Population impacts of conditional financial incentives and a male-targeted digital decision support application on the HIV treatment cascade in rural KwaZulu Natal: findings from the HITS cluster randomized clinical trial

Maxime Inghels; Hae-Young Kim; Thulile Mathenjwa; Maryam Shahmanesh; Janet Seeley; Sally Wyke; Philippa Matthews; Oluwafemi Adeagbo; Dickman Gareta; Nuala McGrath; H Manisha Yapa; Ann Blandford; Thembelihle Zuma; Adrian Dobra; Till Bärnighausen; Frank Tanser

doi:10.1002/jia2.26248

Population impacts of conditional financial incentives and a male-targeted digital decision support application on the HIV treatment cascade in rural KwaZulu Natal: findings from the HITS cluster randomized clinical trial

J Int AIDS Soc. 2024 May;27(5):e26248. doi: 10.1002/jia2.26248.

Authors

Maxime Inghels^{1

2}, Hae-Young Kim^{3

4}, Thulile Mathenjwa³, Maryam Shahmanesh^{3

5}, Janet Seeley^{3

6}, Sally Wyke⁷, Philippa Matthews³, Oluwafemi Adeagbo^{8

9}, Dickman Gareta³, Nuala McGrath^{3

10

11}, H Manisha Yapa^{3

12}, Ann Blandford¹³, Thembelihle Zuma³, Adrian Dobra¹⁴, Till Bärnighausen^{3

15}, Frank Tanser^{3

16

17

18}

Affiliations

¹ Lincoln International Institute for Rural Health, University of Lincoln, Lincoln, UK.
² Centre Population et Développement (UMR 196 Paris Descartes - IRD), SageSud (ERL INSERM 1244), Institut de Recherche pour le Développement, Paris, France.
³ Africa Health Research Institute, KwaZulu-Natal, South Africa.
⁴ Department of Population Health, New York University School of Medicine, New York City, New York, USA.
⁵ Institute for Global Health, University College London, London, UK.
⁶ Department of Global Health and Development, London School of Hygiene and Tropical Medicine, London, UK.
⁷ School of Social and Political Sciences, School of Health and Wellbeing, University of Glasgow, Glasgow, UK.
⁸ Department of Sociology, University of Johannesburg, Johannesburg, South Africa.
⁹ Department of Community and Behavioral Health, College of Public Health, University of Iowa, Iowa City, Iowa, USA.
¹⁰ School of Primary Care, Population Sciences and Medical Education, Faculty of Medicine, University of Southampton, Southampton, UK.
¹¹ Department of Social Statistics and Demography, Faculty of Social Sciences, University of Southampton, Southampton, UK.
¹² Westmead Clinical School, Faculty of Medicine & Health, University of Sydney, Sydney, New South Wales, Australia.
¹³ University College London Interaction Centre, University College London, London, UK.
¹⁴ University of Washington, Seattle, Washington, USA.
¹⁵ Heidelberg Institute of Global Health (HIGH), Heidelberg University, Heidelberg, Germany.
¹⁶ Centre for the AIDS Programme of Research in South Africa (CAPRISA), University of KwaZulu-Natal, Durban, South Africa.
¹⁷ Centre for Epidemic Response and Innovation, School for Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa.
¹⁸ The South African Centre for Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Stellenbosch, South Africa.

Abstract

Introduction: In South Africa, the HIV care cascade remains suboptimal. We investigated the impact of small conditional financial incentives (CFIs) and male-targeted HIV-specific decision-support application (EPIC-HIV) on the HIV care cascade.

Methods: In 2018, in uMkhanyakude district, 45 communities were randomly assigned to one of four arms: (i) CFI for home-based HIV testing and linkage to care within 6 weeks (R50 [US$3] food voucher each); (ii) EPIC-HIV which are based on self-determination theory; (iii) both CFI and EPIC-HIV; and (iv) standard of care. EPIC-HIV consisted of two components: EPIC-HIV 1, provided to men through a tablet before home-based HIV testing, and EPIC-HIV 2, offered 1 month later to men who tested positive but had not yet linked to care. Linking HITS trial data to national antiretroviral treatment (ART) programme data and HIV surveillance programme data, we estimated HIV status awareness after the HITS trial implementation, ART status 3 month after the trial and viral load suppression 1 year later. Analysis included all known individuals living with HIV in the study area including those who did not participated in the HITS trial.

Results: Among the 33,778 residents in the study area, 2763 men and 7266 women were identified as living with HIV by the end of the intervention period and included in the analysis. After the intervention, awareness of HIV-positive status was higher in the CFI arms compared to non-CFI arms (men: 793/908 [87.3%] vs. 1574/1855 [84.9%], RR = 1.03 [95% CI: 0.99-1.07]; women: 2259/2421 [93.3%] vs. 4439/4845 [91.6%], RR = 1.02 [95% CI: 1.00-1.04]). Three months after the intervention, no differences were found for linkage to ART between arms. One year after the intervention, only 1829 viral test results were retrieved. Viral suppression was higher but not significant in the EPIC-HIV intervention arms among men (65/99 [65.7%] vs. 182/308 [59.1%], RR = 1.11 [95% CI: 0.88-1.40]).

Conclusions: Small CFIs can contribute to achieve the first step of the HIV care cascade. However, neither CFIs nor EPIC-HIV was sufficient to increase the number of people on ART. Additional evidence is needed to confirm the impact of EPIC-HIV on viral suppression.

Keywords: HIV; HIV care cascade; counselling; financial incentives; home‐based HIV testing; mHealth.

Publication types

Randomized Controlled Trial
Research Support, Non-U.S. Gov't
Research Support, N.I.H., Extramural

MeSH terms

Adolescent
Adult
Female
HIV Infections* / drug therapy
HIV Testing / methods
Humans
Male
Middle Aged
Motivation*
Rural Population*
South Africa / epidemiology
Young Adult

Abstract

Publication types

MeSH terms

Grants and funding