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.

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.

The impact of a conditional financial incentive on linkage to HIV care: Findings from the HITS cluster randomized clinical trial in rural South Africa.

Introduction: HIV elimination requires innovative approaches to ensure testing and immediate treatment provision. We investigated the effectiveness of conditional financial incentives on increasing linkage to HIV care in a 2×2 factorial cluster randomized controlled trial-Home-Based Intervention to Test and Start (HITS) - in rural South Africa. Methods: Of 45 communities in uMkhanyakude, KwaZulu-Natal, 16 communities were randomly assigned to the arms to receive financial incentives for home-based HIV counseling and testing (HBHCT) and linkage to care within 6 weeks (R50 [US$3] food voucher each) and 29 communities to the arms without financial incentives. We examined linkage to care (i.e., initiation or resumption of antiretroviral therapy after >3 months of care interruption) at local clinics within 6 weeks of a home visit, the eligibility period to receive the second financial incentive. Linkage to care was ascertained from individual clinical records. Intention-to-treat analysis (ITT) was performed using modified Poisson regression with adjustment for receiving another intervention (i.e., male-targeted HIV-specific decision support app) and clustering of standard errors at the community level. Results: Among 13,894 eligible men (i.e., ≥15 years and resident in the 45 communities), 20.7% received HBHCT, which resulted in 122 HIV-positive tests. Of these, 27 linked to care within 6 weeks of HBHCT. Additionally, of eligible men who did not receive HBHCT, 66 linked to care. In the ITT analysis, the proportion of linkage to care among men did not differ in the arms which received financial incentives and those without financial incentives (adjusted Risk Ratio [aRR]=0.78, 95% CI: 0.51-1.21). Among 19,884 eligible women, 29.1% received HBHCT, which resulted in 375 HIV-positive tests. Of these, 75 linked to care. Among eligible women who did not receive HBHCT, 121 linked to care within 6 weeks. Women in the financial incentive arms had a significantly higher probability of linkage to care, compared to those in the arms without financial incentives (aRR=1.50; 95% CI: 1.03-2.21). Conclusion: While a small once-off financial incentive did not increase linkage to care among men during the eligibility period of 6 weeks, it significantly improved linkage to care among women over the same period. Clinical Trial Number: ClinicalTrials.gov # NCT03757104.

Impact of family networks on uptake of health interventions: evidence from a community-randomized control trial aimed at increasing HIV testing in South Africa.

INTRODUCTION: While it is widely acknowledged that family relationships can influence health outcomes, their impact on the uptake of individual health interventions is unclear. In this study, we quantified how the efficacy of a randomized health intervention is shaped by its pattern of distribution in the family network. METHODS: The "Home-Based Intervention to Test and Start" (HITS) was a 2×2 factorial community-randomized controlled trial in Umkhanyakude, KwaZulu-Natal, South Africa, embedded in the Africa Health Research Institute's population-based demographic and HIV surveillance platform (ClinicalTrials.gov # NCT03757104). The study investigated the impact of two interventions: a financial micro-incentive and a male-targeted HIV-specific decision support programme. The surveillance area was divided into 45 community clusters. Individuals aged ≥15 years in 16 randomly selected communities were offered a micro-incentive (R50 [$3] food voucher) for rapid HIV testing (intervention arm). Those living in the remaining 29 communities were offered testing only (control arm). Study data were collected between February and November 2018. Using routinely collected data on parents, conjugal partners, and co-residents, a socio-centric family network was constructed among HITS-eligible individuals. Nodes in this network represent individuals and ties represent family relationships. We estimated the effect of offering the incentive to people with and without family members who also received the offer on the uptake of HIV testing. We fitted a linear probability model with robust standard errors, accounting for clustering at the community level. RESULTS: Overall, 15,675 people participated in the HITS trial. Among those with no family members who received the offer, the incentive's efficacy was a 6.5 percentage point increase (95% CI: 5.3-7.7). The efficacy was higher among those with at least one family member who received the offer (21.1 percentage point increase (95% CI: 19.9-22.3). The difference in efficacy was statistically significant (21.1-6.5 = 14.6%; 95% CI: 9.3-19.9). CONCLUSIONS: Micro-incentives appear to have synergistic effects when distributed within family networks. These effects support family network-based approaches for the design of health interventions.

Can a conditional financial incentive (CFI) reduce socio-demographic inequalities in home-based HIV testing uptake? A secondary analysis of the HITS clinical trial intervention in rural South Africa.

In sub-Saharan Africa, home-based HIV testing interventions are designed to reach sub-populations with low access to HIV testing such as men, younger or less educated people. Combining these interventions with conditional financial incentives (CFI) has been shown to be effective to increase testing uptake. CFI are effective for one-off health behaviour change but whether they operate differentially on different socio-demographic groups is less clear. Using data from the HITS trial in South Africa, we investigated whether a CFI was able to reduce existing home-based HIV testing uptake inequalities observed by socio-demographic groups. Residents aged ≥15 years in the study area were assigned to an intervention arm (16 clusters) or a control arm (29 clusters). In the intervention arm, individuals received a food voucher (∼3.5 US dollars) if they accepted to take a home-based HIV test. Testing uptake differences were considered for socio-demographic (sex, age, education, employment status, marital status, household asset index) and geographical (urban/rural living area, distance from clinic) characteristics. Among the 37,028 residents, 24,793 (9290 men, 15,503 women) were included in the analysis. CFI increased significantly testing uptake among men (39.2% vs 25.2%, p < 0.001) and women (45.9% vs 32.0%, p < 0.001) with similar absolute increase between men and women. Uptake was higher amongst the youngest or least educated individuals, and amongst single (vs in union) or unemployed men. Absolute uptake increase was also significantly higher amongst these groups resulting in increasing socio-demographic differentials for home-based HIV testing uptake. However, because these groups are known to have less access to other public HIV testing services, CFI could reduce inequalities for HIV testing access in our specific context. Although CFI significantly increased home-based HIV testing uptake, it did not do so differentially by socio-demographic group. Future interventions using CFI should make sure that the intervention alone does not increase existing health inequities.

Implementation science in resource-poor countries and communities.

BACKGROUND: Implementation science in resource-poor countries and communities is arguably more important than implementation science in resource-rich settings, because resource poverty requires novel solutions to ensure that research results are translated into routine practice and benefit the largest possible number of people. METHODS: We reviewed the role of resources in the extant implementation science frameworks and literature. We analyzed opportunities for implementation science in resource-poor countries and communities, as well as threats to the realization of these opportunities. RESULTS: Many of the frameworks that provide theoretical guidance for implementation science view resources as contextual factors that are important to (i) predict the feasibility of implementation of research results in routine practice, (ii) explain implementation success and failure, (iii) adapt novel evidence-based practices to local constraints, and (iv) design the implementation process to account for local constraints. Implementation science for resource-poor settings shifts this view from "resources as context" to "resources as primary research object." We find a growing body of implementation research aiming to discover and test novel approaches to generate resources for the delivery of evidence-based practice in routine care, including approaches to create higher-skilled health workers-through tele-education and telemedicine, freeing up higher-skilled health workers-through task-shifting and new technologies and models of care, and increasing laboratory capacity through new technologies and the availability of medicines through supply chain innovations. In contrast, only few studies have investigated approaches to change the behavior and utilization of healthcare resources in resource-poor settings. We identify three specific opportunities for implementation science in resource-poor settings. First, intervention and methods innovations thrive under constraints. Second, reverse innovation transferring novel approaches from resource-poor to research-rich settings will gain in importance. Third, policy makers in resource-poor countries tend to be open for close collaboration with scientists in implementation research projects aimed at informing national and local policy. CONCLUSIONS: Implementation science in resource-poor countries and communities offers important opportunities for future discoveries and reverse innovation. To harness this potential, funders need to strongly support research projects in resource-poor settings, as well as the training of the next generation of implementation scientists working on new ways to create healthcare resources where they lack most and to ensure that those resources are utilized to deliver care that is based on the latest research results.