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Study Population: Characteristics & Sampling Techniques

How do you define a study population?  Research studies require specific groups to draw conclusions and make decisions based on their results. This group of interest is known as a sample. The method used to select respondents is known as sampling.

What is a Study Population?

A study population is a group considered for a study or statistical reasoning. The study population is not limited to the human population only. It is a set of aspects that have something in common. They can be objects, animals, measurements, etc., with many characteristics within a group.

For example, suppose you are interested in the average time a person between the ages of 30 and 35 takes to recover from a particular condition after consuming a specific type of medication. In that case, the study population will be all people between the ages of 30 and 35.

A medical study examines the spread of a specific disease in stray dogs in a city. Here, the stray dogs belonging to that city are the study population. This population or sample represents the entire population you want to conclude about.

How to establish a study population?

Sampling is a powerful technique for collecting opinions from a wide range of people, chosen from a particular group, to learn more about the whole group in general.

For any research study to be effective, it is necessary to select the study population that truly represents the entire population. Before starting your study, the target population must be identified and agreed upon. By appointing and knowing your sample well in advance, any feedback deemed useless to the study will be largely eliminated.

If your survey aims to understand a product’s or service’s effectiveness, then the study population should be the customers who have used it or are best suited to their needs and who will use the product/service.

It would be costly and time-consuming to collect data from the entire population of your target market. By accurately sampling your study population, it is possible to build a true picture of the target market using the trends in the results.

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Choosing an accurate sample from the study population

The decision on an appropriate sample depends on several key factors.

• First, you decide which population parameters you want to estimate.
• Don’t expect estimates from a sample to be exact. Always expect a margin of error when making assumptions based on the results of a sample.
• Understanding the cost of sampling helps us determine how precise our estimates need to be.
• Know how variable the population you want to measure is. It is not necessary to assume that a large sample is required if the study population is large.
• Take into account the response rate of your population. A 20% response rate is considered “good” for an online research study.

Sampling characteristics in the study population

• Sampling is a mechanism to collect data without surveying the entire target population.
• The study population is the entire unit of people you consider for your research. A sample is a subset of this group that represents the population.
• Sampling reduces survey fatigue as it is used to prevent pollsters from conducting too many surveys, thereby increasing response rates.
• Also, it is much cheaper and saves more time than measuring the entire group.
• Tracking the response rate patterns of different groups will help determine how many respondents to select.
• The study is not only limited to the selected part, but is applied to the entire target population.

Sampling techniques for your study population

Now that you understand that you cannot survey the entire study population due to various factors, you should adopt one of the sample selection methodologies that best suits your research study.

In general terms, two methodologies can be applied: probability sampling and non-probability sampling .

Sampling Techniques: Probability Sampling

This method is used to select sample objects from a population based on probability theory. Everyone is included in the sample and has an equal chance of being selected. There is no bias in this type of sample. Every person in the population has the opportunity to be part of the research.

Probability sampling can be categorized into four types:

• Simple Random Sampling : Simple random sampling is the easiest way to select a sample. Here, each member has an equal chance of being part of the sample. The objects in this sample are chosen at random, and each member has exactly the same probability of being selected.
• Cluster sampling : Cluster sampling is a method in which respondents are grouped into clusters. These groups can be defined based on age, gender, location, and demographic parameters.
• Systematic Sampling : In systematic sampling, individuals are chosen at equal intervals from the population. A starting point is selected, and then respondents are chosen at predefined sample intervals.
• Stratified Sampling: S tratified random sampling is a process of dividing respondents into distinct but predefined parameters. In this method, respondents do not overlap but collectively represent the entire population.

Sampling techniques: Non-probabilistic sampling

The non-probability sampling method uses the researcher’s preference regarding sample selection bias . This sampling method derives primarily from the researcher’s ability to access this sample. Here the population members do not have the same opportunities to be part of the sample.

Non-probability sampling can be further classified into four distinct types:

• Convenience Sampling: As the name implies, convenience sampling represents the convenience with which the researcher can reach the respondent. The researchers do not have the authority to select the samples and they are done solely for reasons of proximity and not representativeness.
• Deliberate, critical, or judgmental sampling: In this type of sampling the researcher judges and develops his sample on the nature of the study and the understanding of his target audience. Only people who meet the research criteria and the final objective are selected.
• Snowball Sampling: As a snowball speeds up, it accumulates more snow around itself. Similarly, with snowball sampling, respondents are tasked with providing references or recruiting samples for the study once their participation ends.
• Quota Sampling: Quota sampling is a method where the researcher has the privilege to select a sample based on its strata. In this method, two people cannot exist under two different conditions.

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Advantages and disadvantages of sampling in a study population

In most cases, of the total study population, perceptions can only be obtained from predefined samples. This comes with its own advantages and disadvantages. Some of them are listed below.

• Highly accurate – low probability of sampling errors (if sampled well)
• Economically feasible by nature, highly reliable
• High fitness ratio to different surveys Takes less time compared to surveying the entire population Reduced resource deployment
• Data-intensive and comprehensive Properties are applied to a larger population wideIdeal when the study population is vast.

Disadvantages

• Insufficient samples
• Possibility of bias
• Precision problems (if sampling is poor)
• Difficulty obtaining the typical sample
• Lack of quality sources
• Possibility of making mistakes.

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Methodology

• Population vs. Sample | Definitions, Differences & Examples

Population vs. Sample | Definitions, Differences & Examples

Published on May 14, 2020 by Pritha Bhandari . Revised on June 21, 2023.

A population is the entire group that you want to draw conclusions about.

A sample is the specific group that you will collect data from. The size of the sample is always less than the total size of the population.

In research, a population doesn’t always refer to people. It can mean a group containing elements of anything you want to study, such as objects, events, organizations, countries, species, organisms, etc.

Table of contents

Collecting data from a population, collecting data from a sample, population parameter vs. sample statistic, practice questions : populations vs. samples, other interesting articles, frequently asked questions about samples and populations.

Populations are used when your research question requires, or when you have access to, data from every member of the population.

Usually, it is only straightforward to collect data from a whole population when it is small, accessible and cooperative.

For larger and more dispersed populations, it is often difficult or impossible to collect data from every individual. For example, every 10 years, the federal US government aims to count every person living in the country using the US Census. This data is used to distribute funding across the nation.

However, historically, marginalized and low-income groups have been difficult to contact, locate and encourage participation from. Because of non-responses, the population count is incomplete and biased towards some groups, which results in disproportionate funding across the country.

In cases like this, sampling can be used to make more precise inferences about the population.

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When your population is large in size, geographically dispersed, or difficult to contact, it’s necessary to use a sample. With statistical analysis , you can use sample data to make estimates or test hypotheses about population data.

Ideally, a sample should be randomly selected and representative of the population. Using probability sampling methods (such as simple random sampling or stratified sampling ) reduces the risk of sampling bias and enhances both internal and external validity .

For practical reasons, researchers often use non-probability sampling methods. Non-probability samples are chosen for specific criteria; they may be more convenient or cheaper to access. Because of non-random selection methods, any statistical inferences about the broader population will be weaker than with a probability sample.

Reasons for sampling

• Necessity : Sometimes it’s simply not possible to study the whole population due to its size or inaccessibility.
• Practicality : It’s easier and more efficient to collect data from a sample.
• Cost-effectiveness : There are fewer participant, laboratory, equipment, and researcher costs involved.
• Manageability : Storing and running statistical analyses on smaller datasets is easier and reliable.

When you collect data from a population or a sample, there are various measurements and numbers you can calculate from the data. A parameter is a measure that describes the whole population. A statistic is a measure that describes the sample.

You can use estimation or hypothesis testing to estimate how likely it is that a sample statistic differs from the population parameter.

Sampling error

A sampling error is the difference between a population parameter and a sample statistic. In your study, the sampling error is the difference between the mean political attitude rating of your sample and the true mean political attitude rating of all undergraduate students in the Netherlands.

Sampling errors happen even when you use a randomly selected sample. This is because random samples are not identical to the population in terms of numerical measures like means and standard deviations .

Because the aim of scientific research is to generalize findings from the sample to the population, you want the sampling error to be low. You can reduce sampling error by increasing the sample size.

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If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Likert scale

Research bias

• Implicit bias
• Framing effect
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

Populations are used when a research question requires data from every member of the population. This is usually only feasible when the population is small and easily accessible.

A statistic refers to measures about the sample , while a parameter refers to measures about the population .

A sampling error is the difference between a population parameter and a sample statistic .

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Encyclopedia of Quality of Life and Well-Being Research pp 6412–6414 Cite as

Study Population

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Study population is a subset of the target population from which the sample is actually selected. It is broader than the concept sample frame . It may be appropriate to say that sample frame is an operationalized form of study population. For example, suppose that a study is going to conduct a survey of high school students on their social well-being . High school students all over the world might be considered as the target population. Because of practicalities, researchers decide to only recruit high school students studying in China who are the study population in this example. Suppose there is a list of high school students of China, this list is used as the sample frame .

Description

Study population is the operational definition of target population (Henry, 1990 ; Bickman & Rog, 1998 ). Researchers are seldom in a position to study the entire target population, which is not always readily accessible. Instead, only part of it—respondents who are both eligible for the study...

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Babbie, E. R. (2010). The practice of social research . Belmont, CA: Wadsworth Publishing Company.

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Hu, S. (2014). Study Population. In: Michalos, A.C. (eds) Encyclopedia of Quality of Life and Well-Being Research. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-0753-5_2893

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Research Population

All research questions address issues that are of great relevance to important groups of individuals known as a research population.

This article is a part of the guide:

• Non-Probability Sampling
• Convenience Sampling
• Random Sampling
• Stratified Sampling
• Systematic Sampling

Browse Full Outline

• 1 What is Sampling?
• 2.1 Sample Group
• 2.2 Research Population
• 2.3 Sample Size
• 2.4 Randomization
• 3.1 Statistical Sampling
• 3.2 Sampling Distribution
• 3.3.1 Random Sampling Error
• 4.1 Random Sampling
• 4.2 Stratified Sampling
• 4.3 Systematic Sampling
• 4.4 Cluster Sampling
• 4.5 Disproportional Sampling
• 5.1 Convenience Sampling
• 5.2 Sequential Sampling
• 5.3 Quota Sampling
• 5.4 Judgmental Sampling
• 5.5 Snowball Sampling

A research population is generally a large collection of individuals or objects that is the main focus of a scientific query. It is for the benefit of the population that researches are done. However, due to the large sizes of populations, researchers often cannot test every individual in the population because it is too expensive and time-consuming. This is the reason why researchers rely on sampling techniques .

A research population is also known as a well-defined collection of individuals or objects known to have similar characteristics. All individuals or objects within a certain population usually have a common, binding characteristic or trait.

Usually, the description of the population and the common binding characteristic of its members are the same. "Government officials" is a well-defined group of individuals which can be considered as a population and all the members of this population are indeed officials of the government.

Relationship of Sample and Population in Research

A sample is simply a subset of the population. The concept of sample arises from the inability of the researchers to test all the individuals in a given population. The sample must be representative of the population from which it was drawn and it must have good size to warrant statistical analysis.

The main function of the sample is to allow the researchers to conduct the study to individuals from the population so that the results of their study can be used to derive conclusions that will apply to the entire population. It is much like a give-and-take process. The population “gives” the sample, and then it “takes” conclusions from the results obtained from the sample.

Two Types of Population in Research

Target population.

Target population refers to the ENTIRE group of individuals or objects to which researchers are interested in generalizing the conclusions. The target population usually has varying characteristics and it is also known as the theoretical population.

Accessible Population

The accessible population is the population in research to which the researchers can apply their conclusions. This population is a subset of the target population and is also known as the study population. It is from the accessible population that researchers draw their samples.

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Explorable.com (Nov 15, 2009). Research Population. Retrieved Apr 05, 2024 from Explorable.com: https://explorable.com/research-population

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A short definition for Population Geography

The geographical study of population, including its spatial distribution, dynamics, and movement. As a subdiscipline, it has taken at least three distinct but related forms, the most recent of which appears increasingly integrated with human geography in general. The earliest and most enduring form of population geography emerged from the 1950s onwards, as part of spatial science . Pioneered by Glenn Trewartha, Wilbur Zelinsky , William A. V. Clark, and others in the USA, as well as Jacqueline Beujeau-Garnier and Pierre George in France, it focused on the systematic study of the distribution of population as a whole and the spatial variation in population characteristics such as fertility and mortality . Given the rapidly growing global population as well as the baby boom in affluent countries such as the USA, these geographers studied the relation between demographic growth and resources at an international scale, and population redistribution nationally ( see demographic transition ). An exemplary contribution might be Zelinksy’s mobility transition model (1971) linking migration and demographic change. They used secondary data sources such as censuses to map and describe population change and variation, including such trends as counter-urbanization . Such work could often be distinguished from population studies in general by its use of smaller scale data, below national level. Population projections at national and regional scales could be used to inform public policy debates on resource allocation. The increasing availability of more sophisticated spatial data, including more flexible census geographies, inter-censual surveys, and more detailed cross-tabulations such as the US Public-Use Microdata Samples encouraged more advanced modelling, simulation, and projection techniques ( see geodemographics ). This broad population geography has always been international and therefore comparative in scope, particularly under the auspices of the IGU Commission on Population Geography. To some extent, however, progress in the Global South has been held back by the poor availability of high-quality spatial data (Hugo 2006). Regular international conferences in population geography began in 2002.

A second variant of population geography is narrower in focus, akin to spatial demography. Geographers working in this field stressed the importance of keeping close to demography, its theories and methods, and therefore concentrating more on the core demographic variables of fertility, mortality, and, to a lesser extent, migration. They applied mathematical techniques to describe, infer, and also explain population patterns past and present. A volume edited by British geographers Bob Woods and Phil Rees (1986) Population Structures and Models: Developments in spatial demography typifies this approach. Woods’ own specialism was the historical demography of infant mortality in Victorian Britain. Spatial demography has a strong historical component, not least among French and British geographers. By detailing the spatial (and temporal) variation in mortality, fertility, nuptuality, etc., geographers were able to disrupt many of the generalizations of population change and identify the significance of place.

Many population geographers from the 1980s onwards expressed anxiety that they were marginalized from mainstream human geography and its embrace of social theories from Marxism to feminism , and postmodernism (Findlay and Graham 1991). Not enough research was being done on key issues such as famine, gender, and environment. They also sensed that other human geographers were overlooking the significance of population to wider processes. A ‘retheorization’ of population geography (White and Jackson) gradually took shape, involving more methodological diversity and theoretical plurality. New methods, such as lifecourse analysis , helped integrate biographical and individual-level studies into the field. In recent years there has been greater attention paid to gender, religion, age, disability, generation, sexuality, and race, variables which go beyond the vital statistics of births, deaths, and marriages. Furthermore, population geographers have begun to critique the standard census categories of the field, recognizing the social construction of childhood, whiteness , femininity, etc. Representative of this more theoretical approach is James Tyner’s (2009) War, Violence and Population: making the body count . Tyner argues that population geography should pay more attention to war and violence, using examples from the Vietnam War, Cambodia’s killing fields, and the Rwandan genocide. Grounded in post-colonialism and post-structuralism , he deploys Foucault’s concepts of biopower and disciplinary power to uncover the logics behind such violence.

This more recent form of population geography is increasingly aligned with human geography as a whole. One consequence has been the relative neglect of studies of fertility, mortality, and morbidity , the latter becoming the preserve of medical geography. Of the core demographic topics, migration continued to be the most central to population geographers; most of the papers in the main population geography journals, Population, Space and Place (launched in 1995 as The International Journal of Population Geography ) and Espace, Populations, Sociétés (founded 1983), concern migration and related topics such as transnationalism .

All three forms of population geography outlined here continue side by side. Spatial and historical demography is making increasing use of data sources from outside Europe. Popular textbooks such as Population Geography: Problems, Concept and Prospects (Peters and Larkin 2010) teach new generations the basics of the subject. By contrast, Adrian Bailey’s (2005) Making Population Geography presents a broader, more theoretically informed perspective. Recent conferences and journal special issues have focused on climate change, neo-Malthusianism, children’s geographies, vulnerability, and difference, although migration continues to predominate.

Rogers, A., Castree, N., & Kitchin, R. (2013). "Population geography ." In  A Dictionary of Human Geography . Oxford University Press. Retrieved 25 Jan. 2022

The study of human populations; their composition, growth, distribution, and migratory movements with an emphasis on the last two. It is concerned with the study of demographic processes which affect the environment, but differs from demography in that it is concerned with the spatial expression of such processes. Population, Space and Place is the journal of the UK Population Geography Research Group.

Mayhew, S. (2015). " Population geography ." In  A Dictionary of Geography . Oxford University Press. Retrieved 25 Jan. 2022

DEMOGRAPHY The observed, statistical, and mathematical study of human populations, concerned with the size, distribution, and composition of such populations.

Mayhew, S. (2015). "Demography ." In  A Dictionary of Geography . Oxford University Press. Retrieved 25 Jan. 2022 

In the Library's collections

    Many of the books on Population Studies and Demography are located in the call number range HB 848 through HB 3697 on Berry Level 3 .

    To browse in the library's catalog, do a subject search for Population . That will give a list of the subject headings under Population and the number of items under each heading. You can also do the same for Demography .

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WHY IT'S IMPORTANT

Researchers know that populations vary in their susceptibility to and resilience against heart, lung, blood, and sleep disorders, as well as in disease course and outcomes. These differences sometimes are caused by age, sex, race, ancestry or genetic factors that cannot be changed. In other cases, these differences are due to factors that can be changed or modified, such as lifestyle choices or environment, and some biological factors. Future research will help to better understand the causes of population health differences and to identify strategies that effectively address these differences before they become health disparities. Health disparities are differences in the risk, burden of diseases, and adverse health conditions that exist among specific population groups.

KEY ACCOMPLISHMENTS

• The multi-generational Framingham Heart Study helped discover risk factors and interventions to prevent heart disease, and continues to drive discovery.
• The landmark Women’s Health Initiative (WHI) found that hormone replacement therapy did not prevent heart disease as thought in post-menopausal women.
• The long-term Jackson Heart Study revealed that African Americans who took certain health measures had a lower risk for heart disease.
• The Trans-Omics for Precision Medicine (TOPMed) program is leveraging data from participants in NHLBI’s population and epidemiology studies.

OPPORTUNITIES & CHALLENGES

In 2016, the NHLBI released its Strategic Vision , which will guide the Institute’s research activities for the coming decade. Many of the objectives and compelling questions identified in the plan focus on factors that account for differences in health among populations. For example, researchers are looking at the factors that make individuals or populations resistant or prone to diseases, despite having experienced the same exposures such as diet, smoking, environmental and social factors. Part of NHLBI’s priorities include recruiting and retaining researchers who are interested in epidemiology research and developing a diverse scientific workforce.

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Age, sex, race, genes and biology may account for some differences in health among different populations.Lifestyle choices, behaviors, and socioeconomic status may also play a role in creating differences in health. Our research seeks to better understand the causes of health differences and to identify ways to improve public health and health outcomes.

Population studies have entered an exciting period when advances in assay methods, imaging technologies, and electronic data are creating new scientific opportunities. These tools make it possible for large epidemiology studies to explore what makes individuals susceptible to disease. To capitalize on these opportunities, NHLBI established an Advisory Council Working Group on Epidemiology and Population Science to look at the current landscape, emerging tools, and future opportunities in population science and  make important recommendations that contributed to the Institute’s strategic thinking in this area.

The NHLBI’s large-population cohort studies have been major generators of new knowledge that has informed the molecular basis for disease and identified targets for new treatments. For example, NHLBI research has transformed the way the public approaches cardiovascular disease by conducting numerous studies that focus on diverse populations. The  Women’s Health Initiative (WHI)  continues to yield new insights that advance our understanding of heart disease and other diseases in women.  

It is important that the NHLBI continue to build on its legacy of excellence in population studies research. Our population studies have led to a wide range of discoveries and initiatives that will reduce health disparities and improve health outcomes in  heart and vascular diseases ,  obesity ,  women’s health , and  precision medicine .

Learn about some of NHLBI’s efforts to support and advance population and epidemiology research.

We Perform Research

NHLBI’s  Division of Intramural Research , including its  Epidemiology and Community Health Branch and  Population Sciences Branch , is actively engaged in studying thousands of population cohort study participants to formulate a global view of both the natural history and future trends related to heart, lung, blood, and sleep disorders.

We Fund Research

The research we fund today will help improve our future health. Our  Division of Cardiovascular Sciences ’  Program in Prevention & Population Sciences , including its Epidemiology Branch and Clinical Application & Prevention Branch, supports population and epidemiology research including population studies, disease risk and outcome studies, and clinical trials to prevent disease and improve clinical care and public health. Other  NHLBI Divisions  also fund population and epidemiology research specific to their disease areas.

The Promise of Precision Medicine

Through NHLBI’s  Trans-Omics for Precision Medicine (TOPMed) program , researchers will use data from studies focused on heart, lung, blood and sleep disorders to better predict, prevent, diagnose, and treat diseases based on a patient’s unique genes, environment, and molecular signatures. Learn more about NHLBI  precision medicine activities .

Following Cardiovascular Disease in Generations of Families

The  Framingham Heart Study (FHS)  is a long-term study designed to identify genetic and environmental factors influencing the development of cardiovascular and other diseases in generations of families. Through the FHS, scientists learned of the risk factors for heart disease that are now checked in all routine physicals. This study has contributed discoveries that led to major changes in the prevention and treatment of heart disease.

Leading Women’s Health Research

The  Women's Health Initiative (WHI)  is a long-term study focusing on strategies to prevent the major causes of death and disability among postmenopausal women. Although the original WHI study completed data collection in 2005, the WHI continues to advance women’s health through extension studies and ancillary studies, such as the Women’s Health Initiative Strong and Healthy Study (WHISH) and the Women's Health Initiative Sleep Hypoxia Effects on Resilience (WHISPER).

Informing Improvements to Clinical Care and Public Health

The  Systolic Blood Pressure Intervention Trial (SPRINT)  demonstrated that managing high blood pressure more intensely than recommended significantly lowers the rate of cardiovascular disease and risk of death in a group of high-risk adults who are 50 years or older with high blood pressure. The SPRINT Memory and Cognition in Decreased Hypertension (SPRINT-MIND) Trial is examining whether intensive high blood pressure treatment can reduce the rate of dementia or slow the decline in cognitive function.

Investigating Atherosclerosis Causes and Outcomes

NHLBI’s  Atherosclerosis Risk in Communities Study (ARIC)  study is investigating the causes of atherosclerosis, a disease in which plaque builds up in the arteries, and the clinical outcomes from four U.S. communities. ARIC is also measuring how cardiovascular risk factors, medical care, and outcomes vary by race, sex, place, and time.

Examining Cardiovascular Disease Beginning in Young Adulthood

The  Coronary Artery Risk Development in Young Adults (CARDIA)  study examines the causes, risk factors, and natural history of cardiovascular disease that begin in young adulthood. For over 30 years, CARDIA has followed over 5,000 black and white young adults who were recruited from four centers in 1985 to 1986. The study has helped researchers better understand the importance of early adulthood factors that increase the risk of cardiovascular disease later in life.

Studying Cardiovascular Disease Outcomes

The  Cardiovascular Health Study (CHS)  is a long-term, population-based study of risk factors for the development of coronary heart disease and stroke in men and women aged 65 and older. Annual exams included measures of possible and proven cardiovascular disease risk, including subclinical disease.

Understanding How Diseases Impact Diverse Populations and People who love in Rural South

The NHLBI supports research to better understand the impact of diseases on minorities and to improve health outcomes in diverse populations. Studies include  Hispanic Community Health Study/Study of Latinos (HCHS/SOL) ;  Jackson Heart Study (JHS) ;  Multi-Ethnic Study of Atherosclerosis (MESA) ;  Strong Heart Study (SHS) ;  The Rural Cohort Study ; the CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) Consortium; Consortium on Asthma among African-Ancestry Populations in the Americas (CAAPA); Healthy Communities Study: How Communities Shape Children’s Health (HCS). 

Providing Access to NHLBI Biologic Specimens and Data

The  Biologic Specimen and Data Repository Information Coordinating Center (BioLINCC)  centralizes and integrates biospecimens and clinical data that were once stored in separate repositories. Researchers can find and request available resources on BioLINCC's secure website, which maximizes the value of these resources and advances heart, lung, blood, and sleep research.

Advancing Research on Conditions in People Living with HIV

In 2019, the NHLBI became the primary steward of the new  Men’s AIDS Cohort Study (MACS) / Women’s Interagency HIV Study (WIHS) Combined Cohort Study (MACS/WIHS-CSS) . This study is a trans-NIH collaborative research effort that aims to understand and reduce the impact of chronic health conditions that affect people living with HIV. The MACS/WIHS Combined Cohort Study will build on decades of research in thousands of men and women who are living with and without HIV to further our understanding of chronic heart, lung, blood, sleep, and other disorders in people living with HIV.

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Stanford Cancer Institute

Search stanford cancer institute, research programs, population sciences program, program directors.

Esther M. John

Professor (research) of epidemiology and population health and of medicine (oncology).

Allison W. Kurian, M.D., M.Sc.

Professor of medicine (oncology) and of epidemiology and population health.

Marcia L. Stefanick, Ph.D.

Professor (research) of medicine (stanford prevention research center), of obstetrics and gynecology and, by courtesy, of epidemiology and population health, about population sciences.

The Population Sciences Program is focused on reducing the burden of cancer and improving outcomes of cancer patients. Program members conduct observational and interventional research on cancer etiology, behavioral interventions, screening and outcomes that has a significant translational impact on clinical or public health practice in the SCI catchment area and beyond.

Program Aims:

• Identify Lifestyle, Environmental, and Genetic Risk Factors for Cancer
• Cancer Screening & Detection, and Cancer Treatment & Outcomes
• Behavioral and Intervention Research to Reduce Cancer Risk, Incidence, Recurrence, and Mortality

SCI Population Sciences Program

Available Data and Biospecimen Resources for Collaborative Research

Breast Cancer Family Registry (BCFR)

The BCFR is a multicenter cohort of ~11,000 breast cancer families from the U.S, Canada and Australia established in 1995, with systematic follow-up for new cancers and outcomes, updates of baseline data, and collection of new data items

Resources :  Baseline and follow-up data on family history and risk factors, clinical data, self-reported treatment data, blood samples (DNA, cell lines, plasma), pathology review data, tumor tissue (slides or TMAs), extensive germline molecular characterization (GWAS, Oncoarray, BRCA1, BRCA2, other candidate genes)

Website :  http://www.bcfamilyregistry.org

SCI contact person :  Esther M. John,  [email protected]

Women’s Health Initiative (WHI)

WHI has rich phenotypic data, stored plasma, serum and buffy coat samples and cancer outcomes for 161,808 postmenopausal women (19% non-white), aged 50-79 at baseline (1993-1998) enrolled at one of 40 U.S. centers into either a Clinical Trial (CT, N=68,132) or Observational Study (OS, N=93,676) cohort. Women in the CT participated in one of two menopausal hormone trials (HT, N=27,347) and/or a diet trial (N=48,835) for which breast cancer was a primary safety or prevention outcome, respectively, over half of whom also enrolled in a calcium/vitamin D RCT of fractures and colorectal cancer, also a primary outcome of the diet trial. NCI has funded collection of all cancer outcomes in the ongoing follow-up of participants in WHI Extension Study, many of whom are participating in additional ancillary studies and WHI is part of several cancer consortia.

Website :   https://www.whi.org/SitePages/WHI%20Home.aspx

SCI contact person : Marcia Stefanick,  [email protected]

WHI Life and Longevity after Cancer (LILAC)

LILAC is an NCI infrastructure grant (Multi-PI: G. Anderson, FHCRC; B. Caan, Kaiser RI; E. Paskett, OSU) for a WHI cancer survivor cohort. The LILAC survey is   sent to women (N of cases as of 2016) with colorectal (3074), endometrial (1702), ovarian (1168), breast (12,554), and lung (3514) cancers as well as melanoma (2230), leukemia (975), lymphoma (1630 NH, 69 H) at baseline and annually. In addition to collecting treatment and recurrence data, LILAC is obtaining   paraffin-embedded tumor tissue   for all cases of lung, colorectal, endometrial, ovary cancers, all cases of triple negative breast cancer and a subsample of receptor positive breast cancers (up to 2000 cancers).

Website :   https://www.whi.org/studies/LILAC/Pages/Home.aspx

ONCOSHARE PROJECT

Oncoshare is a comprehensive breast cancer research tool that integrates data from several local and national resources: the statewide, population-based California Cancer Registry; electronic medical records (EMRs) from Stanford University Medical Center and multiple sites of the community-based Sutter-Palo Alto Medical Foundation (PAMF) healthcare system; detailed genomic sequencing results directly from clinical testing laboratories; and patient-reported data. Oncoshare contains de-identified records of more than 28,000 breast cancer patients treated at Stanford and/or PAMF since January 2000.

Website :  http://med.stanford.edu/oncoshare.html

SCI contact person:  Allison Kurian,  [email protected]

Other Cancer Registry Resources

Cancer registry seer.

The Surveillance, Epidemiology, and End-Results  (SEER) program of the NCI collects data on cancer diagnoses in 18 locations across the country, and annually collects information on approximately 1.5 million cancer cases ( https://seer.cancer.gov/ ). Data from the GBACR and CCR have contributed to SEER for decades.

 Scarlett L. Gomez,  [email protected]

Cancer Registry Greater Bay Area

The Greater Bay Area Cancer Registry (GBACR)  gathers information about all cancers diagnosed or treated in a nine-county area (Alameda, Contra Costa, Marin, Monterey, San Benito, San Francisco, San Mateo, Santa Clara and Santa Cruz). Data on approximately 30,000 cases are collected annually ( http://www.cpic.org/ ;  [email protected] ). GBACR data are recognized by national and international registry standard-setting organizations for being of the highest quality.

Scarlett L. Gomez,  [email protected]

Cancer Registry California

The California Cancer Registry  (CCR) includes data from the nine counties of the GBACR and is the state’s population-based cancer surveillance system. It is legally mandated to collect information about all cancers diagnosed in the state. Annually, approximately 150,000 cases are included ( http://ccr.ca.gov/ ;  [email protected] ).

Patient Care

Clinical trials, health equity, shared resources, stanford medicine, health care.

©2024 Stanford Medicine

• Open access
• Published: 02 April 2022

A qualitative study of rural healthcare providers’ views of social, cultural, and programmatic barriers to healthcare access

• Nicholas C. Coombs 1 ,
• Duncan G. Campbell 2 &
• James Caringi 1  

BMC Health Services Research volume  22 , Article number:  438 ( 2022 ) Cite this article

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Ensuring access to healthcare is a complex, multi-dimensional health challenge. Since the inception of the coronavirus pandemic, this challenge is more pressing. Some dimensions of access are difficult to quantify, namely characteristics that influence healthcare services to be both acceptable and appropriate. These link to a patient’s acceptance of services that they are to receive and ensuring appropriate fit between services and a patient’s specific healthcare needs. These dimensions of access are particularly evident in rural health systems where additional structural barriers make accessing healthcare more difficult. Thus, it is important to examine healthcare access barriers in rural-specific areas to understand their origin and implications for resolution.

We used qualitative methods and a convenience sample of healthcare providers who currently practice in the rural US state of Montana. Our sample included 12 healthcare providers from diverse training backgrounds and specialties. All were decision-makers in the development or revision of patients’ treatment plans. Semi-structured interviews and content analysis were used to explore barriers–appropriateness and acceptability–to healthcare access in their patient populations. Our analysis was both deductive and inductive and focused on three analytic domains: cultural considerations, patient-provider communication, and provider-provider communication. Member checks ensured credibility and trustworthiness of our findings.

Five key themes emerged from analysis: 1) a friction exists between aspects of patients’ rural identities and healthcare systems; 2) facilitating access to healthcare requires application of and respect for cultural differences; 3) communication between healthcare providers is systematically fragmented; 4) time and resource constraints disproportionately harm rural health systems; and 5) profits are prioritized over addressing barriers to healthcare access in the US.

Conclusions

Inadequate access to healthcare is an issue in the US, particularly in rural areas. Rural healthcare consumers compose a hard-to-reach patient population. Too few providers exist to meet population health needs, and fragmented communication impairs rural health systems’ ability to function. These issues exacerbate the difficulty of ensuring acceptable and appropriate delivery of healthcare services, which compound all other barriers to healthcare access for rural residents. Each dimension of access must be monitored to improve patient experiences and outcomes for rural Americans.

Peer Review reports

Unequal access to healthcare services is an important element of health disparities in the United States [ 1 ], and there remains much about access that is not fully understood. The lack of understanding is attributable, in part, to the lack of uniformity in how access is defined and evaluated, and the extent to which access is often oversimplified in research [ 2 ]. Subsequently, attempts to address population-level barriers to healthcare access are insufficient, and access remains an unresolved, complex health challenge [ 3 , 4 , 5 ]. This paper presents a study that aims to explore some of the less well studied barriers to healthcare access, particularly those that influence healthcare acceptability and appropriateness.

In truth, healthcare access entails a complicated calculus that combines characteristics of individuals, their households, and their social and physical environments with characteristics of healthcare delivery systems, organizations, and healthcare providers. For one to fully ‘access’ healthcare, they must have the means to identify their healthcare needs and have available to them care providers and the facilities where they work. Further, patients must then reach, obtain, and use the healthcare services in order to have their healthcare needs fulfilled. Levesque and colleagues critically examined access conceptualizations in 2013 and synthesized all ways in which access to healthcare was previously characterized; Levesque et al. proposed five dimensions of access: approachability, acceptability, availability, affordability and appropriateness [ 2 ]. These refer to the ability to perceive, seek, reach, pay for, and engage in services, respectively.

According to Levesque et al.’s framework, the five dimensions combine to facilitate access to care or serve as barriers. Approachability indicates that people facing health needs understand that healthcare services exist and might be helpful. Acceptability represents whether patients see healthcare services as consistent or inconsistent with their own social and cultural values and worldviews. Availability indicates that healthcare services are reached both physically and in a timely manner. Affordability simplifies one’s capacity to pay for healthcare services without compromising basic necessities, and finally, appropriateness represents the fit between healthcare services and a patient’s specific healthcare needs [ 2 ]. This study focused on the acceptability and appropriateness dimensions of access.

Before the novel coronavirus (SARS-CoV-2; COVID-19) pandemic, approximately 13.3% of adults in the US did not have a usual source of healthcare [ 6 ]. Millions more did not utilize services regularly, and close to two-thirds reported that they would be debilitated by an unexpected medical bill [ 7 , 8 , 9 ]. Findings like these emphasized a fragility in the financial security of the American population [ 10 ]. These concerns were exacerbated by the pandemic when a sudden surge in unemployment increased un- and under-insurance rates [ 11 ]. Indeed, employer-sponsored insurance covers close to half of Americans’ total cost of illness [ 12 ]. Unemployment linked to COVID-19 cut off the lone outlet to healthcare access for many. Health-related financial concerns expanded beyond individuals, as healthcare organizations were unequipped to manage a simultaneous increase in demand for specialized healthcare services and a steep drop off for routine revenue-generating healthcare services [ 13 ]. These consequences of the COVID-19 pandemic all put additional, unexpected pressure on an already fragmented US healthcare system.

Other structural barriers to healthcare access exist in relation to the rural–urban divide. Less than 10% of US healthcare resources are located in rural areas where approximately 20% of the American population resides [ 14 ]. In a country with substantially fewer providers per capita compared to many other developed countries, persons in rural areas experience uniquely pressing healthcare provider shortages [ 15 , 16 ]. Rural inhabitants also tend to have lower household income, higher rates of un- or under-insurance, and more difficulty with travel to healthcare clinics than urban dwellers [ 17 ]. Subsequently, persons in rural communities use healthcare services at lower rates, and potentially preventable hospitalizations are more prevalent [ 18 ]. This disparity often leads rural residents to use services primarily for more urgent needs and less so for routine care [ 19 , 20 , 21 ].

The differences in how rural and urban healthcare systems function warranted a federal initiative to focus exclusively on rural health priorities and serve as counterpart to Healthy People objectives [ 22 ]. The rural determinants of health, a more specific expression of general social determinants, add issues of geography and topography to the well-documented social, economic and political factors that influence all Americans’ access to healthcare [ 23 ]. As a result, access is consistently regarded as a top priority in rural areas, and many research efforts have explored the intersection between access and rurality, namely within its less understood dimensions (acceptability and appropriateness) [ 22 ].

Acceptability-related barriers to care

Acceptability represents the dimension of healthcare access that affects a patient’s ability to seek healthcare, particularly linked to one’s professional values, norms and culture [ 2 ]. Access to health information is an influential factor for acceptable healthcare and is essential to promote and maintain a healthy population [ 24 ]. According to the Centers for Disease Control and Prevention, health literacy or a high ‘health IQ’ is the degree to which individuals have the ability to find, understand, and use information and services to inform health-related decisions and actions for themselves and others, which impacts healthcare use and system navigation [ 25 ]. The literature indicates that lower levels of health literacy contribute to health disparities among rural populations [ 26 , 27 , 28 ]. Evidence points to a need for effective health communication between healthcare organizations and patients to improve health literacy [ 24 ]. However, little research has been done in this area, particularly as it relates to technologically-based interventions to disseminate health information [ 29 ].

Stigma, an undesirable position of perceived diminished status in an individual’s social position, is another challenge that influences healthcare acceptability [ 30 ]. Those who may experience stigma fear negative social consequences in relation to care seeking. They are more likely to delay seeking care, especially among ethnic minority populations [ 31 , 32 ]. Social media presents opportunities for the dissemination of misleading medical information; this runs further risk for stigma [ 33 ]. Stigma is difficult to undo, but research has shown that developing a positive relationship with a healthcare provider or organization can work to reduce stigma among patients, thus promoting healthcare acceptability [ 34 ].

A provider’s attempts to engage patients and empower them to be active decision-makers regarding their treatment has also been shown to improve healthcare acceptability. One study found that patients with heart disease who completed a daily diary of weight and self-assessment of symptoms, per correspondence with their provider, had better care outcomes than those who did not [ 35 ]. Engaging with household family members and involved community healers also mitigates barriers to care, emphasizing the importance of a team-based approach that extends beyond those who typically provide healthcare services [ 36 , 37 ]. One study, for instance, explored how individuals closest to a pregnant woman affect the woman’s decision to seek maternity care; partners, female relatives, and community health-workers were among the most influential in promoting negative views, all of which reduced a woman’s likelihood to access care [ 38 ].

Appropriateness-related barriers to care

Appropriateness marks the dimension of healthcare access that affects a patient’s ability to engage, and according to Levesque et al., is of relevance once all other dimensions (the ability to perceive, seek, reach and pay for) are achieved [ 2 ]. The ability to engage in healthcare is influenced by a patient’s level of empowerment, adherence to information, and support received by their healthcare provider. Thus, barriers to healthcare access that relate to appropriateness are often those that indicate a breakdown in communication between a patient with their healthcare provider. Such breakdown can involve a patient experiencing miscommunication, confrontation, and/or a discrepancy between their provider’s goals and their own goals for healthcare. Appropriateness represents a dimension of healthcare access that is widely acknowledged as an area in need of improvement, which indicates a need to rethink how healthcare providers and organizations can adapt to serve the healthcare needs of their communities [ 39 ]. This is especially true for rural, ethnic minority populations, which disproportionately experience an abundance of other barriers to healthcare access. Culturally appropriate care is especially important for members of minority populations [ 40 , 41 , 42 ]. Ultimately, patients value a patient-provider relationship characterized by a welcoming, non-judgmental atmosphere [ 43 , 44 ]. In rural settings especially, level of trust and familiarity are common factors that affect service utilization [ 45 ]. Evidence suggests that kind treatment by a healthcare provider who promotes patient-centered care can have a greater overall effect on a patient’s experience than a provider’s degree of medical knowledge or use of modern equipment [ 46 ]. Of course, investing the time needed to nurture close and caring interpersonal connections is particularly difficult in under-resourced, time-pressured rural health systems [ 47 , 48 ].

The most effective way to evaluate access to healthcare largely depends on which dimensions are explored. For instance, a population-based survey can be used to measure the barrier of healthcare affordability. Survey questions can inquire directly about health insurance coverage, care-related financial burden, concern about healthcare costs, and the feared financial impacts of illness and/or disability. Many national organizations have employed such surveys to measure affordability-related barriers to healthcare. For example, a question may ask explicitly about financial concerns: ‘If you get sick or have an accident, how worried are you that you will not be able to pay your medical bills?’ [ 49 ]. Approachability and availability dimensions of access are also studied using quantitative analysis of survey questions, such as ‘Is there a place that you usually go to when you are sick or need advice about your health?’ or ‘Have you ever delayed getting medical care because you couldn’t get through on the telephone?’ In contrast, the remaining two dimensions–acceptability and appropriateness–require a qualitative approach, as the social and cultural factors that determine a patient’s likelihood of accepting aspects of the services that are to be received (acceptability) and the fit between those services and the patient’s specific healthcare needs (appropriateness) can be more abstract [ 50 , 51 ]. In social science, qualitative methods are appropriate to generate knowledge of what social events mean to individuals and how those individuals interact within them; these methods allow for an exploration of depth rather than breadth [ 52 , 53 ]. Qualitative methods, therefore, are appropriate tools for understanding the depth of healthcare providers’ experiences in the inherently social context of seeking and engaging in healthcare.

In sum, acceptability- and appropriateness-related barriers to healthcare access are multi-layered, complex and abundant. Ensuring access becomes even more challenging if structural barriers to access are factored in. In this study, we aimed to explore barriers to healthcare access among persons in Montana, a historically underserved, under-resourced, rural region of the US. Montana is the fourth largest and third least densely populated state in the country; more than 80% of Montana counties are classified as non-core (the lowest level of urban/rural classification), and over 90% are designated as health professional shortage areas [ 54 , 55 ]. Qualitative methods supported our inquiry to explore barriers to healthcare access related to acceptability and appropriateness.

Participants

Qualitative methods were utilized for this interpretive, exploratory study because knowledge regarding barriers to healthcare access within Montana’s rural health systems is limited. We chose Montana healthcare providers, rather than patients, as the population of interest so we may explore barriers to healthcare access from the perspective of those who serve many persons in rural settings. Inclusion criteria required study participants to provide direct healthcare to patients at least one-half of their time. We defined ‘provider’ as a healthcare organization employee with clinical decision-making power and the qualifications to develop or revise patients’ treatment plans. In an attempt to capture a group of providers with diverse experience, we included providers across several types and specialties. These included advanced practice registered nurses (APRNs), physicians (MDs and DOs), and physician assistants (PAs) who worked in critical care medicine, emergency medicine, family medicine, hospital medicine, internal medicine, pain medicine, palliative medicine, pediatrics, psychiatry, and urgent care medicine. We also included licensed clinical social workers (LCSWs) and clinical psychologists who specialize in behavioral healthcare provision.

Recruitment and Data Collection

We recruited participants via email using a snowball sampling approach [ 56 ]. We opted for this approach because of its effectiveness in time-pressured contexts, such as the COVID-19 pandemic, which has made healthcare provider populations hard to reach [ 57 ]. Considering additional constraints with the pandemic and the rural nature of Montana, interviews were administered virtually via Zoom video or telephone conferencing with Zoom’s audio recording function enabled. All interviews were conducted by the first author between January and September 2021. The average length of interviews was 50 min, ranging from 35 to 70 min. There were occasional challenges experienced during interviews (poor cell phone reception from participants, dropped calls), in which case the interviewer remained on the line until adequate communication was resumed. All interviews were included for analysis and transcribed verbatim into NVivo Version 12 software. All qualitative data were saved and stored on a password-protected University of Montana server. Hard-copy field notes were securely stored in a locked office on the university’s main campus.

Data analysis included a deductive followed by an inductive approach. This dual analysis adheres to Levesque’s framework for qualitative methods, which is discussed in the Definition of Analytic Domains sub-section below. Original synthesis of the literature informed the development of our initial deductive codebook. The deductive approach was derived from a theory-driven hypothesis, which consisted of synthesizing previous research findings regarding acceptability- and appropriateness-related barriers to care. Although the locations, patient populations and specific type of healthcare services varied by study in the existing literature, several recurring barriers to healthcare access were identified. We then operationalized three analytic domains based on these findings: cultural considerations, patient-provider communication, and provider-provider communication. These domains were chosen for two reasons: 1) the terms ‘culture’ and ‘communication’ were the most frequently documented characteristics across the studies examined, and 2) they each align closely with the acceptability and appropriateness dimensions of access to healthcare, respectively. In addition, ‘culture’ is included in the definition of acceptability and ‘communication’ is a quintessential aspect of appropriateness. These domains guided the deductive portion of our analysis, which facilitated the development of an interview guide used for this study.

Interviews were semi-structured to allow broad interpretations from participants and expand the open-ended characterization of study findings. Data were analyzed through a flexible coding approach proposed by Deterding and Waters [ 58 ]. Qualitative content analysis was used, a method particularly beneficial for analyzing large amounts of qualitative data collected through interviews that offers possibility of quantifying categories to identify emerging themes [ 52 , 59 ]. After fifty percent of data were analyzed, we used an inductive approach as a formative check and repeated until data saturation, or the point at which no new information was gathered in interviews [ 60 ]. At each point of inductive analysis, interview questions were added, removed, or revised in consideration of findings gathered [ 61 ]. The Standards for Reporting Qualitative Research (SRQR) was used for reporting all qualitative data for this study [ 62 ]. The first and third authors served as primary and secondary analysts of the qualitative data and collaborated to triangulate these findings. An audit approach was employed, which consisted of coding completed by the first author and then reviewed by the third author. After analyses were complete, member checks ensured credibility and trustworthiness of findings [ 63 ]. Member checks consisted of contacting each study participant to explain the study’s findings; one-third of participants responded and confirmed all findings. All study procedures were reviewed and approved by the Human Subjects Committee of the authors’ institution’s Institutional Review Board.

Definitions of Analytic Domains

Cultural considerations.

Western health systems often fail to consider aspects of patients’ cultural perspectives and histories. This can manifest in the form of a providers’ lack of cultural humility. Cultural humility is a process of preventing imposition of one’s worldview and cultural beliefs on others and recognizing that everyone’s conception of the world is valid. Humility cultivates sensitive approaches in treating patients [ 64 ]. A lack of cultural humility impedes the delivery of acceptable and appropriate healthcare [ 65 ], which can involve low empathy or respect for patients, or dismissal of culture and traditions as superstitions that interfere with standard treatments [ 66 , 67 ]. Ensuring cultural humility among all healthcare employees is a step toward optimal healthcare delivery. Cultural humility is often accomplished through training that can be tailored to particular cultural- or gender-specific populations [ 68 , 69 ]. Since cultural identities and humility have been marked as factors that can heavily influence patients’ access to care, cultural considerations composed our first analytic domain. To assess this domain, we asked participants how they address the unique needs of their patients, how they react when they observe a cultural behavior or attitude from a patient that may not directly align with their treatment plan, and if they have received any multicultural training or training on cultural considerations in their current role.

Patient-provider communication

Other barriers to healthcare access can be linked to ineffective patient-provider communication. Patients who do not feel involved in healthcare decisions are less likely to adhere to treatment recommendations [ 70 ]. Patients who experience communication difficulties with providers may feel coerced, which generates disempowerment and leads patients to employ more covert ways of engagement [ 71 , 72 ]. Language barriers can further compromise communication and hinder outcomes or patient progress [ 73 , 74 ]. Any miscommunication between a patient and provider can affect one’s access to healthcare, namely affecting appropriateness-related barriers. For these reasons, patient-provider communication composed our second analytic domain. We asked participants to highlight the challenges they experience when communicating with their patients, how those complications are addressed, and how communication strategies inform confidentiality in their practice. Confidentiality is a core ethical principle in healthcare, especially in rural areas that have smaller, interconnected patient populations [ 75 ].

Provider-Provider Communication

A patient’s journey through the healthcare system necessitates sufficient correspondence between patients, primary, and secondary providers after discharge and care encounters [ 76 ]. Inter-provider and patient-provider communication are areas of healthcare that are acknowledged to have some gaps. Inconsistent mechanisms for follow up communication with patients in primary care have been documented and emphasized as a concern among those with chronic illness who require close monitoring [ 68 , 77 ]. Similar inconsistencies exist between providers, which can lead to unclear care goals, extended hospital stays, and increased medical costs [ 78 ]. For these reasons, provider-provider communication composed our third analytic domain. We asked participants to describe the approaches they take to streamline communication after a patient’s hospital visit, the methods they use to ensure collaborative communication between primary or secondary providers, and where communication challenges exist.

Healthcare provider characteristics

Our sample included 12 providers: four in family medicine (1 MD, 1 DO, 1 PA & 1 APRN), three in pediatrics (2 MD with specialty in hospital medicine & 1 DO), three in palliative medicine (2 MDs & 1 APRN with specialty in wound care), one in critical care medicine (DO with specialty in pediatric pulmonology) and one in behavioral health (1 LCSW with specialty in trauma). Our participants averaged 9 years (range 2–15) as a healthcare provider; most reported more than 5 years in their current professional role. The diversity of participants extended to their patient populations as well, with each participant reporting a unique distribution of age, race and level of medical complexity among their patients. Most participants reported that a portion of their patients travel up to five hours, sometimes across county- or state-lines, to receive care.

Theme 1: A friction exists between aspects of patients’ rural identities and healthcare systems

Our participants comprised a collection of medical professions and reported variability among health-related reasons their patients seek care. However, most participants acknowledged similar characteristics that influence their patients’ challenges to healthcare access. These identified factors formed categories from which the first theme emerged. There exists a great deal of ‘rugged individualism’ among Montanans, which reflects a self-sufficient and self-reliant way of life. Stoicism marked a primary factor to characterize this quality. One participant explained:

True Montanans are difficult to treat medically because they tend to be a tough group. They don’t see doctors. They don’t want to go, and they don’t want to be sick. That’s an aspect of Montana that makes health culture a little bit difficult.

Another participant echoed this finding by stating:

The backwoods Montana range guy who has an identity of being strong and independent probably doesn’t seek out a lot of medical care or take a lot of medications. Their sense of vitality, independence and identity really come from being able to take care and rely on themselves. When that is threatened, that’s going to create a unique experience of illness.

Similar responses were shared by all twelve participants; stoicism seemed to be heavily embedded in many patient populations in Montana and serves as a key determinant of healthcare acceptability. There are additional factors, however, that may interact with stoicism but are multiply determined. Stigma is an example of this, presented in this context as one’s concern about judgement by the healthcare system. Respondents were openly critical of this perception of the healthcare system as it was widely discussed in interviews. One participant stated:

There is a real perception of a punitive nature in the medical community, particularly if I observe a health issue other than the primary reason for one’s hospital visit, whether that may be predicated on medical neglect, delay of care, or something that may warrant a report to social services. For many of the patients and families I see, it’s not a positive experience and one that is sometimes an uphill barrier that I work hard to circumnavigate.

Analysis of these factors suggest that low use of healthcare services may link to several characteristics, including access problems. Separately, a patient’s perceived stigma from healthcare providers may also impact a patient’s willingness to receive services. One participant put it best by stating

Sometimes, families assume that I didn’t want to see them because they will come in for follow up to meet with me but end up meeting with another provider, which is frustrating because I want to maintain patients on my panel but available time and resource occasionally limits me from doing so. It could be really hard adapting to those needs on the fly, but it’s an honest miss.

When a patient arrives for a healthcare visit and experiences this frustration, it may elicit a patient’s perceptions of neglect or disorganization. This ‘honest miss’ may, in turn, exacerbate other acceptable-related barriers to care.

Theme 2: Facilitating access to healthcare requires application of and respect for cultural differences

The biomedical model is the standard of care utilized in Western medicine [ 79 , 80 ]. However, the US comprises people with diverse social and cultural identities that may not directly align with Western conceptions of health and wellness. Approximately 11.5% of the Montana population falls within an ethnic minority group. 6.4% are of American Indian or Alaska Native origin, 0.5% are of Black or African American origin, 0.8% are of Asian origin and 3.8% are of multiple or other origins. [ 81 ]. Cultural insensitivity is acknowledged in health services research as an active deterrent for appropriate healthcare delivery [ 65 ]. Participants for this study were asked how they react when a patient brings up a cultural attitude or behavior that may impact the proposed treatment plan. Eight participants noted a necessity for humility when this occurs. One participant conceptualized this by stating:

When this happens, I learn about individuals and a way of life that is different to the way I grew up. There is a lot of beauty and health in a non-patriarchal, non-dominating, non-sexist framework, and when we can engage in such, it is really expansive for my own learning process.

The participants who expressed humility emphasized that it is best to work in tandem with their patient, congruently. Especially for those with contrasting worldviews, a provider and a patient working as a team poses an opportunity to develop trust. Without it, a patient can easily fall out of the system, further hindering their ability to access healthcare services in the future. One participant stated:

The approach that ends up being successful for a lot of patients is when we understand their modalities, and they have a sense we understand those things. We have to show understanding and they have to trust. From there, we can make recommendations to help get them there, not decisions for them to obey, rather views based on our experiences and understanding of medicine.

Curiosity was another reaction noted by a handful of participants. One participant said:

I believe patients and their caregivers can be engaged and loving in different ways that don’t always follow the prescribed approach in the ways I’ve been trained, but that doesn’t necessarily mean that they are detrimental. I love what I do, and I love learning new things or new approaches, but I also love being surprised. My style of medicine is not to predict peoples’ lives, rather to empower and support what makes life meaningful for them.

Participants mentioned several other characteristics that they use in practice to prevent cultural insensitivity and support a collaborative approach to healthcare. Table 1 lists these facilitating characteristics and quotes to explain the substance of their benefit.

Consensus among participants indicated that the use of these protective factors to promote cultural sensitivity and apply them in practice is not standardized. When asked, all but two participants said they had not received any culturally-based training since beginning their practice. Instead, they referred to developing skills through “on the job training” or “off the cuff learning.” The general way of medicine, one participant remarked, was to “throw you to the fire.” This suggested that use of standardized cultural humility training modules for healthcare providers was not common practice. Many attributed this to time constraints.

Individual efforts to gain culturally appropriate skills or enhance cultural humility were mentioned, however. For example, three participants reported that they attended medical conferences to discuss cultural challenges within medicine, one participant sought out cultural education within their organization, and another was invited by Native American community members to engage in traditional peace ceremonies. Participants described these additional efforts as uncommon and outside the parameters of a provider’s job responsibilities, as they require time commitments without compensation.

Additionally, eight participants said they share their personal contact information with patients so they may call them directly for medical needs. The conditions and frequency with which this is done was variable and more common among providers in specialized areas of medicine or those who described having a manageable patient panel. All who reported that they shared their personal contact information described it as an aspect of rural health service delivery that is atypical in other, non-rural healthcare systems.

Theme 3: Communication between healthcare providers is systematically fragmented

Healthcare is complex and multi-disciplinary, and patients’ treatment is rarely overseen by a single provider [ 82 ]. The array of provider types and specialties is vast, as is the range of responsibilities ascribed to providers. Thus, open communication among providers both within and between healthcare systems is vital for the success of collaborative healthcare [ 83 ]. Without effective communication achieved between healthcare providers, the appropriate delivery of healthcare services may be become compromised. Our participants noted that they face multiple challenges that complicate communication with other providers. Miscommunication between departments, often implicating the Emergency Department (ED), was a recurring point noted among participants. One participant who is a primary care physician said:

If one of my patients goes to the ER, I don’t always get the notes. They’re supposed to send them to the patient’s primary care doc. The same thing happens with general admissions, but again, I often find out from somebody else that my patient was admitted to the hospital.

This failure to communicate can negatively impact the patient, particularly if time sensitivity or medical complexity is essential to treatment. A patient’s primary care physician is the most accurate source of their medical history; without an effective way to obtain and synthesize a patient’s health information, there may be increased risk of medical error. One participant in a specialty field stated:

One of the biggest barriers I see is obtaining a concise description of a patient’s history and needs. You can imagine if you’re a mom and you’ve got a complicated kid. You head to the ER. The ER doc looks at you with really wide eyes, not knowing how to get information about your child that’s really important.

This concern was highlighted with a specific example from a different participant:

I have been unable to troubleshoot instances when I send people to the ER with a pretty clear indication for admission, and then they’re sent home. For instance, I had an older fellow with pretty severe chronic kidney disease. He presented to another practitioner in my office with shortness of breath and swelling and appeared to have newly onset decompensated heart failure. When I figured this out, I sent him to the ER, called and gave my report. The patient later came back for follow up to find out not only that they had not been admitted but they lost no weight with outpatient dialysis . I feel like a real opportunity was missed to try to optimize the care of the patient simply because there was poor communication between myself and the ER. This poor guy… He ended up going to the ER four times before he got admitted for COVID-19.

In some cases, communication breakdown was reported as the sole cause of a poor outcome. When communication is effective, each essential member of the healthcare team is engaged and collaborating with the same information. Some participants called this process ‘rounds’ when a regularly scheduled meeting is staged between a group of providers to ensure access to accurate patient information. Accurate communication may also help build trust and improve a patient’s experience. In contrast, ineffective communication can result in poor clarity regarding providers’ responsibilities or lost information. Appropriate delivery of healthcare considers the fit between providers and a patient’s specific healthcare needs; the factors noted here suggest that provider-provider miscommunication can adversely affect this dimension of healthcare access.

Another important mechanism of communication is the sharing of electronic medical records (EMRs), a process that continues to shift with technological advances. Innovation is still recent enough, however, for several of our study participants to be able to recall a time when paper charts were standard. Widespread adoption and embrace of the improvements inherent in electronic medical records expanded in the late 2000’s [ 84 ]. EMRs vastly improved the ability to retain, organize, safeguard, and transfer health information. Every participant highlighted EMRs at one point or another and often did so with an underlying sense of anger or frustration. Systematic issues and problems with EMRs were discussed. One participant provided historical context to such records:

Years back, the government aimed to buy an electronic medical record system, whichever was the best, and a number of companies created their own. Each were a reasonable system, so they all got their checks and now we have four completely separate operating systems that do not talk to each other. The idea was to make a router or some type of relay that can share information back and forth. There was no money in that though, so of course, no one did anything about it. Depending on what hospital, clinic or agency you work for, you will most likely work within one of these systems. It was a great idea; it just didn’t get finished.

Seven participants confirmed these points and their impacts on making coordination more difficult, relying on outdated communication strategies more often than not. Many noted this even occurs between facilities within the same city and in separate small metropolitan areas across the state. One participant said:

If my hospital decides to contract with one EMR and the hospital across town contracts with another, correspondence between these hospitals goes back to traditional faxing. As a provider, you’re just taking a ‘fingered crossed’ approach hoping that the fax worked, is picked up, was put in the appropriate inbox and was actually looked at. Information acquisition and making sure it’s timely are unforeseen between EMRs.

Participants reported an “astronomic” number of daily faxes and telephone calls to complete the communication EMRs were initially designed to handle. These challenges are even more burdensome if a patient moves from out of town or out of state; obtaining their medical records was repeatedly referred to as a “chore” so onerous that it often remains undone. Another recurring concern brought up by participants regarded accuracy within EMRs to lend a false sense of security. They are not frequently updated, not designed to be family-centered and not set up to do anything automatically. One participant highlighted these limitations by stating:

I was very proud of a change I made in our EMR system [EPIC], even though it was one I never should have had to make. I was getting very upset because I would find out from my nursing assistant who read the obituary that one of my patients had died. There was a real problem with the way the EMR was notifying PCP’s, so I got an EPIC-level automated notification built into our EMR so that any time a patient died, their status would be changed to deceased and a notification would be sent to their PCP. It’s just really awful to find out a week later that your patient died, especially when you know these people and their families really well. It’s not good care to have blind follow up.

Whether it be a physical or electronic miscommunication between healthcare providers, the appropriate delivery of healthcare can be called to question

Theme 4: Time and resource constraints disproportionately harm rural health systems

Several measures of system capacity suggest the healthcare system in the US is under-resourced. There are fewer physicians and hospital beds per capita compared to most comparable countries, and the growth of healthcare provider populations has stagnated over time [ 15 ]. Rural areas, in particular, are subject to resource limitations [ 16 ]. All participants discussed provider shortages in detail. They described how shortages impact time allocation in their day-to-day operations. Tasks like patient intakes, critical assessments, and recovering information from EMRs take time, of which most participants claimed to not have enough of. There was also a consensus in having inadequate time to spend on medically complex cases. Time pressures were reported to subsequently influence quality of care. One participant stated:

With the constant pace of medicine, time is not on your side. A provider cannot always participate in an enriching dialogue with their patients, so rather than listen and learn, we are often coerced into the mindset of ‘getting through’ this patient so we can move on. This echoes for patient education during discharge, making the whole process more arduous than it otherwise could be if time and resources were not as sparse.

Depending on provider type, specialty, and the size of patient panels, four participants said they have the luxury of extending patient visits to 40 + minutes. Any flexibility with patient visits was regarded as just that: a luxury. Very few providers described the ability to coordinate their schedules as such. This led some study participants to limit the number of patients they serve. One participant said:

We simply don’t have enough clinicians, which is a shame because these people are really skilled, exceptional, brilliant providers but are performing way below their capacity. Because of this, I have a smaller case load so I can engage in a level of care that I feel is in the best interest of my patients. Everything is a tradeoff. Time has to be sacrificed at one point or another. This compromise sets our system up to do ‘ok’ work, not great work.

Of course, managing an overly large number of patients with high complexity is challenging. Especially while enduring the burden of a persisting global pandemic, participants reflected that the general outlook of administering healthcare in the US is to “do more with less.” This often forces providers to delegate responsibilities, which participants noted has potential downsides. One participant described how delegating patient care can cause problems.

Very often will a patient schedule a follow up that needs to happen within a certain time frame, but I am unable to see them myself. So, they are then placed with one of my mid-level providers. However, if additional health issues are introduced, which often happens, there is a high-risk of bounce-back or need to return once again to the hospital. It’s an inefficient vetting process that falls to people who may not have specific training in the labs and imaging that are often included in follow up visits. Unfortunately, it’s a forlorn hope to have a primary care physician be able to attend all levels of a patient’s care.

Several participants described how time constraints stretch all healthcare staff thin and complicate patient care. This was particularly important among participants who reported having a patient panel exceeding 1000. There were some participants, however, who praised the relationships they have with their nurse practitioners and physician’s assistants and mark transparency as the most effective way to coordinate care. Collectively, these clinical relationships were built over long standing periods of time, a disadvantage to providers at the start of their medical career. All but one participant with over a decade of clinical experience mentioned the usefulness of these relationships. The factors discussed in Theme 4 are directly linked to the Availability dimension of access to healthcare. A patient’s ability to reach care is subject to the capacity of their healthcare provider(s). Additionally, further analysis suggests these factors also link to the Appropriateness dimension because the quality of patient-provider relationships may be negatively impacted if a provider’s time is compromised.

Theme 5: Profits are prioritized over addressing barriers to healthcare access in the US.

The US healthcare system functions partially for-profit in the public and private sectors. The federal government provides funding for national programs such as Medicare, but a majority of Americans access healthcare through private employer plans [ 85 ]. As a result, uninsurance rates influence healthcare access. Though the rate of the uninsured has dropped over the last decade through expansion of the Affordable Care Act, it remains above 8 percent [ 86 ]. Historically, there has been ethical criticism in the literature of a for-profit system as it is said to exacerbate healthcare disparities and constitute unfair competition against nonprofit institutions. Specifically, the US healthcare system treats healthcare as a commodity instead of a right, enables organizational controls that adversely affect patient-provider relationships, undermines medical education, and constitutes a medical-industrial complex that threatens influence on healthcare-related public policy [ 87 ]. Though unprompted by the interviewer, participants raised many of these concerns. One participant shared their views on how priorities stand in their practice:

A lot of the higher-ups in the healthcare system where I work see each patient visit as a number. It’s not that they don’t have the capacity to think beyond that, but that’s what their role is, making sure we’re profitable. That’s part of why our healthcare system in the US is as broken as it is. It’s accentuated focus on financially and capitalistically driven factors versus understanding all these other barriers to care.

Eight participants echoed a similar concept, that addressing barriers to healthcare access in their organizations is largely complicated because so much attention is directed on matters that have nothing to do with patients. A few other participants supported this by alluding to a “cherry-picking” process by which those at the top of the hierarchy devote their attention to the easiest tasks. One participant shared an experience where contrasting work demands between administrators and front-line clinical providers produces adverse effects:

We had a new administrator in our hospital. I had been really frustrated with the lack of cultural awareness and curiosity from our other leaders in the past, so I offered to meet and take them on a tour of the reservation. This was meant to introduce them to kids, families and Tribal leaders who live in the area and their interface with healthcare. They declined, which I thought was disappointing and eye-opening.

Analysis of these factors suggest that those who work directly with patients understand patient needs better than those who serve in management roles. This same participant went on to suggest an ulterior motive for a push towards telemedicine, as administrators primarily highlight the benefit of billing for virtual visits instead of the nature of the visits themselves.

This study explored barriers and facilitators to healthcare access from the perspective of rural healthcare providers in Montana. Our qualitative analysis uncovered five key themes: 1) a friction exists between aspects of patients’ rural identities and healthcare systems; 2) facilitating access to healthcare requires application of and respect for cultural differences; 3) communication between healthcare providers is systematically fragmented; 4) time and resource constraints disproportionately harm rural health systems; and 5) profits are prioritized over addressing barriers to healthcare access in the US. Themes 2 and 3 were directly supported by earlier qualitative studies that applied Levesque’s framework, specifically regarding healthcare providers’ poor interpersonal quality and lack of collaboration with other providers that are suspected to result from a lack of provider training [ 67 , 70 ]. This ties back to the importance of cultural humility, which many previous culture-based trainings have referred to as cultural competence. Cultural competence is achieved through a plethora of trainings designed to expose providers to different cultures’ beliefs and values but induces risk of stereotyping and stigmatizing a patient’s views. Therefore, cultural humility is the preferred idea, by which providers reflect and gain open-ended appreciation for a patient’s culture [ 88 ].

Implications for Practice

Perhaps the most substantial takeaway is how embedded rugged individualism is within rural patient populations and how difficult that makes the delivery of care in rural health systems. We heard from participants that stoicism and perceptions of stigma within the system contribute to this, but other resulting factors may be influential at the provider- and organizational-levels. Stoicism and perceived stigma both appear to arise, in part, from an understandable knowledge gap regarding the care system. For instance, healthcare providers understand the relations between primary and secondary care, but many patients may perceive both concepts as elements of a single healthcare system [ 89 ]. Any issue experienced by a patient when tasked to see both a primary and secondary provider may result in a patient becoming confused [ 90 ]. This may also overlap with our third theme, as a disjointed means of communication between healthcare providers can exacerbate patients’ negative experiences. One consideration to improve this is to incorporate telehealth programs into an existing referral framework to reduce unnecessary interfacility transfers; telehealth programs have proven effective in rural and remote settings [ 91 ].

In fact, telehealth has been rolled out in a variety of virtual platforms throughout its evolution, its innovation matched with continued technological advancement. Simply put, telehealth allows health service delivery from a distance; it allows knowledge and practice of clinical care to be in a different space than a patient. Because of this, a primary benefit of telehealth is its impact on improving patient-centered outcomes among those living in rural areas. For instance, text messaging technology improves early infant diagnosis, adherence to recommended diagnostic testing, and participant engagement in lifestyle change interventions [ 92 , 93 , 94 ]. More sophisticated interventions have found their way into smartphone-based technology, some of which are accessible even without an internet connection [ 95 , 96 ]. Internet accessibility is important because a number of study participants noted internet connectivity as a barrier for patients who live in low resource communities. Videoconferencing is another function of telehealth that has delivered a variety of health services, including those which are mental health-specific [ 97 ], and mobile health clinics have been used in rural, hard-to-reach settings to show the delivery of quality healthcare is both feasible and acceptable [ 98 , 99 , 100 ]. While telehealth has potential to reduce a number of healthcare access barriers, it may not always address the most pressing healthcare needs [ 101 ]. However, telehealth does serve as a viable, cost-effective alternative for rural populations with limited physical access to specialized services [ 102 ]. With time and resource limitations acknowledged as a key theme in our study, an emphasis on expanding telehealth services is encouraged as it will likely have significant involvement on advancing healthcare in the future, especially as the COVID-19 pandemic persists [ 103 ].

Implications for Policy

One could argue that most of the areas of fragmentation in the US healthcare system can be linked to the very philosophy on which it is based: an emphasis on profits as highest priority. Americans are, therefore, forced to navigate a health service system that does not work solely in their best interests. It is not surprising to observe lower rates of healthcare usage in rural areas, which may be a result from rural persons’ negative views of the US healthcare system or a perception that the system does not exist to support wellness. These perceptions may interact with ‘rugged individualism’ to squelch rural residents’ engagement in healthcare. Many of the providers we interviewed for this study appeared to understand this and strived to improve their patients’ experiences and outcomes. Though these efforts are admirable, they may not characterize all providers who serve in rural areas of the US. From a policy standpoint, it is important to recognize these expansive efforts from providers. If incentives were offered to encourage maximum efforts be made, it may lessen burden due to physician burnout and fatigue. Of course, there is no easy fix to the persisting limit of time and resources for providers, problems that require workforce expansion. Ultimately, though, the current structure of the US healthcare system is failing rural America and doing little to help the practice of rural healthcare providers.

Implications for Future Research

It is important for future health systems research efforts to consider issues that arise from both individual- and system-level access barriers and where the two intersect. Oftentimes, challenges that appear linked to a patient or provider may actually stem from an overarching system failure. If failures are critically and properly addressed, we may refine our understanding of what we can do in our professional spaces to improve care as practitioners, workforce developers, researchers and advocates. This qualitative study was exploratory in nature. It represents a step forward in knowledge generation regarding challenges in access to healthcare for rural Americans. Although mental health did not come up by design in this study, future efforts exploring barriers to healthcare access in rural systems should focus on access to mental healthcare. In many rural areas, Montana included, rates of suicide, substance use and other mental health disorders are highly prevalent. These characteristics should be part of the overall discussion of access to healthcare in rural areas. Optimally, barriers to healthcare access should continue to be explored through qualitative and mixed study designs to honor its multi-dimensional stature.

Strengths and Limitations

It is important to note first that this study interviewed healthcare providers instead of patients, which served as both a strength and limitation. Healthcare providers were able to draw on numerous patient-provider experiences, enabling an account of the aggregate which would have been impossible for a patient population. However, accounts of healthcare providers’ perceptions of barriers to healthcare access for their patients may differ from patients’ specific views. Future research should examine acceptability- and appropriateness-related barriers to healthcare access in patient populations. Second, study participants were recruited through convenience sampling methods, so results may be biased towards healthcare providers who are more invested in addressing barriers to healthcare access. Particularly, the providers interviewed for this study represented a subset who go beyond expectations of their job descriptions by engaging with their communities and spending additional uncompensated time with their patients. It is likely that a provider who exhibits these behavioral traits is more likely to participate in research aimed at addressing barriers to healthcare access. Third, the inability to conduct face-to-face interviews for our qualitative study may have posed an additional limitation. It is possible, for example, that in-person interviews might have resulted in increased rapport with study participants. Notwithstanding this possibility, the remote interview format was necessary to accommodate health risks to the ongoing COVID-19 pandemic. Ultimately, given our qualitative approach, results from our study cannot be generalizable to all rural providers’ views or other rural health systems. In addition, no causality can be inferred regarding the influence of aspects of rurality on access. The purpose of this exploratory qualitative study was to probe research questions for future efforts. We also acknowledge the authors’ roles in the research, also known as reflexivity. The first author was the only author who administered interviews and had no prior relationships with all but one study participant. Assumptions and pre-dispositions to interview content by the first author were regularly addressed throughout data analysis to maintain study integrity. This was achieved by conducting analysis by unique interview question, rather than by unique participant, and recoding the numerical order of participants for each question. Our commitment to rigorous qualitative methods was a strength for the study for multiple reasons. Conducting member checks with participants ensured trustworthiness of findings. Continuing data collection to data saturation ensured dependability of findings, which was achieved after 10 interviews and confirmed after 2 additional interviews. We further recognize the heterogeneity in our sample of participants, which helped generate variability in responses. To remain consistent with appropriate means of presenting results in qualitative research however, we shared minimal demographic information about our study participants to ensure confidentiality.

The divide between urban and rural health stretches beyond a disproportionate allocation of resources. Rural health systems serve a more complicated and hard-to-reach patient population. They lack sufficient numbers of providers to meet population health needs. These disparities impact collaboration between patients and providers as well as the delivery of acceptable and appropriate healthcare. The marker of rurality complicates the already cumbersome challenge of administering acceptable and appropriate healthcare and impediments stemming from rurality require continued monitoring to improve patient experiences and outcomes. Our qualitative study explored rural healthcare providers’ views on some of the social, cultural, and programmatic factors that influence access to healthcare among their patient populations. We identified five key themes: 1) a friction exists between aspects of patients’ rural identities and healthcare systems; 2) facilitating access to healthcare requires application of and respect for cultural differences; 3) communication between healthcare providers is systematically fragmented; 4) time and resource constraints disproportionately harm rural health systems; and 5) profits are prioritized over addressing barriers to healthcare access in the US. This study provides implications that may shift the landscape of a healthcare provider’s approach to delivering healthcare. Further exploration is required to understand the effects these characteristics have on measurable patient-centered outcomes in rural areas.

Availability of data and materials

The datasets generated and/or analyzed during the current study are not publicly available due to individual privacy could be compromised but are available from the corresponding author on reasonable request.

Ethics approval and consent to participate.

All study procedures and methods were carried out in accordance with relevant guidelines and regulations from the World Medical Association Declaration of Helsinki. Ethics approval was given by exempt review from the Institutional Review Board (IRB) at the University of Montana (IRB Protocol No.: 186–20). Participants received oral and written information about the study prior to interview, which allowed them to provide informed consent for the interviews to be recorded and used for qualitative research purposes. No ethical concerns were experienced in this study pertaining to human subjects.

Consent for publication.

The participants consented to the publication of de-identified material from the interviews.

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Acknowledgements

This research was supported by the Center for Biomedical Research Excellence award (P20GM130418) from the National Institute of General Medical Sciences of the National Institute of Health. The first author was also supported by the University of Montana Burnham Population Health Fellowship. We would like to thank Dr. Christopher Dietrich, Dr. Jennifer Robohm and Dr. Eric Arzubi for their contributions on determining inclusion criteria for the healthcare provider population used for this study.

 This research did not receive any specific grant from funding agencies in the public, commercial, and not-for-profit sectors. 

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The authors confirm contribution to the paper as follows: study conception and design: NC and JC; data collection: NC; analysis and interpretation of results: NC and JC; draft manuscript preparation: NC, DC and JC; and manuscript editing: NC, DC and JC. All authors reviewed the results and approved the final version of the manuscript.

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Coombs, N.C., Campbell, D.G. & Caringi, J. A qualitative study of rural healthcare providers’ views of social, cultural, and programmatic barriers to healthcare access. BMC Health Serv Res 22 , 438 (2022). https://doi.org/10.1186/s12913-022-07829-2

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Kidney stones and dietary intake in adults: a population-based study in southwest Iran

• Bahman Cheraghian 1 ,
• Alipour Meysam 2 ,
• Seyed Jalal Hashemi 3 ,
• Seyed Ahmad Hosseini 4 ,
• Amal Saki Malehi 5 ,
• Dinyar Khazaeli 6 &
• Zahra Rahimi 1  

BMC Public Health volume  24 , Article number:  955 ( 2024 ) Cite this article

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Metrics details

The prevalence of kidney stones is on the rise globally. Several risk factors, including lifestyle, contribute to the formation of kidney stones. Nevertheless, there is a contentious debate about the relationship between diet and kidney stones. Therefore, our study aimed to assess the relationship between macronutrients and micronutrients and the formation of kidney stones.

This population-based cross-sectional study was conducted in the baseline phase of the Hoveyzeh Cohort Study, focusing on adults aged 35–70 in southwest Iran. The information on demographic characteristics, anthropometrics, kidney stone history, and food frequency was collected. Chi-square and t-tests were utilized to assess the relationship between categorical and numerical variables with kidney stones. The ANCOVA and logistic regression models were used to evaluate the relationships while controlling for confounding factors.

Among 10,009 participants, the overall prevalence of kidney stones was 18.77% (95% CI: 17.99–19.53). A higher intake of carbohydrates [OR = 1.02 (95% CI:1.002–1.03), p  = 0.026] and copper [OR = 1.04 (95% CI:1.01–1.09), p  = 0.025] were found to be associated with kidney stones. No associations were found between the other assessed macronutrients or micronutrients and kidney stones ( p -tvalues > 0.05).

Our study’s findings indicate a correlation between diet and the formation of kidney stones. However, the relationship between dietary factors and kidney stones is complex, and further research is needed.

Peer Review reports

Kidney stones are solid mineral deposits formed from dissolved minerals in the urine. They are mainly excreted through the urethra [ 1 ]. The prevalence of kidney stones ranges from 1 to 15% worldwide [ 2 ]. In Iran, it ranges from 1.9 to 5.7%, with a higher rate observed in the western provinces [ 3 , 4 ]. The recurrence rate of kidney stones is high, with approximately 50% of patients experiencing a recurrence within 10 years [ 5 ]. The annual cost of treating kidney stones in the United States was $2.1 billion in 2000 and is projected to exceed $4 billion by 2030 [ 6 ]. The formation of kidney stones can be influenced by various factors, including demographic characteristics and environmental factors [ 7 ].

Previous studies have shown that dietary factors play a significant role in the development of kidney stones. High salt intake, low water intake, low consumption of dairy products, low intake of tea, consumption of oxalate-rich foods, and consumption of processed foods are all associated with an increased risk of kidney stones [ 8 , 9 ]. On the other hand, consuming higher levels of potassium, magnesium, fiber, and vitamin B6 is considered to be a dietary inhibitor for calcium-containing stones [ 10 ]. A study conducted in Switzerland found that the probability of developing kidney stones was higher with increased consumption of cakes, biscuits, and soft drinks, and lower consumption of nuts and seeds, fresh cheese, teas, and alcoholic beverages, particularly wine [ 11 ]. Moreover, reduced consumption of vegetables, coffee, and alcoholic beverages increased the probability of developing kidney stones [ 12 ].

Given the high risk of kidney stone recurrence and the costs of the disease on the healthcare system and society, understanding the risk factors, including nutritional factors, is crucial for the design of prevention and treatment strategies. However, conflicting results have been obtained regarding the factors associated with kidney stone formation [ 1 , 2 , 3 , 7 , 13 , 14 ]. Furthermore, there have been few studies in Iran that have examined the nutritional factors influencing kidney stones among adults in the context of a population-based study. Therefore, the present study investigated the associations between nutritional factors and kidney stone disease among adult residents of southwest Iran.

Design study and participants

This was a population-based cross-sectional study in Southwest Iran. A total of 10,009 individuals, aged 35–70 years, were recruited for the enrollment phase of the Hoveyzeh cohort study (HCS) from May 2016 to August 2018 and were assessed in this analysis [ 15 ]. The Hoveyzeh cohort study is one of the branches of the Prospective Epidemiological Research Studies in Iran (PERSIAN) [ 16 ] focused on non-communicable diseases. Informed consent was obtained from all individuals who wished to participate in the study. The inclusion criteria were individuals aged between 35 and 70 years, residing in the Hoveyzeh district, without severe mental disorders, ability to answer the questionnaires independently. We excluded participants with missing data on kidney stones and frequency of food questionnaire (FFQ), as well as those unwilling to participate in the study.

The Ethics Committee of Ahvaz Jundishapur University of Medical Sciences approved the study protocol (IR.AJUMS.REC.1398.279). This study was conducted in accordance with the Helsinki Declaration and its later amendments. On the day of registration, we obtained written informed consent.

Each participant completed the questionnaires, and well-trained interviewers conducted the anthropometric measurements. The variables in this analysis included age groups (35–39, 40–44, 45–49, 50–54, 55–59, 60–64, and ≥ 65 years), sex (male, female), residence type (urban and rural), and marital status (single, married, widowed, and divorced). The education levels are categorized as illiterate, primary school, secondary school, high school, diploma, and university. The physical activity was assessed using the International Physical Activity Questionnaire (IPAQ), which records participants’ self-reported daily activity over a 1-year period. The physical activity and metabolic equivalent (MET) scores were reported for a 24-hour task [ 17 , 18 ], and then they were categorized into quartiles in our analysis. The validity of the International Physical Activity Questionnaire (IPAQ) in Iran has been evaluated by Moghaddam et al. [ 19 ]. Body mass index (BMI) is calculated by dividing a person’s weight in kilograms by their height in meters squared (kg/m2). A BMI below 18.5 is considered underweight, 18.5–24.9 is within the normal range, 25.0-29.9 is classified as overweight, and over 30 is considered obese. In our study, a smoker is defined as an individual who has smoked at least 100 cigarettes in their lifetime.

Dietary assessment

A semi-quantitative food frequency questionnaire (FFQ) consisting of 130 food items was utilized to evaluate individuals’ typical food consumption. The validity and reproducibility of the questionnaire were evaluated by Eghtesad et al. [ 20 ]. All the questionnaires were completed by a trained nutrition expert during face-to-face interviews with the participants. The participants reported the frequency of their consumption of each item over the past year, indicating whether it was daily, weekly, monthly, or yearly. Portion sizes of each food were converted to grams using household measures [ 21 ].

Kidney stone assessment

The participants self-reported a history of kidney stones if the disease had been previously diagnosed by a doctor. Furthermore, all medical documents, including ultrasounds, photographs, laboratory reports, and surgical records, have been reviewed and verified by the team’s physicians. All medical documentation, including ultrasound images, photographs, laboratory reports, and surgical-related documents, has been thoroughly reviewed and verified by the team’s doctors.

Statistical analysis

The data was described by reporting the frequency and percentage of the categorical variables, as well as the mean and standard deviation of the quantitative variables. The chi-square test evaluated the relationship between categorical variables. The Kolmogorov–Smirnov test and the normal Q-Q plot were utilized to assess the normality hypothesis for all food groups. The independent t-test was utilized to compare continuous variables between the two groups. We investigated the independent associations of the assessed factors with kidney stones using ANCOVA and unconditional multiple logistic regression, while controlling for confounding factors. In the univariate analysis, the criterion for initially entering variables into multiple regression models was set at P  < 0.25. All reported p -values were based on two-tailed tests and were considered significant at a level of 0.05. We performed data analysis using STATA software version 15.

A total of 10,009 individuals who participated in the enrollment phase of the Hoveyzeh cohort study were evaluated in this analysis. The mean age of the participants was 48.76 ± 9.21 years, and approximately 60% of them were women. The overall prevalence of kidney stones was 18.77% (95% CI: 17.99–19.53). The prevalence was significantly higher in men (24.20%) compared to women (15.10%) ( P  < 0.001). Furthermore, individuals in the 50 to 54 age group exhibited the highest prevalence rate (21.9%) of kidney stones (Table  1 ).

Table  2 compares demographic, socioeconomic, and lifestyle variables between individuals with and without kidney stones. The study revealed that individuals with kidney stones were significantly more likely to be male, reside in urban areas, have a higher wealth status, engage in less physical activity, and be smokers compared to those without kidney stones. However, the educational level and BMI were not statistically different between the two groups.

After adjusting for age, sex, body mass index, physical activity, wealth index, and residence type, a significant difference in dairy product consumption between the two groups was observed using ANCOVA. The mean dairy consumption was significantly lower in the group with kidney stones ( p  = 0.014). Additionally, participants with kidney stones had a higher average intake of oils and sweets compared to those without kidney stones ( p  = 0.001). On the other hand, there was no significant difference in the average consumption of the other food items between individuals with and without kidney stones ( p  > 0.05). (Table  3 ).

To control for potential confounding variables, multiple logistic regression analysis was performed using two models. In Model 1, the macronutrients were assessed, while in Model 2, the micronutrients were assessed. According to the logistic regression analysis, the crude and adjusted odds ratios were calculated to evaluate the association between kidney stones and macronutrients. The results are presented in Table  4 . After adjusting for potential confounders, an increased consumption of carbohydrates was associated with significantly higher odds of kidney stones [OR = 1.02 (95%CI:1.002–1.03), p  = 0.026]. On the other hand, no significant associations were found between protein intake, fat intake, and fiber intake with kidney stones (all p -values > 0.05).

In Model 2, after adjusting for potential confounders, among the evaluated micronutrients, the consumption of copper was found to significantly increase the odds of developing kidney stones. On average, for every 10 mg per day increase in copper intake, the odds of developing kidney stones increased by 4% [OR = 1.04 (95% CI:1.01–1.09), p  = 0.025]. Although a higher intake of zinc slightly decreased the odds of developing kidney stones [OR = 0.95 (95% CI: 0.90-1.001) ( p  = 0.056)]. Moreover, no significant associations were found other assessed micronutrients with kidney stones (all p -values > 0.05). The details are presented in Table  5 .

Our study aimed to investigate the relationship between food consumption and kidney stones. The main findings revealed a prevalence of kidney stones at 18.77%, with a higher prevalence among men, individuals residing in urban areas, those with high wealth status, less physical activity, and smokers. After adjusting for potential confounders, the participants with kidney stones had a lower average intake of dairy products and a higher average intake of oils and sweets compared to those without kidney stones. Furthermore, individuals with a higher carbohydrate intake had significantly higher odds of developing kidney stones.

We found a significant inverse relationship between kidney stones and dairy intake. In line with our results, one study found that consuming dairy is associated with a reduced risk of kidney stones [ 22 ]. However, increased milk intake is recommended for uric acid-related stones [ 23 ]. Therefore, policies that promote a diet high in dietary alkali load can help reduce the risk of kidney stone formation. These policies can support public health initiatives aimed at reducing the prevalence of kidney stones.

The results revealed a direct association between carbohydrate intake and kidney stones. In line with the results of our study, some research has suggested that consuming a high-carbohydrate diet may increase the risk of developing kidney stones [ 24 ]. High-carbohydrate diets may contribute to the formation of kidney stones by increasing calcium excretion in the urine. When the body breaks down carbohydrates, it produces an acid called oxalate, which can bind with calcium in the urine and form crystals. This, in turn, can lead to the formation of kidney stones. However, it is important to note that complex carbohydrates, such as those found in whole grains, fruits, and vegetables, are generally healthier than simple carbohydrates found in processed foods and sugary drinks [ 24 , 25 ].

Our study revealed that there was no statistically significant association between kidney stones and vegetable consumption. In line with our study, the UK Biobank study showed that vegetable intake was not associated with the incidence of kidney stones [ 5 ]. However, several studies have indicated that consuming vegetables may reduce the risk of developing kidney stones [ 10 , 26 , 27 , 28 ]. However, specific types of vegetables, especially leafy greens, have been identified as potential risk factors for the condition [ 29 , 30 ]. This is because leafy greens contain a significant amount of oxalates, which can consequently increase the risk of developing oxalate-based stones, the most common type of kidney stone [ 12 , 31 ]. The study conducted by Taylor and Curhan [ 32 ] also found no association between fruit consumption and kidney stones. Several studies have found that specific fruits, such as grapefruit, may elevate the risk of kidney stone formation. However, other fruits, such as oranges, have been found to have a protective effect against stone formation [ 33 ]. These contradictory results may be attributed to the varying levels of nitrates in different fruits.

We found that there was no statistically significant difference in the consumption of meat, beans, protein, fat, and fiber between the individuals with kidney stones and those without the condition. Previous prospective studies that have primarily focused on animal protein intake have yielded inconsistent evidence [ 34 , 35 ]. These conflicting results may depend on the type of meat consumed and the balance of food types in the diet. A high-protein diet results in higher acid levels in the human body, leading to an increase in the excretion of calcium in the urine. Animal protein contains purines, which are broken down into uric acid in the body. Elevated levels of uric acid in the urine can also contribute to the formation of kidney stones [ 36 ]. However, these findings were inconsistent with the results of our study. A study showed that diets high in healthy fats, such as nuts, seeds, fish, and avocados, did not increase the risk of kidney stones [ 37 ]. However, other studies have shown a positive correlation between the consumption of fatty acids, such as arachidonic acid, and the excretion of urinary oxalate, which is a risk factor for the formation of kidney stones [ 38 ]. However, it appears that the type of fat intake can affect the risk of developing kidney stones. In contrast to our findings, some studies have suggested that a high-fiber diet may help reduce the risk of kidney stones [ 5 , 35 , 39 ]. Because these relationships are complex and not yet fully understood, policies that promote a balanced diet may reduce the risk of kidney stone formation.

Among the evaluated micronutrients, copper intake was directly associated with kidney stones. According to our study, research has shown that urinary copper levels were significantly higher in patients with kidney stones compared to healthy controls [ 40 ]. On the other hand, the results showed that intake of manganese, fluoride, zinc, iron, potassium, calcium, and magnesium was not associated with kidney stones. Although, one study showed that manganese deposition could be caused by the hemodialysis method itself [ 41 ]. Excessive fluoride may contribute to the development of calcium oxalate renal calculi [ 42 ]. Therefore, policymakers need to prioritize regulating fluoride levels in drinking water to prevent potential adverse effects on kidney health. On the other hand, a study reported increased odds of kidney stones with higher zinc intake [ 43 ]. The role of dietary potassium in the development of kidney stones is still unclear. A study found that potassium citrate may contribute to the formation of calcium-phosphate stones [ 44 ]. The magnesium level in a 24-hour urine analysis has also been found to be directly associated with oxalate levels, suggesting a role for magnesium in preventing stone formation by binding to oxalate [ 45 ]. It is important to note that while magnesium may be beneficial in preventing kidney stones, it is not a panacea. Some research, such as a prospective study on dietary calcium and other nutrients, found that a low-calcium diet was associated with an increased risk of kidney stone formation in a group of men. The study authors suggested that reducing calcium intake may lead to an increase in the absorption of oxalate, which can contribute to the formation of kidney stones [ 46 ]. It is important to maintain a balanced diet and consult with healthcare professionals for personalized recommendations regarding the intake of these micronutrients to minimize the risk of kidney stones, especially for individuals at high risk.

The results of our study revealed that there is no significant relationship between the consumption of vitamin K, A, B6, B12, and D and kidney stones. Vitamin K2, which is found in fermented foods and produced by bacteria in the intestine, regulates MGP and osteocalcin. These are important for proper mineralization and the inhibition of vascular calcification [ 47 ]. According to our study, research has shown that Vitamin B12 levels are not associated with kidney stones [ 48 ]. Additionally, a prospective analysis of over 193,000 participants found no statistically significant association between vitamin D intake and the risk of kidney stones [ 49 ]. There was no association between vitamin B6 intake and the occurrence of kidney stones in three large prospective cohorts [ 50 , 51 ]. However, a study by Curhan et al. [ 52 ] demonstrated a negative correlation between vitamin B6 intake and the risk of stone formation in women.

This study has several limitations. First, it was a cross-sectional study, which means it cannot establish a cause-and-effect relationship. Second, our study relies on self-reported dietary data, which may be subject to recall bias. Third, we do not consider the composition of kidney stones, which can vary widely and may have different risk factors. On the other hand, the present study had several strengths. Our study had a large sample size, which can increase the statistical power and improve the precision of the estimates. Using a representative sample can improve the generalizability of the results. Additionally, we employed multivariate analysis to adjust for potential confounding factors, thereby enhancing the accuracy of the estimates and identify the independent effects of various risk factors.

Conclusions

Our study’s results suggest a relationship between diet and the formation of kidney stones. It suggests that a high carbohydrate diet and high copper intake may increase the odds of kidney stones. Furthermore, dietary intake can reduce the odds of developing kidney stones. Further research is needed to better understand the complex interplay between genetics, diet, lifestyle, and the risk of kidney stones. It is recommended to seek advice from a healthcare professional before making substantial dietary changes or taking supplements.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

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Acknowledgements

We would like to thank the participants and staff of the Hoveyzeh Cohort Study Center who assisted us in conducting this study and the ethics committee of Ahvaz Jundishapur University of Medical Sciences for approving the study protocol.

This work was supported by the Vice-Chancellor for Research at Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran, grant number HCS-9805.

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Alimentary Tract Research Center, Clinical Sciences Research Institute, School of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran

Seyed Jalal Hashemi

Nutrition and Metabolic Diseases Research Center, Clinical Research Institute, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran

Seyed Ahmad Hosseini

Pain Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran

Amal Saki Malehi

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ZR and MA conceptualized the idea. BCh prepared the design and research instrument. ZR and MA performed data collection and processing. ZR and BCh carried out data analysis.ASM, DKh, SAH, and SJH interpreted research data. All authors reviewed the manuscript.

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Cheraghian, B., Meysam, A., Hashemi, S.J. et al. Kidney stones and dietary intake in adults: a population-based study in southwest Iran. BMC Public Health 24 , 955 (2024). https://doi.org/10.1186/s12889-024-18393-1

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Updated reference values for static lung volumes from a healthy population in Austria

• Tobias Mraz 1 , 2 ,
• Shervin Asgari 2 , 3 ,
• Ahmad Karimi 2 , 3 ,
• Marie-Kathrin Breyer 1 , 2 ,
• Sylvia Hartl 2 , 3 ,
• Owat Sunanta 2 ,
• Alina Ofenheimer 2 , 3 , 4 ,
• Otto C. Burghuber 1 , 2 , 3 ,
• Angela Zacharasiewicz 5 ,
• Bernd Lamprecht 6 , 7 ,
• Caspar Schiffers 2 ,
• Emiel F. M. Wouters 2 , 3 , 4 &
• Robab Breyer-Kohansal 2 , 8  

Respiratory Research volume  25 , Article number:  155 ( 2024 ) Cite this article

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Metrics details

Reference values for lung volumes are necessary to identify and diagnose restrictive lung diseases and hyperinflation, but the values have to be validated in the relevant population. Our aim was to investigate the Global Lung Function Initiative (GLI) reference equations in a representative healthy Austrian population and create population-derived reference equations if poor fit was observed.

We analysed spirometry and body plethysmography data from 5371 respiratory healthy subjects (6–80 years) from the Austrian LEAD Study. Fit with the GLI equations was examined using z-scores and distributions within the limits of normality. LEAD reference equations were then created using the LMS method and the generalized additive model of location shape and scale package according to GLI models.

Good fit, defined as mean z-scores between + 0.5 and -0.5,was not observed for the GLI static lung volume equations, with mean z-scores > 0.5 for residual volume (RV), RV/TLC (total lung capacity) and TLC in both sexes, and for expiratory reserve volume (ERV) and inspiratory capacity in females. Distribution within the limits of normality were shifted to the upper limit except for ERV. Population-derived reference equations from the LEAD cohort showed superior fit for lung volumes and provided reproducible results.

GLI lung volume reference equations demonstrated a poor fit for our cohort, especially in females. Therefore a new set of Austrian reference equations for static lung volumes was developed, that can be applied to both children and adults (6–80 years of age).

Introduction

Respiratory disease conditions are largely based on measurement of lung physiology. A disease can be described as a set of characteristics by which they differ from the norm in such a way that they are biologically disadvantaged [ 1 ]. Reference values are used to help identify and diagnose individuals with abnormal values. Apart from measurement of forced maneuvers in spirometry, lung function can be described using lung volumes, determined by body plethysmography or gas dilution methods. Especially diagnosing restrictive lung disease only is possible by measuring the total lung capacity (TLC), thus requiring lung volumes [ 2 ].

The most commonly used reference values for lung volumes in adult populations are from the European Coal and Steel Community (ECSC), which were derived from data in 1983, and have limitations in terms of the inclusion of smokers and the lack of females [ 2 , 3 ]. These are not applicable to children, and so separate reference values have to be used, the most common being based on work by Zapletal and colleagues published in the 1970s [ 3 ]. Values by Rosenthal et al. were also published more than 20 years ago [ 4 ]. Recognizing the need to update reference values for lung function testing, in 2012 the Global Lung Function Initiative (GLI) published multi-ethnic spirometry reference values that could be used across an age range of 3 to 95 years, with separate calculations for males and females [ 5 ]. Subsequently, the GLI published reference values for static lung volumes that are applicable to assessment either by gas dilution methods or plethysmography [ 6 ]. Whereas the GLI spirometry values are based on data from over 74,000 examinations and have been validated in a number of different populations [ 7 ], the static lung volume reference values are based on a more limited dataset of approximately 7,700 measurements [ 5 , 6 ] and require further validation. We therefore aimed to investigate the fit of the GLI lung volume equations in a cohort of healthy never smokers in Austria. If resulting in a poor fit for the Austrian population, creation of population-derived reference equations was planned.

Material and methods

Population and study design.

The LEAD (Lung, hEart, sociAl, boDy) Study (ClinicalTrials.gov; NCT01727518; http://clinicaltrials.gov ) is an ongoing, longitudinal, observational, population-based cohort study that aims to provide a comprehensive database of risk factors for non-communicable diseases. The study has recruited a random sample (stratified by age, sex, and residential area) of males and females aged 6–80 years from Vienna and lower Austria that are representative of the general Austrian population, and who are being assessed every 4 years [ 8 ] since 2011. LEAD is being carried out according to the Declaration of Helsinki (2008) and has been approved by the Vienna local ethics committee (EK-11–117-0711). Written informed consent was given by all participants (or by parents or legal representatives for those aged under 18 years).

The current analyses focus on pre-bronchodilator data collected from the baseline visit. At each visit, all participants undergo spirometry and body plethysmography lung function testing by trained personnel at the LEAD study centre of the Ludwig Boltzmann Institute for Lung Health at the Clinic Penzing in Vienna, Austria. All measurements were conducted according to international recommendations (European Respiratory Society [ERS]/American Thoracic Society [ATS]) [ 9 , 10 ], using BT-MasterScope Body 0478© (Jaeger, Germany) with the JLAB software. The body plethysmograph was calibrated daily using a 3 L syringe and a box pressure calibrator. Lung volume indices were expressed in body temperature pressure saturated conditions.

The lung function examination started with the subject sitting and breathing steadily, registering the pressure–flow diagrams, and producing at least three reproducible diagrams. Functional residual capacity (FRC) was then measured by closure of the shutter at the end of a normal expiration. At least two FRC loops were obtained, with the subject breathing against the shutter at resting ventilation. The subject then carried out a maximal expiration to measure expiratory reserve volume (ERV), with residual volume (RV) calculated by subtracting ERV from FRC, followed by a slow, maximal inspiration, from which inspiratory capacity (IC) was measured. Finally forced expiratory volume in 1 s (FEV 1 ) and forced vital capacity (FVC) were assessed using forced spirometry, with three acceptable and reproducible loops obtained. Total lung capacity (TLC) was determined by adding RV to the best achieved vital capacity (VC), either from body plethysmography or spirometry. Strict regular quality control was in place for data collection and entry.

Age was registered in full days between the participants day of birth and the date of visit and is expressed in years with two decimals. Height was measured in centimeters without decimals. Weight was measured in kilograms with two decimals.

Definition of healthy never smoking respiratory cohort

All current and ex-smokers were excluded from the analyses. Participants with respiratory symptoms (wheeze, cough, sputum, or dyspnoea) in the last 12-months were also excluded, obtained using an interview-based questionnaire. Further subjects with a doctor’s diagnosis of asthma, chronic obstructive pulmonary disease, chronic bronchitis, or emphysema were also excluded.

In order to avoid extreme outliers, patients with Z-scores ± 5 for height, weight or spirometric values were excluded from the analyses, and lung function reports of outliers were re-checked for errors and were evaluated for quality of the flow diagrams. Finally, we included only subjects with a complete set of pre- and post-bronchodilation spirometry and body plethysmography. As we believed this definition would describe pulmonary healthy subjects, no further exclusion criteria using spirometry or lung volumes were used.

To evaluate the cohort for single centre bias concerning pulmonary function testing, we included data from study participants, who underwent a second pulmonary function testing, using the same protocol, in the pulmonary function testing laboratory of the Clinic Penzing, Vienna. These were selected out of the initial study collective for bronchial challenge testing and do not necessarily correspond to the same subjects as in the healthy study cohort.

Statistical analysis

Z-scores were calculated for the cohort using the available GLI reference equations for pre-bronchodilation spirometry and lung volumes [ 5 , 6 ]. Spirometry was included to check for general comparability to the GLI cohorts. Fit was analysed using the mean Z-scores, the 95% confidence intervals and the percentage above the upper limit of normal (ULN) and below the lower limit of normal (LLN). A good fit was to be concluded if: 1) the mean Z-score was between + 0.5 and -0.5 2) the standard deviation (SD) was approximately 1; and 3) ≤ 5% of the observations were below the LLN and ≤ 5% were above the ULN [ 11 ].

Population-specific reference equations were created based on the same, healthy cohort using the LMS method, consistent with GLI [ 5 ], as described earlier by Cole et al. [ 12 ], and the generalised additive model of location, scale and shape (GAMLSS) package in R (Version 4.2.2, R Foundation, Vienna, Austria, http://www.r-project.org ). Equations were generated separately for males and females, with height and age being the predictive variables. The LMS method allows modelling of the skewness (lamda), the median (mu) and the coefficient of variation (sigma). Fit of the equations was determined using Q-Q plots, worm plots and the distribution of Z-scores. The Kolmogorow-Smirnow test was used to test for normal distribution, indicated by a p -value > 0.05. Degrees of freedom were adapted to achieve the lowest Schwartz-Bayesian-Criterion while avoiding overly complex models.

The analyses used data from 5371 subjects (Fig.  1 ), including 2397 males (43.9%) and 2974 females (56.1%), aged from 6 to 80 years. The baseline characteristics of this cohort are shown in Table  1 for males and Table  2 for females. The majority of included individuals were between 6 to 30 years. A decline of lung function could be observed for both sexes, but more pronounced for FEV1 and FVC than lung volumes. In contrast, RV, RV/TLC and FRC grow larger with increasing age.

Flow chart for selection of a healthy, asymptomatic cohort

In the cohort, 31,2% of adults were overweight (body mass index [BMI] > 25 kg/m 2 ) and 10,7% were obese (BMI > 30 kg/m 2 ). In participants aged < 19 years 18,2% were overweight (BMI WHO Z-score > 1) and 9,6% were obese (BMI WHO Z-score > 2).

In a first step Z-scores were created using the GLI spirometry equations, to check for comparability to the Caucasian GLI cohorts. A good fit could be observed for all spirometry indices (Table  3 ). Females showed slightly lower numbers than the 5% expected under the LLN for FEV1 and FVC, especially at age > 65 years.

Existing reference equations for lung volume data

The fit of the GLI static lung volume equations were poor, as shown by the mean Z-scores in Table  4 . Mean Z-scores for RV and RV/TLC using the GLI reference values were >  ± 0.5 for both males and females, with fit also poor for TLC, IC and ERV in females. Furthermore, there was a shift towards higher values for all indices except ERV, as indicated by a higher proportion of values above the ULN than below the LLN. A absent normal distribution was demonstrated for all indices by an p  < 0.05 in the Kolmogorow-Smirnow test. An acceptable fit could be observed for FRC, IC and ERV in males, especially in the age group between 18–65 years.

Creation of population-specific reference equations

Given the unsatisfactorily fit of the lung volume data when using the GLI reference equations, new equations were created using the LMS method (Table  5 , Supplementary Figures.  1 and 2 ). Consistent with the approach used by GLI, subjects with calculated Z-scores >  ± 5 were excluded before recalculating the equations, to avoid influence by extreme outliers. Look-up tables containing the varying coefficients were created and are available in the online supplement. All equations showed a good fit, with mean Z-scores of 0 and SDs of 1 (Table  6 ). Furthermore, all distributions were even with approximately 5% of subjects above and below ULN and LLN, respectively. All indices were normally distributed in the Kolmogorow-Smirnow test.

Intraindividual variability

As this was a single centre study, a measurement bias by operator or equipment couldn’t be excluded. However, a subgroup of the LEAD cohort underwent an additional pulmonary function testing at a different site: participants with history of atopy, allergy, eosinophilia or positive skin prick test were selected for a bronchial challenge testing, which was carried out at the pulmonary function lab of the Clinic Penzing. The protocol and equipment were the same type as in the study centre, being a BT-MasterScope Body 0478 (Jaeger, Germany). Normal spirometry and plethysmography were carried out, tough only TLC, RV and ERV were available in the database. During Phase 1765 individuals underwent the additional testing, after excluding all with missing or invalid data, 706 participants remained. As the mean interval between the measurements was 40 months, a manual quality check was carried out, to exclude children and adolescents with large differences between the dates due to natural growth, contributing to the high number of exclusions. In the end, data of 602 participants were analysed. As the mean intraindividual difference was < 100 ml for all included parameters (FEV1, FVC, ERV, RV, TLC), a single centre bias of measurements seemed unlikely. (Table  7 ).

These analyses use cross-sectional data obtained from a broad, representative healthy population sample from Austria to investigate the fit of the GLI lung volumes reference equations. As the GLI equations failed to demonstrate a good fit with our population-based data in normal subjects, a new set of sex-specific reference values was created for lung volumes.

Reference values are indispensable when interpreting lung volumes in clinical practice, using the LLN with TLC and ULN with RV for defining restrictive impairment and hyperinflation respectively [ 13 ]. Until recently, assessments in Austria and Europe relied mostly on the ECSC reference equations for adults, despite several studies having demonstrated inconsistencies between these reference equations, so the update by GLI was highly anticipated [ 14 , 15 , 16 ].

When using the GLI spirometry equations in our population a good fit was observed. We therefore considered our cohort comparable to the Caucasian cohorts used by GLI to create equations for spirometry and lung volumes. While small differences exist especially for females, we consider the equations sufficient for the detection of obstructive anomalies in our cohort [ 17 ]. This is consistent with previous analyses reporting a good fit with the GLI spirometry equations for other European cohorts [ 7 , 18 ]. While some authors still report significant differences [ 19 ], the GLI equations, at least for Caucasian populations, offer consistent cut-offs and improved comparability between cohorts. The large amount of collated data, smoothing out small differences between populations, seems one of the main advantages. Additionally, even ethnic-specific equations created by GLI are available for spirometry. But the accuracy of these compared to globally merged equations was questioned lately [ 20 ].

However, GLI lung volume reference values did not fit well within our cohort. Large differences were observed, with mean Z-scores > 0,5 for TLC, RV and RV/TLC. Also, the percentage under the LLN and over the ULN was lower and higher respectively than expected. The difference was even more pronounced in females including significant differences for IC and ERV. These deviations could lead to an under-detection of restrictive disorders and overdiagnosis of hyperinflation in the Austrian population.

So far there is few data about the performance of the new GLI equations in European cohorts. The number of observations for lung volumes was much lower than for spirometry, and no equations are available for different ethnic backgrounds than Caucasian. A recent study from Belgium found similar results, with the GLI equations underestimating especially the values for RV [ 21 ]. Furthermore, the percentage under the LLN was lower than the expected 5% for TLC. A study in Algerian adults also reported, despite good fitting GLI spirometry values, similar results for RV, RV/TLC and TLC [ 22 ].

One potential explanation for the poor fit of the GLI lung volume equations is that our data were collected recently (starting 2011). Longitudinal studies have shown that populations are getting taller and healthier [ 23 ], with average population lung function increasing [ 24 , 25 , 26 , 27 ], potentially influenced by socioeconomic factors, or reduced occupational or environmental exposure [ 25 , 28 ]. While in literature the impact of these developments in lung function is still discussed, the large size of our cohort might especially contribute to visible differences [ 29 ].

There were less obese and overweight individuals in our cohort compared to GLI. As the significance of weight as predictor of static lung volumes is not yet conclusively understood [ 6 ], we used weight as an predictive variable in an early version of the equations. This only minimally altered the coefficients, and so wasn’t used further (data not shown). While weight seems to have only a small impact on overall lung volume reference equations, the effect of body composition could be more important and may explain some of the differences between cohorts.

Future analyses could investigate and include the effect of body compartments on lung volumes.

Other factors contributing to the need to revisit equations could be changes in methods and equipment. Various studies in patients with obstructive lung diseases have demonstrated significant differences between lung volumes measured by gas dilution methods versus plethysmography, although the situation in healthy individuals is less clear [ 30 ]. Indeed, GLI found statistically significant differences between these two methods in their cohort, but regarded the differences as not clinically relevant, although the majority of their data were derived from plethysmography [ 6 ]. In addition, use of different body plethysmography devices and software could potentially impact the results. For example, in GLI devices manufactured by JAEGER (which we used in our study) measured somewhat higher values than those from other manufacturers, especially for RV [ 6 ]. Recently, authors from COSYCONET demonstrated differences in FRC up to 0.67 L between two manufacturers [ 31 ].

So, while the simplicity of one equation spanning different techniques, equipment, and populations is one argument for the use of the GLI equations, this might not appropriately represent all different populations and methods. It is to be expected that reference values derived directly from the specific examined population would fit that population better than standardised equations – although it is important that for such population-based equations to be useful, the examined population has to be representative of the broad population, as has been shown to be the case with the LEAD cohort [ 8 ] Still, adding more data to the GLI equations, may in the future improve the generalizability and render population based equations obsolete.

In this study the population derived reference equations from LEAD demonstrated a superior fit for all lung volume indices compared to the GLI equations. Lung volumes in our cohort were influenced by sex, age and height. Some studies have included weight as predictive variable for lung function [ 15 , 16 , 32 ], but as with GLI we found only a small influence of weight [ 6 ], and our equations therefore do not need to include this parameter. Importantly, we included obese individuals in our analyses, since reference values should be generalisable to the intended population [ 17 ]. Our newly derived equations might be usable in other European countries with similar population characteristics and equipment. This will have to be analysed in future studies.

Strengths and limitations

Our analyses were conducted according to the ERS/ATS workshop report requirements [ 2 ]. While these published already over 20 years ago, they are still the most recent criteria available. We used strict selection criteria for our healthy cohort, only including never smokers, and excluding those reporting any respiratory symptoms. In addition, the population was distributed over all age groups, although with an overrepresentation of children, adolescents and of females, potentially due to the exclusion of those with a smoking history. We used standardised methods for the measurement of lung volumes, with strict quality control [ 8 ], and to create the reference equations we used the same statistical models as GLI. In particular, the LMS model allows the equations to cover the entire age range, avoiding discrepancies when entering the adult age [ 5 ].

The main limitation of our analyses is the single centre aspect of our lung function testing. The comparison of measurements done in another site showed only very small, not clinically relevant differences. Still a systemic bias can’t be ruled out, as only the device and software of one manufacturer was used. This also limits generalisability to other equipment and software. Furthermore, our cohort included no individuals aged < 6 years and > 80 years, so we recommend the use of our equations only between the ages of 6 and 80 years. Ethnicity wasn’t documented, as participants of the LEAD study, corresponding to the Austrian population, were predominantly of European ancestry. The Austrian population is known to consist just a very minor part of subjects different than Caucasian ancestry, so ethnicity wasn’t considered in the initial study design. Therefore the reference values are only applicable to similar Caucasian populations. We used strict exclusion criteria, but still subjects with physiologically abnormal lung function measurements or undiagnosed respiratory disease could have been present in the analysed cohort.

In our cohort the GLI lung volume reference equations demonstrated a poor fit for RV, RV/TLC and TLC, especially in females. We therefore developed a new set of Austrian reference equations for static lung volumes that, unlike most reference values, can be applied to both children and adults, from the ages of 6 to 80 years.

Availability of data and materials

The reference equations generated and analysed during the current study are available in Table 5 . Look-up tables are provided in the online supplement.

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Acknowledgements

The authors would like to thank Sanja Stanojevic and Brendan Cooper from the GLI network for their support and assistance in developing the reference equations

The Austrian LEAD Study is supported by the Ludwig Boltzmann Society, the Municipal Department of Health and Environment of Vienna, the Federal State Governmental Department of Health of Lower Austria, and unrestricted scientific grants from AstraZeneca, Böhringer Ingelheim, Chiesi Pharma, Glaxo Smith Kline and Menarini Pharma. None of the supporting parties had any participation in the data, nor did they contribute to the design or the content of the present manuscript.

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Tobias Mraz, Marie-Kathrin Breyer & Otto C. Burghuber

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Tobias Mraz, Shervin Asgari, Ahmad Karimi, Marie-Kathrin Breyer, Sylvia Hartl, Owat Sunanta, Alina Ofenheimer, Otto C. Burghuber, Caspar Schiffers, Emiel F. M. Wouters & Robab Breyer-Kohansal

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Shervin Asgari, Ahmad Karimi, Sylvia Hartl, Alina Ofenheimer, Otto C. Burghuber & Emiel F. M. Wouters

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Contributions

Contributions to conception and design: TM, MKB, BL, SH, OB, AZ, EW, RBK.  Data analysis: TM, SA, OS, AO, AK.  Interpretation of data: TM, CS, EW, RBK.  Drafting the article or revising it critically for important intellectual content: All authors.  Gave final approval of the version to be published: All authors.  Take responsibility for the integrity of the data and accuracy of the data analysis: All authors.

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LEAD is being carried out according to the Declaration of Helsinki (2008) and has been approved by the Vienna local ethics committee (EK-11–117-0711). Written informed consent was given by all participants (or by parents or legal representatives for those aged under 18 years).

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Supplementary Information

Additional file 1..

 Mean predicted lung volumes males

Additional file 2.

 Mean predicted lung volumes females

Additional file 3.

 LEAD Lookup tables lung volumes submitted

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Mraz, T., Asgari, S., Karimi, A. et al. Updated reference values for static lung volumes from a healthy population in Austria. Respir Res 25 , 155 (2024). https://doi.org/10.1186/s12931-024-02782-6

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DOI : https://doi.org/10.1186/s12931-024-02782-6

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Outdoors | Wisconsin loon decline spurred by more rain,…

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Outdoors | wisconsin loon decline spurred by more rain, less water clarity, study finds, research finds heavier rainfall and murkier lakes, fueled by climate change, are reducing the survival of young loon chicks.

RHINELANDER, Wis. — A cascading series of environmental factors pushed by climate change are causing a decline in northern Wisconsin’s loon population and may be limiting loon numbers in Minnesota as well.

That’s the finding of research published in the recent edition of the journal Ecology and conducted on lakes in northeastern Wisconsin. Researchers already knew there has been a 22% decline in loon numbers over the past 27 years in the study area, and now they say the problem is driven by declining water clarity.

That declining water clarity is caused by more runoff and erosion on land, a rush of dissolved organic matter into the lakes, caused by much heavier summer rain events fueled by climate change, said Walter Piper, lead researcher on the long-term project and a professor of biology at Chapman University in California.

Adult loons are finding it harder to find fish to feed their chicks, the study concludes, and the population of young non-breeding loons, which scientists call “floaters,” is down 46% since the study began. Meanwhile, chick weight has declined by 11%, a sign they aren’t getting enough to eat.

“Loss of mass by chicks — which shows up as emaciated chicks that often fail to fledge — appears to be a major driver of the well-documented decline in survival rates of young loons in Wisconsin recently and overall population decline in the region,” researchers concluded. While other studies have shown that loons prefer clear water, this is believed to be the first study to directly link declining water quality and declining loons.

July water clarity decreased by nearly 0.6 meters in the lakes studied from 1995 to 2021. July is the critical month for newly hatched loon chicks to survive toward adulthood, Piper said.

Piper has been studying loons in the project area, around Rhinelander, for 31 years and previously documented declining loon numbers there, especially fewer chicks surviving to make the flight south their first fall. Those findings were published in a 2020 edition of the Condor, the journal of the American Ornithological Society.

But it’s only been in recent years that Piper confirmed the disturbing trend of reduced water quality in July. Loons, which depend on their sight to find and catch fish for food, often pick the clearest lakes to nest on because they can see their prey underwater. Other researchers in the project from Rensselaer Polytechnic Institute in New York used decades of satellite imagery to help track water clarity in the loon lakes studied.

“The hidden nature of the Wisconsin decline points out that floater populations can mask major drops in breeding populations and throws into question population trends in other loon populations, whose floater populations are largely unknown,” researchers wrote.

Piper began expanding his study to include north-central Minnesota lakes in 2021. There are now about 250 Minnesota loons banded and part of the study along with 400 in Wisconsin.

“I have been color-marking and studying the behavior and ecology of common loons in northern Wisconsin since 1993. Four years ago, I sensed a decline was afoot in my Wisconsin study population, and this fear was confirmed when I looked at my data,” Piper noted. ”This decline was part of what inspired me to establish a second marked study population of loons in Crow Wing County, Minnesota. We now have about two-thirds of the adult breeders banded in the new Minnesota study area, which includes part of Cass County as well.”

“I’m worried we’re going to see the same problem in Minnesota, but we don’t have enough data yet,” he added, noting state researchers already have seen some decline in loon chicks in Minnesota.

While Piper said other issues are problems for loons — eagles, nest predators like raccoons and lead poisoning — but none have the widespread impact of water clarity declines. He said controlling shoreline runoff along lakes might help curb the issue, as could reducing fertilizers that may be spurring algal blooms and adding to the water clarity problem.

“This is all grim news, I know. But I think we need to get the word out about it,’’ Piper said. “If we narrow down the cause of the loon decline even more, we might take steps to fix the problem. Although, admittedly, we will not fix climate change overnight.”

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Your environment. your health., climate change and human health literature portal mediterranean built environment and precipitation as modulator factors on physical activity in obese mid-age and old-age adults with metabolic syndrome: cross-sectional study, climate change and human health literature portal.

• Publisher https://dx.doi.org/10.3390/ijerph16050854
• PubMed https://www.ncbi.nlm.nih.gov/pubmed/30857222

When promoting physical activity (PA) participation, it is important to consider the plausible environmental determinants that may affect this practice. The impact of objectively-measured public open spaces (POS) and walk-friendly routes on objectively-measured and self-reported PA was explored alongside the influence of rainy conditions on this association, in a Mediterranean sample of overweight or obese senior adults with metabolic syndrome. Cross-sectional analyses were undertaken on 218 PREDIMED-Plus trial participants aged 55(-)75 years, from the city of Palma, in Mallorca (Spain). Indicators of access to POS and walk-friendly routes were assessed in a 1.0 and 0.5 km sausage network walkable buffers around each participant's residence using geographic information systems. Mean daily minutes of self-reported leisure-time brisk walking, and accelerometer objectively-measured moderate-to-vigorous PA in bouts of at least 10 min (OM-MVPA) were measured. To investigate the association between access to POS and walk-friendly routes with PA, generalized additive models with a Gaussian link function were used. Interaction of rainy conditions with the association between access to POS and walk-friendly routes with OM-MVPA was also examined. Better access to POS was not statistically significantly associated with self-reported leisure-time brisk walking or OM-MVPA. A positive significant association was observed only between distance of walk-friendly routes contained or intersected by buffer and OM-MVPA, and was solely evident on non-rainy days. In this elderly Mediterranean population, only access to walk-friendly routes had an influence on accelerometer-measured PA. Rainy conditions during the accelerometer wear period did appear to modify this association.

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Tier 1 Pilot Research Grants from the Population Health Initiative (Due 4/15/24)

Posted: 3/28/2024 ()

The Population Health Initiative is offering Tier 1 Pilot research grants, with Letters of Intent (LOI) due in April 15th. Tier 1 grants are meant to support researchers in laying the foundation for a future project to generate proof-of-concept. Awards of up to $25,000 in total expenses per project are available from the Population Health Initiative. Learn more about the grant, including past funded projects here .

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Sample Size and its Importance in Research

Chittaranjan andrade.

Clinical Psychopharmacology Unit, Department of Clinical Psychopharmacology and Neurotoxicology, National Institute of Mental Health and Neurosciences, Bengaluru, Karnataka, India

The sample size for a study needs to be estimated at the time the study is proposed; too large a sample is unnecessary and unethical, and too small a sample is unscientific and also unethical. The necessary sample size can be calculated, using statistical software, based on certain assumptions. If no assumptions can be made, then an arbitrary sample size is set for a pilot study. This article discusses sample size and how it relates to matters such as ethics, statistical power, the primary and secondary hypotheses in a study, and findings from larger vs. smaller samples.

Studies are conducted on samples because it is usually impossible to study the entire population. Conclusions drawn from samples are intended to be generalized to the population, and sometimes to the future as well. The sample must therefore be representative of the population. This is best ensured by the use of proper methods of sampling. The sample must also be adequate in size – in fact, no more and no less.

SAMPLE SIZE AND ETHICS

A sample that is larger than necessary will be better representative of the population and will hence provide more accurate results. However, beyond a certain point, the increase in accuracy will be small and hence not worth the effort and expense involved in recruiting the extra patients. Furthermore, an overly large sample would inconvenience more patients than might be necessary for the study objectives; this is unethical. In contrast, a sample that is smaller than necessary would have insufficient statistical power to answer the primary research question, and a statistically nonsignificant result could merely be because of inadequate sample size (Type 2 or false negative error). Thus, a small sample could result in the patients in the study being inconvenienced with no benefit to future patients or to science. This is also unethical.

In this regard, inconvenience to patients refers to the time that they spend in clinical assessments and to the psychological and physical discomfort that they experience in assessments such as interviews, blood sampling, and other procedures.

ESTIMATING SAMPLE SIZE

So how large should a sample be? In hypothesis testing studies, this is mathematically calculated, conventionally, as the sample size necessary to be 80% certain of identifying a statistically significant outcome should the hypothesis be true for the population, with P for statistical significance set at 0.05. Some investigators power their studies for 90% instead of 80%, and some set the threshold for significance at 0.01 rather than 0.05. Both choices are uncommon because the necessary sample size becomes large, and the study becomes more expensive and more difficult to conduct. Many investigators increase the sample size by 10%, or by whatever proportion they can justify, to compensate for expected dropout, incomplete records, biological specimens that do not meet laboratory requirements for testing, and other study-related problems.

Sample size calculations require assumptions about expected means and standard deviations, or event risks, in different groups; or, upon expected effect sizes. For example, a study may be powered to detect an effect size of 0.5; or a response rate of 60% with drug vs. 40% with placebo.[ 1 ] When no guesstimates or expectations are possible, pilot studies are conducted on a sample that is arbitrary in size but what might be considered reasonable for the field.

The sample size may need to be larger in multicenter studies because of statistical noise (due to variations in patient characteristics, nonspecific treatment characteristics, rating practices, environments, etc. between study centers).[ 2 ] Sample size calculations can be performed manually or using statistical software; online calculators that provide free service can easily be identified by search engines. G*Power is an example of a free, downloadable program for sample size estimation. The manual and tutorial for G*Power can also be downloaded.

PRIMARY AND SECONDARY ANALYSES

The sample size is calculated for the primary hypothesis of the study. What is the difference between the primary hypothesis, primary outcome and primary outcome measure? As an example, the primary outcome may be a reduction in the severity of depression, the primary outcome measure may be the Montgomery-Asberg Depression Rating Scale (MADRS) and the primary hypothesis may be that reduction in MADRS scores is greater with the drug than with placebo. The primary hypothesis is tested in the primary analysis.

Studies almost always have many hypotheses; for example, that the study drug will outperform placebo on measures of depression, suicidality, anxiety, disability and quality of life. The sample size necessary for adequate statistical power to test each of these hypotheses will be different. Because a study can have only one sample size, it can be powered for only one outcome, the primary outcome. Therefore, the study would be either overpowered or underpowered for the other outcomes. These outcomes are therefore called secondary outcomes, and are associated with secondary hypotheses, and are tested in secondary analyses. Secondary analyses are generally considered exploratory because when many hypotheses in a study are each tested at a P < 0.05 level for significance, some may emerge statistically significant by chance (Type 1 or false positive errors).[ 3 ]

INTERPRETING RESULTS

Here is an interesting question. A test of the primary hypothesis yielded a P value of 0.07. Might we conclude that our sample was underpowered for the study and that, had our sample been larger, we would have identified a significant result? No! The reason is that larger samples will more accurately represent the population value, whereas smaller samples could be off the mark in either direction – towards or away from the population value. In this context, readers should also note that no matter how small the P value for an estimate is, the population value of that estimate remains the same.[ 4 ]

On a parting note, it is unlikely that population values will be null. That is, for example, that the response rate to the drug will be exactly the same as that to placebo, or that the correlation between height and age at onset of schizophrenia will be zero. If the sample size is large enough, even such small differences between groups, or trivial correlations, would be detected as being statistically significant. This does not mean that the findings are clinically significant.

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