The Arts and Tools for Using Routine Health Data to Establish HIV High Burden Areas: The Pilot Case of KwaZulu-Natal South Africa - Wabiri, Njeri; Naidoo, Inbarani; Mungai, Esther; Samuel, Candice; Ngwenya, Tryphinah (Frontiers in Public Health, 2019)
Background: To optimally allocate limited health resources in responding to the HIV epidemic, South Africa has undertaken to generate local epidemiological profiles identifying high disease burden areas. Central to achieving this, is the need for readily available quality health data linked to both large and small geographic scales. South Africa has relied on national population-based surveys: the Household HIV Survey and the Antenatal Care (ANC) Sentinel Surveillance amongst others for such data for informing policy decisions. However, the data resources are conducted approximately every two and three years creating a gap in data and evidence required for policy. At subnational levels, timely decisions are required with frequent course corrections in the interim. Routinely collected HIV testing data at health facilities have the potential to provide this much needed information, as a proxy measure of HIV prevalence in the population, when survey data is not available. The South African District health information system (DHIS) contains aggregated routine health data from public health facilities which is used in the article. Methods: Using spatial interpolation methods we combine three “types” of data: 1) 2015 gridded high-resolution population data, 2) age-structure data as defined in 2015 South Africa mid-year population estimates; and 3) georeferenced health facilities HIV-testing data from DHIS for individuals (15-49 years old) who tested in health care facilities in the district in 2015 to generate a density surface of people living with HIV (PLHIV). For validation, we extracted interpolated values at the facilities locations and compared with the real observed values calculating the residuals. Lower residuals means the IDW interpolator provided reliable prediction at unknown locations. Results were adjusted to national South Africa‘s published HIV estimates and aggregated to subnational levels. Results: Results shows the HIV burden at local municipality level, with high disease burden in municipalities in eThekwini, King Cetshwayo, and uMgungundlovu districts. Conclusion: The methods provide accurate estimates of the local HIV burden at the third subnational level. The method allows decision makers to routinely update and use facility level data for understanding the epidemic