Spatial Heterogeneity Association of HIV Incidence with Socio-economic Factors in Zimbabwe

Tawanda Manyangadze (Geography Department, Faculty of Science and Engineering, Bindura University of Science Education, Bag 1020,Bindura, Zimbabwe;School of Nursing and Public Health, Department of Public Health Medicine University of KwaZulu-Natal, Durban,South Africa)
Moses J Chimbari (School of Nursing and Public Health, Department of Public Health Medicine University of KwaZulu-Natal, Durban, South Africa)
Emmanuel Mavhura (Geography Department, Faculty of Science and Engineering, Bindura University of Science Education, Bag 1020,Bindura, Zimbabwe)

Article ID: 3466

DOI: https://doi.org/10.30564/jgr.v4i3.3466

Abstract


This study examined the spatial heterogeneity association of HIV incidence and socio-economic factors including poverty severity index,permanently employed females and males, unemployed females, percentage of poor households i.e., poverty prevalence, night lights index, literacy rate,household food security, and Gini index at district level in Zimbabwe.A mix of spatial analysis methods including Poisson model based on original log likelihood ratios (LLR), global Moran’s I, local indicator of spatial association - LISA were employed to determine the HIV hotspots.Geographically Weighted Poisson Regression (GWPR) and semi-parametric GWPR (s-GWPR) were used to determine the spatial association between HIV incidence and socio-economic factors. HIV incidence (number of cases per 1000) ranged from 0.6 (Buhera district) to 13.30 (Mangwe district). Spatial clustering of HIV incidence was observed (Global Moran’s I = - 0.150; Z score 3.038; p-value 0.002). Significant clusters of HIV were observed at district level. HIV incidence and its association with socioeconomic factors varied across the districts except percentage of females unemployed. Intervention programmes to reduce HIV incidence should address the identified socio-economic factors at district level.


Keywords


HIV and AIDS;Spatial modelling;Geographical weighted Poisson regression model;Socio-economic factors;Zimbabwe

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References


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