https://doi.org/10.5061/dryad.qjq2bvqrz

Description of the data and file structure

Source data for: Analysis of travel-time to HIV treatment in sub-Saharan Africa reveals inequities in access to antiretrovirals

Okano JT, Low A, Dullie L, Mzumara W, Nuwagaba-Biribonwoha H, Blower S. 

Communications Medicine (2025).

The source data includes geospatial data in raster (.tif) format on HIV prevalence and Density of Infection as estimated from the Population-Based HIV Impact Assessment (PHIA) surveys conducted in Eswatini, Malawi, and Zambia in 2015–2016. It also includes tabular (.csv) data that can be used to plot Epidemic Concentration Curves and fitted Logistic cumulative distribution functions.

Files and variables

File: Source_data.zip

Directory of files included: 

1. Raster maps for HIV prevalence in adults aged 15-59

   a. Fig1a_Eswatini-Prev.tif

   b. Fig1b_Malawi-Prev.tif

   c. Fig1c_Zambia-Prev.tif

2. Raster maps for HIV prevalence in women aged 15-59

   a. Fig2a_Eswatini-Prev-Women.tif

   b. Fig2b_Malawi-Prev-Women.tif

   c. Fig2c_Zambia-Prev-Women.tif

3. Raster maps for HIV prevalence in men aged 15-59

   a. Fig2d_Eswatini-Prev-Men.tif

   b. Fig2e_Malawi-Prev-Men.tif

   c. Fig2f_Zambia-Prev-Men.tif

4. Raster maps for Density of Infection in adults 15-59

   a. Fig3a_Eswatini-DoI.tif

   b. Fig3b_Malawi-DoI.tif

   c. Fig3c_Zambia-DoI.tif

   Description: All rasters in #1-4 are projected in the World Geodetic System 1984 (WGS84) coordinate reference system. The Density of Infection rasters in #4 contains data at a resolution of 1 km x 1 km.

5. Tabular data for Epidemic Concentration Curves

   a. Fig3d_Eswatini-cover-data.csv

   b. Fig3e_Malawi-cover-data.csv

   c. Fig3f_Zambia-cover-data.csv

   Description: Column 1 is the Density of Infection, and Column 2 is the corresponding proportion of people with HIV (PWH) living at that Density of Infection level (or higher).

6. Tabular data for fitted Logistic CDFs

   a. Fig4a_Logistic-fit.csv

   b. Fig4b_Eswatini-UR-fit.csv

   c. Fig4c_Malawi-UR-fit.csv

   d. Fig4d_Zambia-UR-fit.csv

   Description: For the file in #6a, the fields are: ‘Time’ = Travel-time; ‘E_Logfit’ = Fitted Logistic CDF values for Eswatini; ‘M_Logfit’ = Fitted Logistic CDF values for Malawi; ‘Z_Logfit’ = Fitted Logistic CDF values for Zambia. For the files in #6b–d, the fields are: ‘Time’ = Travel-time; ‘Pred’ = Fitted Logistic CDF values; ‘Low’ = Lower 95% bound of fitted Logistic CDF; ‘High’ = Upper 95% bound of fitted Logistic CDF; ‘Urban’ = If fit used urban or rural data.

Code/software

The geospatial data in raster (.tif) format can be viewed in ArcGIS, R, or many photo-viewing applications. The tabular (.csv) data can be viewed in any statistical software or basic text editor. Example R code to import and explore these files can be found in ‘R code_Commun-Med [2025].R’ within the associated Zenodo repository.

Sources:

Data was derived from the following sources:

• ICAP at Columbia University. Population-based Impact Surveys (PHIA). https://phia-data.icap.columbia.edu/ (2022).
• WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High-Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00646