Background: The objective of this study was to model the predictors of HIV prevalence in Malawi through a complex sample logistic regression and spatial mapping approach using the national Demographic and Health Survey datasets.
Methods: We conducted a secondary data analysis using the 2015-2016 Malawi Demographic and Health Survey and AIDS Indicator Survey. The analysis was performed in three stages while incorporating population survey sampling weights to: i) interpolate HIV data, ii) identify the spatial clusters with the high prevalence of HIV infection, and iii) perform a multivariate complex sample logistic regression.
Results: In all, 14,779 participants were included in the analysis with an overall HIV prevalence of 9% (7.0% in males and 10.8% in females). The highest prevalence was found in the southern region of Malawi (13.2%), and the spatial interpolation revealed that the HIV epidemic is worse at the south-eastern part of Malawi. The districts in the high HIV prevalent zone of Malawi are Thyolo, Zomba, Mulanje, Phalombe and Blantyre. In central and northern region, the district HIV prevalence map identified Lilongwe in the central region and Karonga in the northern region as districts that equally deserve attention. People residing in urban areas had a 2.2 times greater risk of being HIV-positive compared to their counterparts in the rural areas (AOR = 2.16; 95%CI = 1.57-2.97). Other independent predictors of HIV prevalence were gender, age, marital status, number of lifetime sexual partners, extramarital partners, the region of residence, condom use, history of STI in the last 12 months, and household wealth index. Disaggregated analysis showed in-depth sociodemographic regional variations in HIV prevalence.
Conclusion: These findings identify high-risk populations and regions to be targeted for Pre-Exposure Prophylaxis (PrEP) campaigns, HIV testing, treatment and education to decrease incidence, morbidity, and mortality related to HIV infection in Malawi.
Bibliographical noteFunding Information:
This work was supported by University of California, San Francisco Population Health and Health Equity Scholar program under Grant number 7504575.
© 2020 The Author(s).
- Pre-exposure prophylaxis
- Risk behavior