Pan African Medical Journal · 2019 · 21 citations · 15 references
Public HealthHealth SciencesPopulation Health SciencesEpidemiologyHiv PreventionInfectious Disease EpidemiologyHiv DiagnosisHiv EpidemiologyHiv TestingCross-sectional EpidemiologyCultural CharacteristicsHiv PrevalenceLogistic Regression AnalysisPopulation-based Hiv SurveysSocio-demographic Inequalities
INTRODUCTION: Sub-Saharan Africa accounts for 66% of 36.7 million individuals living with HIV in 2015 with Nigeria having the second highest prevalence in Africa. The study aimed to find the prevalence and socio-demographic factors associated with HIV infection and compare these findings between high and low prevalence areas. METHODS: We conducted a cross-sectional study among adults aged 15 to 49 years from March to April 2015. We administered a questionnaire to collect linked anonymous data on socio-demographic and socio-cultural characteristics and screened all respondents for HIV infection. We defined a high HIV prevalence area as area with prevalence consistently above 5% and an area with prevalence consistently below 2% as low prevalence area. We performed univariate, bivariate and logistic regration analysis to assess factors associated with HIV infection. RESULTS: We screened and interviewed all 480 respondents. Majority 344 (71.7%) were females, mean age was 30.1 years (±7.4 years), high proportion were employed 246 (51.2%). In high HIV prevalence area, aged <30 years (Adjusted Odd Ratio (AOR) = 4.2, 95% Confidence Interval (CI) = 1.1-20.4) and being employed (AOR= 3.7, 95% CI=1.0-58.8) increased the likelihood of HIV infection. In low HIV prevalence area, lack of education (AOR=7.1, 95% CI= 0.9-32) was the only predictor of HIV infection. CONCLUSION: Interplay of socio-demographic factors was responsible for differences in HIV prevalence. To further decrease prevalence in low prevalence areas (below 1%), government should make universal basic education mandatory and in high prevalence areas, interventions should target the young and the employed.
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Statistical Methods for Rates and Proportions.
B. S. Everitt, J. L. Fleiss · Biometrics · 1981 · 9.1K citations
Nyovani Madise, Abdhalah Ziraba, Joseph Inungu et al. · Health & Place · 2012 · 163 citations · Full text
Public Health, Community Health, Population Health Sciences +15