Optimizing community linkage to care and antiretroviral therapy Initiation: Lessons from the Nigeria HIV/AIDS Indicator and Impact Survey (NAIIS) and their adaptation in Nigeria ART Surge.

BACKGROUND: Ineffective linkage to care (LTC) is a known challenge for community HIV testing. To overcome this challenge, a robust linkage to care strategy was adopted by the 2018 Nigeria HIV/AIDS Indicator and Impact Survey (NAIIS). The NAIIS linkage to care strategy was further adapted to improve Nigeria's programmatic efforts to achieve the 1st 90 as part of the Nigeria Antiretroviral Therapy (ART) Surge initiative, which also included targeted community testing. In this paper we provide an overview of the NAIIS LTC strategy and describe the impact of this strategy on both the NAIIS and the Surge initiatives. METHODS: The NAIIS collaborated with community-based organizations (CBOs) and deployed mobile health (mHealth) technology with real-time dashboards to manage and optimize community LTC for people living with HIV (PLHIV) diagnosed during the survey. In NAIIS, CBOs' role was to facilitate linkage of identified PLHIV in community to facility of their choice. For the ART Surge, we modified the NAIIS LTC strategy by empowering both CBOs and mobile community teams as responsible for not only active LTC but also for community testing, ART initiation, and retention in care. RESULTS: Of the 2,739 PLHIV 15 years and above identified in NAIIS, 1,975 (72.1%) were either unaware of their HIV-positive status (N = 1890) or were aware of their HIV-positive status but not receiving treatment (N = 85). Of these, 1,342 (67.9%) were linked to care, of which 952 (70.9%) were initiated on ART. Among 1,890 newly diagnosed PLHIV, 1,278 (67.6%) were linked to care, 33.7% self-linked and 66.3% were linked by CBOs. Among 85 known PLHIV not on treatment, 64 (75.3%) were linked; 32.8% self-linked and 67.2% were linked by a CBO. In the ART Surge, LTC and treatment initiation rates were 98% and 100%, respectively. Three-month retention for monthly treatment initiation cohorts improved from 76% to 90% over 6 months. CONCLUSIONS: Active LTC strategies by local CBOs and mobile community teams improved LTC and ART initiation in the ART Surge initiative. The use of mHealth technology resulted in timely and accurate documentation of results in NAIIS. By deploying mHealth in addition to active LTC, CBOs and mobile community teams could effectively scale up ART with real-time documentation of client-level outcomes.

Social barriers in accessing care by clients who returned to HIV care after transient loss to follow-up.

BACKGROUND: People living with HIV (PLHIV) constantly need to address social issues such as the cost of accessing care, stigma, and lack of social support which impacts on their level of adherence to clinic visits or antiretroviral treatment leading to adverse health outcomes. This study examined the social barriers in accessing care by clients who returned to care after transient loss to follow-up. METHODS: This study was a cross-sectional survey of PLHIV from 99 US CDC PEPFAR-supported HIV clinics located in 10 of Nigeria's 36 states and Federal Capital Territory, who were momentarily lost to follow-up but returned to care after tracking. Demographic and social factors at bivariate and multivariate level were analyzed to determine the predictors of difficulty in accessing HIV clinics. RESULTS: Of the 7483 clients tracked, 1386 (18.5%) were confirmed to be in care, 2846 (38.2%) were lost to follow-up (LTFU), 562 (7.5%) returned to care, 843 (11.2%) discontinued care, 827 (11.1%) transferred out to other facilities for care, 514 (6.8%) had died while 505 (6.7%) could not be reached by phone or located at their addresses. 438 out of the 562 (78%) returnee PLHIV gave consent and participated in the study. 216 out of the 438 (50%) clients who returned to care were transiently lost to follow-up because they had difficulty accessing their HIV clinic. Also, 126/438 (29%) of returnee PLHIV were previously lost to follow-up. Difficult access to a HIV clinic was significantly influenced by prior LTFU (OR 2.5 [95% CI 1.3-4.8], p = 0.008), history of being stigmatized (OR 2.1 [95% CI 1.1-3.8], p = 0.02), lack of social or financial support (OR 2.8 [95% CI 1.3-6.0], p = 0.01) and perceived in-adequate healthcare workers support (OR 3.8 [95% CI 1.2-11.2], p = 0.02). Age (p = 0.218) and gender (p = 0.771) were not significant determinants of difficult access to an HIV clinic. CONCLUSION: Stigma, lack of support and prior loss to follow-up event are essential factors affecting retention in care. Social constructs such as home-based visits, community-based care services, transportation subsidies, and robust strong social systems should be built into HIV service delivery models to improve retention in care of people on HIV treatment. The authors advocate for further studies on how differentiated care models impact on retention of patients in care.