Everyday discrimination is a potent source of stress for racial minorities, and is associated with a wide range of negative health outcomes, spanning both mental and physical health. Few studies have examined the relationships linking race and discrimination to mortality in later life. We examined the longitudinal association among race, everyday discrimination, and all-cause mortality in 12,081 respondents participating in the Health and Retirement Study. Cox proportional hazards models showed that everyday discrimination, but not race, was positively associated with mortality; depressive symptoms and lifestyle factors partially accounted for the relationship between everyday discrimination and mortality; and race did not moderate the association between everyday discrimination and mortality. These findings contribute to a growing body of evidence on the role that discrimination plays in shaping the life chances, resources, and health of people, and, in particular, minority members, who are continuously exposed to unfair treatment in their everyday lives.

Decades of research have produced evidence that parental divorce is negatively associated with offspring outcomes from early childhood, through adolescence, and into the adult years. This study adds to the literature on the effects of parental divorce by examining how the timing of a parental divorce influences the total effect on adult health. Furthermore, we look at how this long-term effect of parental divorce depends on mediators such as the family’s socioeconomic status, parental involvement, cognitive test scores, behavioural problems, smoking, and the offspring’s own experience with divorce. The analyses use data from the National Child Development Study, which includes nine waves of data beginning at birth in 1958 and continuing through age 50. Results from a structural equation model suggest that a parental divorce experienced before age 7 does influence adult health by operating primarily through family socioeconomic status and smoking in adulthood.

This article presents an extension of the cohort-component model of population projection (CCMPP) first formulated by Heuveline (2003) that is capable of modeling a population affected by HIV. Heuveline proposes a maximum likelihood approach to estimate the age profile of HIV incidence that produced the HIV epidemics in East Africa during the 1990s. We extend this work by developing the Leslie matrix representation of the CCMPP, which greatly facilitates the implementation of the model for parameter estimation and projection. The Leslie matrix also contains information about the stable tendencies of the corresponding population, such as the stable age distribution and time to stability. Another contribution of this work is that we update the sources of data used to estimate the parameters, and use these data to estimate a modified version of the CCMPP that includes (estimated) parameters governing the survival experience of the infected population. A further application of the model to a small population with high HIV prevalence in rural South Africa is presented as an additional demonstration. This work lays the foundation for development of more robust and flexible Bayesian estimation methods that will greatly enhance the utility of this and similar models.

BACKGROUND Much of our knowledge of the epidemiology and demography of HIV epidemics in Africa is derived from models fit to sparse, non-representative data. These often average over age and other important dimensions, rarely quantify uncertainty, and typically do not impose consistency on the epidemiology and the demography of the population. OBJECTIVE This work conducts an empirical investigation of the history of the HIV epidemic in Uganda and Tanzania through the late 1990s, focusing on sex-age-specific incidence, uses those results to produce probabilistic forecasts of HIV prevalence ten years later, and compares those to measures of HIV prevalence at the later time to describe the sex-age pattern of changes in prevalence over the intervening period. METHODS We adapt an epidemographic model of a population affected by HIV so that its parameters can be estimated using both the Bayesian melding with IMIS estimation method and maximum likelihood methods. Using the Bayesian version of the model we produce probabilistic forecasts of the population with HIV. RESULTS We produce estimates of sex-age-specific HIV incidence in Uganda and Tanzania in the late 1990s, produce probabilistic forecasts of the HIV epidemics in Uganda and Tanzania during the early 2000s, describe the sex-age pattern of changes in HIV prevalence in Uganda during the early 2000s, and compare the performance and results of the Bayesian and maximum likelihood estimation procedures. CONCLUSIONS We demonstrate that: (1) it is possible to model HIV epidemics in Africa taking account of sex and age, (2) there are important advantages to the Bayesian estimation method, including rigorous quantification of uncertainty and the ability to make probabilistic forecasts, and (3) that there were important age-specific changes in HIV incidence in Uganda during the early 2000s.

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