The epidemiology of HIV infection in Zambia

Population surveys of health and fertility are an important source of information about demographic trends and their likely impact on the HIV/AIDS epidemic. In contrast to groups sampled at health facilities they can provide nationally and regionally representative estimates of a range of variables. Data on HIV sero-status were collected in the 2001-2 Zambia Demographic and Health Survey (ZDHS) and made available in a separate data file in which HIV status was linked to a very limited set of demographic variables. We utilized this data set to examine associations between HIV prevalence, gender, age and geographical location. We apply the generalized geo-additive semi-parametric model as an alternative to the common linear model, in the context of analyzing the prevalence of HIV infection. This model enables us to account for spatial auto-correlation, non-linear, location effects on the prevalence of HIV infection at the disaggregated provincial level (9 provinces) and assess temporal and geographical variation in the prevalence of HIV infection, while simultaneously controlling for important risk factors. 54 % of the overall sample of 3950 was female. The overall HIV positivity rate was 565 (14.3%). The mean age at HIV diagnosis for male was 30.3 (SD: 11.2) and 27.7 (SD: 9.3) for female respectively. Lusaka and Copperbelt have the first and second highest prevalence of AIDS/HIV (marginal odds ratios of 3.24 and 2.88 respectively) but when the younger age of the urban population and the spatial auto-correlation was taken into account Lusaka and Copper belt were no longer among the areas with the highest prevalence. Nonlinear effects of age at HIV diagnosis were also discussed and the importance of spatial residual effects and control of confounders on the prevalence of HIV infection. The study was conducted to assess the spatia pattern and the effect of confounding risk factors on AIDS/HIV prevalence and to develop a means of adjusting estimates of AIDS/HIV prevalence on the important risk factors. Controlling for important risk factors such as geographical location (spatial auto-correlation), age structure of the population, gender gave estimates of prevalence that are statistically robust. Researchers should be encouraged to use all available information in the data to account for important risk factors when reporting AIDS/HIV prevalence. Where this is not possible, correction factors should be applied, particularly where estimates of AIDS/HIV prevalence are pooled in systematic reviews. Our maps can be used for policy planning and management of AIDS/HIV in Zambia

Millennium development goal 6 and HIV infection in Zambia : what can we learn from successive household surveys?

Background: Geographic location represents an ecological measure of HIV status and is a strong predictor of HIV prevalence. Given the complex nature of location effects, there is limited understanding of their impact on policies to reduce HIV prevalence. Methods: Participants were 3949 and 10 874 respondents from two consecutive Zambia Demographic and Health Surveys from 2001/2007 (mean age for men and women: 30.3 and 27.7 years, HIV prevalence 14.3% in 2001/2002; 30.3 and 28.0 years, HIV prevalence of 14.7% in 2007). A Bayesian geo-additive mixed model based on Markov Chain Monte Carlo techniques was used to map the change in the spatial distribution of HIV/AIDS prevalence at the provincial level during the 6-year period, accounting for important risk factors. Results: Overall HIV/AIDS prevalence changed little over the 6-year period, but the mapping of residual spatial effects at the provincial level suggested different regional patterns. A pronounced change in odds ratios in Lusaka and Copperbelt provinces in 2001/2002 and in Lusaka and Central provinces in 2007 was observed following adjustment for spatial autocorrelation. Western province went from a lower prevalence area in 2001 (13.4%) to a higher prevalence area in 2007 (17.3%). Southern province went from the highest prevalence area in 2001 (17.3%) to a lower prevalence area in 2007 (15.9%). Conclusion: Findings from two consecutive surveys corroborate the Zambian government's effort to achieve Millennium Developing Goal (MDG) 6. The novel finding of increased prevalence in Western province warrants further investigation. Spatially adjusted provincial-level HIV/AIDS prevalence maps are a useful tool for informing policies to achieve MDG 6 in Zambia. (C) 2011 Wolters Kluwer Health vertical bar Lippincott Williams & Wilkin

Racial residential segregation, socioeconomic disparities, and the White-Black survival gap.

ObjectiveTo evaluate the association between racial residential segregation, a prominent manifestation of systemic racism, and the White-Black survival gap in a contemporary cohort of adults, and to assess the extent to which socioeconomic inequality explains this association.DesignThis was a cross sectional study of White and Black men and women aged 35-75 living in 102 large US Core Based Statistical Areas. The main outcome was the White-Black survival gap. We used 2009-2013 CDC mortality data for Black and White men and women to calculate age-, sex- and race adjusted White and Black mortality rates. We measured segregation using the Dissimilarity index, obtained from the Manhattan Institute. We used the 2009-2013 American Community Survey to define indicators of socioeconomic inequality. We estimated the CBSA-level White-Black gap in probability of survival using sequential linear regression models accounting for the CBSA dissimilarity index and race-specific socioeconomic indicators.ResultsBlack men and women had a 14% and 9% lower probability of survival from age 35 to 75 than their white counterparts. Residential segregation was strongly associated with the survival gap, and this relationship was partly, but not fully, explained by socioeconomic inequality. At the lowest observed level of segregation, and with the Black socioeconomic status (SES) assumed to be at the White SES level scenario, the survival gap is essentially eliminated.ConclusionWhite-Black differences in survival remain wide notwithstanding public health efforts to improve life expectancy and initiatives to reduce health disparities. Eliminating racial residential segregation and bringing Black socioeconomic status (SES) to White SES levels would eliminate the White-Black survival gap

Greater response variability in adolescents is associated with increased white matter development.

Adolescence is a period of learning, exploration, and continuous adaptation to fluctuating environments. Response variability during adolescence is an important, understudied, and developmentally appropriate behavior. The purpose of this study was to identify the association between performance on a dynamic risky decision making task and white matter microstructure in a sample of 48 adolescents (14-16 years). Individuals with the greatest response variability on the task obtained the widest range of experience with potential outcomes to risky choice. When compared with their more behaviorally consistent peers, adolescents with greater response variability rated real-world examples of risk taking behaviors as less risky via self-report. Tract-Based Spatial Statistics (TBSS) were used to examine fractional anisotropy (FA) and mean diffusivity (MD). Greater FA in long-range, late-maturing tracts was associated with higher response variability. Greater FA and lower MD were associated with lower riskiness ratings of real-world risky behaviors. Results suggest that response variability and lower perceived risk attitudes of real-world risk are supported by neural maturation in adolescents

Geo-additive models of childhood undernutrition in three sub-Saharan African countries

We investigate the geographical and socioeconomic determinants of childhood undernutrition in Malawi, Tanzania and Zambia, three neighbouring countries in southern Africa, using the 1992 Demographic and Health Surveys. In particular, we estimate models of undernutrition jointly for the three countries to explore regional patterns of undernutrition that transcend boundaries, while allowing for country-specific interactions. We use geo-additive regression models to flexibly model the effects of selected socioeconomic covariates and spatial effects. Inference is fully Bayesian based on recent Markov chain Monte Carlo techniques. While the socioeconomic determinants generally confirm findings from the literature, we find distinct residual spatial patterns that are not explained by the socioeconomic determinants. In particular, there appears to be a belt transcending boundaries and running from southern Tanzania to northeastern Zambia which exhibits much worse undernutrition. These findings have important implications for planning, as well as in the search for left-out variables that might account for these residual spatial patterns

Mesoscopic structure and social aspects of human mobility

The individual movements of large numbers of people are important in many contexts, from urban planning to disease spreading. Datasets that capture human mobility are now available and many interesting features have been discovered, including the ultra-slow spatial growth of individual mobility. However, the detailed substructures and spatiotemporal flows of mobility - the sets and sequences of visited locations - have not been well studied. We show that individual mobility is dominated by small groups of frequently visited, dynamically close locations, forming primary "habitats" capturing typical daily activity, along with subsidiary habitats representing additional travel. These habitats do not correspond to typical contexts such as home or work. The temporal evolution of mobility within habitats, which constitutes most motion, is universal across habitats and exhibits scaling patterns both distinct from all previous observations and unpredicted by current models. The delay to enter subsidiary habitats is a primary factor in the spatiotemporal growth of human travel. Interestingly, habitats correlate with non-mobility dynamics such as communication activity, implying that habitats may influence processes such as information spreading and revealing new connections between human mobility and social networks.Comment: 7 pages, 5 figures (main text); 11 pages, 9 figures, 1 table (supporting information

Evidence of Competition in Research Activity among Economic Department using Spatial Econometric Techniques

Despite the prevalence of both competitive forces and patterns of collaboration within academic communities, studies on research productivity generally treat universities as independent entities. By exploring the research productivity of all academic economists employed at 81 universities and 17 economic research institutes in Austria, Germany, and German-speaking Switzerland, this study determines whether a research unit’s productivity depends on that of neighboring research units. The significant negative relationship that is found implies competition for priority of discovery among individual researchers, as well as the universities and research institutes that employ them. In addition, the empirical results support the hypotheses that collaboration and the existence of economies of scale increase research productivity.Research productivity, Competition, Collaboration, Negative spatial autocorrelation, Geo-referenced point data