Prevalence, spatial patterns and factors associated with HIV infection in Zimbabwe, 2011.
Date
2014-04-24
Authors
Atilola, Glory
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Abstract
Like in other Sub-Sahara African countries, HIV has had significant devastating effects in
Zimbabwe. Understanding the underlying and proximate determinants of infections in the
population is important for knowing who needs intervention, where interventions are needed and for designing specific and relevant interventions.
No studies appear to have assessed distributional patterns and risk factors of HIV prevalence simultaneously across the ten provinces in Zimbabwe using a combination of the proximatedeterminant framework approach and modern geostatistical techniques to identify spatial HIV hotspots. The overall aim of this study was to investigate the prevalence, spatial patterns and factors associated with HIV infection in Zimbabwe using the proximate-determinant framework approach and modern geostatistical techniques.
Methods
This study used the Zimbabwe demographic and health survey of 2010/2011 which included a representative sample of 9171 women and 7104 men- aged 15 to 49. Following the proximate determinants framework, multilevel models were fitted separately for men and women. Global and local spatial autocorrelations were assessed. Spatial regression models were also fitted to adjust for spatial random effects and non-random effects. Significant difference between hotspots and cold spots was examined at community level.
Results
The overall prevalence estimate was 15.4% (95%CI: 14.8% - 16.0%) [17.7% among women and 12.2% among men]. While prevalence was highest in women in the middle age categories 29.3%), a corresponding dose response relationship was observed among men. Highest prevalence was obtained in urban dwellers, widowed men (60%) and women (56%), and Matabeleland South. A dose response association was found between HIV prevalence and duration of cohabitation, total lifetime partners in both gender populations, and age of most recent partner in men.
Evidence of global and local spatial autocorrelation was found. Spatial scan techniques identified three hotspots including Matabeleland South province. Prominent significant underlying risk factors of HIV infection in both gender populations were age group [men: AOR40-44: 5.19;(95%CI:1.92 – 14.02); AOR45-49 : 4.76 (95%CI: 1.70 – 13.30)] [women: AOR30-34: 2.17;(95%CI:1.32 – 3.54); AOR35-39: 1.91; (95%CI:1.14 – 3.19)] and widowed marital status[men:AOR: 7.37; (95%CI: 3.10 – 17.52)][women: AOR: 4.13; (95%CI: 2.49 – 6.85)], likewise Matabeleland South region in men [AOR: 2.32; (95%CI: 1.49-3.62)]. On the other hand, total lifetime partners [men: AOR5-9: 3.15; (95%CI: 2.08 – 4.77)][women: AOR5-9: 3.74; (95%CI:2.52 – 5.53)], and symptoms of sexually transmitted infections emerged as proximate predictors of the epidemic.
No evidence of significant heterogeneity in geographical distribution of HIV prevalence was found after adjusting for significant underlying and proximate risk factors. However, persons living within hotspots are on the average 30% more likely to be within the richest wealth quintile (OR: 1.30; p: <0.001), more likely to have higher total lifetime partners and symptoms of sexually transmitted infections.
Conclusions
A combined application of traditional statistical procedures and modern geostatistical techniques to identify significant predictors of HIV infection and prevalence hotspots provide a more robust approach to investigate HIV spread in a generalized epidemic setting. The results showed excess risk of infections in certain demographic sub-groups, reinforcing the need for programmatic interventions to be directed at these locations and populations in order to maximize impact. The interventions should address the proximate determinants of infection in the population. Further analysis should examine the independent effect of condom use on HIV outcome in the context of marital status and sexual partnership. Proximate determinants of HIV infection should be
explored at community level.