Clinics register based HIV prevalence in Jimma zone, Ethiopia: applications of likelihood and Bayesian approaches

• Published in: BMC infectious diseases Vol. 22; no. 1; p. 281
• Main Authors: Bedaso, Nemso Geda, Debusho, Legesse Kassa
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 24-03-2022; BioMed Central; BMC
• Subjects: Acquired immune deficiency syndrome; Age; AIDS; Bayes Theorem; Bayesian analysis; Bayesian approach; Control; Disaggregation; Disease control; Disease transmission; Distribution; Epidemics; Epidemiology; Ethiopia - epidemiology; Generalized linear mixed effects model; Heterogeneity; Heterogeneity in HIV infection; HIV; HIV infection; HIV Infections - prevention & control; Human immunodeficiency virus; Humans; Immune system; Infections; Infectious diseases; Mathematical models; Maximum likelihood estimation; Medical tests; Odd ratios; Patients; Prevalence; Public health; Risk factors; Sexual intercourse; Statistical tests; Ethiopia; Addis Ababa Ethiopia; Maximum likelihood estimation; Bayesian approach; Generalized linear mixed effects model; HIV; Odd ratios; Heterogeneity in HIV infection
• ISSN: 1471-2334; 1471-2334
• DOI: 10.1186/s12879-021-06965-0

The distribution of HIV is not uniform in Ethiopia with some regions recording higher prevalence than others. However, reported regional HIV prevalence estimates mask the heterogeneity of the epidemic within regions. The main purpose of this study was to assess the district differences in HIV prevalence and other factors that affect the prevalence of HIV infection in Jimma zone, Oromia region of Ethiopia. We aimed to identify districts which had higher or lower than zone average HIV prevalence. Such in-depth analysis of HIV data at district level may help to develop effective strategies to reduce the HIV transmission rate.
Data collected from 8440 patients who were tested for HIV status in government clinics at the 22 Districts between September 2018 to August 2019 in Jimma zone were used for the analyses. A generalized linear mixed effects model with district random effects was applied to assess the factors associated with HIV infection and the best linear unbiased prediction was used to identify districts that had higher or lower HIV infection. Both likelihood and Bayesian methods were considered.
The statistical test on district random effects variance suggested the need for district random effects in all the models. The results from applying both methods on full data show that the odds of HIV infection are significantly associated with covariates considered in this study. Disaggregation of prevalence by gender also highlighted the persistent features of the HIV epidemic in Jimma zone. After controlling for covariates effects, the results from both techniques revealed that there was heterogeneity in HIV infection prevalence among districts within Jimma zone, where some of them had higher and some had lower HIV infection prevalence compared to the zone average HIV infection prevalence.
The study recommends government to give attention to those districts which had higher HIV infection and to conduct further research to improve their intervention strategies. Further, related to those districts which had lower infection, it would be advantageous to identify reasons for their performance and may apply them to overcome HIV infection among residents in those districts which had higher HIV infection. The approach used in this study can also help to assess the effect of interventions introduced by the authorities to control the epidemic and it can easily be extended to assess the regions HIV infection rate relative to the rate at the national level, or zones HIV infection rate relative to the rate at a region level.