Day: June 5, 2014

Spatial Analysis of Factors Associated with HIV Infection among Young People in Uganda, 2011

/ Matt Artz / 1 Comment

BMC Public HealthBMC Public Health, 14:555, Published 05 June 2014

By Lucy A Chimoyi and Eustasius Musenge

“Background

The HIV epidemic in East Africa is of public health importance with an increasing number of young people getting infected. This study sought to identify spatial clusters and examine the geographical variation of HIV infection at a regional level while accounting for risk factors associated with HIV/AIDS among young people in Uganda.

“Methods

A secondary data analysis was conducted on a survey cross-sectional design whose data were obtained from the 2011 Uganda Demographic and Health Survey (DHS) and AIDS Indicator Survey (AIS) for 7 518 young people aged 15-24 years. The analysis was performed in three stages while incorporating population survey sampling weights. Maximum likelihood-based logistic regression models were used to explore the non-spatially adjusted factors associated with HIV infection. Spatial scan statistic was used to identify geographical clusters of elevated HIV infections which justified modelling using a spatial random effects model by Bayesian-based logistic regression models.

“Results

In this study, 309/533 HIV sero-positive female participants were selected with majority residing in the rural areas [386(72%)]. Compared to singles, those currently [Adjusted Odds Ratio (AOR) =3.64; (95%CI; 1.25-10.27)] and previously married [AOR = 5.62; (95%CI: 1.52-20.75)] participants had significantly higher likelihood of HIV infections. Sexually Transmitted Infections [AOR = 2.21; (95%CI: 1.35-3.60)] were more than twice likely associated with HIV infection. One significant (p < 0.05) primary cluster of HIV prevalence around central Uganda emerged from the SaTScan cluster analysis. Spatial analysis disclosed behavioural factors associated with greater odds of HIV infection such as; alcohol use before sexual intercourse [Posterior Odds Ratio (POR) =1.32; 95% (BCI: 1.11-1.63)]. Condom use [POR = 0.54; (95%BCI: 0.41-0.69)] and circumcision [POR = 0.66; (95%BCI: 0.45-0.99)] provided a protective effect against HIV.

uganda

“Conclusions

The study revealed associations between high-risk sexual behaviour and HIV infection. Behavioural change interventions should therefore be pertinent to the prevention of HIV. Spatial analysis further revealed a significant HIV cluster towards the Central and Eastern areas of Uganda. We propose that interventions targeting young people should initially focus on these regions and subsequently spread out across Uganda.”

Global Patterns of Marine Mammal, Seabird, and Sea Turtle Bycatch Reveal Taxa-specific and Cumulative Megafauna Hotspots

/ Matt Artz / 1 Comment

14.coverProceedings of the National Academy of Sciences, vol. 111 no. 14, 08 April 2014

By Rebecca L. Lewison, Larry B. Crowder, Bryan P. Wallace, Jeffrey E. Moore, Tara Cox, Ramunas Zydelis, Sara McDonald, Andrew DiMatteo, Daniel C. Dunn, Connie Y. Kot, Rhema Bjorkland, Shaleyla Kelez, Candan Soykan, Kelly R. Stewart, Michelle Sims, Andre Boustany, Andrew J. Read, Patrick Halpin, W. J. Nichols, and Carl Safina

“Recent research on ocean health has found large predator abundance to be a key element of ocean condition. Fisheries can impact large predator abundance directly through targeted capture and indirectly through incidental capture of nontarget species or bycatch. However, measures of the global nature of bycatch are lacking for air-breathing megafauna. We fill this knowledge gap and present a synoptic global assessment of the distribution and intensity of bycatch of seabirds, marine mammals, and sea turtles based on empirical data from the three most commonly used types of fishing gears worldwide. We identify taxa-specific hotspots of bycatch intensity and find evidence of cumulative impacts across fishing fleets and gears.

Calculated cumulative bycatch intensity (Eq. 1) for all taxonomic groups and gear types. Bycatch intensity values were used to generate a raster surface from an inverse weighted distance function for polygons with available data. Interpolated values are displayed using the same color ramp as in Fig. 1. Numbers represent the number of data records represented within each polygon.

Calculated cumulative bycatch intensity (Eq. 1) for all taxonomic groups and gear types. Bycatch intensity values were used to generate a raster surface from an inverse weighted distance function for polygons with available data.  Numbers represent the number of data records represented within each polygon.

“This global map of bycatch illustrates where data are particularly scarce—in coastal and small-scale fisheries and ocean regions that support developed industrial fisheries and millions of small-scale fishers—and identifies fishing areas where, given the evidence of cumulative hotspots across gear and taxa, traditional species or gear-specific bycatch management and mitigation efforts may be necessary but not sufficient. Given the global distribution of bycatch and the mitigation success achieved by some fleets, the reduction of air-breathing megafauna bycatch is both an urgent and achievable conservation priority.”