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Differences Between Human Life Span and ExpectancyThe human lifespan is the maximum number of years an individual from the human species can live based on observed examples. Though this definition of lifespan may seem simple enough, it is often confused with other common concepts in the study of the aging , life, and death of living organisms. In order to better understand the human lifespan, let's dive a little deeper into the concept and its important distinctions from other commonly used terms. Human Life Span vs. Human Life ExpectancyThe term lifespan is most commonly confused with another important concept: life expectancy . While both terms relate to the number of living years, they actually define very different concepts. While the term lifespan refers to the maximum number of years an individual can live, life expectancy refers to an estimate or an average number of years a person can expect to live. Most simply put, life expectancy can be attributed to and impacted by an individual and their personal health history, genetics, and lifestyle, whereas lifespan holds for all living humans. For example, a person's life expectancy is affected by personal factors like family history, environment, diet, and even age and sex. One person's life expectancy might be different from your life expectancy and it may even change over time. Your life spans, however, are one in the same. We all share it as members of the same species. So what is the human life span? What Is the Human Life Span?Given that the human lifespan is defined by the longest observed human life from birth to death, it is a figure that has changed over the years . For humans, the current accepted maximum lifespan is 122 years. This age was achieved by Jeane Louise Calment of France. Calment lived from February 21, 1875, to August 4, 1997, until she was exactly 122 years and 164 days old. Remarkably, Calment remained relatively healthy and mentally intact until her 122nd birthday. Though there have certainly been claims of longer lives, none of the claims were acceptably documented and verified. Closing the Gap Between Life Expectancy and Life SpanWith the United State's average life expectancy currently hovering at around 79 years, the age to which most Americans can expect to live is still forty-four years younger than the human lifespan . So how do we close that gap and elongate our lives? There will always be factors that are out of our individual control like our inherited genes, but we shouldn't discount the impact of those that we can control. It is generally understood that closing the gap between life expectancy and lifespan can be done through healthier living, less exposure to toxins, the prevention of chronic illnesses, and a little bit of luck. Wilhelm P. Jeanne Calment: Validation of the Duration of Her Life . Validation of Exceptional Longevity. Odense University Press. ISBN 87-7838-466-4 United Nations Development Programme. Human Development Report 2019 (PDF). 10 December 2019. By Mark Stibich, PhD Mark Stibich, PhD, FIDSA, is a behavior change expert with experience helping individuals make lasting lifestyle improvements. The Edvocate- Lynch Educational Consulting
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Teaching Students About Lassi: A Refreshing Cultural ExperienceTeaching students about the origin of the word “meme”, teaching students about land mines: an important lesson in global awareness, teaching students about how the atmosphere acquires most of its energy from the sun, teaching students about tim minchin: a multidisciplinary approach, teaching students about the antoinette perry award for excellence, teaching students about the jean seberg’s legacy, teaching students about the volkswagen thing: an unconventional approach, teaching students about the american renaissance, simple & easy lifespan development essay topics. Simple & Easy Lifespan Development Essay Titles - Life Expectancy Growth of Howard Hughes
- Lifecycle Growth And Adult Lifespan of Various Beetles
- A Research of the Personality Outlook of Lifecycle Development Concepts
- Adulthood and Growth Across Life Time
- A Particular Research on Life Expectancy Growth of a Couple
- Subjective and Professional Growth Throughout the Lifespan
- Monitoring Childhood Throughout The Lifecycle
- Comprehension of Life Expectancy and Development and How It Can Aid Those Giving Care to Various Client Groups
- Growth in the Lifecycle/Toddler & Senior
- Philosophies and Philosophers On Lifespan Development
- Innovative Lifecycle and Growth by John Santrock
- Physical, Intellectual, Psychological Growth Through Life Expectancy
- Character and Life Expectancy and Growth of Edgar Allan Poe
Good Essay Topics on Lifespan Development - The Development of Human Lifecycle During Emotional Growth
- The Nature of Children on Human Lifetime and Growth
- The Puzzling Issues, Ideas, and Descriptions of Development Notions in Fundamentals of Lifetime Progress
- Lifecycle Growth: Analysing Child and Adolescent Growth Impact on Adulthood
- Inspiring Concept Of Lifecycle Growth
- The Influence Of Piaget On The Subject of Lifecycle and Growth
- Principal Developmental Functions and Breakthroughs with Each Period in Human Growth
- Schizophrenia and Psychosis and Lifecycle Growth Patterns
- Adolf Hitler – Lifespan Grownth and Behaviour
- Concepts Of Lifespan Growth In Psychology
- Combination of Ideologies Describing the Lifecycle Development
- Human Growth and Attainment Throughout the Lifecycle
- Schizophrenia and Irregularities of Lifespan Growth
- Human Progression: The Significance of Human and Lifecycle
Fascinating Chronicle Of A Death Foretold Essay ...Computational thinking: everything you need to know. Matthew LynchRelated articles more from author. Arranged Marriage Essay TopicsEnvironmental issues essay topic ideas & examples, simple & easy music essay topics, interesting risk assessment essay topics, simple & easy moral development essay topics, fascinating booker t. washington essay topics to write about. Module 2: Developmental TheoriesComparing and evaluating lifespan theories, learning outcomes. - Contrast the main psychological theories that apply to human development
Developmental theories provide a set of guiding principles and concepts that describe and explain human development. Some developmental theories focus on the formation of a particular quality, such as Piaget’s theory of cognitive development. Other developmental theories focus on growth that happens throughout the lifespan, such as Erikson’s theory of psychosocial development. It would be natural to wonder which of the perspectives provides the most accurate account of human development, but clearly, each perspective is based on its own premises and focuses on different aspects of development. Many lifespan developmentalists use an eclectic approach, drawing on several perspectives at the same time because the same developmental phenomenon can be looked at from a number of perspectives. In the table below, we’ll review some of the major theories that you learned about in this module. Recall that three key issues considered in human development examine if development is continuous or discontinuous, if it is the same for everyone or distinct for individuals (one course of development or many), and if development is more influenced by nature or by nurture. The table below reviews how each of these major theories approaches each of these issues. Table 1. Major Theories in Human Development [1] | | | | | | Psychosexual theory | Behavior is motivated by inner forces, memories, and conflicts that are generally beyond people’s awareness and control. Emphasizes the unconscious, defense mechanisms, and influences of the id, ego, and superego. | Discontinuous; there are distinct stages of development | One course; stages are universal for everyone | Both; natural impulses combined with early childhood experiences impact development | Sigmund Freud | Psychosocial theory | A person negotiates biological and sociocultural influences as they move through eight stages, each characterized by a psychosocial crisis: trust vs. mistrust, autonomy vs. shame/doubt, initiative vs. guilt, industry vs. inferiority, identity vs. role confusion, intimacy vs. isolation, generativity vs. stagnation, ego integrity vs. despair. | Discontinuous; there are distinct stages of development | One course; stages are universal for everyone | Both; natural impulses combined with sociocultural experiences impact development | Erik Erikson | Classical conditioning | Learning by the association of a response with a stimulus; a person comes to respond in a particular way to a neutral stimulus that normally does not bring about that type of response. | Continuous; learning is ongoing without distinct stages | Many courses; learned behaviors vary by person | Mostly nurture; behavior is conditioned | Ivan Pavlov, John Watson | Operant conditioning | Learning that occurs when a voluntary response is strengthened or weakened by its association with positive or negative consequences. Rewards and punishments can strengthen or discourage behaviors. | Continuous; learning is ongoing without distinct stages | Many courses; learned behaviors vary by person | Mostly nurture; behavior is conditioned | B.F. Skinner | Social cognitive theory (social learning theory) | Learning occurs in a social context; considering the relationship between the environment and a person’s behavior. Learning can occur through observation. | Continuous; learning is gradual and ongoing without distinct stages | Many courses; learned behaviors vary by person | Mostly nurture; behavior is observed and learned | Albert Bandura | Piaget’s theory of cognitive development | A theory about how people come to gradually acquire, construct, and use knowledge and information. It describes cognitive development through four distinct stages: sensorimotor, preoperational, concrete, and formal. | Discontinuous; there are distinct stages of development | One course; stages are universal for everyone | Both; natural impulses combined with experiences that challenge the existing schemas | Jean Piaget | Information processing | A theory that seeks to identify the ways individuals take in, use, and store information (sometimes compared to a computer). It is based on the idea that humans process the information they receive, rather than merely respond to stimuli. | Continuous; cognitive development is gradual and ongoing without distinct stages | One course; the model applies to everyone | Both; natural cognitive development combined with experiences of processing information in new and different ways | Richard Atkinson, Richard Shiffrin | Humanistic theories | Theories that emphasizes an individual’s inherent drive towards self-actualization and contend that people have a natural capacity to make decisions about their lives and control their own behavior. Key terms and concepts include unconditional positive regard, striving for “the good life,” and the hierarchy of needs. | Continuous; development is ongoing without distinct stages and can be multidirectional depending on environmental circumstances | Mostly one course; Maslow’s hierarchy of needs is universally applied, but there is an individual course for self-actualization | Mostly nurture; development is influenced by environmental circumstances and social interactions | Carl Rogers, Abraham Maslow | Sociocultural theory | Vygotsky’s theory that emphasizes how cognitive development proceeds as a result of social interactions between members of a culture. Key terms and concepts include the zone of proximal development and scaffolding. | Both, but mostly continuous as an individual learns and progresses | Many courses; there are variations between individuals and cultures | Both; development is influenced by biological preparation and social experiences | Lev Vygotsky | Bioecological systems model | Urie Bronfenbrenner’s theory stressing the importance of studying a child in the context of multiple environments, or ecological systems. It is organized into five levels of external influence: microsystem, mesosystem, exosystem, macrosystem, and chronosystem. | Both; the influence of each system can be continuous or discontinuous depending on the system in question | Many courses; the interaction of people and the environment varies | Both; a person’s biological potential and the environment interact to impact development | Urie Bronfenbrenner, Stephen Ceci | Evolutionary psychology theory | A theory that seeks to identify behavior that is a result of our genetic inheritance from our ancestors. | Continuous; current behaviors have been shaped over multiple generations based on successful survival and reproduction | Both; behavioral genetics show similarities across the species, but our unique family history also plays a role in development | Both; our genetic history and biological impulses interact with life experiences to produce individual development and development across the history and future of the species | Charles Darwin, David Buss, Konrad Lorenz, Robert Sapolsky | - Berk, L. E. (1998). "Stances of Major Theories on Basic Issues in Human Development."Development through the lifespan. Boston: Allyn and Bacon. p. 26. ↵
- Comparing and Evaluating Lifespan Theories. Authored by : Lumen Learning. License : CC BY: Attribution
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Dynamics of life expectancy and life span equalityJosé manuel aburto. a Interdisciplinary Centre on Population Dynamics, University of Southern Denmark, 5000 Odense, Denmark; b Lifespan Inequalities Research Group, Max Planck Institute for Demographic Research, 18057 Rostock, Germany; Francisco Villavicencioc Department of International Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, 21205; Ugofilippo Basellinid Laboratory of Digital and Computational Demography, Max Planck Institute for Demographic Research, 18057 Rostock, Germany; e Mortality, Health and Epidemiology Unit, Institut National d’Études Démographiques (INED), 93322 Aubervilliers, France; Søren Kjærgaardf Center for Research in Econometric Analysis of Time Series (CREATES), Aarhus University, 8000 Aarhus, Denmark; James W. Vaupelg Duke University Population Research Institute, Duke University, Durham, NC, 27708; h Emeritus Research Group, Max Planck Institute for Demographic Research, 18057 Rostock, Germany Author contributions: J.M.A. and J.W.V. designed research; J.M.A., F.V., U.B., and S.K. performed research; J.M.A., F.V., U.B., and S.K. contributed new reagents/analytic tools; J.M.A., U.B., and S.K. analyzed data; and J.M.A., F.V., U.B., and J.W.V. wrote the paper. Reviewers: C.M., University of Oxford; M.M., London School of Economics; and C.R., University of Oxford. Associated DataDescription to access the data and the code to reproduce results are in a permanent repository, accessible via the following link: https://zenodo.org/record/3571095 . All data are publicly available. SignificanceWhy life expectancy and life span equality have increased together is a question of scientific interest. Both measures are calculated for a calendar year and might not describe a cohort’s actual life course. Nonetheless, life expectancy provides a useful measure of average life spans, and life span equality gives insights into uncertainty about age at death. We show how patterns of change in life expectancy and life span equality are described by trajectories of mortality improvements over age and time. The strength of the relationship between life expectancy and life span equality is not coincidental but rather a result of progress in saving lives at specific ages: the more lives saved at the youngest ages, the stronger the relationship is. As people live longer, ages at death are becoming more similar. This dual advance over the last two centuries, a central aim of public health policies, is a major achievement of modern civilization. Some recent exceptions to the joint rise of life expectancy and life span equality, however, make it difficult to determine the underlying causes of this relationship. Here, we develop a unifying framework to study life expectancy and life span equality over time, relying on concepts about the pace and shape of aging. We study the dynamic relationship between life expectancy and life span equality with reliable data from the Human Mortality Database for 49 countries and regions with emphasis on the long time series from Sweden. Our results demonstrate that both changes in life expectancy and life span equality are weighted totals of rates of progress in reducing mortality. This finding holds for three different measures of the variability of life spans. The weights evolve over time and indicate the ages at which reductions in mortality increase life expectancy and life span equality: the more progress at the youngest ages, the tighter the relationship. The link between life expectancy and life span equality is especially strong when life expectancy is less than 70 y. In recent decades, life expectancy and life span equality have occasionally moved in opposite directions due to larger improvements in mortality at older ages or a slowdown in declines in midlife mortality. Saving lives at ages below life expectancy is the key to increasing both life expectancy and life span equality. The rise in human life expectancy over the past two centuries is a remarkable accomplishment of modern civilization ( 1 , 2 ). This progress was achieved during the demographic transition of societies from regimes of high mortality and fertility to regimes of low mortality and fertility ( 3 , 4 ). At present, among the world’s nations, Japanese women have the highest life expectancy at birth, above 87 y. In 1840, the record was held by Swedish women, with an average life span of 46 y ( 5 ). This advance has been accompanied by an increase in life span equality: In low mortality populations today, most individuals survive to similar ages ( 6 – 11 ). Life span equality matters because it captures a fundamental type of inequality: variation in length of life. This variation is not revealed by life expectancy and other measures of average mortality levels ( 12 ). Two populations that share the same level of life expectancy could experience substantial differences in the timing of death, e.g., deaths could be more evenly spread over age in one population than another. Although life expectancy is monitored in every country in the world, few countries have begun to monitor and acknowledge the importance of disparities in age at death. For values of life expectancy at birth from under 20 to above 85 y, life span equality rises linearly ( Fig. 1 ). This relationship between life expectancy and life span equality has been found to hold in a life span continuum over millions of years of primate evolution, in many countries and among subgroups in a population ( 6 – 11 , 13 – 15 ). The dual advance, however, might be coincidental rather than causal. Even though both life expectancy and life span equality are computed from the same information, namely age-specific death rates, doubt about a common causal link is sown by messier and sometimes negative relationships between them in various datasets and using alternative indicators of life span equality ( 16 ). The United States, for example, has relatively low equality in life spans in comparison with other countries that have similar levels of life expectancy ( 17 ). Scotland reached similar levels of life expectancy with 10% higher life span inequality than England and Wales since 1980 ( 18 ). Finnish females from lower educational levels experienced increases in life expectancy, while life span equality decreased at age 30 since the 1970s ( 12 ). In Denmark, life span equality decreased among the lowest income subgroup over the period of 1986 to 2014 despite the increase in life expectancy ( 19 ). In some countries in Eastern Europe and Latin America, life expectancy and life span equality moved independently over periods of slow improvements in life expectancy ( 20 – 22 ). Indeed, in many countries and subgroups within a country in recent decades, life span equality declined, although the average life span rose or vice versa (as indicated by the points in the second and fourth quadrants of Fig. 2 A and B ). In addition, causes of death that contributed to increasing life expectancy somewhat differ from those that increased equality in life spans in developed countries after 1970 ( 23 , 24 ). Nonetheless, despite these exceptions and discrepancies, life expectancy and life span equality generally move in the same direction ( 11 ). Association between life expectancy at birth e o and life span equality h . ( A ) Association between changes in life expectancy at birth e o and life span equality h . ( B ) Association between changes over 10-y rolling periods. In this article, we develop a mathematical framework to explore how life expectancy at birth and life span equality relate to each other and evolve over time. We rely on two dimensions of aging: the average length of life (pace) and the relative variation in length of life (shape) ( 25 ). The former refers to how fast aging occurs, while the latter describes how sharply populations age. The shape of mortality pertains to the distribution of life spans. Statisticians and demographers, based on both theoretical and practical considerations, have developed different indicators to summarize the distribution of life spans ( 26 , 27 ). Here, we measure average length of life by life expectancy, and we analyze the distribution of life spans with three different indicators of life span equality. These indicators are variants of 1) the life table entropy, 2) the Gini coefficient, and 3) the coefficient of variation of the age-at-death distribution ( 28 , 29 ). Other indicators of absolute dispersion in life spans exist, such as the variance of the age-at-death distribution, its SD, or life years lost due to death ( 30 , 31 ). However, these are pace indicators measured in units of time and do not capture the dimensionless shape of aging ( 26 ). We focus on how age-specific mortality improvements change life span equality and life expectancy at birth. We analyze changes over time in these two longevity measures for Swedish females since the 18th century, and 48 additional populations from the Human Mortality Database with reliable data, in many cases since the beginning of the 20th century, for females and males separately ( 5 ). Mortality risks implied by a period life table generally differ from the risks individuals will experience over their lifetimes. Nonetheless, life table information on life expectancy and life span equality may provide information individuals use to make life course decisions, and information policymakers use to assess population health and well-being ( 32 – 34 ). Trends in Life Expectancy and Life Span EqualityLife expectancy at birth for both men and women increased throughout the 20th century ( 5 , 35 ). Paralleling the rise of life expectancy, all countries included in our study became more equal in life spans ( Fig. 1 ). This is a significant advance in giving people more equitable opportunities. Furthermore, the rise in life span equality has influenced the decisions individuals make over their life course, such as when to have children, study, work, or retire, and how much to save for retirement, because such decisions are based not only on expected lifetime but also on uncertainty about age at death ( 14 ). Analysis of the relationship between life expectancy at birth e o and life span equality, as measured by h , a log-transformation of life table entropy H ¯ ( Materials and Methods and Box 1 ), indicates a strong correlation (Pearson coefficient of 0.985 for the data in Fig. 1 ). We also analyzed the relationship between average life span and two other measures of life span equality based on the Gini coefficient and the coefficient of variation, and found similarly high correlations, 0.981 and 0.975, respectively ( SI Appendix , Fig. S1 ). Although life expectancy and life span equality have been positively correlated, it is apparent that the relationship is less strong and often reversed in recent decades, resulting in negative correlations in some countries in yearly and 10-y changes ( Fig. 2 ). Box 1. The Threshold Age and the Life Expectancy at BirthLife span equality measured by h refers to an indicator closely related to the life table entropy, which was first developed by Leser ( 29 ) and further explored by Demetrius ( 62 ), Keyfitz ( 42 ), and Keyfitz and Golini ( 63 ). The life table entropy is a dimensionless indicator of the relative variation in the length of life compared to life expectancy at birth, and can be expressed as follows: Function ℓ ( x ) denotes the probability of surviving from birth to age x , whereas e † refers to life disparity—the average remaining life expectancy at ages of death ( 31 , 45 , 46 )—and e o is the life expectancy at birth. Life span equality measured by h = − ln H ¯ has previously been used as an indicator of life span equality ( 11 ). If mortality improvements over time occur at all ages, there exists a unique threshold age that separates positive from negative contributions to H ¯ as a result of those improvements ( 52 ). Because h is a logarithmic transformation of H ¯ , it has the same threshold age, which we denote by a h (vertical dashed lines in Fig. 3 ). This threshold is reached when Weights for the changes in life expectancy w ( x ) ( A and B ) and life span equality w ( x ) W h ( x ) ( C and D ). Each line refers to a given period and represents how life expectancy and life span equality react to age-specific mortality improvements for Swedish women in selected periods. where H ( a h ) is the cumulative hazard to age a h and H ¯ ( a h ) is the life table entropy conditional on surviving to age a h ( 52 ). Box 1, Fig. 1 shows the evolution of life expectancy at birth e o , the threshold age a h , and the most common age at death after infancy, M , for Swedish females since 1900 ( A ). The figure highlights how the three measures move together. The threshold age in A is the age that separates “early” from “late” deaths in terms of the effect on life span equality. Averting deaths before a h increases equality, while averting deaths after this age has the opposite effect. It is a population-specific measure that depends on the observed mortality pattern. The threshold age and the life expectancy at birth move in the same direction, either increasing or decreasing together; note that they are very close in recent decades. The modal age at death M was fairly constant before 1950 and rose in tandem with e o and a h thereafter. More than 40% of deaths occur below e o and a h , whereas more than 60% of deaths occur below M ( B ). C and D show that mortality improvements below e o and a h were responsible for around 80% of gains in life expectancy at birth and life span equality in the beginning of the 20th century, while they are responsible for around 60% in contemporary Sweden. Box 1, Fig. 1. ( A ) Life expectancy at birth e o , threshold age a h , and modal age at death M . ( B ) Proportion of deaths below life expectancy at birth e o , threshold age a h , and modal age at death M . ( C ) Percentage of changes in life span equality due to changes in death rates below life expectancy at birth e o , threshold age a h , and modal age at death M . ( D ) Percentage of changes in life expectancy at birth due to changes in death rates below life expectancy at birth e o , threshold age a h , and modal age at death M . How Strong Is the Relationship Between Life Expectancy and Life Span Equality over Time?To study how strongly life expectancy and life span equality are related over time and whether they respond in the same direction to age-specific mortality changes, we complement demographic analysis with time series analysis (see SI Appendix , section A for details). This framework is designed to integrate the stochastic properties of dynamics over time ( 8 , 9 ). Focusing on changes over time improves our analysis by avoiding misleading inferences from correlations, such as confounding due to unobserved or unmeasured variables ( 36 ). Econometric time series theory indicates that life expectancy and life span equality have a long-run relationship if there exists a single process that drives both indicators toward a long-term equilibrium, even if temporary departures from it occur (as observed more often in recent decades). If this equilibrium exists, changes over time in life span equality are proportional to changes in life expectancy in the long term. In other words, while life expectancy and life span equality increase over time, a linear combination of both leads to a residual time series consistent with stationarity (i.e., with stable mean and variance), referred to as cointegration in time series analysis ( SI Appendix , section A.2 ). The results reveal that, in most populations, life expectancy and life span equality are linked by a long-run relationship for both sexes ( SI Appendix , Fig. S2 ). In 91% of the populations we investigated (males and females from 45 countries and regions by sex), this relationship holds under the same model specifications ( SI Appendix , section A.2 ); similar results are exhibited for all three indicators of life span equality ( SI Appendix , Fig. S2 ). At the 5% significance level, negative results are expected for 5% of the cases due to random variations. We got negative results in 9% cases. So, the importance of negative results in specific populations should not be overly emphasized ( SI Appendix , section A.3 ). These results hold for countries that have experienced substantially different mortality patterns, including women in Japan; men in the United States with life expectancy of about 77 y and relatively high life span inequality ( 17 ); and men in Russia and Ukraine with the lowest levels in life expectancy in this study (about 65 and 66 y in 2013, respectively) and high inequality ( 21 ). Importantly, for every population in our study, females’ lives tend to be longer and more equal compared to males’ lives in a given year, consistent with previous research ( 11 , 37 ). This underscores the advantage of females over males not only in average life span but also in lower uncertainty about age at death. Age-Specific Dynamics of Mortality.The field of demography has long been known within the social sciences for its innovations in decomposition analysis ( 38 ). Decomposition analysis is based on the principle of separating demographic measures, e.g., life expectancy or life span equality, into components that contribute to their dynamics, such as age-specific mortality. Several methods to analyze change in life expectancy over time have been developed. Pollard ( 39 ), Arriaga ( 40 ), and Andreev et al. ( 41 ), among others, focused on discrete differences in life expectancy, while other authors considered continuous change ( 42 – 46 ). Some of these methods have been extensively used in population health studies to disentangle age- and cause-specific effects because they are easy to implement ( 40 , 47 , 48 ). Here, we relate changes in both life expectancy and life span equality to the average pace of improvement of mortality and the average number of years lost at death ( 31 ). We are able to describe specific properties of both indicators. Changes in life expectancy and in life span equality over time are weighted averages of rates of progress in reducing age-specific mortality, ρ ( x ) , albeit with different weights ( Materials and Methods ). These weights— w ( x ) for life expectancy at birth and the product w ( x ) W h ( x ) for life span equality—evolve over time and vary by age. They indicate the potential gain (loss) in life expectancy and life span equality if lives are saved at a specific age and in a given period. Fig. 3 A and B shows the weights for life expectancy at birth and from age 5 for Swedish women. From the 18th to the first part of 20th century, the largest potential increases in life expectancy were concentrated in infancy. The effect on life expectancy improvements due to saving lives in midlife was higher than at older ages. This changed dramatically after 1950, when the effect of infant mortality decreased significantly. By 2010, the effect of reducing mortality by 1% at birth was the same as reducing mortality by 1% at age 71. In the 21st century, saving lives between ages 5 and 40 y had a negligible effect on life expectancy, as opposed to the relatively high impact of these ages before 1900. A shift over time toward the importance of older ages is clear. This ongoing wave toward older ages is in line with recent evidence documenting an advancing front of old-age survival that has driven recent increases in average life span ( 49 ). Indeed, the postponement of old-age mortality is an ongoing process that started more than 50 y ago ( 50 , 51 ). Fig. 3 A and B shows that whenever mortality improvements occur life expectancy increases. The size of the increase depends on the ages at which lives are saved. These improvements ρ ( x ) and the weights w ( x ) are the drivers of life expectancy at birth over time ( 31 ). Fig. 3 C and D shows the weights w ( x ) W h ( x ) for life span equality h . As in A and B , each value represents the effect on life span equality of reducing mortality at a given age. Saving lives at very young ages had the largest effect on increasing equality of life spans throughout the 18th, 19th, and first half of the 20th centuries. In contemporary Sweden, the impact of reducing mortality at birth on life span equality is the same as saving lives at all ages between 76 and 80 y. As with life expectancy, there is an ongoing shift toward older ages, but with an important difference. At older ages, there is a threshold age above which saving lives decreases life span equality ( Box 1 ). This age is depicted with the dashed lines colored according to the respective period: An increase of this age over time clearly appears in the graphs. The threshold age gives valuable information for understanding of the relationship between life expectancy at birth and life span equality: To the extent that improvements at ages below the threshold age outpace those above it, life expectancy will move in the same direction as life span equality ( 52 ). The shift from positive to negative effects has previously been explored using other indicators ( 53 , 54 ). The three life span equality indicators that we analyze behave similarly ( SI Appendix , Fig. S3 ); their sensitivity to changes in age-specific mortality resembles that of other indices of life span variation ( 27 ). Fig. 4 A shows the contributions, in years, of mortality fluctuations below the threshold age (early component), and Fig. 4 B shows contributions above the threshold age (late component) to changes in life expectancy and life span equality in 10-y rolling periods for all countries included in our study. The points in the first and third quadrants in Fig. 4 A and the second and fourth quadrants in Fig. 4 B reflect a mix of reductions in death rates at some ages below and above the threshold and increases at other ages. Because the weights for specific ages differ for life expectancy and life span equality, the aggregate effect of such a mix of mortality changes can be positive (negative) for life expectancy and negative (positive) for life span equality. The sum of the early and late components gives the total change in each indicator ( Fig. 2 A ). We report similar results for the two other indicators of life span equality in SI Appendix , Fig. S4 . There is a strong positive association between changes in life expectancy and life span equality below the threshold age, while the relationship is negative above that threshold. Since the two effects oppose each other, as shown by the regression lines, the relationship is driven by the component that makes the larger contribution. Reductions in death rates below the threshold age were significantly larger than reductions above it before 1960, resulting in a strong positive association between life expectancy and life span equality. Since 1960, mortality reductions above the threshold age have become more comparable in magnitude to the early-life component, with more increases in life expectancy coinciding with decreases in life span equality. Until now, the absolute change in both indicators per decade is mainly driven by mortality changes below the threshold age (83.7% and 82.0% on average per decade for life span equality and life expectancy, respectively [ Fig. 4 and Box 1, Fig. 1 B and C ]). ( A ) Association between 10-y changes in life expectancy at birth e o and life span equality h because of mortality changes below the threshold age. ( B ) Association between 10-y changes in e o and h because of mortality changes above the threshold age. The dotted lines show the directions of the relationship below and above the threshold age. As life expectancy increases, the threshold age also increases ( Box 1 and SI Appendix , Fig. S5 ). There is then more scope to save early lives below the threshold age and maintain the positive relationship between life expectancy and life span equality. This is an essential characteristic of the long-run equilibrium. Progress, however, after the threshold age has been increasing. For example, in Sweden the most common age at death at older ages was stagnant up until the 1950s when it started rising with life expectancy ( Box 1, Fig. 1 A ), and contributions to changes in life expectancy and life span equality increased above the threshold age ( Box 1, Fig. 1 C ). These results underscore the effect of mortality improvements at advanced ages (i.e., above the threshold age) in recent years and shed light on recent interruptions in the relationship between changes in life expectancy and life span equality. This process follows a redistribution of mortality over age and causes of death ( 23 , 55 , 56 ). In the past, deaths were concentrated at young and working ages, mainly due to infectious diseases and to some extent wars and famines that resulted in high inequality of life spans ( 57 ). In recent decades, because of major improvements in health services and medical treatment, living standards, sanitation, and various social determinants of health ( 58 – 61 ), lifesaving is concentrated at older ages, sometimes above the threshold age. The dynamics of life expectancy and of life span equality are driven by changes in age-specific death rates. The impact of the change at some age differs somewhat for the two measures. At younger ages, the impacts are similar. After a threshold age late in life, a reduction in age-specific death rates increases life expectancy but decreases life span equality. Because of progress in recent decades in reducing death rates above the threshold age, rises in life expectancy more often coincide with declines in life span equality. For the populations we analyzed, in the period 1900 to 1950 less than 16% of the annual changes in average life span coincided with opposite changes in life span equality. In the 1960s, this discrepancy rose to 47%; and thereafter the average has been around 32%. These trends were driven by Eastern and Central European countries and by Nordic countries, which experienced divergent patterns in mortality at different ages ( 21 , 24 ). Since 1960, life span inequality was high and fluctuated strongly in Central and Eastern Europe. A recent study shows that in the decades 1960 to 1980, life expectancy and life span equality changed in opposite directions in half the years and populations analyzed, largely because trends in age-specific death rates were positive at some ages and negative at other ages ( 21 ). This is consistent with our findings. Previous evidence suggests that alcohol-related and cardiovascular diseases might have been important in driving the observed trends in Central and Eastern Europe ( 21 , 64 – 66 ). Danish males experienced deterioration caused by smoking-related and cardiovascular mortality between ages 35 and 80, while negative trends in Norway and Sweden were mostly caused by an increase in cardiovascular mortality ( 24 ). Are there paths other than the joint, linear rise in Fig. 1 that might have been followed if social conditions and public policies had been different? This is an intriguing question that can be examined in our framework. Fig. 5 shows the relationship between life expectancy and life span equality for Swedish women from 1751 to 2017 under three different scenarios. Blue points refer to observed life expectancy from values below 20 y in 1773 to 84.1 y in 2017. The process of increasing life expectancy with greater equality in individual life spans has been referred to as the compression of mortality or the rectangularization of survivorship, and has been studied from various perspectives over the last couple of decades ( 7 – 11 , 21 , 67 ). Understanding the dynamics of the compression of mortality is important for forecasting heterogeneity in future age patterns of population health as well as for assessments of the timing of individual mortality ( 12 ). Life expectancy at birth e o and life span equality h for three different scenarios: 1) observed points: Swedish females, 1751 to 2017; 2) youngest equality: life span equality derived by matching observed life expectancy levels by reducing the youngest age; and 3) constant change over age: death rates in each year at all ages are reduced at the rate ρ to achieve the observed change in life expectancy at birth. Consider the difference of life expectancy and life span equality between two consecutive years. The regression line in Fig. 5 indicates that the average change in life expectancy is about 25.4 times the life span equality change, a value close to the 27 reported elsewhere ( 11 ). Here, we demonstrate that each of these first differences, as an approximation to the time derivative ( Materials and Methods ), is a weighted total of mortality improvements in a given year ( Fig. 3 ). Our main motivation lies on the remarkably tight relationship between life expectancy and life span equality through time illustrated by the regression line (slope, 0.04; P < 0.001). For example, in 1773 Sweden underwent the last major famine that caused starvation across the country ( 68 ). Approximately 50% of excess deaths were due to dysentery, and most deaths (20%) were concentrated in infancy ( 57 ). Even under periods of such mortality stress, observed life expectancy and life span equality fall on the linear trend that holds in more favorable years. Is this tight connection coincidental or a result of fundamental social and physiological forces? We have shown that the connection is largely due to change in death rates at younger ages. Can more be said? The observed path (blue points, Fig. 5 ) is a combination of age-specific mortality improvements and the weights shown in Fig. 3 . Improvements in mortality are uneven across ages ( 31 ). Hence, we explored an alternative scenario in which the same rate of mortality reduction (or increase) occurred at all ages, the “constant scenario,” with the rate chosen to be consistent with observed levels of life expectancy over time. The red rhombuses in Fig. 5 illustrate the resulting trajectory for Sweden. When the average life span rises above 40 y, levels of life span equality start to diverge and become lower than the observed ones. The relationship between life expectancy becomes nonlinear and levels off at around a life expectancy at birth of 70 y. Another hypothetical scenario is represented by the purple squares labeled “youngest equality.” This curve refers to the case when all progress in reducing death rates is concentrated at the youngest ages. For example, to get the 1752 life expectancy level from 1751, only deaths at age zero are reduced. Then when deaths at birth are zero, deaths are reduced at age 1, then age 2, and so on, to match the observed life expectancy in the following years. That is, all lifesaving is concentrated at the youngest age(s) at which deaths still occur. Results yield a steeper slope (0.051; P < 0.001), which translates into larger equality in individual life spans at levels of life expectancy after age 50. Consider now another scenario, the “potential scenario.” From the level of life expectancy in 1950 to contemporary Sweden, age-specific rates of improvement are chosen such that 1) life expectancy increases continuously match the observed levels every decade, and 2) life span equality increases optimally. That is, when life expectancy increases, progress is concentrated at the ages when change in death rates most increases life span equality. Also consider the “constant scenario” in which the life expectancy improvement every decade was achieved by reducing mortality at the same rate for every age. Table 1 shows life span equality under these scenarios for Swedish females from 1960 as well as the actual observed trajectory of life span equality. The potential scenario leads to the highest attained life span equality, while the constant scenario shows the lowest equality in life spans. Interestingly, what was observed in Sweden is close to 50% on average of the difference between the potential and constant scenarios. Hence, the observed trajectory might be called the “semioptimal scenario.” These alternative scenarios show that the narrow passageway that describes the relationship between life expectancy at birth and life span equality is not a coincidence. The transition from low levels of average life span and high variation in length of life to longer and more equal life spans is a result of saving lives at ages that matter—but semioptimally. The tight link between life expectancy and life span equality has been shaped by improvements in mortality at the most important ages for life expectancy and for life span equality: early ages in the 18th century and adult ages today. Life expectancy at birth e o and life span equality h for three different scenarios Year | | Life span equality by scenario | (Observed − Constant)/(Potential − Constant), % | Observed | Potential | Constant | 1960 | 74.88 | 1,84 | 1,90 | 1,76 | 57 | 1970 | 77.21 | 1,87 | 1,99 | 1,86 | 8 | 1980 | 78.86 | 1,93 | 1,98 | 1,88 | 50 | 1990 | 80.39 | 1,98 | 2,03 | 1,94 | 44 | 2000 | 82.01 | 2,05 | 2,09 | 1,99 | 60 | 2010 | 83.47 | 2,11 | 2,15 | 2,05 | 60 | 2017 | 84.12 | 2,13 | 2,16 | 2,11 | 40 |
The three different scenarios are as follows: 1) observed points: Swedish females, 1960 to 2017; 2) potential equality: life span equality derived by matching observed life expectancy levels by reducing death rates that increase life span equality the most; and 3) constant change in mortality improvements ρ ( x ) over age matching observed life expectancy levels every decade. In recent years, more instances of a temporary reversal of the relationship between life expectancy and life span equality have been observed in several countries and subgroups of populations ( 12 , 20 – 22 ). Often these cases were due to midlife mortality deterioration or to major improvements in old-age mortality above the threshold age. In Sweden, death rates among octogenarians and nonagenarians have fallen since 1950 ( 69 ). For other developed countries, the pattern has been similar ( 70 ). If improvements at advanced ages continue and if they outpace those made at younger ages, the pattern of the relationship between life expectancy and life span equality could reverse in the future. It is, however, unlikely that rates of improvement above the threshold age will outpace progress at younger ages in the long term. Furthermore, as life expectancy increases, the threshold age will increase. Across primate species, there is a rough association of life expectancy and life span equality. Several instances, however, where a relationship between the pace and shape of aging is not found have been documented in other species. Across the tree of life, 46 diverse species did not show a strong correlation between life expectancy and life span equality ( 71 ), and among plants a nonlinear, but weak, positive association has been reported ( 72 ). These findings compare different species, whereas our results are for a single species in a changing environment. Two studies, one of the nematode worm Caenorhabditis elegans and the other of Drosophila melanogaster , of individuals held under different conditions, found that life span equality appeared to be independent of life expectancy ( 73 , 74 ). For humans, a sharp worsening of conditions tends to lead to substantial increases in infant and child mortality ( 57 ), and in some cases mortality at young adult ages, e.g., as experienced in the former Soviet Union after the end of the anti-alcohol campaign and the dissolution of the USSR ( 21 ), lowering both life expectancy and life span equality. On the other hand, improvements in standards of living, nutrition, education, public health, and other environmental conditions tend, at least when life expectancy is less than 70, to predominately affect life expectancy—and life span equality—through reductions in death rates at young ages ( 2 ). A key question is whether changes in environmental conditions have their biggest effects on mortality in infancy and childhood because of human agency or because of human physiology. Do societies act to focus mortality improvements at the ages that matter the most, or is human mortality for physiological reasons most sensitive at younger ages to environmental changes? Study of the impact of environmental change on life expectancy and life span equality in nonhuman primate species, being undertaken by Fernando Colchero, Susan Alberts, and colleagues, could shed light on the role of agency versus physiology. More generally, our findings—coupled with the mathematical relationships we derived to analyze how changes in age-specific death rates affect life expectancy and life span equality—suggest that a link may be found for species in which environmental change affects life expectancy largely because of changes in death rates at young ages. Materials and MethodsWe used death rates by age and sex from the Human Mortality Database ( 5 ) for 49 countries and regions by single age and year, with data available from the beginning of the 20th century for some of the countries and regions and later in the 20th century for others and with data up to the most recent year available (see SI Appendix , Table S1 for detailed information). We constructed life tables following standard demographic procedures (7,717 life tables) ( 75 ). For each population, we investigated life expectancy at birth and life span equality by sex. The analysis is restricted to countries with data available for consecutive years (without gaps in the information over time) in order to study age-specific mortality patterns on a yearly basis. We decided not to analyze dispersion at death conditional on survival to any older age because of major improvements made in early ages during the 20th century ( 76 ). In addition, we did not include Chile, South Korea, and Croatia in the cointegration analysis due to limited data availability, spanning less than 20 y. All of the analyses were carried out with R software ( 77 ) and are fully reproducible, including data handling, from the public repository at https://zenodo.org/record/3571095 . Contributions to Mathematical Demography.Changes over time in life expectancy.. Changes over time in life expectancy at birth are a weighted average of rates of progress in reducing mortality ( 31 ). Letting ℓ ( x , t ) be the period life table probability at time t of surviving from birth to age x , life expectancy at birth can be expressed as follows: Because ℓ ( x , t ) = exp [ − ∫ 0 x μ ( a , t ) d a ] , where μ ( a , t ) is the force of mortality (hazard rate) at age a at time t , changes over time in e o ( t ) are given by the following: A dot over a function denotes its partial derivative with respect to time. For simplicity, variable t will be omitted as an argument in the following. We define the following: as the age-specific rates of mortality improvement over time and the remaining life expectancy at age x , respectively. Then, Eq. 1 can be expressed in terms of these two functions as follows: This last result shows that changes over time in life expectancy at birth are a weighted total of rates of progress in reducing mortality, with weights given by the function w ( x ) = μ ( x ) ℓ ( x ) e ( x ) , as shown by Vaupel and Canudas-Romo ( 31 ). Measures of life span equality and their change over time.Several indicators have been proposed to measure variation in age at death ( 27 , 78 , 79 ). Selecting the best measure when comparing aging patterns among populations that differ in length of life is of great importance, since indicators vary in their sensitivity to mortality fluctuations and in their mathematical interpretation ( 27 ). In this study, we use three indicators based on the pace and shape of aging framework ( 25 ), which suggests a set of properties that indicators should satisfy ( 26 , 80 ). A variant of the life table entropy: h .A measure of life span inequality is the life table entropy H ¯ ( 29 , 62 , 63 ), which can be defined as follows: where c ( x ) = ℓ ( x ) / ∫ x ∞ ℓ ( a ) d a is the life table age composition, and H ( x ) = ∫ 0 x μ ( a ) d a is the cumulative hazard to age x . Hence, H ¯ can be interpreted as an average value of the cumulative hazard. It can also be expressed as follows: where e † = − ∫ 0 ∞ ℓ ( x ) ln ℓ ( x ) d x accounts for “life disparity,” the average number of life-years lost as a result of death or the average remaining life expectancy at ages of death ( 9 ). For instance, an individual dying at age 50 in a population with remaining life expectancy at age 50 of 20 y would have lost those 20 y of life. This definition of entropy provides a dimensionless indicator of relative variation in the length of life compared to life expectancy at birth, permitting comparison of populations with different age-at-death distributions ( 26 ). An alternative measure to H ¯ is the following: which has previously been used to study life span equality across different primate populations, including humans ( 11 ). Note that H ¯ can be interpreted as an indicator of “life span inequality,” given that higher values represent more variation in life spans, whereas h (the logarithm of the inverse) is a measure of “life span equality.” From Eq. 3 , the variation over time in h is given by the following: An equivalent expression to Eq. 4 was previously derived using calculus of variation by Fernandez and Beltrán-Sánchez ( 81 ), who found that This shows that changes over time in h are equal to minus the relative change in the life table entropy H ¯ . Similarly to life expectancy at birth, Aburto et al. ( 52 ) proved that where w ( x ) = μ ( x ) ℓ ( x ) e ( x ) are the same weights for changes over time in e o defined in Eq. 2 , and Function H ¯ ( x ) = e † ( x ) / e ( x ) is the entropy conditional on surviving to age x , where e † ( x ) refers to life disparity above age x , and e ( x ) is the remaining life expectancy at age x ( 52 ). Because h ˙ = − H ¯ ˙ / H ¯ , it follows that with W h ( x ) = − W ( x ) . This result shows that changes in life span equality over time are weighted totals of rates of progress in reducing mortality ρ ( x ) , with weights given by the product w ( x ) W h ( x ) . A variant of the Gini coefficient: g .The Gini coefficient is a popular index in social science used to measure distributions of positive variables, such as income ( 82 ). It has also been used to describe inequality in life spans as a measure of population health and in survival analysis as an indicator of concentration in survival times ( 26 , 28 , 64 , 83 , 84 ). In life table notation, the Gini coefficient G is given by the following: Function ϑ = ∫ 0 ∞ ℓ ( x ) 2 d x relates to perturbation theory as it measures life expectancy from doubling the risk of death at all ages. From Eq. 6 , G can also be expressed in terms of the life table age distribution, Note that ℓ ¯ = ϑ / e o = ∫ 0 ∞ c ( x ) ℓ ( x ) d x is a dimensionless indicator of life span equality, bounded between 0 and 1. If life spans are completely concentrated, all individuals die at the same age, the indicator equals 1; if they are equally spread the indicator tends to 0. In addition, if two babies are born at the same time in a population, then ℓ ¯ measures their shared life span as a proportion of life expectancy ( 85 ). An alternative indicator to the Gini coefficient is the logarithm of its inverse: which is also a measure of equality rather than inequality. Note that the derivative of ℓ ¯ with respect to time is as follows: Hence, changes over time in g are given by the following: Similar to h , the time derivative of g can be reexpressed as follows: where w ( x ) = μ ( x ) ℓ ( x ) e ( x ) are the same weights for changes over time in e o , and Function ℓ ¯ ( x ) is defined as follows: and can be interpreted as life span equality above age x . A detailed proof of Eq. 9 can be found in SI Appendix , section B . This result shows that changes in life span equality over time, measured by g , are a weighted total of the rates of progress in reducing mortality ρ ( x ) , with weights given by the product w ( x ) W g ( x ) . A variant of the coefficient of variation: v .The coefficient of variation of the age-at-death distribution is the quotient of its SD σ and the life expectancy at birth: This indicator has been previously used to measure life span inequality ( 24 , 26 ). Here, we define a measure of life span equality as the logarithm of the inverse of the coefficient of variation, Similar to life table entropy and the Gini coefficient, changes over time in v are given by the following: which can be reexpressed as follows: As before, w ( x ) are the weights for e o , whereas W v ( x ) are weights defined as follows: Note that C V ( x ) is a weighted average of deviations from life expectancy at age x , which can be expressed as the difference between the average age of the population above age x ( a ¯ x ) and the life expectancy at birth. A detailed proof of Eq. 12 can be found in SI Appendix , section C . This result shows that changes over time in the alternative measure v of the coefficient of variation are a weighted total of the rates of progress in reducing mortality ρ ( x ) , with weights given by the product w ( x ) W v ( x ) . Demographic Methods to Calculate Threshold Ages and Age-Specific Contributions.From life tables, we calculated for each of the three indicators the threshold age below which averting deaths increases life span equality, and above which equality decreases. Eqs. 5 , 9 , and 12 indicate that the age-specific contribution to changes over time in life span equality can be expressed as the product ρ ( x ) w ( x ) W k ( x ) , for k ∈ { h , g , v } . Note that w ( x ) is a strictly positive function, whereas the indicator-specific weights W k ( x ) are strictly decreasing. Hence, under the assumption that death rates remain constant or decline at all ages [i.e., ρ ( x ) ≥ 0 for all x ] or remain constant or increase at all ages [i.e., ρ ( x ) ≤ 0 for all x ], for each indicator there is unique threshold age that we denote by a h , a g , and a v , respectively. These threshold ages are reached when the corresponding weights equal 0; that is, when W h ( x ) = 0 , W g ( x ) = 0 or W v ( x ) = 0 . The assumption that death rates need to decline (or increase) at all ages is necessary to have a unique threshold age. If death rates increase for some ages and decline for others, there may be several threshold ages that separate positive from negative contributions to life span equality, since the product ρ ( x ) w ( x ) W k ( x ) may switch from positive to negative several times across ages. For instance, whenever ρ ( x ) and W ( x ) are both positive (or both negative), contributions will be positive; on the contrary, whenever W ( x ) > 0 and ρ ( x ) < 0 , or W ( x ) < 0 and ρ ( x ) > 0 , contributions will be negative. We quantified age-specific contributions to yearly changes in life expectancy and life span equality for all of the data available and estimated contributions above and below those thresholds. We used a model defined on a continuous framework that assumes gradual change in mortality over time ( 86 ) used in previous studies of life span inequality ( 13 , 20 , 21 , 24 ). Stochastic Properties of Life Expectancy and Life Span Equality.We analyzed the stochastic properties of e o and life span equality over time to determine whether they are stationary processes (for further details, see SI Appendix , section A ). In case of nonstationarity, we also find the order of integration. We performed the Kwiatkowski–Phillips–Schmidt–Shin test ( 87 ) for e o and the three measures of life span equality, and the augmented Dickey–Fuller test ( 88 ) in their levels and first differences, respectively (we also perform tests against higher orders of integration but could not reject the hypothesis that the variables were integrated at a lower level). Using the 95% critical values, the null hypothesis of stationarity can be rejected in 94.9% of the cases for life expectancy and 93.9% for life span equality h . Moreover, at the same level, the null hypothesis of a unit root in their first differences is rejected in 97% of the cases for e o and h . These analyses suggest that the variables are nonstationary processes and achieve stationarity after differencing once for both females and males. In the statistical analysis, we treat both variables as integrated of order one. The concept of cointegration was developed to avoid misleading interpretations regarding the relationship between two integrated variables ( 89 ). It refers to the case of a model that can adjust for stochastic trends to produce stationary residuals, and it permits detection of stable long-run relationships among integrated variables. Formally, two cointegrated variables can be expressed using a two-dimensional vector autoregressive model in its error correction form, defined as follows: Operator Δ denotes the first differences; z t is a 2 × 1 vector of stochastic variables ( e o and life span equality in our case) at time t ; Γ contains the cumulative long-run impacts; α and β are two 2 × 1 vectors of full rank; μ is a vector of constants; and ε t is a vector of normally, independently, and identically distributed errors with zero means and constant variances ( 90 ). We specify the model with an unrestricted constant in the cointegration space and dummy variables in contexts where life expectancy experienced historical shocks, such as world wars and epidemics (see SI Appendix , Table S2 and section A , for additional details and sensitivity analyses). Data Availability.Supplementary material, supplementary file, acknowledgments. The research was funded by the Max Planck Society and the University of Southern Denmark. J.M.A. was partially supported by the Lifespan Inequalities research group at the Max Planck Institute for Demographic Research (European Research Council Grant 716323). J.M.A., U.B., and S.K. acknowledge support from the European Doctoral School of Demography when it was hosted at Sapienza University of Rome. Researchers at the Interdisciplinary Centre on Population Dynamics, University of Southern Denmark, and Alyson van Raalte provided helpful input. The authors declare no competing interest. Database deposition: A description to access the data and the code to reproduce results have been deposited on Zenodo ( https://zenodo.org/record/3571095 ). This article contains supporting information online at https://www.pnas.org/lookup/suppl/doi:10.1073/pnas.1915884117/-/DCSupplemental . - Monmouth County
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Live longer in the Garden State: Top NJ counties for longevity revealedEven though the fountain of youth has yet to be discovered, Americans have still found longevity. According to the Center for Disease and Control, back in 1970 the average American life span was 70.8 and in 2022 it jumped up to 77.5. That's almost a seven year increase over 50 years. Aside from genetics, factors such as diets, lifestyles and environments can determine how long you live and also where you live. A new study conducted by stacker.com, a news and entertainment site, findings revealed that out of 21 counties in the state there are eight counties with the longest life expectancy in New Jersey . Editors from stacker.com say life expectancy measures the average number of years from birth a person can expect to live calculated by the number of deaths and the average number of people at risk of dying during a specific time frame. By using data from two sources — the 2024 County Health Rankings & Roadmaps from the University of Wisconsin Population Health Institute and the mortality data from the National Vital Statistics System — the site found the following: Longest life expectancy in New JerseySome counties that had unreliable or insufficient data were not included in this research according to stacker.com. Hunterdon County: No. 1- Life expectancy: 82.9 years
- 3.7 years higher than the state average
Bergen County: No. 2- Life expectancy: 81.9 years
- 2.7 years higher than the state average
Somerset County: No. 3- Life expectancy: 81.8 years
- 2.6 years higher than the state average
Morris County: No. 4- Life expectancy: 81.7 years
- 2.5 years higher than the state average
Middlesex County: No. 5- Life expectancy: 80.3 years
- 1.1 years higher than the state average
Hudson County: No. 6- Life expectancy: 80.0 years
- 0.8 years higher than the state average
Monmouth County: No. 7- Life expectancy: 79.8 years
- 0.6 years higher than the state average
Union County No. 8- Life expectancy: 79.4 years
- 0.2 years higher than the state average
Shortest life expectancy in New JerseySalem county: no. 1. - Life expectancy: 73.5 years
- 5.7 years lower than the state average
Cumberland County: No. 2- Life expectancy: 74.0 years
- 5.2 years lower than the state average
Atlantic County: No. 3- Life expectancy: 75.9 years
- 3.3 years lower than the state average
Camden County: No. 4- Life expectancy: 76.2 years
- 3.0 years lower than the state average
Cape May County: No. 5- Life expectancy: 76.8 years
- 2.4 years lower than the state average
Gloucester County: No. 6- Life expectancy: 77.0 years
- 2.2 years lower than the state average
Essex County: No. 7- Life expectancy: 77.8 years
- 1.4 years lower than the state average
Ocean County: No. 8- Life expectancy: 78.1 years
- 1.1 years lower than the state average
Sussex County: No. 9- Life expectancy: 78.4 years
- 0.8 years lower than the state average
Passaic County: No. 10- Life expectancy: 78.6 years
- 0.6 years lower than the state average
According to the CDC , the average life expectancy in New Jersey (2023) is 77.5 years for both sexes, 74.8 years for males and 80.2 years for females. I Knew Diddy for Years. What I Now Remember Haunts Me.Looking back on my life as a woman in the music industry, I’m unsettled by the inescapable sexism perpetrated by Sean Combs and others. Credit... Artwork by David Samuel Stern Supported by By Danyel Smith A thing happened between Sean Combs and me. Unlike what he has been accused of over the last eight months, what occurred between us was not sexual. It was professional — demonstrative of the way dynamic and domineering men moved in our heyday. Combs and I worked together a lot. Competed, in our way. So often I thought I came out on top. I was mistaken. I had reason to fear for my life. What happened was insidious. It broke my brain. I forgot the worst of it for 27 years. It was July 1997. In the fading smoke of the murders of Tupac Shakur and the Notorious B.I.G., I was named editor in chief of a music magazine called Vibe. Started by Quincy Jones and Time Inc. in 1992, the magazine chronicled Black music and culture with rigor and beauty, 10 issues a year, for an audience that was relentlessly underserved. When I took over, we thought hip-hop might have died with our heroes, and we were determined not only to keep it alive but also to give it the cultural credit it was due. Hip-hop was both in mourning and in marketing meetings. Combs, Biggie’s creative partner and label boss, was the personification of this dichotomy. His Bad Boy Records was having a $100 million year — much due to the work of Biggie and Mase, as well as Combs’s own debut album, “No Way Out,” which was anchored by the blockbuster Biggie tribute “I’ll Be Missing You” featuring Faith Evans. Other singles, “It’s All About the Benjamins” and “Been Around the World,” functioned as a score for hip-hop’s megawatt moment — its commercial evolution and international expansion. (“No Way Out” would go on to sell over seven million copies.) So I wanted Combs on the cover of Vibe’s December 1997/January 1998 double issue. And I wanted him to wear white feathered wings. My point of reference was the poster for “Heaven Can Wait,” a 1978 film starring Warren Beatty. The movie is about a quarterback who dies before his time and is reincarnated as an idiosyncratic and callous billionaire. Vibe’s working cover line for Sacha Jenkins’s article was “The Good, the Bad and the Puffy.” Not so elegant, but it would work if the fashion director Emil Wilbekin and I got Combs (then known as Puffy, or Puff Daddy) to put on the angel wings. And if we also got a shot that looked even slightly mischievous, we could do a split run of the cover — one with heavenly signifiers and another with hellish ones. Possible cover line: “Bad Boy, Bad Boy, Whatcha Gonna Do?” The photo shoot took place in Manhattan in September 1997. I had probably said hello to Combs at an event, but the shoot was the first time I was around him for an extended period. Either it was a crowded set or I just felt claustrophobic. I wore yoga pants and an oversize T-shirt. I remember wanting to minimize my bust more than my bra was already doing. I remember cajoling. And I remember knowing that as a Black woman, I was in a no-win situation: to fail was to live up to my male bosses’ low expectations, and to succeed was to invite their resentment. That day, Combs was begrudgingly compliant. We finally got him to shrug on the white feathered wings. We are having trouble retrieving the article content. Please enable JavaScript in your browser settings. Thank you for your patience while we verify access. If you are in Reader mode please exit and log into your Times account, or subscribe for all of The Times. Thank you for your patience while we verify access. Already a subscriber? Log in . Want all of The Times? Subscribe . Advertisement What is Life Expectancy? Term Paper- To find inspiration for your paper and overcome writer’s block
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Mathematics of life expectancyFactors and their correlation, works cited. Life expectancy may be defined as the possible number of years remaining at a given age. It is denoted by e x, which means the average number of complete years of life remaining. It is calculated from the analysis of life tables. The global blueprint of life expectancy differs with the expansion prowess and socio-economic conditions of various nations. For example, the life expectancy at birth in the USA is 79 years. The mortality rate during the years of life span determines the life expectancy. There is a high mortality rate during the early years of life as compared to the later years because of lesser immune competence in infants and children. The problem is more applicable in poorer nations. There is the Gompertz-Makeham law of mortality which states that the death rate is the sum of an age-independent component and an age-dependent component. So accordingly, there is an exponential increase in death rates with age. The factors affecting life expectancy are diet, lifestyle, medical care, stress and injuries, pollution, genetic disorders, obesity, exercise, smoking, drug use, and alcohol abuse. Life span differs from life expectancy in the fact that it represents the maximum years of life that an individual survives, while life expectancy is an average. The goal of this essay is to understand the mathematics of life expectancy and its correlation with factors. Life expectancies are usually calculated from a life table. A life table shows for each age, the probability that a person of that age will die before the next birthday. Life tables are constructed using projections of future mortality rates. A hypothetical simplified example of life expectancy is given below. A (Age) | B (No. of people beginning each age.) | C (Death rate during that age.) | D (Number dying) (B*C) | E (Contribution to an average life.) ((A+1/2)*D) | 0 | 1000 | 0.30 | 300 | 150 | 1 | 700 | 0.20 | 140 | 210 | 2 | 560 | 0.10 | 56 | 140 | 3 | 504 | 0 | 0 | 0 | … | … | … | … | … | 69 | 504 | 0 | 0 | 0 | 70 | 504 | 0 | 0 | 0 | 71 | 504 | 1 | 504 | 36036 | 72 | 0 | 0 | 0 | 0 | | | | Total | 36536 |
So the average life expectancy, e x =Total/No. of people=36536/1000= 36.536 years For the sake of simplicity, we began with 1000 individuals. The numbers that determine the outcome are in column C, the death rates. In this hypothetical example, 1000 people are born, marked age 0. 30% die in the first year, so only 700 appear in the second line at age 1. 20% of these die in the second year, or 140 deaths between age 1 and 2. 560 appear on the third line at age 2 and 10% die in the third year, so only 504 for the next line. All the rest live up to age 71. The calculations in columns D and E are done as mentioned in the table above, and life expectancy is calculated by dividing the total obtained in Column E by a total number of persons, here 1000. Scientists have been trying to find prospective ways to human longevity (Olshansky 1491). Economist Julian Simon had mentioned that scientific progress was constantly improving human life but Olshansky argued that USA life expectancy would level off by 2050 (Olshansky 1138). The lifestyle disorders such as obesity, diabetes, and hypertension would just level off the increasing life expectancy curve in the future. Even Leonard Hayflick had mentioned that the life expectancy of individuals would level off at around 85 years, with 82 years for males and 88 years for females. Age-specific mortality causes a major change in life expectancy. Consider a case where there are more deaths in 0-10 years span, there would be more loss in life expectancy than the deaths reported in 60-70 years span. But if one has to reduce the death rate by 1%, then the elderly life span would be the best. The correlation of how mortality can reduce life expectancy has been explained (Vaupel 147). Thus life expectancy can improve due to specific policies adopted at individual and at state levels, and there would be an upper limit to it despite the innovations in public health and medical care. The crux is to lower the mortality rates at all age spans, infants, children, and the elderly alike. The mathematical calculation to points to counter mortality rates to bolster the life expectancy of individuals. For this, a collective approach needs to be adopted in sustained collaboration between the family and the nation. Olshansky Jay, Carnes Bruce, and Desesquelles Aline. “Prospects for Human Longevity.” Science 291.5508 (2001): 1491-1492. Print. Olshansky, Jay et al. “A Potential Decline in Life Expectancy in the United States in the 21 st Century.” The New England Journal of Medicine 352 (2005): 1138-1145. Print. Vaupel, J. W. “How Change in Age-specific Mortality Affects Life Expectancy.” Population Studies 40 (1986): 147-157. Print. - Theory Definition, Building, and Conflict With Practice
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This paper will discuss lifespan development perspective, theories of lifespan development, and the interaction between heredity and environment. Culture and the context in which the changes occur must be considered when analyzing the changes. Lifespan Development Psychology: Observation at Cosmo Park.
Life-Span Development: From Birth to Death. One's personal, physical and emotional development is cumulative. The nature of experiences and development during each state of the life-span will have an impact on how subsequent stages are experienced for better or for worse. The following is a concise overview of the changes and normative ...
On February 17, 2016, our Life-Span Development class observed the physical, cognitive, and social development of Asher, a 5.5-month-old male. The observation was conducted at Dordt College in room CL2260 and the infant's parent consented to participate in the live observation. Asher was quite sick and later saw a doctor, so the findings of ...
lifespan perspective: an approach to studying development which emphasizes that development is lifelong, multidimensional, multidirectional, plastic, contextual, and multidisciplinary. nonnormative influences: unpredictable influences not tied to a certain developmental time, personally or historical period.
Lifespan development is a progressive process of development in a human being involving an increase in age, which begins at conception and ends with death (Sugarman, 2000, p. 56). In addition, lifespan development can be divided into four levels depicting advanced functionality and character changes as an individual moves from one level to ...
Developmental Psychology, also known as Human Development or Lifespan Development, is the scientific study of ways in which people change, as well as stay the same, from conception to death. You will no doubt discover in the course of studying that the field examines change across a broad range of topics. These include physical and other psychophysiological processes, cognition, language, and ...
Standard Area: Life Span Development Content Standards: After concluding this unit, students understand: 1. Methods and issues in life span development 2. Theories of life span development 3. Prenatal development and the newborn 4. Infancy (i.e., the first 2 years of life) 5. Childhood 6. Adolescence 7. Adulthood and aging
Piaget's theory of cognitive development. A theory about how people come to gradually acquire, construct, and use knowledge and information. It describes cognitive development through four distinct stages: sensorimotor, preoperational, concrete, and formal. Discontinuous; there are distinct stages of development.
study of development using norms, or average ages, when most children reach specific developmental milestones. nurture. environment and culture. physical development. domain of lifespan development that examines growth and changes in the body and brain, the senses, motor skills, and health and wellness. psychosocial development.
The life span perspective of the human development is based on the idea that a person moves through several stages of development during the whole life (Berger, 2011, p. 7). Get a custom essay on The Life Span Perspective of Development. Thus, certain changes are typical for the definite stages of life, but it is also important to pay attention ...
In this post, I'm going to write an IELTS Writing Task 2 model essay in response to this question about increased life expectancy from Test 2 of The Official Cambridge Guide To IELTS:. You should spend about 40 minutes on this task. Write about the following topic: One of the consequences of improved medical care is that people are living longer and life expectancy is increasing.
Within the lifespan perspective, three developmental domains emerge as crucial lenses through which we discern the intricacies of human development. The physical domain extends beyond the evident changes in height, weight, puberty, and menopause. It encompasses an individual's perception and experiential lens in engaging with the world.
Essay Questions. 1. At a family gathering one evening, one of your uncles says he has heard you are taking a course in life-span development. He scoffs at this course saying, "What a waste of time! Everyone knows that children are basically mindless creatures until they get to be around six years old.
Most simply put, life expectancy can be attributed to and impacted by an individual and their personal health history, genetics, and lifestyle, whereas lifespan holds for all living humans. For example, a person's life expectancy is affected by personal factors like family history, environment, diet, and even age and sex. One person's life ...
WORDS 584. ## Essay Topics on Alzheimer's and Lifespan Development. 1. The Impact of Alzheimer's on Cognitive Functioning and the Aging Process. Explore the neurobiological changes associated with Alzheimer's disease and their effects on cognitive abilities, such as memory, attention, and language.
Discussion Every one goes through many stages in life beginning at the time of conception, throughout life, and finally in death. Human development is important to psychologists because it can provide insight about a person and the stage he or she may be experiencing in life based on age-related changes in behavior, emotions, personality, and thought processes (Boyd & Bee, 2009).
Good Essay Topics on Lifespan Development. The Development of Human Lifecycle During Emotional Growth. The Nature of Children on Human Lifetime and Growth. The Puzzling Issues, Ideas, and Descriptions of Development Notions in Fundamentals of Lifetime Progress. Lifecycle Growth: Analysing Child and Adolescent Growth Impact on Adulthood.
Albert Bandura. Piaget's theory of cognitive development. A theory about how people come to gradually acquire, construct, and use knowledge and information. It describes cognitive development through four distinct stages: sensorimotor, preoperational, concrete, and formal. Discontinuous; there are distinct stages of development.
Life Span Essays. Illness and Disease Management Across Life Span. Managing the complex health problem of diabetes may be more accessible by attending to patients' support requirements. Because diabetes requires rigorous attention to exercise, nutrition, and medicine, support facilitates the development of crucial coping mechanisms. Adverse ...
The rise in human life expectancy over the past two centuries is a remarkable accomplishment of modern civilization (1, 2).This progress was achieved during the demographic transition of societies from regimes of high mortality and fertility to regimes of low mortality and fertility (3, 4).At present, among the world's nations, Japanese women have the highest life expectancy at birth, above ...
Lifespan development. Development is progressive physical, mental/cognitive, social, and emotional growth. Traditionally, development was thought to occur only in childhood. However, changes occur throughout life. This paper will discuss lifespan development perspective, theories of lifespan development, and the interaction between heredity and ...
Human lifespan has a limit and we might have reached it. S. Jay Olshansky, who studies the upper bounds of human longevity at the University of Illinois Chicago, believes people shouldn't expect ...
The average life expectancy for women living this trio of lifestyle behaviors was just shy of 89 years. For men, it was nearly 86 years. ... Guest Essays; Op-Docs; Letters; Sunday Opinion; Opinion ...
Life Span Essay; Life Span Essay. Sort By: Page 1 of 50 - About 500 essays. Decent Essays. Is the Prolonged Span of Life by Medic Care Technology a Triumph or a Tragedy? 864 Words; 4 Pages; Is the Prolonged Span of Life by Medic Care Technology a Triumph or a Tragedy? has long been debated whether the longer life span granted by the advanced ...
Her work often focused on women in different stages of life, mixing "ordinary people and extraordinary themes," according to her New York Times obituary. She was awarded the Nobel in 2013 when ...
According to the Center for Disease and Control, back in 1970 the average American life span was 70.8 and in 2022 it jumped up to 77.5. That's almost a seven year increase over 50 years.
This meta-analytic review investigated the development of narcissism across the life span, by synthesizing the available longitudinal data on mean-level change and rank-order stability. Three factors of narcissism were examined: agentic, antagonistic, and neurotic narcissism. Analyses were based on data from 51 samples, including 37,247 participants. As effect size measures, we used the ...
In November 2023, Casandra Ventura, who performs as Cassie, sued Combs under New York's Adult Survivors Act, claiming that he raped, abused and sex-trafficked her over the span of a decade.
Life tables are constructed using projections of future mortality rates. A hypothetical simplified example of life expectancy is given below. beginning each age.) during that age.) (Contribution to an average life.) So the average life expectancy, e x =Total/No. of people=36536/1000= 36.536 years.
Andrea Robin Skinner, one of Munro's daughters, published an essay in the Toronto Star on Sunday that brought to light a long-held secret in the author's own family: Munro's husband ...