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

Chimoyi LA, Musenge E - BMC Public Health (2014)

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Division of Epidemiology and Biostatistics, School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa. chimoyi@hotmail.com.

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.

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Posterior odds ratio of HIV risk among young people in Uganda.
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Figure 2: Posterior odds ratio of HIV risk among young people in Uganda.

Mentions: The results from the Bayesian and cluster analysis were superimposed to produce a map that displayed low and high rate geographical clusters of HIV in the study area. High (red) and low (green) HIV risk areas were identified as shown in Figure 1. Figure 2 depicted the variations of HIV in different regions in Uganda from the BayesX output. A standard Geographical Information System (GIS) programme [38], Quantum GIS was used to translate the outputs into maps that depicted the distribution of HIV prevalence in Uganda.


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

Chimoyi LA, Musenge E - BMC Public Health (2014)

Posterior odds ratio of HIV risk among young people in Uganda.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4061924&req=5

Figure 2: Posterior odds ratio of HIV risk among young people in Uganda.
Mentions: The results from the Bayesian and cluster analysis were superimposed to produce a map that displayed low and high rate geographical clusters of HIV in the study area. High (red) and low (green) HIV risk areas were identified as shown in Figure 1. Figure 2 depicted the variations of HIV in different regions in Uganda from the BayesX output. A standard Geographical Information System (GIS) programme [38], Quantum GIS was used to translate the outputs into maps that depicted the distribution of HIV prevalence in Uganda.

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Division of Epidemiology and Biostatistics, School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa. chimoyi@hotmail.com.

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.

Show MeSH
Related in: MedlinePlus