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Beyond HIV prevalence: identifying people living with HIV within underserved areas in South Africa

Introduction Despite progress towards the Joint United Nations Programme on HIV/AIDS 95-95-95 targets, South Africa is still suffering from one of the largest HIV epidemics globally. In this study, we generated high-resolution HIV prevalence maps and identified people living with HIV (PLHIV) in underserved areas to provide essential information for the optimal allocation of HIV-related services. Methods The data come from the South Africa Demographic and Health Survey conducted in 2016 and spatial variables from other published literature. We produced high-resolution maps of HIV prevalence and underserved areas, defined as a greater than 30 min travel time to the nearest healthcare facility. Using these maps and the population density, we mapped PLHIV and the PLHIV within underserved areas for 30, 60 and 120 min thresholds. Results There was substantial geographic variation in HIV prevalence, ranging from 1.4% to 24.2%, with a median of 11.5% for men, and from 2.1% to 48.1%, with a median of 20.6% for women. Gauteng province showed the highest density for both HIV prevalence and PLHIV. 80% of all areas in the country were identified as underserved areas (30 min threshold), which contained more than 16% and 20% of the total men and women living with HIV, respectively. KwaZulu-Natal province had the largest number of PLHIV in underserved areas (30 min threshold) and showed less than one healthcare facility per 1000 PLHIV. Conclusion Our study showed extensive spatial variation of HIV prevalence and significant numbers of PLHIV in underserved areas in South Africa. Moreover, we identified locations where HIV-related services need to be intensified to reach the ~1.5 million PLHIV in underserved areas, particularly in KwaZulu-Natal province, with less than one healthcare facility per 1000 PLHIV.

SADHS. As such, the sample locations represent 80% and 84% for men and women, respectively, not 25%. Additionally, SADHS includes sampling weights at household and individual levels in order to restore representativeness of the sample and to prevent bias due to over-and undersampling depend on region size. 1 Thus, among the total population for 2016 SADHS, 2,136 men and 2,726 women who interviewed HIV testing at the sample locations (601 for men and 633 for women) are the population of this study.
In terms of the HIV testing, the 2016 SADHS incorporates the HIV testing biomarkers. 1 For each biomarker test for which an individual was eligible, the respondent was required to provide written consent before the test proceed. The SADHS provides brochure including information of HIV and the national AIDS hotline number and nearby facilities for HIV testing and counselling.
The HIV testing algorithm (figure1) called for testing all samples with an enzyme-linked immunosorbent assay (ELISA). All samples that tested positive on the ELISA 1 were subjected to a second ELISA (ELISA 2). Similar to samples that tested positive on the ELISA 1, 5% of the samples that tested negative on the ELISA 1 were also subjected to the ELISA 2 for internal quality control, while the other 95% were recorded as negative. All the concordant negative results on the ELISA 1 and ELISA 2 were recorded as negative. If the results on the ELISA 1 and ELISA 2 were mismatched, the two ELISAs were repeated. If the results remained discordant, the specimen was classified as inconclusive. All the concordant positive results on the ELISA 1 and ELISA 2 were subjected to a third assay. When both the ELISA 1 and ELISA 2 were positive, the sample was classified as positive if the confirmatory rapid test was positive, and inconclusive if the confirmatory rapid test was negative or indeterminate. For the detailed description of HIV testing and ELISA test, please see the 2016 SADHS reports. 1 BMJ Publishing Group Limited (BMJ) disclaims all liability and responsibility arising from any reliance Supplemental material placed on this supplemental material which has been supplied by the author(s) BMJ Global Health doi: 10.1136/bmjgh-2020-004089 :e004089.

Definition of socio-economic and behavioral variables
To access the association between socioeconomic and behavioral factors, and the prevalence of HIV infection, we selected a variety of socio-economic and behavioral attributes from the South Africa Demographic and Health Survey (SADHS) dataset. Six variables measured for men and five for women included: education level, poverty, condom use, number of lifetime sexual partners, if the participants had ever been tested for HIV, and male circumcision (men only).
Socioeconomic and behavioral variables were quantified as percentages at each SADHS sample location for men and women separately. Specifically, level of education is individual's highest educational level. SADHS standardized the variable into No education, Primary, Secondary, and Higher. 2 We classified individuals with secondary or higher education as a higher education and calculated the percent of individuals at each DHS sample location. We used DHS wealth index to identify poverty variable. DHS wealth index is an ordinal variable that describes standard of living as determined by material possessions. The index is calculated based on the questions about wealth status, such as an ownership of a television and car, housing construction materials, type of drinking water source, and access to sanitation facilities. 2 3 This index were used to classify individuals in five categories: poorest, poorer, middle, richer, and richest. We classified poorest and poorer as a poverty and calculated the percent of those individuals at each DHS sample location. Behavioral variables, such as condom use, ever been tested for HIV, and male circumcision, are from the binary questions. The three questions for the behavioral variables are: 1) Did you use condom during last sex with most recent partner?, 2) Have you ever been tested for HIV?, 3) Are you circumcised? (men only). The binary, yes or no, answer to the questions was used as the measure of individuals, and we calculated the percent of individuals who answered yes to the question. The number of lifetime sexual partners is continuous variable in DHS, and we calculated the percent of individuals whose lifetime sexual partners are less than three at each DHS sample location.

Definition of environmental variables
Environmental variables included the normalized difference vegetation index (NDVI), distance to main roads, distance to the nearest dams, night light, global human influence index (GH-I), friction map, and population density. NDVI serves as a proxy for degree of urbanization at a sampled location, and it has been shown to be associated with the potential risk of coinfection with diseases transmitted by parasites, such as malaria, and could lead to increased HIV transmission efficiency in SSA. 4 5 Night light represents dynamics of urban settlements and related economic activities since urban areas are expected to have higher night light intensity than rural areas. We collected NDVI and night light datasets from the National Aeronautics and Space Administration (NASA) Earth Observatory Group. 6 Those datasets were used to extract the corresponding variables at each DHS sample location. Distance to main roads was derived from a road networks dataset (in vector format) from the World Bank. 7 Distance to dams was derived from a dam dataset from NASA. The dams were geospatially referenced and assigned to polygons depicting reservoir outlines with multiple attributes 8 A raster layer of distance to main roads and dams were calculated as Euclidean distance for each pixel to the nearest main road and dams, respectively. We further resampled these raster layers to 5X5km spatial resolution with the same projection (WGS_1984_UTM_Zone_35S). GH-I provides an updated information of anthropogenic impacts on the environment based on global data of human population pressure, human land use and infrastructure (built-up areas, land use), and human access (coastlines, railroads, and roads). 9 Population density was obtained from the WorldPop database. 10 Population count data with detailed satellite-derived settlement extents was resampled to 100m X 100m resolution is used to generate population distribution. Estimates of population that we used in the analysis were summarized in raster image with a resolution of 100m x 100m pixel. For consistency with the resolution of the maps generated previously, we reduced the resolution of the raster file to 5km x 5km pixel. We included the friction map from Malaria Atlas project (MAP) to represent accessibility and mobility in the model. 11 The friction map is expressed in units of minutes required to travel on meter, representing allocated fastest speed of travel based on the types of travel mode estimated within the pixel. 12 It used mainly the most updated data until 2015 for road, railroad and water travel distances from the Open Street Maps (OMS) and Google roads projects. The datasets include roads, railways, rivers, lakes, oceans, topographic conditions (e.g., slope and elevation), landcover types, and national borders. 12 We For selection of variables, first we conducted preliminary bivariate logistic regression analyses to assess the existence of associations between each variable and HIV prevalence for each gender. Second, since we were interested in variables that included spatial information, we explored the randomness of the geographical distribution (spatial structure) of each variable using Global Moran's Index for spatial autocorrelation. We selected variables that satisfied both criteria: 1) the bivariate logistic regression slope coefficient must have p-value < 0.1; and 2) the significance test for spatial autocorrelation must have p-value < 0.1. Then, we used multivariable logistic regression models, one for women and another for men, to examine the associations between each selected variables and HIV prevalence adjusting for the other variables. For this model, the dependent variable, , is the percentage of HIV seropositive individuals for women and men at the sample location i, respectively. The HIV prevalence at sample location i was modeled as ~ ( , ), where is the total number of individuals sampled at sample location i and is the HIV prevalence at sample location i. The multivariable regression model for the HIV prevalence is where is the value of predictor variable j at sample location i and is the corresponding regression coefficient.
Supplementary Figure 1 represents the cofactors in the final logit model for men, and the cofactors are poverty, lifetime number of sexual partners more than two, ever been HIV tested, GH-I, and male circumcision. Each variable shows the spatial variance in the country. For example, the high percentage of poverty tend to focus on the northern and eastern part of the country across the Limpopo, KwaZulu-Natal, and Eastern Cape Provinces. In contrast, the southeastern part of the country has very low poverty percentages. Supplementary Figure 2 shows the cofactors in the final logit model for women. The cofactors are poverty, condom use, lifetime number of sexual partners more than two, NDVI, and friction. These cofactors show spatial variance. The poverty and the high number of lifetime sexual partners which are also selected for men show similar spatial patterns with men, showing KwaZulu-Natal province has the large areas of high prevalence.

Mapping predictor variables and HIV prevalence
We used ordinary kriging mapping methods, which is one of the most widely used interpolation methods, to generate continuous surface maps for each significant socioeconomic, demographic, and behavioral factor identified in the multivariable regression model. This method interpolates the values of variables at unmeasured locations by estimating a variogram of weighted averages of the data. 13 Using this method, we generated continuous surface maps of each selected variable with 5km x 5km pixel resolution. We generated an HIV prevalence map for each gender by substituting values from all continuous surface maps into the gender-specific multivariable logistic regression model using Map Algebra and the inverse logistic equation: = 0 + 1 1 + 2 2 +⋯+ 1 + 0 + 1 1 + 2 2 +⋯++ We used this method to generate a map of HIV prevalence in raster format with 5-kilometer grid resolution for each gender, implemented in the software ArcGIS version 10.5. Finally, a 5km x 5km pixel resolution map of the density of women and men living with HIV was generated by combining the HIV prevalence map with the women and men density maps. We obtained estimates of population densities from the WorldPop dataset as a raster image with a resolution of 100m x 100 m pixel. For consistency with the resolution of the maps generated previously, we reduced the resolution of the raster image to 5km x 5km pixel. Since the survey dataset restricted the respondents' age from 15-49 for women and 15-59 for men, we estimated the population for each gender to represent precise estimates of people living with HIV. We obtained the ratio of women aged 15-49 and men aged 15-59 among the total population in 2016 from PopulationPyramid dataset 14 and combined the ratio with the population density to generate the estimates of study population, women aged 15-49 and men 15-59.

Definition of variables for underserved areas
To measure underserved areas from health care facilities, we used two datasets: the friction map that we previously addressed for from MAP and the locations of health care facilities in South Africa. 15 We obtained all public health care facilities and private-not-for-profit sectors managed by government (e.g., the Ministry of Health, MoH), local authorities, and faith-based (FBO) and non-governmental organization (NGO) to represent public health services. Those facilities serve expanded immunization programs, health data surveillance, and receive government funding to provide services to the general population. Facilities that provided services for special groups and for private were excluded from the analysis. The main source of the locations of health care facilities is MoH, and other relevant sources such as other government agencies, website of FBOs and NGOs, or personal contact were used if MoH had inadequate facility listing information. 15 A total of 4,287 health care facilities were included in the analysis.

Mapping HIV underserved areas
We modeled travel time as a function of distance and travel speed. It can be conceptualized as the cost of movement using the cell dimensions (pixel resolution) and travel time assigned to each cell. In this model, travel occurs through cell to cell in both cardinal and diagonal directions, and a travel time was designated for each cell, representing the time required to traverse the cell estimated from the friction map from MAP. For example, if we travel from cell A to contiguous cell B in cardinal directions, travel time from cell A to cell B ( ) would be calculated as following, where is the travel time assigned to the cell i, and is the distance between cell centers, which is equal to the cell resolution. The movement consists in two steps because half of each cell is traversed with each step. In case of diagonal direction movement, travel time from cell A to cell C, the program could choose either a direct route between the two cells or a route traversing another cell, which is located at a right angle. The movement of direct route between the two points would be calculated using the Pythagorean theorem to adjust the distance term as following, .
Travel time or cost for traversing each cell was calculated using the cell length and travel time assigned to each cell. An accumulated cost surface was created wherein cell values represented the total travel time from the cell to the nearest hospital location (i.e., least cost path for each cell). To identify underserved areas, we reclassified the accumulated travel time surface into a Boolean surface based on whether the cell was greater than thresholds (30, 60, and 120 minutes) from a health care location. Then, we filtered the grid representing underserved areas to remove any groups of less than three contiguous cells. We conducted the filtering process in an effort to remove single cells and very small areas generally inside the catchment areas. After the filtering process, we converted the underserved areas from a raster grid to a vector data form (polygons) to identify the women and men living with HIV in these areas.
We combined the continuous surface maps of women and men living with HIV generated in the previous step with the maps of underserved areas to generate density maps of women living with HIV and men living with HIV in the underserved areas, and calculate the number of women living with HIV and men living with HIV in these areas.

Supplementary Tables
Supplementary Table S1. Descriptive statistics of socio-behavioral variables included in the analysis.