» Articles » PMID: 24898872

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

Overview
Publisher Biomed Central
Specialty Public Health
Date 2014 Jun 6
PMID 24898872
Citations 37
Authors
Affiliations
Soon will be listed here.
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.

Citing Articles

Willingness to Use Long-Acting Injectable Pre-Exposure Prophylaxis among Adolescent Girls and Young Women in Kampala, Uganda.

Lunkuse J, Lwanga C, Wamono F, Muturi-Kioi V, Price M, Mayanja Y AIDS Behav. 2025; .

PMID: 39883369 DOI: 10.1007/s10461-025-04616-y.


Geospatial pattern of HIV seropositivity and its predictors among women in Ethiopia. A spatial and multiscale geographically weighted regression analysis.

Kitaw T, Abate B, Tilahun B, Haile R PLoS One. 2024; 19(7):e0306645.

PMID: 38990932 PMC: 11239007. DOI: 10.1371/journal.pone.0306645.


Predicting the individualized risk of human immunodeficiency virus infection among sexually active women in Ethiopia using a nomogram: prediction model development and validation.

Tesfie T, Yehuala T, Agimas M, Yismaw G, Maru Wubante S, Fente B Front Public Health. 2024; 12:1375270.

PMID: 38979038 PMC: 11229785. DOI: 10.3389/fpubh.2024.1375270.


Associations between disordered eating behaviour and sexual behaviour amongst emerging adults attending a tertiary education institution in Coastal Kenya.

Chea S, Kazienga A, Oyugi E, Menza I, Nasambu C, Ibrahim F PLoS One. 2024; 19(6):e0301436.

PMID: 38861516 PMC: 11166344. DOI: 10.1371/journal.pone.0301436.


Spatial distribution and determinants of HIV high burden in the Southern African sub-region.

Adetokunboh O, Are E PLoS One. 2024; 19(4):e0301850.

PMID: 38669230 PMC: 11051620. DOI: 10.1371/journal.pone.0301850.


References
1.
Agardh A, Tumwine G, Ostergren P . The impact of socio-demographic and religious factors upon sexual behavior among Ugandan university students. PLoS One. 2011; 6(8):e23670. PMC: 3161050. DOI: 10.1371/journal.pone.0023670. View

2.
Kazembe L, Clarke A, Kandala N . Childhood mortality in sub-Saharan Africa: cross-sectional insight into small-scale geographical inequalities from Census data. BMJ Open. 2012; 2(5). PMC: 3488715. DOI: 10.1136/bmjopen-2012-001421. View

3.
Touray K, Adetifa I, Jallow A, Rigby J, Jeffries D, Cheung Y . Spatial analysis of tuberculosis in an urban west African setting: is there evidence of clustering?. Trop Med Int Health. 2010; 15(6):664-72. DOI: 10.1111/j.1365-3156.2010.02533.x. View

4.
Tanser F, le Sueur D . The application of geographical information systems to important public health problems in Africa. Int J Health Geogr. 2003; 1(1):4. PMC: 149399. DOI: 10.1186/1476-072x-1-4. View

5.
Bailey R, Moses S, Parker C, Agot K, Maclean I, Krieger J . Male circumcision for HIV prevention in young men in Kisumu, Kenya: a randomised controlled trial. Lancet. 2007; 369(9562):643-56. DOI: 10.1016/S0140-6736(07)60312-2. View