Spatial clustering of HIV/AIDS mortality events in rural South Africa population between 2000-2006

MSc (Med), Population-Based Field Epidemiology, Faculty of Health Sciences, University of the Witwatersrand, 2009Background: Cluster detection analysis could be an appropriate approach to identify critical AIDS mortality locations for public health intervention. Methods: GIS and Kulldorff’s spatial scan statistic was used to investigate statistically significant AIDS mortality clusters (p 0.05). SaTScan was used to perform the spatial analysis scanning while MapInfo was used as a visualizing tool. Mortality data between 2000- 2006 were analyzed. Results: AIDS exhibit strong spatial clustering tendencies as measured by the Kulldorff’s spatial scan statistic method. Conclusion: Further work is needed to understand the underlying mechanisms responsible for the spatial clustering

Spatial Clustering of All-Cause and HIV-Related Mortality in a Rural South African Population (2000-2006)

Background Sub-Saharan Africa bears a disproportionate burden of HIV infection. Knowledge of the spatial distribution of HIV outcomes is vital so that appropriate public health interventions can be directed at locations most in need. In this regard, spatial clustering analysis of HIV-related mortality events has not been performed in a rural sub-Saharan African setting. Methodology and Results Kulldorff’s spatial scan statistic was used to identify HIV-related and all-cause mortality clusters (p<0.05) in a population-based demographic surveillance survey in rural KwaZulu Natal, South Africa (2000–2006). The analysis was split pre (2000–2003) and post (2004–2006) rollout of antiretroviral therapy, respectively. Between 2000–2006 a total of 86,175 resident individuals ≥15 years of age were under surveillance and 5,875 deaths were recorded (of which 2,938 were HIV-related) over 343,060 person-years of observation (crude all-cause mortality rate 17.1/1000). During both time periods a cluster of high HIV-related (RR = 1.46/1.51, p = 0.001) and high all-cause mortality (RR = 1.35/1.38, p = 0.001) was identified in peri-urban communities near the National Road. A consistent low-risk cluster was detected in the urban township in both time periods (RR = 0.60/0.39, p = 0.003/0.005) and in the first time period (2000–2003) a large cluster of low HIV-related and all-cause mortality in a remote rural area was identified. Conclusions HIV-related and all-cause mortality exhibit strong spatial clustering tendencies in this population. Highest HIV-related mortality and all-cause mortality occurred in the peri-urban communities along the National Road and was lowest in the urban township and remote rural communities. The geography of HIV-related mortality corresponded closely to the geography of HIV prevalence, with the notable exception of the urban township where high HIV-related mortality would have been expected on the basis of the high HIV prevalence. Our results suggest that HIV treatment and care programmes should be strengthened in easy-to-reach high density, peri-urban populations near National Roads where both HIV-related and all-cause mortality are highest

Space-time variations in child mortality in a rural South African population with high HIV prevalence (2000–2014)

Objective The aim of the study was to identify the key determinants of child mortality ‘hot-spots’ in space and time. Methods Comprehensive population-based mortality data collected between 2000 and 2014 by the Africa Centre Demographic Information System located in the UMkhanyakude District of KwaZulu-Natal Province, South Africa, was analysed. We assigned all mortality events and person-time of observation for children <5 years of age to an exact homestead of residence (mapped to <2m accuracy as part of the DSA platform). Using these exact locations, both the Kulldorff and Tango spatial scan statistics for regular and irregular shaped cluster detection were used to identify clusters of childhood mortality events in both space and time. Findings Of the 49 986 children aged 20 per 1000 person-years in 2001–2003 to 4 per 1000 person-years in 2014. The two scanning spatial techniques identified two high-risk clusters for child mortality along the eastern border of the study site near the national highway, with a relative risk of 2.10 and 1.91 respectively. Conclusions The high-risk communities detected in this work, and the differential risk factor profile of these communities, can assist public health professionals to identify similar populations in other parts of rural South Africa. Identifying child mortality hot-spots will potentially guide policy interventions in rural, resource-limited settings