Email updates

Keep up to date with the latest news and content from BMC Public Health and BioMed Central.

Open Access Research article

Spatial analysis of factors associated with HIV infection among young people in Uganda, 2011

Lucy A Chimoyi* and Eustasius Musenge

Author Affiliations

Division of Epidemiology and Biostatistics, School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa

For all author emails, please log on.

BMC Public Health 2014, 14:555  doi:10.1186/1471-2458-14-555

Published: 5 June 2014

Abstract

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.

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.

Keywords:
HIV/AIDS; Spatial; Clusters; Uganda; Young people; 15-24 years