Genetic associations with longevity are on average stronger in females than in males

It is long observed that females tend to live longer than males in nearly every country. However, the underlying mechanism remains elusive. In this study, we discovered that genetic associations with longevity are on average stronger in females than in males through bio-demographic analyses of genome-wide association studies (GWAS) dataset of 2178 centenarians and 2299 middle-age controls of Chinese Longitudinal Healthy Longevity Study (CLHLS). This discovery is replicated across North and South regions of China, and is further confirmed by North-South discovery/replication analyses of different and independent datasets of Chinese healthy aging candidate genes with CLHLS participants who are not in CLHLS GWAS, including 2972 centenarians and 1992 middle-age controls. Our polygenic risk score analyses of eight exclusive groups of sex-specific genes, analyses of sex-specific and not-sex-specific individual genes, and Genome-wide Complex Trait Analysis using all SNPs all reconfirm that genetic associations with longevity are on average stronger in females than in males. Our discovery/replication analyses are based on genetic datasets of in total 5150 centenarians and compatible middle-age controls, which comprises the worldwide largest sample of centenarians. The present study's findings may partially explain the well-known male-female health-survival paradox and suggest that genetic variants may be associated with different reactions between males and females to the same vaccine, drug treatment and/or nutritional intervention. Thus, our findings provide evidence to steer away from traditional view that "one-size-fits-all" for clinical interventions, and to consider sex differences for improving healthcare efficiency. We suggest future investigations focusing on effects of interactions between sex-specific genetic variants and environment on longevity as well as biological function.

THE USES AND ABUSES OF AN AGE-PERIOD-COHORT METHOD: ON THE LINEAR ALGEBRA AND STATISTICAL PROPERTIES OF INTRINSIC AND RELATED ESTIMATORS

As a sophisticated and popular age-period-cohort method, the Intrinsic Estimator (IE) and related estimators have evoked intense debate in demography, sociology, epidemiology and statistics. This study aims to provide a more holistic review and critical assessment of the overall methodological significance of the IE and related estimators in age-period-cohort analysis. We derive the statistical properties of the IE from a linear algebraic perspective, provide more precise mathematical proofs relevant to the current debate, and demonstrate the essential, yet overlooked, link between the IE and classical statistical tools that have been employed by scholars for decades. This study offers guidelines for the future use of the IE and related estimators in demographic research. The exposition of the IE and related estimators may help redirect, if not settle, the logic of the debate.

Optimizing Count Responses in Surveys: A Machine-learning Approach

Count responses with grouping and right censoring have long been used in surveys to study a variety of behaviors, status, and attitudes. Yet grouping or right-censoring decisions of count responses still rely on arbitrary choices made by researchers. We develop a new method for evaluating grouping and right-censoring decisions of count responses from a (semisupervised) machine-learning perspective. This article uses Poisson multinomial mixture models to conceptualize the data-generating process of count responses with grouping and right censoring and demonstrates the link between grouping-scheme choices and asymptotic distributions of the Poisson mixture. To search for the optimal grouping scheme maximizing objective functions of the Fisher information (matrix), an innovative three-step M algorithm is then proposed to process infinitely many grouping schemes based on Bayesian A-, D-, and E-optimalities. A new R package is developed to implement this algorithm and evaluate grouping schemes of count responses. Results show that an optimal grouping scheme not only leads to a more efficient sampling design but also outperforms a nonoptimal one even if the latter has more groups.

Recent trends in US mortality in early and middle adulthood: racial/ethnic disparities in inter-cohort patterns

BACKGROUND

A striking increase in the all-cause mortality of US middle-aged non-Hispanic Whites in the past two decades has been documented by previous studies. The inter-cohort patterns in US mortality, as well as their racial/ethnic disparities, are still unclear.

METHODS

Using official mortality data, we study US annual mortality rates for ages 25-54 from 1990 to 2016 by gender and race/ethnicity. We conduct an age-period-cohort analysis to disentangle the period and cohort forces driving the absolute changes in mortality across cohorts. Nine leading causes of death are also explored to explain the inter-cohort mortality patterns and their racial/ethnic disparities.

RESULTS

We find cohort-specific elevated mortality trends for gender- and race/ethnicity-specific populations. For non-Hispanic Blacks and Hispanics, Baby Boomers have increased mortality trends compared with other cohorts. For non-Hispanic White females, it is late-Gen Xers and early-Gen Yers for whom the mortality trends are higher than other cohorts. For non-Hispanic White males, the elevated mortality pattern is found for Baby Boomers, late-Gen Xers, and early-Gen Yers. The mortality pattern among Baby Boomers is at least partially driven by mortality related to drug poisoning, suicide, external causes, chronic obstructive pulmonary disease and HIV/AIDS for all race and gender groups affected. The elevated mortality patterns among late-Gen Xers and early-Gen Yers are at least partially driven by mortality related to drug poisonings and alcohol-related diseases for non-Hispanic Whites. Differential patterns of drug poisoning-related mortality play an important role in the racial/ethnic disparities in these mortality patterns.

CONCLUSIONS

We find substantial racial/ethnic disparities in inter-cohort mortality patterns. Our findings also point to the unique challenges faced by younger generations.

Latent Trajectories of Cross-National Homicide Trends: Structural Characteristics of Underlying Groups

Relative to studies of U.S. homicide trends, few have investigated cross-national trends. We explore hidden heterogeneity across a sample of 82 nations between 1980 and 2010, and examine (a) what distinct latent trajectories are represented among these nations? and (b) what structural factors characterize these latent trajectory groups? World Health Organization mortality data were used for the trajectory analyses wherein three distinct groups were identified. Structural characteristics of each group are compared to determine which factors account for their trajectories. Characteristics that predicted group placement include a development index, divorced males, female labor force participation, and Latin American region.

Sex Differences in Genetic Associations With Longevity

IMPORTANCE

Sex differences in genetic associations with human longevity remain largely unknown; investigations on this topic are important for individualized health care.

OBJECTIVE

To explore sex differences in genetic associations with longevity.

DESIGN SETTING AND PARTICIPANTS

This population-based case-control study used sex-specific genome-wide association study and polygenic risk score (PRS) analyses to examine sex differences in genetic associations with longevity. Five hundred sixty-four male and 1614 female participants older than 100 years were compared with a control group of 773 male and 1526 female individuals aged 40 to 64 years. All were Chinese Longitudinal Healthy Longevity Study participants with Han ethnicity who were recruited in 1998 and 2008 to 2014.

MAIN OUTCOMES AND MEASURES

Sex-specific loci and pathways associated with longevity and PRS measures of joint effects of sex-specific loci.

RESULTS

Eleven male-specific and 11 female-specific longevity loci (P < 10-5) and 35 male-specific and 25 female-specific longevity loci (10-5 ≤ P < 10-4) were identified. Each of these loci's associations with longevity were replicated in north and south regions of China in one sex but were not significant in the other sex (P = .13-.97), and loci-sex interaction effects were significant (P < .05). The associations of loci rs60210535 of the LINC00871 gene with longevity were replicated in Chinese women (P = 9.0 × 10-5) and US women (P = 4.6 × 10-5) but not significant in Chinese and US men. The associations of the loci rs2622624 of the ABCG2 gene were replicated in Chinese women (P = 6.8 × 10-5) and European women (P = .003) but not significant in both Chinese and European men. Eleven male-specific pathways (inflammation and immunity genes) and 34 female-specific pathways (tryptophan metabolism and PGC-1α induced) were significantly associated with longevity (P < .005; false discovery rate < 0.05). The PRS analyses demonstrated that sex-specific associations with longevity of the 4 exclusive groups of 11 male-specific and 11 female-specific loci (P < 10-5) and 35 male-specific and 25 female-specific loci (10-5 ≤P < 10-4) were jointly replicated across north and south discovery and target samples. Analyses using the combined data set of north and south showed that these 4 groups of sex-specific loci were jointly and significantly associated with longevity in one sex (P = 2.9 × 10-70 to 1.3 × 10-39) but not jointly significant in the other sex (P = .11 to .70), while interaction effects between PRS and sex were significant (P = 4.8 × 10-50 to 1.2 × 10-16).

CONCLUSION AND RELEVANCE

The sex differences in genetic associations with longevity are remarkable, but have been overlooked by previously published genome-wide association studies on longevity. This study contributes to filling this research gap and provides a scientific basis for further investigating effects of sex-specific genetic variants and their interactions with environment on healthy aging, which may substantially contribute to more effective and targeted individualized health care for male and female elderly individuals.

The Importance of the Baby Boom Cohort and the Great Recession in Understanding Age, Period, and Cohort Patterns in Happiness

Twenge, Sherman, and Lyubomirsky (TSL) claim that long-term cultural changes have increased young adults' happiness while reducing mature adults' happiness. To establish their conclusion, TSL use trend analyses, as well as more sophisticated mixed-effects models, but their analyses are problematic. In particular, TSL's trend analyses ignore a crucial cohort effect: well-known lower happiness among baby boomers. Furthermore, their data aggregation obscures the ephemerality of a recent period effect: the Great Recession. Finally, TSL overlook a key finding of their mixed-effects models that both pre- and post-Boomer cohorts became happier as they aged from young to mature adults. Our reanalyses of the data establish that the Baby Boomer cohort, the short-lived Great Recession, and unfortunate data aggregation account for TSL's results. The well-established, long-term relationship between age and happiness remains as it has been for decades despite any cultural shifts that may have occurred disfavoring mature adults.

Interaction Between the FOXO1A-209 Genotype and Tea Drinking Is Significantly Associated with Reduced Mortality at Advanced Ages

On the basis of the genotypic/phenotypic data from Chinese Longitudinal Healthy Longevity Survey (CLHLS) and Cox proportional hazard model, the present study demonstrates that interactions between carrying FOXO1A-209 genotypes and tea drinking are significantly associated with lower risk of mortality at advanced ages. Such a significant association is replicated in two independent Han Chinese CLHLS cohorts (p = 0.028-0.048 in the discovery and replication cohorts, and p = 0.003-0.016 in the combined dataset). We found the associations between tea drinking and reduced mortality are much stronger among carriers of the FOXO1A-209 genotype compared to non-carriers, and drinking tea is associated with a reversal of the negative effects of carrying FOXO1A-209 minor alleles, that is, from a substantially increased mortality risk to substantially reduced mortality risk at advanced ages. The impacts are considerably stronger among those who carry two copies of the FOXO1A minor allele than those who carry one copy. On the basis of previously reported experiments on human cell models concerning FOXO1A-by-tea-compounds interactions, we speculate that results in the present study indicate that tea drinking may inhibit FOXO1A-209 gene expression and its biological functions, which reduces the negative impacts of FOXO1A-209 gene on longevity (as reported in the literature) and offers protection against mortality risk at oldest-old ages. Our empirical findings imply that the health outcomes of particular nutritional interventions, including tea drinking, may, in part, depend upon individual genetic profiles, and the research on the effects of nutrigenomics interactions could potentially be useful for rejuvenation therapies in the clinic or associated healthy aging intervention programs.

Age-Specific Variation in Adult Mortality Rates in Developed Countries

This paper investigates historical changes in both single-year-of-age adult mortality rates and variation of the single-year mortality rates around expected values within age intervals over the past two centuries in 15 developed countries. We apply an integrated Hierarchical Age-Period-Cohort-Variance Function Regression Model to data from the Human Mortality Database. We find increasing variation of the single-year rates within broader age intervals over the life course for all countries, but the increasing variation slows down at age 90 and then increases again after age 100 for some countries; the variation significantly declined across cohorts born after the early 20th century; and the variation continuously declined over much of the last two centuries but has substantially increased since 1980. Our further analysis finds the recent increases in mortality variation are not due to increasing proportions of older adults in the population, trends in mortality rates, or disproportionate delays in deaths from degenerative and man-made diseases, but rather due to increasing variations in young and middle-age adults.

Multicollinearity in hierarchical linear models

This study investigates an ill-posed problem (multicollinearity) in Hierarchical Linear Models from both the data and the model perspectives. We propose an intuitive, effective approach to diagnosing the presence of multicollinearity and its remedies in this class of models. A simulation study demonstrates the impacts of multicollinearity on coefficient estimates, associated standard errors, and variance components at various levels of multicollinearity for finite sample sizes typical in social science studies. We further investigate the role multicollinearity plays at each level for estimation of coefficient parameters in terms of shrinkage. Based on these analyses, we recommend a top-down method for assessing multicollinearity in HLMs that first examines the contextual predictors (Level-2 in a two-level model) and then the individual predictors (Level-1) and uses the results for data collection, research problem redefinition, model re-specification, variable selection and estimation of a final model.

Clarifying hierarchical age-period-cohort models: A rejoinder to Bell and Jones

Previously, Reither et al. (2015) demonstrated that hierarchical age-period-cohort (HAPC) models perform well when basic assumptions are satisfied. To contest this finding, Bell and Jones (2015) invent a data generating process (DGP) that borrows age, period and cohort effects from different equations in Reither et al. (2015). When HAPC models applied to data simulated from this DGP fail to recover the patterning of APC effects, B&J reiterate their view that these models provide "misleading evidence dressed up as science." Despite such strong words, B&J show no curiosity about their own simulated data--and therefore once again misapply HAPC models to data that violate important assumptions. In this response, we illustrate how a careful analyst could have used simple descriptive plots and model selection statistics to verify that (a) period effects are not present in these data, and (b) age and cohort effects are conflated. By accounting for the characteristics of B&J's artificial data structure, we successfully recover the "true" DGP through an appropriately specified model. We conclude that B&Js main contribution to science is to remind analysts that APC models will fail in the presence of exact algebraic effects (i.e., effects with no random/stochastic components), and when collinear temporal dimensions are included without taking special care in the modeling process. The expanded list of coauthors on this commentary represents an emerging consensus among APC scholars that B&J's essential strategy--testing HAPC models with data simulated from contrived DGPs that violate important assumptions--is not a productive way to advance the discussion about innovative APC methods in epidemiology and the social sciences.

Should age-period-cohort studies return to the methodologies of the 1970s?

Social scientists have recognized the importance of age-period-cohort (APC) models for half a century, but have spent much of this time mired in debates about the feasibility of APC methods. Recently, a new class of APC methods based on modern statistical knowledge has emerged, offering potential solutions. In 2009, Reither, Hauser and Yang used one of these new methods - hierarchical APC (HAPC) modeling - to study how birth cohorts may have contributed to the U.S. obesity epidemic. They found that recent birth cohorts experience higher odds of obesity than their predecessors, but that ubiquitous period-based changes are primarily responsible for the rising prevalence of obesity. Although these findings have been replicated elsewhere, recent commentaries by Bell and Jones call them into question - along with the new class of APC methods. Specifically, Bell and Jones claim that new APC methods do not adequately address model identification and suggest that "solid theory" is often sufficient to remove one of the three temporal dimensions from empirical consideration. They also present a series of simulation models that purportedly show how the HAPC models estimated by Reither et al. (2009) could have produced misleading results. However, these simulation models rest on assumptions that there were no period effects, and associations between period and cohort variables and the outcome were perfectly linear. Those are conditions under which APC models should never be used. Under more tenable assumptions, our own simulations show that HAPC methods perform well, both in recovering the main findings presented by Reither et al. (2009) and the results reported by Bell and Jones. We also respond to critiques about model identification and theoretically-imposed constraints, finding little pragmatic support for such arguments. We conclude by encouraging social scientists to move beyond the debates of the 1970s and toward a deeper appreciation for modern APC methodologies.

Implications of changes in households and living arrangements for future home-based care needs and costs for disabled elders in China

OBJECTIVES

To better understand future home-based care needs and costs for disabled elders in China.

METHOD

To further develop and apply the ProFamy extended cohort-component method and the most recent census and survey data.

RESULTS

(a) Chinese disabled elders and the annual growth rate of the percentage of national gross domestic product (GDP) devoted to home-based care costs for disabled elders will increase much more rapidly than the growth of total elderly population; (b) home-based care needs and costs for disabled oldest-old aged 80+ will increase much faster than that for disabled young-old aged 65-79 after 2030; (c) disabled unmarried elders living alone and their home-based care costs increase substantially faster than those disabled unmarried elders living with children; (d) percent of rural disabled oldest-old will be substantially higher than that of rural population after 2030; (e) sensitivity analyses show that possible changes in mortality and elderly disability status are the major direct factors affecting home-based care needs and costs; (f) caregivers resources under the universal two-child policy will be substantially better than that under the rigorous fertility policy unchanged.

DISCUSSION

We discuss policy recommendations concerning pathways to healthy aging with relatively reduced care costs, including reductions of the prevalence of disability, gender equality, the universal two-child policy and resources of caregivers, encouragements of rural-to-urban family migration and elder's residential proximity to their adult children, and remarriages of not-married elders.

GxE interactions between FOXO genotypes and drinking tea are significantly associated with prevention of cognitive decline in advanced age in China

Logistic regression analysis based on data from 822 Han Chinese oldest old aged 92+ demonstrated that interactions between carrying FOXO1A-266 or FOXO3-310 or FOXO3-292 and tea drinking at around age 60 or at present time were significantly associated with lower risk of cognitive disability at advanced ages. Associations between tea drinking and reduced cognitive disability were much stronger among carriers of the genotypes of FOXO1A-266 or FOXO3-310 or FOXO3-292 compared with noncarriers, and it was reconfirmed by analysis of three-way interactions across FOXO genotypes, tea drinking at around age 60, and at present time. Based on prior findings from animal and human cell models, we postulate that intake of tea compounds may activate FOXO gene expression, which in turn may positively affect cognitive function in the oldest old population. Our empirical findings imply that the health benefits of particular nutritional interventions, including tea drinking, may, in part, depend upon individual genetic profiles.

Household and living arrangement projections at the subnational level: an extended cohort-component approach

This article presents the core methodological ideas and empirical assessments of an extended cohort-component approach (known as the "ProFamy model"), and applications to simultaneously project household composition, living arrangements, and population sizes-gender structures at the subnational level in the United States. Comparisons of projections from 1990 to 2000 using this approach with census counts in 2000 for each of the 50 states and Washington, DC show that 68.0 %, 17.0 %, 11.2 %, and 3.8 % of the absolute percentage errors are <3.0 %, 3.0 % to 4.99 %, 5.0 % to 9.99 %, and ≥10.0 %, respectively. Another analysis compares average forecast errors between the extended cohort-component approach and the still widely used classic headship-rate method, by projecting number-of-bedrooms-specific housing demands from 1990 to 2000 and then comparing those projections with census counts in 2000 for each of the 50 states and Washington, DC. The results demonstrate that, compared with the extended cohort-component approach, the headship-rate method produces substantially more serious forecast errors because it cannot project households by size while the extended cohort-component approach projects detailed household sizes. We also present illustrative household and living arrangement projections for the five decades from 2000 to 2050, with medium-, small-, and large-family scenarios for each of the 50 states; Washington, DC; six counties of southern California; and the Minneapolis-St. Paul metropolitan area. Among many interesting numerical outcomes of household and living arrangement projections with medium, low, and high bounds, the aging of American households over the next few decades across all states/areas is particularly striking. Finally, the limitations of the present study and potential future lines of research are discussed.

Assessing the Significance of Cohort and Period Effects in Hierarchical Age-Period-Cohort Models: Applications to Verbal Test Scores and Voter Turnout in U.S. Presidential Elections

In recently developed hierarchical age-period-cohort (HAPC) models, inferential questions arise: How can one assess or judge the significance of estimates of individual cohort and period effects in such models? And how does one assess the overall statistical significance of the cohort and/or the period effects? Beyond statistical significance is the question of substantive significance. This paper addresses these questions. In the context of empirical applications of linear and generalized linear mixed-model specifications of HAPC models using data on verbal test scores and voter turnout in U.S. presidential elections, respectively, we describe a two-step approach and a set of guidelines for assessing statistical significance. The guidelines include assessments of patterns of effects and statistical tests both for the effects of individual cohorts and time periods as well as for entire sets of cohorts and periods. The empirical applications show strong evidence that trends in verbal test scores are primarily cohort driven, while voter turnout is primarily a period phenomenon.

How genes influence life span: the biodemography of human survival

BACKGROUND

In genome-wide association studies (GWAS) of human life span, none of the genetic variants has reached the level of genome-wide statistical significance. The roles of such variants in life span regulation remain unclear.

DATA AND METHOD

A biodemographic analyses was done of genetic regulation of life span using data on low-significance longevity alleles selected in the earlier GWAS of the original Framingham cohort.

RESULTS

Age-specific survival curves considered as functions of the number of longevity alleles exhibit regularities known in demography as "rectangularization" of survival curves. The presence of such pattern confirms observations from experimental studies that regulation of life span involves genes responsible for stress resistance.

CONCLUSION

Biodemographic analyses could provide important information about the properties of genes affecting phenotypic traits.

The quadratic hazard model for analyzing longitudinal data on aging, health, and the life span

A better understanding of processes and mechanisms linking human aging with changes in health status and survival requires methods capable of analyzing new data that take into account knowledge about these processes accumulated in the field. In this paper, we describe an approach to analyses of longitudinal data based on the use of stochastic process models of human aging, health, and longevity which allows for incorporating state of the art advances in aging research into the model structure. In particular, the model incorporates the notions of resistance to stresses, adaptive capacity, and "optimal" (normal) physiological states. To capture the effects of exposure to persistent external disturbances, the notions of allostatic adaptation and allostatic load are introduced. These notions facilitate the description and explanation of deviations of individuals' physiological indices from their normal states, which increase the chances of disease development and death. The model provides a convenient conceptual framework for comprehensive systemic analyses of aging-related changes in humans using longitudinal data and linking these changes with genotyping profiles, morbidity, and mortality risks. The model is used for developing new statistical methods for analyzing longitudinal data on aging, health, and longevity.

The link between unemployment and crime rate fluctuations: An analysis at the county, state, and national levels

Cantor and Land (1985) developed a theoretical model that proposed two pathways through which economic activity - as indexed by the aggregate unemployment rate - could affect the rate of criminal activity. The first is by increasing levels of criminal motivation within the population as deteriorating economic conditions affect social strain and social control; the second is by influencing the availability and vulnerability of criminal targets and thus the number of criminal opportunities. Although much empirical research has applied this theoretical model, few analyses have done so at disaggregated units of analysis. We present the most comprehensive analysis to date by empirically evaluating this model with data on 400 of the largest US counties - and examine the effects of aggregation on results as these county data are combined to the state and national levels - for the years 1978-2005. For seven Index crimes at each of the three levels of analysis, and with or without controls for structural covariates at each level, the directional effects hypothesized by Cantor and Land are found for 78 out of 84 estimated relationships. Even after taking into account the lack of statistical independence of these estimates by drawing on recently developed statistical theory, this is a very unlikely outcome. In accordance with expectations based on theory and prior research, (a) some of these relationships are weak and not statistically significant, and (b) the strongest and most consistent patterns of relationships for both the crime opportunity and crime motivation effects are found for three property crimes: burglary, larceny, and motor vehicle theft. Suggestions for further research on this topic are given.

Heterogeneity in the Strehler-Mildvan general theory of mortality and aging

This study examines and further develops the classic Strehler-Mildvan (SM) general theory of mortality and aging. Three predictions from the SM theory are tested by examining the age dependence of mortality patterns for 42 countries (including developed and developing countries) over the period 1955-2003. By applying finite mixture regression models, principal component analysis, and random-effects panel regression models, we find that (1) the negative correlation between the initial adulthood mortality rate and the rate of increase in mortality with age derived in the SM theory exists but is not constant; (2) within the SM framework, the implied age of expected zero vitality (expected maximum survival age) also is variable over time; (3) longevity trajectories are not homogeneous among the countries; (4) Central American and Southeast Asian countries have higher expected age of zero vitality than other countries in spite of relatively disadvantageous national ecological systems; (5) within the group of Central American and Southeast Asian countries, a more disadvantageous national ecological system is associated with a higher expected age of zero vitality; and (6) larger agricultural and food productivities, higher labor participation rates, higher percentages of population living in urban areas, and larger GDP per capita and GDP per unit of energy use are important beneficial national ecological system factors that can promote survival. These findings indicate that the SM theory needs to be generalized to incorporate heterogeneity among human populations.

Composition and decomposition in US gender-specific self-reported health disparities, 1984-2007

Variance function regression models and demographic decomposition methods are applied to identify two dimensions of changes in health disparities (SES-demographic effects vs. compositional effects, between-group disparities vs. within-group disparities) in the US from 1984 to 2007. Using National Health Interview Survey data on self-reported health, we find that disparities in men's health increased, while those of women decreased, for the whole period. Widening men's health disparities are largely driven by increases in the effects of SES-demographic statuses on within-group disparities. These increases are moderated by increasing levels of men's college attainment. But decreasing middle and upper income attainment and a decreasing employment rate further increase men's health disparities. For women, the effects of SES-demographic statuses on health disparities also increased over time. This, however, was outweighed by increases in women's college attainment, middle and upper income attainment, and employment rate. The result is overall declining self-reported health disparities for women.

Variance Function Regression in Hierarchical Age-Period-Cohort Models: Applications to the Study of Self-Reported Health

Two long-standing research problems of interest to sociologists are sources of variations in social inequalities and differential contributions of the temporal dimensions of age, time period, and cohort to variations in social phenomena. Recently, scholars have introduced a model called Variance Function Regression for the study of the former problem, and a model called Hierarchical Age-Period-Cohort regression has been developed for the study of the latter. This article presents an integration of these two models as a means to study the evolution of social inequalities along distinct temporal dimensions. We apply the integrated model to survey data on subjective health status. We find substantial age, period, and cohort effects, as well as gender differences, not only for the conditional mean of self-rated health (i.e., between-group disparities), but also for the variance in this mean (i.e., within-group disparities)-and it is detection of age, period, and cohort variations in the latter disparities that application of the integrated model permits. Net of effects of age and individual-level covariates, in recent decades, cohort differences in conditional means of self-rated health have been less important than period differences that cut across all cohorts. By contrast, cohort differences of variances in these conditional means have dominated period differences. In particular, post-baby boom birth cohorts show significant and increasing levels of within-group disparities. These findings illustrate how the integrated model provides a powerful framework through which to identify and study the evolution of variations in social inequalities across age, period, and cohort temporal dimensions. Accordingly, this model should be broadly applicable to the study of social inequality in many different substantive contexts.

MODELING REPEATED MEASURES OF DICHOTOMOUS DATA: Testing Whether the Within-Person Trajectory of Change Varies Across Levels of Between-Person Factors

In this paper, we consider the following question for the analysis of data obtained in longitudinal panel designs: How should repeated-measures data be modeled and interpreted when the outcome or dependent variable is dichotomous and the objective is to determine whether the within-person rate of change over time varies across levels of one or more between-person factors? Standard approaches address this issue by means of generalized estimating equations or generalized linear mixed models with logistic links. Using an empirical example and simulated data, we show (1) that cross-level product terms from these models can produce misleading results with respect to whether the within-person rate of change varies across levels of a dichotomous between-person factor; and (2) that subgroup differences in the rate of change should be assessed on an additive scale (using group differences in the effects of predictors on the probability of disease) rather than on a multiplicative scale (using group differences in the effects of predictors on the odds of disease). Because usual approaches do not provide a significance test for whether the rate of additive change varies across levels of a between-person factor, sample differences in the rate of additive change may be due to sampling error. We illustrate how standard software can be used to estimate and test whether additive changes vary across levels of a between-person factor.

Trade-off in the effects of the apolipoprotein E polymorphism on the ages at onset of CVD and cancer influences human lifespan

Progress in unraveling the genetic origins of healthy aging is tempered, in part, by a lack of replication of effects, which is often considered a signature of false-positive findings. We convincingly demonstrate that the lack of genetic effects on an aging-related trait can be because of trade-offs in the gene action. We focus on the well-studied apolipoprotein E (APOE) e2/3/4 polymorphism and on lifespan and ages at onset of cardiovascular diseases (CVD) and cancer, using data on 3924 participants of the Framingham Heart Study Offspring cohort. Kaplan-Meier estimates show that the e4 allele carriers live shorter lives than the non-e4 allele carriers (log rank = 0.016). The adverse effect was attributed to the poor survival of the e4 homozygotes, whereas the effect of the common e3/4 genotype was insignificant. The e3/4 genotype, however, was antagonistically associated with onsets of those diseases predisposing to an earlier onset of CVD and a later onset of cancer compared to the non-e4 allele genotypes. This trade-off explains the lack of a significant effect of the e3/4 genotype on survival; adjustment for it in the Cox regression model makes the detrimental effect of the e4 allele highly significant (P = 0.002). This trade-off is likely caused by the lipid-metabolism-related (for CVD) and nonrelated (for cancer) mechanisms. An evolutionary rationale suggests that genetic trade-offs should not be an exception in studies of aging-related traits. Deeper insights into biological mechanisms mediating gene action are critical for understanding the genetic regulation of a healthy lifespan and for personalizing medical care.

Polymorphisms in the ACE and ADRB2 genes and risks of aging-associated phenotypes: the case of myocardial infarction

Multiple functions of the beta2-adrenergic receptor (ADRB2) and angiotensin-converting enzyme (ACE) genes warrant studies of their associations with aging-related phenotypes. We focus on multimarker analyses and analyses of the effects of compound genotypes of two polymorphisms in the ADRB2 gene, rs1042713 and rs1042714, and 11 polymorphisms of the ACE gene, on the risk of such an aging-associated phenotype as myocardial infarction (MI). We used the data from a genotyped sample of the Framingham Heart Study Offspring (FHSO) cohort (n = 1500) followed for about 36 years with six examinations. The ADRB2 rs1042714 (C-->G) polymorphism and two moderately correlated (r(2) = 0.77) ACE polymorphisms, rs4363 (A-->G) and rs12449782 (A-->G), were significantly associated with risks of MI in this aging cohort in multimarker models. Predominantly linked ACE genotypes exhibited opposite effects on MI risks, e.g., the AA (rs12449782) genotype had a detrimental effect, whereas the predominantly linked AA (rs4363) genotype exhibited a protective effect. This trade-off occurs as a result of the opposite effects of rare compound genotypes of the ACE polymorphisms with a single dose of the AG heterozygote. This genetic trade-off is further augmented by the selective modulating effect of the rs1042714 ADRB2 polymorphism. The associations were not altered by adjustment for common MI risk factors. The results suggest that effects of single specific genetic variants of the ADRB2 and ACE genes on MI can be readily altered by gene-gene or/and gene-environmental interactions, especially in large heterogeneous samples. Multimarker genetic analyses should benefit studies of complex aging-associated phenotypes.

Beta2-adrenergic receptor gene polymorphisms as systemic determinants of healthy aging in an evolutionary context

The Gln(27)Glu polymorphism but not the Arg(16)Gly polymorphism of the beta2-adrenergic receptor (ADRB2) gene appears to be associated with a broad range of aging-associated phenotypes, including cancers at different sites, myocardial infarction (MI), intermittent claudication (IC), and overall/healthy longevity in the Framingham Heart Study Offspring cohort. The Gln(27)Gln genotype increases risks of cancer, MI and IC, whereas the Glu(27) allele or, equivalently, the Gly(16)Glu(27) haplotype tends to be protective against these diseases. Genetic associations with longevity are of opposite nature at young-old and oldest-old ages highlighting the phenomenon of antagonistic pleiotropy. The mechanism of antagonistic pleiotropy is associated with an evolutionary-driven advantage of carriers of a derived Gln(27) allele at younger ages and their survival disadvantage at older ages as a result of increased risks of cancer, MI and IC. The ADRB2 gene can play an important systemic role in healthy aging in evolutionary context that warrants exploration in other populations.

Area-based socioeconomic characteristics of industries at high risk for violence in the workplace

This study examined socioeconomic factors associated with the presence of workplaces belonging to industries reported to be at high risk for worker homicide. The proportion of 2004 North Carolina workplaces in high-risk industries was computed following spatial linkage of individual workplaces to 2000 United States Census Block Groups (n = 3,925). Thirty census-derived socioeconomic variables (selected a priori as potentially predictive of violence) were summarized using exploratory factor analysis into poverty/deprivation, human/economic capital, and transience/instability. Multinomial logistic regression models indicate associations between higher proportion of workplaces belonging to high-risk industries and Block Groups with more poverty/deprivation or transience/instability and less human/economic capital. The relationship between human/economic capital and Block Groups proportion of high-risk industry workplaces was modified by levels of transience/instability. Community characteristics therefore contribute to the potential for workplace violence, and future research should continue to understand the relationship between social context and workplace violence risk.

Cumulative deficits and physiological indices as predictors of mortality and long life

We evaluated the predictive potential for long-term (24-year) survival and longevity (85+ years) of an index of cumulative deficits (DI) and six physiological indices (pulse pressure, diastolic blood pressure, pulse rate, serum cholesterol, blood glucose, and hematocrit) measured in mid- to late life (44-88 years) for participants of the 9th and 14th Framingham Heart Study examinations. For all ages combined, the DI, pulse pressure, and blood glucose are the strongest determinants of both long-term survival and longevity, contributing cumulatively to their explanation. Diastolic blood pressure and hematocrit are less significant determinants of both of these outcomes. The pulse rate is more relevant to survival, whereas serum cholesterol is more relevant to longevity. Only the DI is a significant predictor of longevity and mortality for each 5-year age group ranging from 45 to 85 years. The DI appears to be a more important determinant of long-term risks of death and longevity than are the physiological indices.

The association of childhood socioeconomic conditions with healthy longevity at the oldest-old ages in China

Based on unique data from the largest-ever sample of the Chinese oldest-old aged 80 and older, our multivariate logistic regression analyses show that either receiving adequate medical service during sickness in childhood or never/rarely suffering from serious illness during childhood significantly reduces the risk of being ADL (activities of daily living) impaired, being cognitively impaired, and self-reporting poor health by 18%-33% at the oldest-old ages. Estimates of effects for five other indicators of childhood conditions are similarly positive but mostly not statistically significant. Multivariate survival analysis shows that better childhood socioeconomic conditions in general tend to reduce the four-year period mortality risk among the oldest-old. But after additional controls for 14 covariates are put into the model, the effects are not statistically significant, thus suggesting that most of the effects of childhood conditions on oldest-old mortality are indirect-at least to the point of affecting current health status at the oldest-old ages, which itself is strongly associated with mortality. While acknowledging limitations of the present analyses due to a lack of information on childhood illness, the oldest-olds'recollection errors, and other data problems, we conclude, based on this and other studies, that policies that enhance childhood health care and children's socioeconomic well-being can have large and long-lasting benefits up to the oldest-old ages.

A new method for correcting under-estimation of disabled life expectancy and an application to the Chinese oldest-old

This article demonstrates that disabled life expectancies that are based on conventional multistate life-table methods are significantly underestimated because of the assumption of no changes in functional status between age x and death. We present a new method to correct the bias and apply it to data from a longitudinal survey of about 9,000 oldest-old Chinese aged 80-105 collected in 1998 and 2000. In our application, the age trajectories of disability (activities of daily living--ADL), status-specific death rates, and the probabilities of transitions between ADL states of the oldest-old were investigated for the first time in a developing country. In this article, we report estimates of bias-corrected disabled and active life expectancies of the Chinese oldest-old and demonstrate patterns of large differences associated with initial status, gender, and advances in ages. Using combined information on ADL disabilities and length of having been bedridden before dying, we analyze gender and age patterns of the extent of morbidity before dying among the oldest-old and their implications for debates on the hypothesis of compression of morbidity.

Diverse trajectories of cocaine use through early adulthood among rebellious and socially conforming youth

This paper tests predictions of continuity and change in antisocial behavior over time as derived from population heterogeneity and life-course perspectives. These predictions are assessed with respect to a rarely studied form of delinquent/criminal behavior, cocaine use during the late-teenage and young adult years. We first examine the extent to which differential propensities toward antisocial behavior can be detected in a nationally representative sample of youth aged 14-16 in 1979. Based on self-reported delinquent and criminal activities in late adolescense, traditional cross-sectional latent-class analysis identifies three groups of antisocial/rebellious respondents and a group of non-offenders. We then follow these groups into early adulthood, examining age trajectories of cocaine usage between 1984 and 1998. Latent-class trajectory models identify clusters of respondents who show similar age trajectories of cocaine use over time and provide parameter estimates that predict membership in those clusters. In support of the population heterogeneity perspective, we find that antisocial/rebellious youth have higher probabilities of cocaine use throughout early adulthood than non-of-fending youth. There is, however, much variation in drug use patterns among the groups as they aged. In support of a life-course perspective, we find that social ties to schools, families, religion, and the labor market help differentiate youth who refrain from, maintain, or desist from using cocaine through early adulthood.

Trends in environmental lead exposure and troubled youth, 1960-1995: an age-period-cohort-characteristic analysis

Beginning in the mid-1980s and extending into the early 1990s, the United States experienced a wave of increased youth violence and teenage pregnancy. Nevin (2000) proffers a cohort-based explanation that these trends can be attributed to corresponding trends in gasoline lead exposure during the youths' early years. He contends that the increased consumption of adversely impacted their intelligence levels (IQs). This decreased their intellectual ability, resulted in poor decisions made during their teen and young adult years, and in turn, led to disproportionally high level of criminal involvement and unwed pregnancies among this cohort. The present study evaluates Nevin's causal model by testing the connection between trends in lead exposure and youthful problem behavior with age-period-cohort-characteristic (APCC) models. Our research finds no support for this cohort explanation.

Adjusting period tempo changes with an extension of Ryder's basic translation equation

We show that the observed changes in the period tempo of fertility are biased and derive a new formula for adjusting such bias. We present illustrative applications of our proposed method to the cases of the United States and Taiwan. We then describe the relevance of adjustments of observed period fertility tempo for evaluating family planning programs aiming at delaying and reducing births to slow down population growth in developing countries. The work reported in this article also can be regarded as an extension of Ryder's basic translation equation. The extension provides a set of formulas expressing relationships of quantum-tempo between cohorts and periods under specified assumptions.

A sensitivity analysis of the Bongaarts-Feeney method for adjusting bias in observed period total fertility rates

Our sensitivity analysis shows that the adjusted TFR'(t) using the formula of Bongaarts and Feeney (1998), which assumes an invariant shape for the fertility schedule, usually does not differ significantly from an adjusted TFR"(t) that allows the shape of the fertility schedule to change at a constant annual rate. Because annual changes in the shape of the fertility schedules often are approximately constant except in abnormal conditions, the Bongaarts-Feeney (B-F) method is generally robust for producing reasonable estimates of the adjusted TFR'(t). The adjusted TFR'(t) neither represents any real cohort experiences from the past nor forecasts any future trend. It merely provides an improved reading of the period fertility measure, which reduces the tempo distortion.

Active life expectancy estimates for the U.S. elderly population: a multidimensional continuous-mixture model of functional change applied to completed cohorts, 1982-1996

An increment-decrement stochastic-process life table model that continuously mixes measures of functional change is developed to represent age transitions among highly refined disability states interacting simultaneously with mortality. The model is applied to data from the National Long Term Care Surveys of elderly persons in the years 1982 to 1996 to produce active life expectancy estimates based on completed-cohort life tables. At ages 65 and 85, comparisons with extant period estimates for 1990 show that our active life expectancy estimates are larger for both males and females than are extant period estimates based on coarse disability states.

Active life expectancy estimates for the U.S. elderly population: A multidimensional continuous-mixture model of functional change applied to completed Cohorts, 1982–1996

An increment-decrement stochastic-process life table model that continuously mixes measures of functional change is developed to represent age transitions among highly refined disability states interacting simultaneously with mortality. The model is applied to data from the National Long Term Care Surveys of elderly persons in the years 1982 to 1996 to produce active life expectancy estimates based on completed-cohort life tables. At ages 65 and 85, comparisons with extant period estimates for 1990 show that our active life expectancy estimates are larger for both males and females than are extant period estimates based on coarse disability states.

Effects of the status of women on the first-birth interval in Indian urban society

The status of women, which is relative and multidimensional, has an important bearing on any long-term reduction in fertility. In Indian society, where cohabitation and childbearing are socially sanctioned only after marriage, the length of the first-birth interval affects the completed family size by influencing the spacing and childbearing pattern of a family. This study examines the influence of certain aspects of the status of married women--education, employment, role in family decision making, and age at marriage--along with three socioeconomic variables--per capita income of the family, social position of the household, and the caste system--on the duration of the first-birth interval in an urban Hindu society of the north-east Indian state of Assam. The data were analysed by applying life table and hazard regression techniques. The results indicate that a female's age at marriage, education, current age, role in decision making, and the per capita income of the household are the main covariates that strongly influence the length of the first-birth interval of Hindu females of urban Assam. Of all the covariates studied, a female's education appears to be a key mediating factor, through its influence on her probability of employment outside the home and thereby an earned income and on her role in family decision making. Unlike other Indian communities, the effect of the caste system does not have a significant effect on first-birth timing in this urban Hindu society.

Social correlates of the dimensions of depression in the elderly

Few investigations of the social correlates of depressive symptomatology have addressed variation in the correlates across multiple dimensions of depression scales. We examined the relationships of selected social, clinical, and demographic correlates with four dimensions of the Center for Epidemiologic Studies-Depression (CES-D) scale in 3,401 community-dwelling elders in the Piedmont area of North Carolina. These correlates explained significant variation in somatic complaints and depressed affect; effects of chronic disability and recent negative events were particularly robust. Having a confidant explained reduced symptomatology for all four dimensions, but particularly for low positive affect and interpersonal problems. Positive affect was also buttressed by helping others. These patterns have particular relevance where treatment for depression is divorced from considerations of the social environment of the elderly patient.

Wealth Accumulation across the Adult Life Course: Stability and Change in Sociodemographic Covariate Structures of Net Worth Data in the Survey of Income and Program Participation, 1984-1991

This paper reports microlevel Tobit regression analyses of sociodemographic covariates of the life course accumulation of total household net worth data in eight waves of five distinct panels-spanning over 6 years from late 1984 through early 1991-of the Survey of Income and Program Participation (SIPP). It is found that the quadratic age-wealth relationship predicted by Modigliani's Life Cycle Hypothesis is evident in aggregate age-median wealth profiles as well as in the micro data for households with positive net worth. However, when adult status attainment variables are entered into the regression models either by themselves or in combination with marital/family status variables, the age of household head at which net worth begins to decline is far beyond the typical retirement age. In addition, the traditional criterion variables of sociological status attainment theory-educational attainment, occupational status, and earnings-are found to be positively associated with household net worth, although the net effect of occupational status generally is not statistically significant and the earnings effect is nonlinear. Further, consistent with status attainment theory, householder minority status (black, Hispanic) is negatively associated with the accumulation of net worth. It is found that both single male and single female householder status are negatively associated with the accumulation of household net worth (relative to married couple households) as is the size of the household (measured by the number of children under age 18 present). Separate logistic regression analyses show that households with zero and negative net worth are more likely than households with positive net worth to be black and have low earnings. Higher levels of educational and occupational status attainment reduce the probability of zero net worth but not the probability of negative net worth. Male- and female-headed households and households headed by Hispanics also are more likely to have zero net worth, but not negative net worth. The estimated sociodemographic covariate structures of household net worth are found to exhibit substantial stability across both waves and panels in the SIPP-although effects of the 1990-1991 recession are detectable in estimates for the 1990 panel. Possible applications of the estimated models in demographic projections of household net worth are suggested.