Technical efficiency of national HIV/AIDS spending in 78 countries between 2010 and 2018: A data envelopment analysis

Abstract

HIV/AIDS remains a leading global cause of disease burden, especially in low- and middle-income countries (LMICs). In 2020, more than 80% of all people living with HIV (PLHIV) lived in LMICs. While progress has been made in extending coverage of HIV/AIDS services, only 66% of all PLHIV were virally suppressed at the end of 2020. In addition to more resources, the efficiency of spending is key to accelerating progress towards global 2030 targets for HIV/AIDs, including viral load suppression. This study aims to estimate the efficiency of HIV/AIDS spending across 78 countries. We employed a data envelopment analysis (DEA) and a truncated regression to estimate the technical efficiency of 78 countries, mostly low- and middle-income, in delivering HIV/AIDS services from 2010 to 2018. Publicly available data informed the model. We considered national HIV/AIDS spending as the DEA input, and prevention of mother to child transmission (PMTCT) and antiretroviral treatment (ART) as outputs. The model was adjusted by independent variables to account for country characteristics and investigate associations with technical efficiency. On average, there has been substantial improvement in technical efficiency over time. Spending was converted into outputs almost twice as efficiently in 2018 (81.8%; 95% CI = 77.64, 85.99) compared with 2010 (47.5%; 95% CI = 43.4, 51.6). Average technical efficiency was 66.9% between 2010 and 2018, in other words 33.1% more outputs could have been produced relative to existing levels for the same amount of spending. There is also some variation between WHO/UNAIDS regions. European and Eastern and Southern Africa regions converted spending into outputs most efficiently between 2010 and 2018. Rule of Law, Gross National Income, Human Development Index, HIV prevalence and out-of-pocket expenditures were all significantly associated with efficiency scores. The technical efficiency of HIV investments has improved over time. However, there remains scope to substantially increase HIV/AIDS spending efficiency and improve progress towards 2030 global targets for HIV/AIDS. Given that many of the most efficient countries did not meet 2020 global HIV targets, our study supports the WHO call for additional investment in HIV/AIDS prevention and control to meet the 2030 HIV/AIDS and eradication of the AIDS epidemic.

Fig 1. Sample schematic.

Note: ARV: Antiretroviral, ART: Antiretroviral therapy, PLHIV: People living with HIV, PMTCT: Prevention of Mother to Child Transmission.

Fig 2. Raw input and output variables terciles by country (N = 78).

Notes: Raw variables presented before transforming them into proportions. * White areas for HIV prevalence were missing but some of them were imputed afterwards for analytical purposes. Data used to generate the maps is included in S1 and S2 Data. Maps were created using “QGIS Geographic Information System” from the Open Source Geospatial Foundation Project (http://qgis.osgeo.org). The QGIS Geographic Information System is a free and open source software, for more details on copyright: https://www.qgis.org/en/site/getinvolved/governance/trademark/index.html#:~:text=QGIS%20trademarks%2C%20service%20marks%2C%20logos,all%20uses%20of%20QGIS%20Marks.

Fig 3. Mean and 95% CI of our adjusted technical efficiency scores over the years.

Notes: black dots stand represent observations per year. Bottom figure presents the robust reciprocal of bias-corrected efficiency scores. Horizontal lines represent median values, boxes show the IQR, whiskers show data points within 1.5x|IQR|. DEA scores stand for efficiency scores.

Fig 4. Bias-corrected efficiency scores from the base model by year, WB income group and WHO region.

Notes: Robust reciprocal of bias-corrected efficiency scores were used. Horizontal lines represent median values, boxes show the IQR, whiskers show data points within 1.5x|IQR|. E&S stands for Easter and Southern, W&C for Western and Central, A&P is Asia and the Pacific, E.E. & C.A. is Eastern Europe and Central Asia, L.A.&C.A. Latin America and the Caribbean, N.A.&M.E. is North Africa and Middle East, and WE.&NA. is Western and Central Europe and North America.

Fig 5. Average bias-corrected efficiency scores by WHO region and WB income group.

Notes: Values extracted from the base model. Same graph by UNAIDS region is shown in Fig E of Section B in S1 Text. DEA scores stand for efficiency scores.

Fig 6. Average bias-corrected efficiency scores by country (N = 78).

Notes: Average technical efficiency across country-years is depicted by the middle vertical line. Dots present each observation per country. Y-axis shows country codes. SDN has below 0.1 values (see Table C of Section B in S1 Text for further details on scores estimated per country).

Fig 7. Average bias-corrected efficiency scores by sensitivity analysis model.

Notes: 95% CIs were not added as y-axis scale is small. No notable differences are observed in average bias-corrected efficiency scores. T-test between the values were applied and p-value>0.1 for all comparisons. There were 76, 77, 77, 74, 72 number of countries and 643, 619, 659, 617, and 571 observations included for Model C, D, E, F, and G, respectively. DEA scores stand for technical efficiency scores.