Spatial patterns of HIV prevalence and service use in East Zimbabwe: implications for future targeting of interventions

Abstract

Introduction: Focusing resources for HIV control on geographic areas of greatest need in countries with generalized epidemics has been recommended to increase cost-effectiveness. However, socioeconomic inequalities between areas of high and low prevalence could raise equity concerns and have been largely overlooked. We describe spatial patterns in HIV prevalence in east Zimbabwe and test for inequalities in accessibility and uptake of HIV services prior to the introduction of spatially-targeted programmes. Methods: 8092 participants in an open-cohort study were geo-located to 110 locations. HIV prevalence and HIV testing and counselling (HTC) uptake were mapped with ordinary kriging. Clusters of high or low HIV prevalence were detected with Kulldorff statistics, and the socioeconomic characteristics and sexual risk behaviours of their populations, and levels of local HIV service availability (measured in travel distance) and uptake were compared. Kulldorff statistics were also determined for HTC, antiretroviral therapy (ART), and voluntary medical male circumcision (VMMC) uptake. Results: One large and one small high HIV prevalence cluster (relative risk [RR] = 1.78, 95% confidence interval [CI] = 1.53–2.07; RR = 2.50, 95% CI = 2.08–3.01) and one low-prevalence cluster (RR = 0.70, 95% CI = 0.60–0.82) were detected. The larger high-prevalence cluster was urban with a wealthier population and more high-risk sexual behaviour than outside the cluster. Despite better access to HIV services, there was lower HTC uptake in the high-prevalence cluster (odds ratio [OR] of HTC in past three years: OR = 0.80, 95% CI = 0.66–0.97). The low-prevalence cluster was predominantly rural with a poorer population and longer travel distances to HIV services; however, uptake of HIV services was not reduced. Conclusions: High-prevalence clusters can be identified to which HIV control resources could be targeted. To date, poorer access to HIV services in the poorer low-prevalence areas has not resulted in lower service uptake, whilst there is significantly lower uptake of HTC in the high-prevalence cluster where health service access is better. Given the high levels of risky sexual behaviour and lower uptake of HTC services, targeting high-prevalence clusters may be cost-effective in this setting. If spatial targeting is introduced, inequalities in HIV service uptake may be avoided through mobile service provision for lower prevalence areas.

Geographic Information Systems, spatial analysis, and HIV in Africa: A scoping review

“…Thirteen studies used the Kulldorff spatial scan statistic [15] to identify clustering of HIV infection, [16–24], HIV-specific and all-cause mortality [25–27], and sero-discordant partnerships [28]. In these studies, the spatial scan statistic identified statistically significant “hotspots” or “cold spots” by systematically scanning circular windows of varying sizes across the study area, comparing the number of cases within the window to those outside the window.…”

Spatial analysis of factors associated with HIV infection in Malawi: indicators for effective prevention

“…Evidence has shown that socioeconomic and demographic factors are important predictors of HIV transmission, which necessitates interprofessional collaborations between basic and social scientists, including geographers in HIV and AIDS, in resource-limited countries. There has been considerable research to investigate the geographical variation of the HIV epidemic in sub-Saharan Africa [7][8][9][10][11]. After almost four decades of research, we have comprehensive knowledge of the factors associated with HIV infection in sub-Saharan Africa [12][13][14].…”

“…These models identified significant geographically-focused patterns that would not have been accounted for by standard regression procedures [ 18 ]. The use of spatial cluster techniques to identify clusters of HIV prevalence enhanced the understanding of the determinants of HIV infection and geographic patterns, leading to improved resource allocation in Zimbabwe [ 10 ].…”

Opportunities for Enhanced Strategic Use of Surveys, Medical Records, and Program Data for HIV Surveillance of Key Populations: Scoping Review

“…Properly designed and executed bio-behavioral surveys aim to obtain a probability sample of the population in contrast to health sector or program data, which have data only from clients using their services. Surveys can provide an independent evidence-based assessment of gaps in coverage for programs and health facilities and identify emerging epidemics [ 37 , 38 ]. Moreover, validated survey modules on HIV stigma, the accessibility of health services, violence, and sexual behavior can provide more in-depth information on sensitive topics and allow analysis to identify and explore associations between HIV infection and barriers to accessing prevention and other services to guide the implementation of programs.…”