The Arts and Tools for Using Routine Health Data to Establish HIV High Burden Areas: The Pilot Case of KwaZulu-Natal South Africa

Abstract

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 areas. South Africa has relied on national population-based surveys: the Household HIV Survey and the National Antenatal Sentinel HIV and Syphilis Prevalence Survey (ANC) amongst others for such data for informing policy decisions. However, these surveys are conducted approximately every 2 and 3 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 public 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 this 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 South Africa mid-year population estimates, 2015; 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 delineate high HIV disease burden areas using density surface of either HIV positivity and/or number of people living with HIV (PLHIV). For validation, we extracted interpolated values at the facility locations and compared with the real observed values calculating the residuals. Lower residuals means the Inverse Weighted Distance (IDW) interpolator provided reliable prediction at unknown locations. Results were adjusted to provincial published HIV estimates and aggregated to municipalities. Uncertainty measures map at municipalities is provided. Data on major cities and roads networks was only included for orientation and better visualization of the high burden areas.Results: Results shows the HIV burden at local municipality level, with high disease burden in municipalities in eThekwini, iLembe and uMngundgudlovu; and around major cities and national routes.Conclusion: The methods provide accurate estimates of the local HIV burden at the municipality level. Areas with high population density have high numbers of PLHIV. The analysis puts into the hand of decision makers a tool that they can use to generate evidence for HIV programming. The method allows decision makers to routinely update and use facility level data in understanding the local epidemic.

Provision of HIV testing services and its impact on the HIV positivity rate in the public health sector in KwaZulu-Natal: a ten-year review

“…Study results found geographic variation between urban and rural districts and population density that may account for the difference in the number of tests conducted per 100 000. The effect of area population size between urban and rural areas as implied by Wabiri et al ( 2019 ), may account for the discrepancy in the rate of HIV tests being conducted per 100 000 population and the HIV testing rates (Wabiri et al, 2019 ). Therefore, in order to achieve ideal HIV testing coverage, it is necessary to provide more equitable geographic distribution of HIV testing services.…”

“…One of the goals of South Africa’s National Strategic Plan (NSP) for HIV, TB and STIs 2017–2022 is to address the burden of HIV in the country through the use of strategic information such as routinely collected data for monitoring the progress towards achieving the United Nations (UN) Sustainable Development Goals (SDGs) as well as to determine the utilisation of services (National Department of Health, 2016c ). There is also a need to evaluate the impact of interventions on programmes that are implemented in response to the burden of disease and to ensure that interventions are focused on high-burden areas with populations that are most at risk (Wabiri, Naidoo, Mungai, Samuel, & Ngwenya, 2019 ). Routinely collected data from public health facilities are housed on the District Health Information System (DHIS).…”

“…Routinely collected data from public health facilities are housed on the District Health Information System (DHIS). The aggregated data from DHIS can be used for epidemiological monitoring as well as estimating the burden of HIV at various geographic levels (Wabiri et al, 2019 ).…”

“…The rate of HIV infections within the public health system in KZN can be continuously monitored using an indicator called the HIV positivity rate which represents the annual proportion of clients at public health facilities who tested positive for the first time in the 15–49 years age category from amongst the total number of 15–49 year old clients that underwent HIV testing. This excluded antenatal clients (ANC) who were found to be HIV positive after undergoing HIV testing (Wabiri et al, 2019 ).…”

Mapping HIV prevalence in Nigeria using small area estimates to develop a targeted HIV intervention strategy

“…Interventions adapted to the sub-state or district level can be especially effective. Studies in Botswana [10], Kenya [6], Lesotho [11], Malawi [12], Rwanda [13], and South Africa [14] indicate that geographic targeting could increase the impact of prevention interventions, with some studies identifying "hotspots" for greater prioritization in geographic areas as small as the community level [15]. Despite the recent evidence that shows the usefulness of geospatial estimates for targeted intervention planning and policymaking [16,17], there are few examples describing the derivation and practical application of sub-state geospatial estimates for targeted intervention planning.…”