Alcohol Tax Policy and Related Mortality. An Age-Period-Cohort Analysis of a Rapidly Developed Chinese Population, 1981–2010

To delineate the temporal dynamics between alcohol tax policy changes and related health outcomes, this study examined the age, period and cohort effects on alcohol-related mortality in relation to changes in government alcohol policies. We used the age-period-cohort modeling to analyze retrospective mortality data over 30 years from 1981 to 2010 in a rapidly developed Chinese population, Hong Kong. Alcohol-related mortality from 1) chronic causes, 2) acute causes, 3) all (chronic+acute) causes and 4) causes 100% attributable to alcohol, as defined according to the Alcohol-Related Disease Impact (ARDI) criteria developed by the US Centers for Disease Control and Prevention, were examined. The findings illustrated the possible effects of alcohol policy changes on adult alcohol-related mortality. The age-standardized mortality trends were generally in decline, with fluctuations that coincided with the timing of the alcohol policy changes. The age-period-cohort analyses demonstrated possible temporal dynamics between alcohol policy changes and alcohol-related mortality through the period effects, and also generational impact of alcohol policy changes through the cohort effects. Based on the illustrated association between the dramatic increase of alcohol imports in the mid-1980s and the increased alcohol-related mortality risk of the generations coming of age of majority at that time, attention should be paid to generations coming of drinking age during the 2007–2008 duty reduction.     

Bayesian extrapolation of space-time trends in cancer registry data

We apply a full Bayesian model framework to a dataset on stomach cancer mortality in West Germany. The data are stratified by age group, year, and district. Using an age-period-cohort model with an additional spatial component, our goal is to investigate whether there is evidence for space-time interactions in these data. Furthermore, we will determine whether a period-space or a cohort-space interaction model is more appropriate to predict future mortality rates. The setup will be fully Bayesian based on a series of Gaussian Markov random field priors for each of the components. Statistical inference is based on efficient algorithms to block update Gaussian Markov random fields, which have recently been proposed in the literature.     

When in America,do as the Americans? The evolution of health behaviors and outcomes across immigrant cohorts

This study seeks to understand US immigrants’ health-related behaviors and outcomes across arrival cohorts. We simultaneously examine risky consumption choices (smoking and drinking) and physical health conditions (asthma, diabetes, vision problems, and coronary heart diseases) using data from the National Health Interview Surveys (1989–2018). We incorporate cohort fixed-effects and the interactions between cohort effects and years since immigration into our empirical framework to capture the dynamics of immigrant health over time. For all health indicators, we find that there are important differences between arriving immigrants and natives. Despite some heterogeneity in the dynamics of unhealthy behaviors, this heterogeneity seems to dissipate as we explore longer-term health outcomes. Overall, our findings provide an interesting outlook on how the integration into the host society affects American immigrants’ health. We contribute new results to the immigrant assimilation literature, which has primarily focused on obesity and wages.     

Longitudinal profiling of health care units based on continuous and discrete patient outcomes

Monitoring health care quality involves combining continuous and discrete outcomes measured on subjects across health care units over time. This article describes a Bayesian approach to jointly modeling multilevel multidimensional continuous and discrete outcomes with serial dependence. The overall goal is to characterize trajectories of traits of each unit. Underlying normal regression models for each outcome are used and dependence among different outcomes is induced through latent variables. Serial dependence is accommodated through modeling the pairwise correlations of the latent variables. Methods are illustrated to assess trends in quality of health care units using continuous and discrete outcomes from a sample of adult veterans discharged from 1 of 22 Veterans Integrated Service Networks with a psychiatric diagnosis between 1993 and 1998.     

Relative Contributions of Geographic,Socioeconomic, and Lifestyle Factors to Quality of Life,Frailty, and Mortality in Elderly

Background

To date, few studies address disparities in older populations specifically using frailty as one of the health outcomes and examining the relative contributions of individual and environmental factors to health outcomes.

Methodology/Principal Findings

Using a data set from a health survey of 4,000 people aged 65 years and over living in all regions of Hong Kong, we examined regional variations in self-rated health, frailty, and four-year mortality, and analyzed the relative contributions of lifestyle, socioeconomic status, and geographical location of residence to these outcomes using path analysis. We hypothesize that lifestyle, socioeconomic status, and regional characteristics directly and indirectly through interactions contribute to self-rated physical and psychological health, frailty, and four-year mortality.District variations directly affect self-rated physical health, and also exert an effect through socioeconomic position as well as lifestyle factors. Socioeconomic position in turn directly affects self-rated physical health, as well as indirectly through lifestyle factors. A similar pattern of interaction is observed for self-rated mental health, frailty, and mortality, although there are differences in different lifestyle factors and district associations. Lifestyle factors also directly affect physical and mental components of health, frailty, and mortality. The magnitude of direct district effect is comparable to those of lifestyle and socioeconomic position.

Conclusions/Significance

We conclude that district variations in health outcomes exist in the Hong Kong elderly population, and these variations result directly from district factors, and are also indirectly mediated through socioeconomic position as well as lifestyle. Provision and accessibility to health services are unlikely to play a significant role. Future studies on these district factors would be important in reducing health disparities in the older population.     

Education as policy: the impact of education on marriage, contraception, and fertility in Colombia, Peru, and Bolivia

Using data from the World Fertility and Demographic and Health Surveys of Colombia, Peru, and Bolivia, we model the effects of education on three demographic outcomes: the timing of first sexual union, contraceptive use, and fertility. These effects are examined over time and across geographic areas using a multivariate framework. We find substantial improvements in female educational attainment over the last fifty years and a strong relationship between education and the demographic outcomes. Each successive increment in education is associated with declines in the marriage rate, increased contraceptive use, and lower fertility. Education accounts for some of the changes over time in the demographic outcomes, but the pattern varies by outcome, time period, and geographic area. In support of the social diffusion hypothesis, our results indicate that educational differences in reproductive behavior are reduced as the level of development increases and societies pass through their demographic transition.     

Influence of maternal age, parity and social class on perinatal mortality in Scotland: 1960-82

This study examines secular changes in the influence of maternal age, parity and social class on perinatal mortality in Scotland. Using cross-sectional national data on all Scottish legitimate births the effects of these factors are estimated on the risk of stillbirths, neonatal and perinatal deaths, and the extent to which the current pattern of relative risks in the early 1980s has changed over the past 2 decades is investigated. Social class is used as a crude measure of relative as opposed to absolute differences in socioeconomic conditions which may influence reproductive outcomes. The effects of age, parity and social class are estimated using logistic models. The most parsimonious model adequately describing the data is provided by a main effects model without interactions. Despite changes in reproductive behavior, improved access to maternity services and more effective perinatal care, the influence of maternal age and social class on perinatal mortality remained unchanged between 1960 and 1982. Although the absolute risks of stillbirths and neonatal deaths declined in all maternal age groups, this improvement was not accompained by a significant change in the relative risks traditionally associated with age. Despite no significant changes in the traditional J-shaped association between parity and stillbirths, cross-sectional analysis shows that in the early 1980s the risk of both neonatal and perinatal deaths decreased as parity increased. This finding is consistent with the pattern of risks observed in longitudinal studies and retrospective surveys of reproductive histories. In view of the stability of age, parity and social class effects on the risk of perinatal mortality, little if any of the overall decrease in Scottish stillbirth and neonatal death rates can be attributed to a significant narrowing of relative risks. The results suggest that the attributable risk of high maternal age or low social class on perinatal mortality is negligible. Future improvements in perinatal mortality are thus likely to result from a continuation of the uniform decrease in perinatal mortality for women of all ages, parities and social classes and not from a diminishing of differences in relative risks which are now virtually identical for a large and growing % of women in Scotland.     

Capitation funding: population,age, and mortality adjustments for regional and district health authorities in England.

OBJECTIVE--To assess the effect of each of the components of the national capitation funding formula--population projections and age and mortality weighting--at regional and district level. DESIGN--Application of age-cost and mortality weights to the projected 1997 populations of regions and districts, based on the Department of Health public health common dataset. SETTING--Regional and district health authorities in England. RESULTS--The application of the age-cost and mortality weights to projected populations resulted in greater changes in the shares of weighted populations relative to the estimated 1991 population at district level (mean 0, range -17% to 28%) than at regional level (mean 0, range -9% to 6%). At district level mortality weights had less scope for influence (mean 0, range -9% to 14%) than population projections (mean 0, range -16% to 31%) or age weights (mean 0, range -20% to 30%). CONCLUSIONS--The adjustments to the 1991 population shares due to the application of the national capitation funding formula depend on the interaction of three elements: the projected population by age group, the age-cost weight, and the mortality weight. Since each is open to uncertainty, either in terms of measurement (projected population, particularly for births and the over 85s) or method (derivation of the age-cost and mortality weights), the formula should be implemented cautiously at district level. Ways should be considered of incorporating elements of uncertainty into the model. Further research is required on the elements and degree of uncertainty in each of these components, as well as on the relative health needs of different populations.     

The Neighbourhood Method for Measuring Differences in Maternal Mortality,Infant Mortality and Other Rare Demographic Events

In the absence of reliable systems for registering rare types of vital events large surveys are required to measure changes in their rates. However some events such as maternal deaths are widely known about in the community. This study examined the utility of asking respondents about events in their neighbourhood as an efficient method for measuring relative rates of rare health events such as maternal and infant deaths. A survey was conducted in the health and demographic surveillance system (HDSS) in Matlab, Bangladesh, which includes two areas with different health care regimes. Adult women were asked about any maternal deaths; multiple births; infant deaths, live births and some other events they knew of in a small specified area around their home. Agreement between HDSS records and survey responses was moderate or better (kappa≥0.44) for all the events and greatest for maternal deaths (kappa = 0.77) with 84% being reported. Most events were more likely to be reported if they were recent (p<0.05). Infant mortality rate in one area was 0.56 times that in the other which was well reflected by the ratio of survey results (0.53). Simulations were used to study the ability of the method to detect differences in maternal mortality ratio. These suggested that a sample size around 5000 would give 80% power to detect a 50% decrease from a baseline of 183 which compared well with an estimated sample size around 10 times larger using the direct sisterhood method. The findings suggest that the Neighbourhood Method has potential for monitoring relative differences between areas or changes over time in the rates of rare demographic events, requiring considerably smaller sample sizes than traditional methods. This raises the possibility for interventions to demonstrate real effects on outcomes such as maternal deaths where previously this was only feasible by indirect methods.     

Convergence of body mass with aging: The longitudinal interrelationship of health, weight, and survival

There has been ongoing debate about the health risks associated with increased body weight among the elderly population. One issue has not been investigated thoroughly is that body weight changes over time, as both the reasons and results of, the development of chronic diseases and functional disabilities. Structural models have the ability to unravel the complicated simultaneous relationship between body weight, disability, and mortality along the aging process. Using longitudinal data from the Medicare Current Beneficiary Survey from 1992 to 2001, we constructed a structural model to estimate the longitudinal dynamic relationship between weight, chronic diseases, functional status, and mortality among the aging population. A simulation of an age cohort from 65 to 100 was conducted to show the changes in weight and health outcomes among the cohorts with different baseline weight based on the parameters estimated by the model. The elderly with normal weight at age 65 experience higher life expectancy and lower disability rates than the same age cohorts in other weight categories. The interesting prediction of our model is that the average body size of an elderly cohort will converge to the normal weight range through a process of survival, senescence, and behavioral adjustment.     

Spatial multistate transitional models for longitudinal event data

Summary .   Follow-up medical studies often collect longitudinal data on patients. Multistate transitional models are useful for analysis in such studies where at any point in time, individuals may be said to occupy one of a discrete set of states and interest centers on the transition process between states. For example, states may refer to the number of recurrences of an event, or the stage of a disease. We develop a hierarchical modeling framework for the analysis of such longitudinal data when the processes corresponding to different subjects may be correlated spatially over a region. Continuous-time Markov chains incorporating spatially correlated random effects are introduced. Here, joint modeling of both spatial dependence as well as dependence between different transition rates is required and a multivariate spatial approach is employed. A proportional intensities frailty model is developed where baseline intensity functions are modeled using parametric Weibull forms, piecewise-exponential formulations, and flexible representations based on cubic B-splines. The methodology is developed within the context of a study examining invasive cardiac procedures in Quebec. We consider patients admitted for acute coronary syndrome throughout the 139 local health units of the province and examine readmission and mortality rates over a 4-year period.     

The disease triangle: pathogens, the environment and society

The primary means to define any disease is by naming a pathogen or agent that negatively affects the health of the host organism. Another assumed, but often overlooked, determinant of disease is the environment, which includes deleterious physical and social effects on mankind. The disease triangle is a conceptual model that shows the interactions between the environment, the host and an infectious (or abiotic) agent. This model can be used to predict epidemiological outcomes in plant health and public health, both in local and global communities. Here, the Irish potato famine of the mid-nineteenth century is used as an example to show how the disease triangle, originally devised to interpret plant disease outcomes, can be applied to public health. In parallel, malaria is used to discuss the role of the environment in disease transmission and control. In both examples, the disease triangle is used as a tool to discuss parameters that influence socioeconomic outcomes as a result of host-pathogen interactions involving plants and humans.     

Tracking the dynamics of pathogen interactions: modeling ecological and immune-mediated processes in a two-pathogen single-host system

Traditionally, epidemiological studies have focused on understanding the dynamics of a single pathogen, assuming no interactions with other pathogens. Recently, a large body of work has begun to explore the effects of immune-mediated interactions, arising from cross-immunity and antibody-dependent enhancement, between related pathogen strains. In addition, ecological processes such as a temporary period of convalescence and pathogen-induced mortality have led to the concept of ecological interference between unrelated diseases. There remains, however, the need for a systematic study of both immunological and ecological processes within a single framework. In this paper, we develop a general two-pathogen single-host model of pathogen interactions that simultaneously incorporates these mechanisms. We are then able to mechanistically explore how immunoecological processes mediate interactions between diseases for a pool of susceptible individuals. We show that the precise nature of the interaction can induce either competitive or cooperative associations between pathogens. Understanding the dynamic implications of multi-pathogen associations has potentially important public health consequences. Such a framework may be especially helpful in disentangling the effects of partially cross-immunizing infections that affect populations with a pre-disposition towards immunosuppression such as children and the elderly.     

Improving the Rank Precision of Population Health Measures for Small Areas with Longitudinal and Joint Outcome Models

Objectives

The University of Wisconsin Population Health Institute has published the County Health Rankings since 2010. These rankings use population-based data to highlight health outcomes and the multiple determinants of these outcomes and to encourage in-depth health assessment for all United States counties. A significant methodological limitation, however, is the uncertainty of rank estimates, particularly for small counties. To address this challenge, we explore the use of longitudinal and pooled outcome data in hierarchical Bayesian models to generate county ranks with greater precision.

Methods

In our models we used pooled outcome data for three measure groups: (1) Poor physical and poor mental health days; (2) percent of births with low birth weight and fair or poor health prevalence; and (3) age-specific mortality rates for nine age groups. We used the fixed and random effects components of these models to generate posterior samples of rates for each measure. We also used time-series data in longitudinal random effects models for age-specific mortality. Based on the posterior samples from these models, we estimate ranks and rank quartiles for each measure, as well as the probability of a county ranking in its assigned quartile. Rank quartile probabilities for univariate, joint outcome, and/or longitudinal models were compared to assess improvements in rank precision.

Results

The joint outcome model for poor physical and poor mental health days resulted in improved rank precision, as did the longitudinal model for age-specific mortality rates. Rank precision for low birth weight births and fair/poor health prevalence based on the univariate and joint outcome models were equivalent.

Conclusion

Incorporating longitudinal or pooled outcome data may improve rank certainty, depending on characteristics of the measures selected. For measures with different determinants, joint modeling neither improved nor degraded rank precision. This approach suggests a simple way to use existing information to improve the precision of small-area measures of population health.     

Kin influences on fertility in Thailand: Effects and mechanisms

It has been suggested that human mothers are cooperative breeders, as they need help from others to successfully raise offspring. Studies working under this framework have found correlations between the presence of kin and both child survival and female fertility rates. This study seeks to understand the proximate mechanisms by which kin influence fertility using data from the 1987 Thailand Demographic and Health Survey (DHS), a nationally representative sample of 6775 women. Kin influence is measured by the length of time couples live with the husband's or wife's parents after marriage. Event history analysis, multilevel modeling and structural equation modeling are used to investigate both fertility outcomes and potential pathways through which postnuptial residence may influence fertility outcomes, including employment status, maternal and child outcomes, contraceptive use, breastfeeding duration, and age at marriage. We show that living virilocally (with husband's kin after marriage) increases total fertility by shortening time from marriage to first birth, and increasing the likelihood of progression to each subsequent birth. These effects are mediated through correlations between virilocal residence and earlier age at marriage as well as delayed initiation of contraceptive use. We find no influence of husband's kin on maternal or child outcomes. Living uxorilocally (with wife's kin after marriage) also reduces age at marriage, shortens time from marriage to first birth and (marginally) improves child survivorship, but has no effect on other child and maternal outcomes or progression to subsequent births and results in a similar number of living children as women living neolocally.     

Individual and Population Level Effects of Partner Notification for Chlamydia trachomatis

Partner notification (PN or contact tracing) is an important aspect of treating bacterial sexually transmitted infections (STIs), such as Chlamydia trachomatis. It facilitates the identification of new infected cases that can be treated through individual case management. PN also acts indirectly by limiting onward transmission in the general population. However, the impact of PN, both at the level of individuals and the population, remains unclear. Since it is difficult to study the effects of PN empirically, mathematical and computational models are useful tools for investigating its potential as a public health intervention. To this end, we developed an individual-based modeling framework called Rstisim. It allows the implementation of different models of STI transmission with various levels of complexity and the reconstruction of the complete dynamic sexual partnership network over any time period. A key feature of this framework is that we can trace an individual’s partnership history in detail and investigate the outcome of different PN strategies for C. trachomatis. For individual case management, the results suggest that notifying three or more partners from the preceding 18 months yields substantial numbers of new cases. In contrast, the successful treatment of current partners is most important for preventing re-infection of index cases and reducing further transmission of C. trachomatis at the population level. The findings of this study demonstrate the difference between individual and population level outcomes of public health interventions for STIs.     

Physiological dysregulation and somatic decline among elders: modeling, applying and re-interpreting allostatic load

Mortality rates continue to decline among post-reproductive individuals. This makes understanding long-term physiological responses to stress increasingly important. Allostatic load (AL) was developed to assess detrimental effects on the soma of responding to multiple stressors over a lifetime. AL arises from developmental experiences, genetic predispositions, environmental, psychosocial, life style and other stressors. In early life stress responsive systems are initiated that produce hormones that maintain the soma through continual allostatic responses. Later in life, systems designed to mitigate stressors may fail or be compromised, promoting unwanted somatic changes and dysregulation. This places a load on the regulatory system that impedes day-to-day stress responses, predisposing to cellular damage and degenerative diseases. Here we review 44 peer-reviewed 2005-2010 publications reportedly examining relationships between AL and risk factors, chronic diseases, morbidity and mortality in samples of elderly adults. The sum of results suggests that AL does assess aspects of physiological dysregulation and somatic decline, predicts detrimental age-related declines, and is associated with negative sociocultural attributes and psychological outcomes. Such consistent results and wide application of AL, while it is still being modeled and re-interpreted, suggest its perceived usefulness as a research and clinical tool. AL provides a possible biomarker of senescence, assessing it over the life span will aid in predicting future negative health outcomes.     

The intersection of social determinants of health,the microbiome,and health outcomes in immigrants: A scoping review

In the present scoping review, we explore whether existing evidence supports the premise that social determinants of health (SDoH) affect immigrant health outcomes through their effects on the microbiome. We adapt the National Institute on Minority Health and Health Disparities' research framework to propose a conceptual model that considers the intersection of SDoH, the microbiome, and health outcomes in immigrants. We use this conceptual model as a lens through which to explore recent research about SDoH, biological factors associated with changes to immigrants' microbiomes, and long-term health outcomes. In the 17 articles reviewed, dietary acculturation, physical activity, ethnicity, birthplace, age at migration and length of time in the host country, socioeconomic status, and social/linguistic acculturation were important determinants of postmigration microbiome-related transformations. These factors are associated with progressive shifts in microbiome profile with time in host country, increasing the risks for cardiometabolic, mental, immune, and inflammatory disorders and antibiotic resistance. The evidence thus supports the premise that SDoH influence immigrants' health postmigration, at least in part, through their effects on the microbiome. Omission of important postmigration social-ecological variables (e.g., stress, racism, social/family relationships, and environment), limited research among minoritized subgroups of immigrants, complexity and inter- and intra-individual differences in the microbiome, and limited interdisciplinary and biosocial collaboration restrict our understanding of this area of study. To identify potential microbiome-based interventions and promote immigrants' well-being, more research is necessary to understand the intersections of immigrant health with factors from the biological, behavioral/psychosocial, physical/built environment, and sociocultural environment domains at all social-ecological levels.     

Climate change and health with an emphasis on interactions with ultraviolet radiation: a review

Climate change is increasingly recognized as a major risk to human health, and health concerns are assuming more importance in international debates on mitigation and adaptation strategies. Health consequences of climate change will occur through direct and indirect routes, and as a result of interactions with other environmental exposures. Heatwaves will become more common and are associated with higher mortality particularly in the elderly and those with pre‐existing cardiovascular and respiratory illnesses. Warmer ambient temperatures will result in more dehydration episodes and increased risks of renal disease and, through effects on pollen seasons, there may be an increase in allergic disease such as asthma and hayfever. Other adverse effects including on air quality, food safety and security and an expanding distribution of some infectious diseases, including vector‐borne diseases, are postulated. A related but separate environmental exposure is that of ultraviolet radiation (UVR). Interactions between climate change and stratospheric ozone (and the causes of ozone depletion) will cause changes to levels of ambient UVR in the future and warmer temperatures are likely to change sun exposure behaviour. Co‐occurring effects on aquatic and terrestrial ecosystems have potential consequences for food safety, quality and supply. Climate change‐related exposures are likely to affect the incidence and distribution of diseases usually considered as caused by UVR exposure; and changes in UVR exposure will modulate the climate change effects on human health. For example, in some regions warmer temperatures due to climate change will encourage more outdoor behaviour, with likely consequences for increasing skin cancer incidence. Although many of the health outcomes of both climate change and the interaction of climate change and UVR exposure are somewhat speculative, there are risks to over‐ or under‐estimations of health risks if synergistic and antagonistic effects of co‐occurring environmental changes are not considered.     

Caregiving within and beyond the family is associated with lower mortality for the caregiver: A prospective study

Grandparenting has been proposed as an ultimate evolutionary mechanism that has contributed to the increase in human life expectancy (see the grandmother hypothesis). The neural and hormonal system – originally rooted in parenting and thus grandparenting – that is activated in the process of caregiving has been suggested as a potential proximate mechanism that promotes engagement in prosocial behavior towards kin and non-kin alike. Evidence and theory suggest that activating this caregiving system positively impacts health and may reduce the mortality of the helper. Although some studies have found grandparental care to have beneficial effects on grandparents' health outcomes, most studies have focused on the detrimental health consequences of providing custodial care for grandchildren. Little is known about how non-custodial grandparental and other forms of caregiving relate to mortality hazards for the care provider. Using an evolutionary framework, we examined whether caregiving within and beyond the family is related to mortality in older adults. Survival analyses based on data from the Berlin Aging Study revealed that mortality hazards for grandparents who provided non-custodial childcare were 37% lower than for grandparents who did not provide childcare and for non-grandparents. These associations held after controlling for physical health, age, socioeconomic status and various characteristics of the children and grandchildren. Furthermore, the effect of caregiving extended to non-grandparents and to childless older adults who helped beyond their families. Potential ultimate and proximate mechanisms underlying these effects are discussed.