares say the same about the political system (42%), the economic system (37%) and the health care system (34%), according to the October survey.

While Black Americans are in favor of significant changes to policing, most want spending on police departments in their communities to stay the same (39%) or increase (35%). A little more than one-in-five (23%) think spending on police departments in their area should be decreased.

Black adults who favor decreases in police spending are most likely to name medical, mental health and social services (40%) as the top priority for those reappropriated funds. Smaller shares say K-12 schools (25%), roads, water systems and other infrastructure (12%), and reducing taxes (13%) should be the top priority.

Voting and ‘buying Black’ viewed as important strategies for Black community advancement

Black Americans also have clear views on the types of political and civic engagement they believe will move Black communities forward. About six-in-ten Black adults say voting (63%) and supporting Black businesses or “buying Black” (58%) are extremely or very effective strategies for moving Black people toward equality in the U.S. Smaller though still significant shares say the same about volunteering with organizations dedicated to Black equality (48%), protesting (42%) and contacting elected officials (40%).

Black adults were also asked about the effectiveness of Black economic and political independence in moving them toward equality. About four-in-ten (39%) say Black ownership of all businesses in Black neighborhoods would be an extremely or very effective strategy for moving toward racial equality, while roughly three-in-ten (31%) say the same about establishing a national Black political party. And about a quarter of Black adults (27%) say having Black neighborhoods governed entirely by Black elected officials would be extremely or very effective in moving Black people toward equality.

Most Black Americans support repayment for slavery

Discussions about atonement for slavery predate the founding of the United States. As early as 1672 , Quaker abolitionists advocated for enslaved people to be paid for their labor once they were free. And in recent years, some U.S. cities and institutions have implemented reparations policies to do just that.

Most Black Americans say the legacy of slavery affects the position of Black people in the U.S. either a great deal (55%) or a fair amount (30%), according to the survey. And roughly three-quarters (77%) say descendants of people enslaved in the U.S. should be repaid in some way.

Black adults who say descendants of the enslaved should be repaid support doing so in different ways. About eight-in-ten say repayment in the forms of educational scholarships (80%), financial assistance for starting or improving a business (77%), and financial assistance for buying or remodeling a home (76%) would be extremely or very helpful. A slightly smaller share (69%) say cash payments would be extremely or very helpful forms of repayment for the descendants of enslaved people.

Where the responsibility for repayment lies is also clear for Black Americans. Among those who say the descendants of enslaved people should be repaid, 81% say the U.S. federal government should have all or most of the responsibility for repayment. About three-quarters (76%) say businesses and banks that profited from slavery should bear all or most of the responsibility for repayment. And roughly six-in-ten say the same about colleges and universities that benefited from slavery (63%) and descendants of families who engaged in the slave trade (60%).

Black Americans are skeptical change will happen

Even though Black Americans’ visions for social change are clear, very few expect them to be implemented. Overall, 44% of Black adults say equality for Black people in the U.S. is a little or not at all likely. A little over a third (38%) say it is somewhat likely and only 13% say it is extremely or very likely.

They also do not think specific institutions will change. Two-thirds of Black adults say changes to the prison system (67%) and the courts and judicial process (65%) that would ensure fair treatment for Black people are a little or not at all likely in their lifetime. About six-in-ten (58%) say the same about policing. Only about one-in-ten say changes to policing (13%), the courts and judicial process (12%), and the prison system (11%) are extremely or very likely.

This pessimism is not only about the criminal justice system. The majority of Black adults say the political (63%), economic (62%) and health care (51%) systems are also unlikely to change in their lifetime.

Black Americans’ vision for social change includes reparations. However, much like their pessimism about institutional change, very few think they will see reparations in their lifetime. Among Black adults who say the descendants of people enslaved in the U.S. should be repaid, 82% say reparations for slavery are unlikely to occur in their lifetime. About one-in-ten (11%) say repayment is somewhat likely, while only 7% say repayment is extremely or very likely to happen in their lifetime.

Black Democrats, Republicans differ on assessments of inequality and visions for social change

Party affiliation is one key point of difference among Black Americans in their assessments of racial inequality and their visions for social change. Black Republicans and Republican leaners are more likely than Black Democrats and Democratic leaners to focus on the acts of individuals. For example, when summarizing the nature of racism against Black people in the U.S., the majority of Black Republicans (59%) say racist acts committed by individual people is a bigger problem for Black people than racism in our laws. Black Democrats (41%) are less likely to hold this view.

Black Republicans (45%) are also more likely than Black Democrats (21%) to say that Black people who cannot get ahead in the U.S. are mostly responsible for their own condition. And while similar shares of Black Republicans (79%) and Democrats (80%) say they experience racial discrimination on a regular basis, Republicans (64%) are more likely than Democrats (36%) to say that most Black people who want to get ahead can make it if they are willing to work hard.

On the other hand, Black Democrats are more likely than Black Republicans to focus on the impact that racial inequality has on Black Americans. Seven-in-ten Black Democrats (73%) say racial discrimination is the main reason many Black people cannot get ahead in the U.S, while about four-in-ten Black Republicans (44%) say the same. And Black Democrats are more likely than Black Republicans to say racism (67% vs. 46%) and police brutality (65% vs. 44%) are extremely big problems for Black people today.

Black Democrats are also more critical of U.S. institutions than Black Republicans are. For example, Black Democrats are more likely than Black Republicans to say the prison system (57% vs. 35%), policing (52% vs. 29%) and the courts and judicial process (50% vs. 35%) should be completely rebuilt for Black people to be treated fairly.

While the share of Black Democrats who want to see large-scale changes to the criminal justice system exceeds that of Black Republicans, they share similar views on police funding. Four-in-ten each of Black Democrats and Black Republicans say funding for police departments in their communities should remain the same, while around a third of each partisan coalition (36% and 37%, respectively) says funding should increase. Only about one-in-four Black Democrats (24%) and one-in-five Black Republicans (21%) say funding for police departments in their communities should decrease.

Among the survey’s other findings:

Black adults differ by age in their views on political strategies. Black adults ages 65 and older (77%) are most likely to say voting is an extremely or very effective strategy for moving Black people toward equality. They are significantly more likely than Black adults ages 18 to 29 (48%) and 30 to 49 (60%) to say this. Black adults 65 and older (48%) are also more likely than those ages 30 to 49 (38%) and 50 to 64 (42%) to say protesting is an extremely or very effective strategy. Roughly four-in-ten Black adults ages 18 to 29 say this (44%).

Gender plays a role in how Black adults view policing. Though majorities of Black women (65%) and men (56%) say police brutality is an extremely big problem for Black people living in the U.S. today, Black women are more likely than Black men to hold this view. When it comes to criminal justice, Black women (56%) and men (51%) are about equally likely to share the view that the prison system should be completely rebuilt to ensure fair treatment of Black people. However, Black women (52%) are slightly more likely than Black men (45%) to say this about policing. On the matter of police funding, Black women (39%) are slightly more likely than Black men (31%) to say police funding in their communities should be increased. On the other hand, Black men are more likely than Black women to prefer that funding stay the same (44% vs. 36%). Smaller shares of both Black men (23%) and women (22%) would like to see police funding decreased.

Income impacts Black adults’ views on reparations. Roughly eight-in-ten Black adults with lower (78%), middle (77%) and upper incomes (79%) say the descendants of people enslaved in the U.S. should receive reparations. Among those who support reparations, Black adults with upper and middle incomes (both 84%) are more likely than those with lower incomes (75%) to say educational scholarships would be an extremely or very helpful form of repayment. However, of those who support reparations, Black adults with lower (72%) and middle incomes (68%) are more likely than those with higher incomes (57%) to say cash payments would be an extremely or very helpful form of repayment for slavery.

• Black adults in the September 2020 survey only include those who say their race is Black alone and are non-Hispanic. The same is true only for the questions of improvements to Black people’s lives and equality in the United States in the October 2021 survey. Throughout the rest of this report, Black adults include those who say their race is Black alone and non-Hispanic; those who say their race is Black and at least one other race and non-Hispanic; or Black and Hispanic, unless otherwise noted. ↩

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How soviet legacies shape russia’s response to the pandemic: ethical consequences of a culture of non-disclosure.

Nataliya Shok & Nadezhda Beliakova

[This is an advance copy of an article that will appear in print in September 2020 as part of the KIEJ’s special double issue on Ethics, Pandemics, and COVID-19.]

ABSTRACT. The COVID-19 pandemic required strong state responsibility for the health of its citizens and the effective allocation of healthcare resources. In Russia, extreme circumstances reveal hidden Soviet patterns of public health. This article illuminates how Russia has implemented some changes within its health insurance structures but also has maintained the paternalistic style of state governing within public health practices. The authors examine key neo-Soviet trends in Russian society revealed during the pandemic: the ethics of silence, a culture of non-disclosure, and doublethinking. Additionally, we argue that both modern Russian medicine and healthcare demonstrate gaps in implementing robust bioethical frameworks compared with the United States. Using a robust analysis of healthcare and state practice during the COVID-19 pandemic within the framework of global bioethics, this article aims to respond to Russian history and culture in order to advance the development of bioethics.

INTRODUCTION

The global COVID-19 pandemic has quickly and radically changed the world. The healthcare system in Russia, as in other countries, is under incredible pressure, and Russian society likewise is tested by “social distancing” practices. The unceasing adaptation and mobilization of resources has become part of our everyday lives. The struggle against the epidemic continues to emphasize the priority of global social health. Accordingly, we must address questions recently raised by The Hastings Center in “What Values Should Guide Us?”. What will be the new ethical norm after the pandemic? What compromises will be in place between civil liberties and public health? Will such biomedical ethical themes concerning the priority of the patient’s interests, the principal of minimizing harm, the disclosure of medical errors, and balance of equality and justice receive new recognition? Should we equally weigh the basic ethical principles, and are they universal? The global context of the pandemic allowed us to see that the practice of applying bioethical principles varies greatly, and the development of these principles depends on the history and culture of each respective country.

The differences in fundamental ethical commitments and historical forces that shape cultures result in different priorities, goals, and understandings of appropriate restrictions on behavior. These gaps can be difficult to bridge in the context of international guideline development, and they pose special concerns where lack of coordination threatens public health or undermines the ability to pursue collaborative research to advance health interests. One of the greatest barriers to developing globally shared guidelines is a lack of understanding and appreciation of local history and culture. In reviewing global responses to the COVID-19 pandemic, we understand the importance of dialogue and information exchange between countries in extreme circumstances. Responses can drastically differ between countries, as evidenced in both European Union countries (Hirsch 2020) and China (Lei and Qiu 2020), even though the scientific conclusions and state measures may share some overlap. Russia is no exception. The struggle against the pandemic exposed the deep rift between the global bioethical discourse and Russia’s response to the pandemic. In the development of public health guidelines, we cannot simply replicate the experiences and responses of foreign countries.

Russia imposed “the regime of self-isolation,” which limited citizens’ mobility rights, as the main pandemic countermeasure (Stanovaya 2020a; 2020b); however, there is no such applicable legal term in Russian law. The Russian President called this “a days off regime,” meaning a vacation from work obligations. He did not declare a state of emergency or impose quarantine restrictions. Instead, the bulk of the responsibility for anti-pandemic countermeasures was placed on the regional governments. In our opinion, such decisions and shifting of responsibility can be ethically justified only if the measures are proportional to the severity of the epidemic, taken with no violation of individual freedom and rights, and transparent to the public. These requirements were not met: the state refused to offer financial aid to businesses or to the vulnerable population. Instead of supporting its citizens, the state imposed a series of administrative measures (i.e. penalties) that were aimed at punishing the healthy population for going out of their homes without a special digital code during the non-imposed quarantine. For those who were diagnosed with COVID-19 and their families, they were forced to use a social monitoring app that required sending selfies five times per day, even at night. These penalties increased social anxiety and mistrust.

While many have studied the collapse of the Soviet Union, few realize that Soviet policy and practice in healthcare is alive and has significant implications in Russia. Although the democratic state assumed the monopoly in the fight against the COVID-19 pandemic, it quickly returned to the patterns of Soviet policy in implementing public health strategies. In the mid-1990s The New York Times used the term ‘neo-soviet style of state governing’ to describe a pattern of returning to the Soviet Kremlin medical treatment practice and assumptions for responsibility within state leadership (Stanley 1995). The budget of this health bureaucracy, then and now, is secret. The current pandemic has brought about the return of several negative trends from the Soviet past such as ethics of silence, double thinking, state paternalism, and a culture of non-disclosure.

We will begin by sharing the meaning of these four key features of the Soviet legacy that influence medicine and healthcare. We then will highlight the implications of these commitments for medicine and bioethics within the context of the COVID-19 pandemic. We consider the history of Russian medicine and explore Soviet commandeering within health systems. We argue that by identifying pathways that respond to our history and culture, we can advance the development of bioethics in Russia by using robust analyses of healthcare and state practice during the COVID-19 pandemic.

Ethics of Silence, or Why Does Russia Not have Bioethics?

It is well-known that bioethics—as a research field—originated as part of the academic and social discourse in the United States. Although there are some research groups, independent researchers, and journalists working to study and implement bioethics in Russia, the country has not seen widespread academic development or widespread public discourse. Why does Russia lack organizations similar to the American Society of Bioethics and Humanities, The Hastings Center, the Kennedy Institute of Ethics, etc.? The answer is quite complicated (Stepanova 2019).

Until the late 1980s, bioethics was not represented in the Soviet academic discourse. This was, in many ways, due to the fact that it was a product of American academic culture (Callahan 1993). As the medical sociologists Fox and Swazey stated in their mid-1980s study, we should not suppose “that bioethics is a sufficiently neutral and universalistic term for it to be applied to medical morality in China or, for that matter, to medical ethical concern in whatever society or form it may now occur” (1984, 336–37). This perspective was critical of Professor Engelhardt’s material, published after the delegation of the US Kennedy Institute of Ethics returned from China (Engelhardt 1980). Sociologists described Professor Engelhardt’s approach as “cultural myopia”; according to them, Professor Engelhardt did not adequately appreciate the deep Western and American cultural influences on bioethics. These sociologists argued that he inappropriately considered bioethics as neutral and universal (Fox and Swazey 1984, 337). Sociologists emphasized that the term ‘bioethics’ was a neologism, which appeared primarily in American culture. In other cultures, its philosophical bases can be perceived as “acultural or transcultural,” which “can significantly limit its application in practice, allowing for the use of other terms” (337–38). In many ways, this is true for both Soviet and post-Soviet Russian development of medical ethics.

Perspectives within bioethical Soviet studies in the fields of Communist morality, medical ethics, and deontology differed radically from those that emerged within the United States (De George 1969; Tsaregorodtsev 1966; Petrovsky 1988). These differences are deeply intertwined within history and culture; differences are especially evident when we examine the issue of freedom. Soviet philosophy largely encompasses a determinist doctrine, which means that a person’s choice is an illusion, as it was determined by the interests of society (De George 1969, 35-38). Another important difference concerning Soviet ethics was noted by the Professor Graham in his Nature essay. He noted numerous attempts by Soviet philosophers to transfer the solution of complex problems to a future society, which would presumably be more just (Graham 1991). He agreed that this position allowed for neutrality in the struggle of conflicting concepts, but such abstracts proved to be of no use for those who were bound to make decisions in current society. In other words, this ethical model was not applicable. The problem was made more complex by a rather special understanding of the basic ethical categories. For example, freedom was not individual, but collective, which in turn complicated the perception of such categories as responsibility, wellbeing, duty, and consciousness—which were directly dependent on the understanding of freedom (De George 1969). Thus, the individual value of a human being was bound to the needs of society, and morality was an instrument of social control that guaranteed social stability. In the public discourse there was an “embryological perspective” of a person and the inner life of a human being, which were seen as “underdeveloped” and in the need of being “raised.”

The desire of the enlightened government to educate the “ignorant” people has deep historic roots, starting from the reign of Peter the Great. At that time, numerous disciplinary and oppressive campaigns were conducted under the guise of “enlightenment.” In the Soviet period, society was evaluated on an evolutionary scale, spanning from the primitive to the communist forms, which meant that there was no need to give a detailed analysis of its contemporary state. The ideas of constructing a new society, educating the new human being for the common socialist happiness, manifested its limitations as early as the 1950s, since this ideology could boast a purely humanitarian ideal for the future, but only offer limited guidance on how this would allow contemporary problems to be solved.

Relatedly, developments concerning a culture of silence share this intertwined history. The ethics of silence was shaped by the expansion of the “classified” zones and non-disclosure demands. In the Soviet Union, the term “state secret” received a maximally-broad interpretation, especially the context of the non-stop mobilization campaigns, carried out under pretense of war. Safeguarding “state secrets and showing discretion was the duty of every Soviet citizen” (Zelenov 2012, 147). Record keeping in Soviet institutions was also aimed at secrecy; indeed, entire departments were kept secret. In more open institutions, so-called “first departments” monitored the storage of state secrets.

The non-disclosure culture was enforced through institutions, whose employees internalized the non-disclosure norms, as well as through censorship and the state’s monopoly on mass media. As a result, Mikhailova describes the following:

Silence as a form of social protest behavior revealed its duplicity: under certain circumstances it allowed the individual to counter society’s oppressive machines, and to defend if not one’s life, then at least one’s honor. On the other hand, it provided the opportunity to sustain the fragile peace (even a questionable one), which was still preferable to the ‘good old fight,’ yet at the same time this very tendency aided the spread of lies, the formation of a pseudo-reality and the deepening of the anthropological crisis. (2011)

The “ethics of silence” and “non-disclosure culture” mechanisms, shaped in the Soviet Union, were supplemented by the phenomenon of duplicitous thinking. Soviet sociologist Yuri Levada actively used George Orwell’s formulation ‘doublethink’ to describe this practice (Orwell 1961). Levada also noted the following about late-Soviet society:

The doublethink dictatorship became total and unlimited: it was cemented that one must separate the sphere of social norms (to act, speak and think as you ‘were supposed to’), and the sphere of what was tolerated, which was basically dubbed as the ‘private’ life…The ‘minor’ and ‘major’ truths were locked together, struggling against one another, yet supplementing each other. (2006, 266)

In Levada’s opinion, post-Soviet society cemented the behavioral qualities of the Soviet period. In 2000, he called the post-Soviet person “sly”:

The sly person—on every level and in every essence of his or her existence – not only tolerates deceit, but is ready to be deceived, moreover—he or she has a need for self-delusion for reasons of the same (including psychological) self-preservation, for the overcoming of one’s internal split, for the justification of one’s own deceit. (2000, 20)

With the absence of strict barriers that would separate the proper and improper behavior, the dominant situation for post-Soviet society was not playing by the rules, but rather—playing with the rules.

The ideology of the totalitarian state, which routinely uses policies to impose state pressure on all spheres of life, also transformed the understanding of a doctor’s duty and medical ethics. This influence denied the essence of the physician’s mission and transformed the mission into a form of social work aimed at the benefit of the state, not individual interest. The late 1980s and early 1990s witnessed the appearance of first Western studies dedicated to the peculiar traits of the Soviet and post-Soviet healthcare systems (Rowland, and Telyukov 1991; Chazov 1992; Barr and Field 1996; Field 1995), especially medical ethics (Veatch 1989; Ryan 1990; Barr 1996).

In Soviet medicine, “medical privacy” received peculiar interpretations. The main ethical goal of the doctor was to aid in the creation of the “major” truth of the state. The strict paternalist model, the medical structure centralized on a pan-Soviet scale, and its moral obligation to ensure society’s healthcare (and not the healthcare of the individual patient) completely changed the term “medical privacy” throughout society. Policymakers did not protect the patient’s rights to confidentiality but rather strengthened the non-disclosure culture of the medical corporation. This naturally did not allow for the formation of bioethical institutions, as it was understood from a Western perspective. Still, academics were also interested in investigating the “second economy on an unofficial barter system” of healthcare (Barr 1996). This was a major part of the public consensus—underground payments to the doctors in underfinanced conditions and the low quality of the publicly free healthcare. This phenomenon is hard to understand, if one only depends on the official Marxist dogmas and Communist morality of the Soviet professions, which were supposed to be driven by the primacy of duty. For doctors, the expectation and acceptance of payment from a patient in exchange for medical services was officially considered bourgeois and unethical (Barr 1996, 38). Yet this also can be easily understood as a manifestation of the “doublethink” and the non-disclosure ethics in medicine.

Strict centralized control, the disregard for the specifics of regional healthcare, the priority of quantity over quality, the overall lack of resources, and lack of financing could not stimulate the doctors to improve their work. At this time, the average doctor’s salary equaled about 70% of the non-agricultural worker’s paycheck. These factors only led to an increase in mortality, a low life expectancy, and the public mistrust of the healthcare system (Rowland and Telyukov 1991). Further, independent professional associations of health care workers could not exist under the Soviet state. The state could not and did not evaluate the professional work of doctors. Soviet doctors, as state employees, were subject to disciplinary orders and could not fully control the field of their professional expertise, which was a necessity for their work. The social standing of doctors in USSR exhibited a “paradoxical combination of corporate powerlessness and bureaucratic power” (Field 1989).

In the post-Soviet landscape, and especially in Russia, the government—until recently —kept the Soviet organizational model for mass healthcare. The government began to gradually transfer the system into the sphere of “services” yet retained the paternalistic watch of the state over the medical institutions. It’s important to remember that by the 1980s, Soviet medicine was falling behind the healthcare levels of the developed countries. Researchers see the main reasons for this in the overbearing centralized management system of healthcare, the low levels of professional education, and insufficient funding (3.4% of USSR GDP in 1989, in comparison with the 11.4% in the United States) (Rowland and Telyukov 1991). As Dr. Rowland notes, “in theory, Soviet healthcare is a model of regional medical aid, which is based on local clinics. In reality, this system falls apart due to lack of finance, the indolence of centralized control, and lack of innovation” (Rowland and Telyukov 1991). The doctor’s clinical practice was not autonomous; the patient did not have the right to choose where and from whom he or she will receive medical aid. This, obviously, meant that there were no objective motivations for improving the quality of medical aid, and numerous services would only be available for an additional, informal payment. As The New York Times stated, “Communism may be dead…but its disparities and inefficiencies remain,” and “health care had never been one of the Soviet Union’s great successes….[D]octors were always relatively badly treated in the Soviet Union” (Erlanger 1992). The situation would not change for several years.

After 1991, Russia started developing the private medical care sector, which was more flexible but also expensive for population-based health needs. The last healthcare reform in Russia, which took place during 2015-2017, was driven by the logic of “optimizing” costs and raising the “economic efficiency” of the system. In Russia, the main direction of reform concerns the reduction and merger of clinics, the partial privatization of insurance, and in some cases, privatization of the actual delivery of services. In contrast, in the United States, the most important part of healthcare reform lies in the field of insurance, not in the actual delivery of healthcare services (Filatova and Schultz 2016). Russian policy changes have led to the bureaucratization of the system, a decrease in number of hospitals and hospitals’ enlargement and specialization, with a marked and forced decrease in numbers of doctors and nurses (Nurik 2015). Nurik notes the following figures from between 2005 and 2013:

The number of health facilities in rural areas fell by 75 percent from 8,249 to 2,085. That number includes a 95 percent drop in the number of district hospitals, from 2,631 to only 124, and a 65 percent decline in the number of local health clinics, from 7,404 to 2,561. (2015)

In 2014, a broad public campaign against the collapse of the Russian healthcare system was launched by the Deystvie Union, the Russian Confederation of Labour, the Pirogov doctors’ movement, the “Together for Decent Medicine” protest group, and other civil society organizations. This did not yield the desired results. Instead, with Russia’s huge territories and disproportionate population spread, the reduction or merger of hospitals partially led to the rise in inequality of people’s access to healthcare.

On one side, as Kleinert and Horton state in their editorial, “the health system itself is marred by an insufficient skill level of its too many doctors who are still underpaid and demotivated” (2017). On another, the Russian Constitution guarantees the right to health and free medicine in state hospitals for every citizen. But the scope of guarantees is not defined by law. The budgets for medical care are scattered: they are partly paid by the federal budget and partly by regional budgets. A citizen does not know who pays, when they pay, or for what. The population does not participate in financing public health care. Health resources are disintegrated, and their allocation cannot be considered equitable. Availability of health care depends on one’s location within the country and social status. As Sheiman and Shishkin (2010) noticed, an important part of the reforms should be raising the level of legal education in the field of healthcare. It is necessary to create conditions to facilitate patients’ choice of medical organizations on the basis of accessible and transparent information and ratings of clinics. This information might also include the results of assessing the quality of medical care based on patient’s opinions. It is necessary to specify what it means for the state to guarantee free medical care, to strengthen regulation of paid medical services, to make a serious revision of medical education on all levels.

The consequences of this healthcare reform became clear in light of the pandemic, which shed light on the most vulnerable groups and re-activated the ethics of silence. Professor V. Vlasov calls this “the continued misuse of health care for political purposes” (Vlasov 2017). For many years, the government used healthcare programs to solve demographic problems (e.g., suppressing access to abortions, increasing funding for in-vitro fertilization). Healthcare was a tool, aimed at the increase of manpower, instead of being an instrument focused on aiding sick people (Vlassov 2017).

L. Roshal, the chief physician in the National Institute of Emergency Children’s Surgery, recently confirmed that Russian healthcare was not ready for emergencies like COVID-19. According to him, a meeting took place almost a year ago on the preparedness of Russian healthcare to offer aid in various emergency situations. It was hosted by the All-Russian People’s Front, a civil initiative, with the participation of representatives from the Ministry of Defense, Ministry of Emergencies, the “Defense” Movement (Zaschita), the Ministry of Healthcare, the managers of the ambulance doctors’ union, and chief physicians of various clinics. The conclusion was extremely depressing: “We are not ready to provide aid to the country’s population.” Due to a combination of numerous state initiatives to “optimize” health are and decreases in total healthcare workers, Russia was primed to experience difficulties throughout a pandemic.

Moreover, this commentary also demonstrates another example of the non-disclosure culture: no one wants to bring healthcare reform failure to the public’s attention. The regime of “secrecy” (which demands silence) reemerged in Russian everyday life with the threat of COVID-19. Public discourse on responses to the pandemic focused on the high level of social mobilization and heroism, typical for the Soviet past and in tune with communist morality. Russians’ realities were penetrated by something that is directly connected with the ethics of silence and non-disclosure culture: “ The crash of Communism instilled hope that science would transform Soviet health care in the interests of humanity, but still, it serves mostly the ideology of the Russian State” (Vlasov 2017). The culture of non-disclosure persists in the neo-Soviet era.

Social Disparity and Culture of Non-Disclosure in COVID-19 Russia

The regime of “secrecy” came back into Russian reality with the threat of COVID-19, as part of the public discourse of mobilization that was a well-known part of Soviet morality accompanied with “the culture of non-disclosure.” According to the Blavatnik School of Government at Oxford University, the measures taken by the Russian government were among the strictest in the world, according to the global scale of state countermeasures to the coronavirus (2020).

The Russian National COVID Response Team was created on January 29, 2020. On January 31, Russia closed its border with China. Following that measure, self-isolation regimes and electronic passes were introduced. The government took unprecedented measures, initially stating that its care for its citizens was the prerogative. At the first stage, Russian society responsibly accepted the social isolation guidelines in spite of their numerous drawbacks. Yet the federal government refused to impose nationwide quarantine measures or even impose them on the large metropolitan areas. Responsibility for such measures was instead routed to the regional governments. Additionally, the state refused to offer financial aid to businesses or to vulnerable groups of the population. In the aftermath, the state continued to pursue the disciplinary-oppressive course, which was represented as an “educational” measure aimed at “irresponsible” citizens. Instead of supporting its citizens, the state imposed a series of administrative measures that were aimed at punishing the population for violating the non-imposed quarantine measures and for the spread of unverified information. The state also created a special procedure for registering and selling medicine, while maintaining limitations in trade and businesses.

The legal system has imposed strict measures in response to COVID-19. One of the specific measures was the Russian Supreme Court’s decision that forbade the discussion and spread of any coronavirus information under threat of criminal charges. Article 207.1 (“Public dissemination of knowingly false information about circumstances posing a threat to the life and safety of citizens”) was introduced into the Criminal Code on April 1, 2020. It focuses on filtering coronavirus distribution data. Accordingly, information concerning the pandemic that does not originate from official state channels is presupposed to be aimed at political and social destabilization.

Digital passes, isolation, and fines have also been imposed in Moscow vigorously. Regional authorities try to follow the capital’s example as best as they can. All of this was accompanied by the strengthening of social inequality—state officials of all levels, members of security services, journalists, and jurists were exempt from the restriction measures. The elder generation of Russian citizens characterized this situation as the imposition of “martial law.” Society’s perception of this time-period as a “time of war” allows us to analyze these events from the point of view of the history of Russian collective mentality . This approach allows us to see the “content plane” that lies beyond the “expression plane” of the social mindset (Ariès 1981, XIII–XVII). With the original cooperation of the population (who agreed to social distance and limit their movement), the level of the state’s communication with vulnerable people facing economic instability deteriorated to its usual forms—fines, bans, threats, and pressure. Under the conditions of the epidemic, the mental predisposition of almost all state officials becomes quite clear: the Russian president calls for social isolation, and thus it is unacceptable to say anything against social control. Following this idea, President Vladimir Putin signed the new federal law No. 123 on April 24, 2020:

On conducting an experiment to establish special regulation in order to create the necessary conditions for the development and implementation of artificial intelligence technologies in the subject of the Russian Federation—the city of federal significance Moscow.

This law amended Article 6 and 10 of the Federal Law “On Personal Data.” As a result, Russian citizens are facing a new ethical challenge to their rights to privacy; this is partially why the state’s actions are aimed at further enforcement of isolation. Nevertheless, the measures taken by the authorities in their fight against the coronavirus vary from region to region, as does the level of the pandemic’s spread (Volkov 2020).

In order to properly evaluate perception of the imposed restrictions, we must understand which measures will be repealed once the quarantine ends and which will remain permanently. For now, we can only say that only a minor percentage of the Russian population considers the state’s anti-COVID measures extreme. Here, we should examine Russians’ attitude towards similar restrictions: the law allowing security services to monitor private correspondence, the blocking of several foreign websites (including some universities), and punishment for “extremist” social media posts. These previous initiatives were actually met with support by a bulk of the population; however, public opinion on the matter was dominated by representatives of older generations and residents of rural regions. Both groups tend to traditionally see the state as the institution responsible for the safety and morality of the people. Younger, more educated, and socially active Russians and residents of major cities, on the contrary, were highly unhappy with these laws. They had a better understanding of the restrictions’ essence and “extrapolated” on themselves the possible punishments.

These contemporary state measures reinforce isolation measures. The lack of bioethics and a professional community of specialists, capable of articulating society’s interests who seek to balance individual freedom with social interests, has led to the restoration of the “ethics of silence.” This is verified by public opinion surveys. For example, Lev Gudkov, the director of the Levada Center notes that “over 60% of the population is neutral in their assessment of the President” (2020). Gudkov calls this the “base construction,” where this population concludes that “there is nothing we can do, so I will not say anything good or bad” (2020). In the post-Soviet period, Russian society went through a radical transformation and assumed a highly unstable structure. Post-Soviet Russia experienced several crises—e.g., 1998, 2000, 2014—with each one deepening the social and economic divide, creating new vulnerable groups in Russian society. Yet the problem of poverty, questions of inequality, or a search for solidarity did not become subjects of public discourse. Russian society exhibits a far greater level of income inequality than European and the majority of post-Socialist countries. The “middle class” is absent, as are the conditions that would allow for its formation, while the population is subject to a high mortality rate.

Meanwhile, individuals living in poverty were further marginalized. Poverty remained adamantly overlooked by Russian society. In 2014, sociologists marked the stigmatization of the poor; society would bestow them with highly negative characteristics (Mareeva and Tikhonova 2016). The concept of a two-level social inequality in modern Russia is applicable to healthcare access. Inequalities in health are not limited to inequalities in access to health services, however. The difference in the social status of patients creates different attitudes regarding health, reinforcing inequalities in maintaining the health of Russians. A high social status allows the patient to establish equal relations with the doctor and affects the degree of satisfaction they experience in contact with the doctor and treatment. Low social status limits the patient’s choice: they usually use the state medicine, not only because the low costs, but also because of prejudices in private medical services. The COVID-19 pandemic—aside from emphasizing the divide of the population of the large metropolitan areas with large and small towns and rural areas, intertwined with demographic factors such as age, education, and income—also brought into the open the crisis of public trust towards official medical institutions and doctors (the lack of transparency in medical statistics, malfunctioning tests, the cancellation of any medical treatment aside from COVID-19). The lack of professional associations and the political subjectivity of regional authorities leads to little legal accountability . With this in mind, it is noteworthy that the Moscow City Administration is actively promoting the values of transhumanism, that is, the improvement of a human being with digital technology and AI, through measures such as the “Smart City 2030” program and the new digital pass system testing that has been ongoing throughout the COVID-19 epidemic.

 In addition, there is a lack of accountability for the social, immunological, and psychological consequences for isolated people. Using their own clinical observations, several doctors have recently discussed the risks of prolonged isolation that directly challenge the currently imposed measures (Jalsevac 2020). However, information questioning the state’s responses will often be met with hostility on social media. The military mobilization of the health services allowed the state to go back to the comfortable and familiar forms of communication with society—a format of strength, stemming from paternalism and police control.

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The Moscow Social Space: Features and Structure

• URBAN DEVELOPMENT
• Published: 26 December 2019
• Volume 9 , pages 383–395, ( 2019 )

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• O. I. Vendina 1 ,
• A. N. Panin 2 &
• V. S. Tikunov 2  

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The article presents the results of a study on analyzing intracity differences in Moscow. The concept of “social space” as a dual reality is used as the theoretical framework of the work, derived simultaneously from social relations and properties of an urban area. In the study, heterogeneous quantitative indicators were used for each of Moscow’s 125 districts. Sources of information are a census; current socioeconomic, demographic, migration, and electoral statistics; real estate data; surveys of residents in districts of the city. Based on these, the indices of the ethnic mosaic, demographic shifts, development of the urban amenities, people’s moods, and the reputation of place are calculated; districts are categorized by typology, taking into account factors of location and territorial proximity; maps are compiled, reflecting different dimensions of the city’s social space. Comparative analysis showed that the rather egalitarian social space of Soviet Moscow in past years has become more fragmented and polarized: the boundaries of differences have become more marked. The increase in unevenness has led to tangible divisions in improvement of the urban environment, saturation of the urban well-being of some districts, and the impoverishment of others. The authors conclude that, in order to reduce the risks of urban segregation, it is necessary to strengthen the coherence of the urban space and social environments, and to bring the level of diversity of the urban environment in line with that of the population of Moscow districts. Such policies and activity are most required where rapid growth of ethnocultural diversity occurs against a lack of development, relative transport isolation of districts, and social exclusion.

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The study was carried out at the Russian Presidential Academy of National Economy and Public Administration with the financial support of the Russian Science Foundation (project no. 15-18-00064 “New Approaches and Methods for Regulating Ethno-Political Relations in Russia’s Largest Urban Agglomerations”). Analytical part of the research was fulfilled within the framework of the state-ordered research theme of the Institute of Geography RAS, no. 0148-2019-0008 (“Problems and Prospects of the Russia’s Territorial Development in Terms of its Unevenness and Global Instability”).

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Institute of Geography, Russian Academy of Sciences, 119017, Moscow, Russia

O. I. Vendina

Moscow State University, Faculty of Geography, 119991, Moscow, Russia

A. N. Panin & V. S. Tikunov

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Correspondence to O. I. Vendina , A. N. Panin or V. S. Tikunov .

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Vendina, O.I., Panin, A.N. & Tikunov, V.S. The Moscow Social Space: Features and Structure. Reg. Res. Russ. 9 , 383–395 (2019). https://doi.org/10.1134/S2079970519040117

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Received : 13 February 2019

Revised : 29 May 2019

Accepted : 11 July 2019

Published : 26 December 2019

Issue Date : October 2019

DOI : https://doi.org/10.1134/S2079970519040117

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