Quantifying the individual-level association between income and mortality risk in the United States using the National Longitudinal Mortality Study.

Policy makers would benefit from being able to estimate the likely impact of potential interventions to reverse the effects of rapidly rising income inequality on mortality rates. Using multiple cohorts of the National Longitudinal Mortality Study (NLMS), we estimate the absolute income effect on premature mortality in the United States. A multivariate Poisson regression using the natural logarithm of equivilized household income establishes the magnitude of the absolute income effect on mortality. We calculate mortality rates for each income decile of the study sample and mortality rate ratios relative to the decile containing mean income. We then apply the estimated income effect to two kinds of hypothetical interventions that would redistribute income. The first lifts everyone with an equivalized household income at or below the U.S. poverty line (in 2000$) out of poverty, to the income category just above the poverty line. The second shifts each family's equivalized income by, in turn, 10%, 20%, 30%, or 40% toward the mean household income, equivalent to reducing the Gini coefficient by the same percentage in each scenario. We also assess mortality disparities of the hypothetical interventions using ratios of mortality rates of the ninth and second income deciles, and test sensitivity to the assumption of causality of income on mortality by halving the mortality effect per unit of equivalized household income. The estimated absolute income effect would produce a three to four percent reduction in mortality for a 10% reduction in the Gini coefficient. Larger mortality reductions result from larger reductions in the Gini, but with diminishing returns. Inequalities in estimated mortality rates are reduced by a larger percentage than overall estimated mortality rates under the same hypothetical redistributions.

Association Between Schistosoma haematobium Exposure and Human Immunodeficiency Virus Infection Among Females in Mozambique.

Recent evidence suggests an association between human immunodeficiency virus (HIV) and female genital schistosomiasis (FGS) in sub-Saharan Africa, especially in Mozambique, South Africa, Tanzania, and Zimbabwe. Women with FGS have increased numbers of HIV target cells and cell receptors in genital and blood compartments, potentially increasing the risk of HIV transmission per sexual exposure, and the association may explain the high female:male ratio of HIV prevalence unique to sub-Saharan Africa. We investigate this association in Mozambique by linking two georeferenced, high-quality secondary data sources on HIV prevalence and Schistosoma haematobium: the AIDS Indicator Survey, and the Global Neglected Tropical Diseases (GNTD) open-source database, respectively. We construct a schistosomiasis exposure covariate indicating women reporting "unimproved" daily drinking water sources and living no more than 2-5 km from high-endemic global positioning system (GPS) coordinates in the GNTD. In logistic regression analyses predicting HIV-positive status, we show that exposure increases the odds of HIV-positive status by three times, controlling for demographic and sexual risk factors.

An association between neighbourhood wealth inequality and HIV prevalence in sub-Saharan Africa.

This paper investigates whether community-level wealth inequality predicts HIV serostatus using DHS household survey and HIV biomarker data for men and women ages 15-59 pooled from six sub-Saharan African countries with HIV prevalence rates exceeding 5%. The analysis relates the binary dependent variable HIV-positive serostatus and two weighted aggregate predictors generated from the DHS Wealth Index: the Gini coefficient, and the ratio of the wealth of households in the top 20% wealth quintile to that of those in the bottom 20%. In separate multilevel logistic regression models, wealth inequality is used to predict HIV prevalence within each statistical enumeration area, controlling for known individual-level demographic predictors of HIV serostatus. Potential individual-level sexual behaviour mediating variables are added to assess attenuation, and ordered logit models investigate whether the effect is mediated through extramarital sexual partnerships. Both the cluster-level wealth Gini coefficient and wealth ratio significantly predict positive HIV serostatus: a 1 point increase in the cluster-level Gini coefficient and in the cluster-level wealth ratio is associated with a 2.35 and 1.3 times increased likelihood of being HIV positive, respectively, controlling for individual-level demographic predictors, and associations are stronger in models including only males. Adding sexual behaviour variables attenuates the effects of both inequality measures. Reporting eleven plus lifetime sexual partners increases the odds of being HIV positive over five-fold. The likelihood of having more extramarital partners is significantly higher in clusters with greater wealth inequality measured by the wealth ratio. Disaggregating logit models by sex indicates important risk behaviour differences. Household wealth inequality within DHS clusters predicts HIV serostatus, and the relationship is partially mediated by more extramarital partners. These results emphasize the importance of incorporating higher-level contextual factors, investigating behavioural mediators, and disaggregating by sex in assessing HIV risk in order to uncover potential mechanisms of action and points of preventive intervention.

An association between ethnic diversity and HIV prevalence in sub-Saharan Africa.

This paper investigates whether ethnic diversity at the Demographic and Health Surveys (DHS) cluster level predicts HIV serostatus in three sub-Saharan African countries (Kenya, Malawi and Zambia), using DHS household survey and HIV biomarker data for men and women aged 15­59 collected since 2006. The analysis relates a binary dependent variable (HIV positive serostatus) and a weighted aggregate predictor variable representing the number of different ethnic groups within a DHS Statistical Enumeration Area (SEA) or cluster, which roughly corresponds to a neighbourhood. Multilevel logistic regression is used to predict HIV prevalence within each SEA, controlling for known demographic, social and behavioural predictors of HIV serostatus. The key finding was that the cluster-level ethnic diversity measure was a significant predictor of HIV serostatus in Malawi and Zambia but not in Kenya. Additional results reflected the heterogeneity of the epidemics: male gender, marriage (Kenya), number of extramarital partners in the past year (Kenya and Malawi, but probably confounded with younger age) and Muslim religion (Zambia) were associated with lower odds of positive HIV serostatus. Condom use at last intercourse (a spurious result probably reflecting endogeneity), STD in the past year, number of lifetime sexual partners, age (Malawi and Zambia), education (Zambia), urban residence (Malawi and Zambia) and employment (Kenya and Malawi) were associated with higher odds of positive serostatus. Future studies might continue to employ multilevel models and incorporate additional, more robust, controls for individual behavioural risk factors and for higher-level social and economic factors, in order to verify and further clarify the association between neighbourhood ethnic diversity and HIV serostatus.