Introduction

Over the last 15 years (2004–2019), Nigeria identified and initiated treatment for at least 1.2 million people living with HIV (PLHIV) [1]. In 2014, the Joint United Nations Program on HIV/AIDS (UNAIDS) set 90-90-90 targets for 2020, whereby 90% of PLHIV are aware of their HIV status (1^st 90), 90% of those aware of their HIV status are on antiretroviral therapy (ART) (2^nd 90), and 90% of those on ART are virally suppressed (3^rd 90) [2]. The 2018 Nigeria HIV/AIDS Indicator and Impact Survey (NAIIS), a population-based household survey, estimated Nigeria’s ARV adjusted progress (by detecting presence of ARVs in blood of all identified PLHIV during NAIIS) towards the UNAIDS 90-90-90 targets to be at 47-96-81 [1]. Nigeria’s 1^st 90 was among the lowest in Africa [3]. The 1^st 90 for West and Central Africa was estimated to be 64% [3]. Before NAIIS, facility-based testing (HIV testing in clinic settings) was used to identify most new PLHIV in Nigeria. After NAIIS, this approach was considered inadequate for accelerating progress toward the 1^st 90 targets because of low healthcare facility attendance among Nigerians, especially in rural settings, which have lower socio-economic status when compared to most urban areas [4]. To be successful, any HIV testing approach needed to include an efficient strategy for identifying new PLHIV and linking them to care and treatment [5].

Community-based HIV testing (HIV testing in non-clinic settings) is highly effective in improving uptake of HIV testing services [6]. However, one of the key challenges with this HIV testing approach is linking persons diagnosed with HIV in the community to a health facility for care and treatment. Broadly, these challenges can be divided into three categories, patient-related barriers, service provision barriers, and documenting and monitoring linkage to care (LTC) barriers. Key patient-related barriers include distance to healthcare facility, transportation costs, stigma and fear of disclosure, denial of HIV diagnosis, and being asymptomatic [7–9]. Key service provision barriers include suboptimal patient-staff relationships, inadequate counseling, absence of clear referral and clinic procedures, disorganized visit schedules, overcrowding, and long waiting times [9]. Documentation and monitoring challenges include use of paper-based records with potential for compromised confidentiality, transcription errors, and duplication of records resulting in inability to determine if PLHIV have successfully linked or to track patients in real-time.

To determine progress toward UNAIDS 90-90-90 target and to characterize the epidemics fully, Nigeria implemented NAIIS in 2018. Following NAIIS, Nigeria realized huge gap in the first 90 target (whereby only 47% are aware of their HIV status) with about 1million PLHIV yet to be identified [1]. To bridge this gap, Nigeria implemented the ART Surge. The NAIIS included community-based HIV testing and implemented a robust linkage to care strategy designed to overcome these barriers related to community-based HIV testing. Similarly, the Nigeria Antiretroviral Therapy (ART) Surge was an ambitious strategy leveraged on NAIIS data to identify and initiate treatment for 500,000 (half of the total first 90 gap) previously undiagnosed PLHIV in 18 months. The ART Surge adopted the linkage to care strategy developed for NAIIS as a complement to community HIV testing activities conducted during this initiative. The strategy primarily focused in six high HIV prevalence states and four states with the highest ART coverage [10]. The aim of this paper is to provide an overview of the structure and achievements of the community linkage to care strategy and describe the impact of this strategy in identifying and linking new PLHIV to care in the NAIIS and the Nigeria Surge initiatives.

Methods

NAIIS ALTC operational management structure, tools and dashboards

Linkage to care processes started with the counsellor-interviewer (Fig 1) who provided counselling to participants before and after testing. The counsellor-interviewer was responsible for a) referring HIV-positive participants to healthcare facilities of their choice from a directory of HIV care and treatment facilities, using approved paper-based referral forms and b) completion of the electronic consent and participant locator forms for each participant who consented for ALTC. Only participants who consented to sharing personal information with CBOs were provided ALTC. Information entered into the electronic participant locator form appears on the LC dashboard in near real-time and is used by the CBO for active tracking of participants who consented for ALTC. Participants were allowed seven days to visit a health care facility through passive linkage. Those who did not visit a health care facility within seven days were assigned to a CBO by the LC using the LC dashboard to start active linkage to care.

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Fig 1. Active linkage to care processes, NAIIS 2018.

Abbreviations: LC–Linkage Coordinator; LTC–Linkage to Care; CBO–Community Based Organization; ALTC–Active Linkage to Care; FRFP–Facility Referral Focal Person; USSD–Unstructured Supplementary Service Data; VL–Viral Load; EID–Early Infant Diagnosis.

https://doi.org/10.1371/journal.pone.0257476.g001

Once assigned to a CBO, participant information on the LC dashboard was available on the CBO dashboard. Upon receipt of the information, the CBO contacted the participant, developed a linkage plan, and initiated ALTC processes. When at their chosen health facility, regardless of mode of linkage (active or passive), participants presented the facility referral focal person the hardcopy referral form they received from the counselor-interviewer with the participant’s unique identification number (unique ID) and their HIV-positive test result. There is no difference in documentation and enrollment of participants referred through the two LTC approaches at the facility by the facility referral focal person. Participants referred through ALTC will appear in both CBO and LC dashboards while those referred through passive linkage will only appear in the LC dashboard. For all participants, an HIV rapid test using the same testing algorithm as that in the field was repeated to confirm the positive test result and a plan of action was agreed upon between the participant and the clinician. Following enrollment at the facility, the participant is considered as a registered (enrolled) patient and in line with National ART Guidelines, the participant is initiated on ART and scheduled for routine periodic ARV refills and monitoring at the facility. LTC outcomes (option 1 –participant enrolled, option 2 –participant commenced ART) were documented by the facility referral focal person using a handset and a combination of USSD code and participant’s unique ID.

Results

Of 2,739 PLHIV age 15 years and above identified during NAIIS, 1,890 (69%) were newly diagnosed PLHIV and 849 (31%) were known PLHIV who were re-confirmed as HIV positive during the survey, of which 85 (10%) reported not being on ART. Of the newly diagnosed PLHIV, 1,278 (67.6%) were linked to care (Table 1), with more using ALTC through CBOs (66.3%) than using passive linkage (33.7%). Similarly, more PLHIV were linked to care using ALTC than passive linkage among known PLHIV not on ART (67.2% and 32.8%), respectively. Of the total PLHIV linked to care, 952 (70.9%) were initiated on ART. ART initiation rates among newly identified PLHIV and known PLHIV not on ART were 70.7% and 75.0%, respectively (Table 1).

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Table 1. People living with HIV (PLHIV) age ≥15 years linkage to care (LTC) and antiretroviral therapy (ART) initiation by category of self-report HIV status and sex, NAIIS 2018.

https://doi.org/10.1371/journal.pone.0257476.t001

During the first 30 weeks of the ART Surge, a total of 71,995 PLHIV were identified with 39,288 (54.6%) PLHIV identified through community testing. Weekly case identification ranged from 156 in week 1 to 2,277 in week 30. There was an observed increase in the contribution from community case finding from 8.0% in week 1 to 80.2% in week 30. Of the total 39,288 PLHIV identified in the community, 38,504 were linked to care, resulting in a 98% linkage rate, range (week 10 [94%]–week 17 [100%]). We observed 100% weekly ART initiation rate in each of the 30 weeks of the surge (Fig 2). Of the 38,504 who initiated ART, only 3,404 (8.8%) were from hard-to-reach locations and hence eligible for community treatment initiation. Three-month retention for the monthly treatment initiation cohorts showed progressive improvement in retention: 76% (October 2019 treatment cohort, 3-month retention evaluation was in January 2020) to 90% (April 2020 treatment cohorts, 3-month retention evaluation was in July 2020) (Fig 2).

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Fig 2. Linkage, Antiretroviral (ART) initiation, and retention rates during ART Surge, Nigeria, October 2019 –May 2020.

Abbreviations: PLHIV–People Living with HIV; ART–Antiretroviral Therapy; HTS–HIV Testing Service.

https://doi.org/10.1371/journal.pone.0257476.g002

Discussions

Actively monitoring and evaluating linkage to care data during NAIIS and the Surge were associated with a high percentage of PLHIV accessing ART through active LTC interventions. Using the mHealth strategy in NAIIS during the short time the large survey was in the field, we were able to efficiently monitor and manage all PLHIV identified from time of HIV diagnosis to ART initiation, using CBOs to ensure those not accessing services within one week were provided assistance. Leveraging this same network of CBOs during the ART Surge with close tracking of all identified PLHIV to ensure enrollment in care and initiation on treatment and client-centered services to those who needed assistance may have resulted in the high linkage to care rate of 98%.

The linkage and ART initiation rates achieved by the NAIIS model were much higher than than LTC rates obtained in conventional HIV community outreaches in Nigeria [12]. For example, a study in Rivers State indicated linkage to care rates of 25% and 43% for passive and community ART respectively [12]. Similarly, in rural Tanzania, community linkage to care was as low as 28% due to stigma, denial and gap in knowledge and awareness of the value of early linkage to care [13]. In a provincial surveillance survey in Kwazulu-Natal, South Africa, the community linkage to care rate was estimated at 55% with 21% of eligible participants initiating ART [14]. The higher linkage rates reported among those who were actively linked can be attributed to the reported effectiveness of using CBOs in community health initiatives, which help with stigma reduction that serves as major obstacle for successful linkage to care [15,16]. In both the NAIIS and the ART Surge, the CBOs that supported active linkage to care provided interim solutions to major linkage to care barriers. Some of these solutions included support for transport and/or escort to health facilities to facilitate linkage, as well as provision of other supportive services, such as psychosocial and education support. This is consistent with other studies that have found these interventions to be effective for improving LTC [17–19]. It is likely that involving CBOs in linkage to care also reduced the level of mistrust and reservations observed in communities, whenever community health activities including unfamiliar persons, such as NAIIS staff, enter communities.

Among NAIIS participants testing HIV-positive, the difference in linkage rates between those who reported knowing their HIV-positive status and those who did not can be explained in various ways. The higher linkage to care rates observed among previously known HIV-positive participants not on ART may be attributed to the reconfirmation of positive test results during the survey because they didn’t think they were positive before. It may also be that the CBOs providing active support and interventions helped these persons to overcome barriers to access that may have hindered their linkage to care in the past. On the other hand, the lower linkage rates seen among those who did not know their status may have been either because they were known HIV-positive persons on ART but refused to disclose their HIV-positive status during the survey, asymptomatic, or not ready to accept their HIV-positive status. A study from South Africa reported that PLHIV who reported being in good health were less likely to link to care [20]. Similarly, denial was shown to be an important factor impeding linkage to care and treatment initiation; acceptance of a positive result and disclosure are associated with higher rate of linkage to care [21,22].

Following scale-up of community testing through engagement of more CBOs and community testing teams, we observed progressive increases in the community contribution to overall case finding efforts of almost 10 fold from week 1 to week 30 (8.0% versus 80.2%). Targeted community testing was shown to be an efficient way of rapid identification of new PLHIV in Nigeria [23]. The high linkage and treatment initiation rates observed in the Nigeria ART Surge might have been influenced by four key changes implemented during the Surge based on lessons learned from NAIIS linkage strategies: 1) active strategies to link PlHIV to care and treatment services were used 2) mobile community testing and linkage teams were deployed to complement the CBOs’ efforts. as the Surge covers larger geographical locations, which available CBOs were not able cover alone, 3) CBOs and community testing teams were responsible for ensuring all PLHIV identified were initiated and retained on treatment, and 4) PLHIV in hard-to-reach areas had the opportunity to initiate ART in the community. To ensure that PLHIV who were identified, linked and initiated ART were retained, patient education and adherence counselling were emphasized during training of CBOs and community teams and implementation was monitored during the Surge. Therefore, it is likely that CBOs provided useful counselling and patient education on the benefits of early and consistent treatment, which might have influenced PLHIV to accept linkage and treatment initiation since good client education, awareness, and overall understanding of treatment benefits and needs for lifelong treatment have been shown to improve treatment initiation uptake and retention [24,25].

Although 100% of new and known HIV-positive persons identified in the Surge who were not on ART were offered treatment, the 3-month retention data suggest that not all of them started or remained on treatment. These data suggest that additional early follow-up of newly initiated persons may be necessary to ensure that they start and remain on treatment. We observed progressive improvement in three-month retention among monthly cohorts of PLHIV who were linked and initiated on ART in the Surge. During the Surge, CBOs and community teams deployed innovative strategies to ensure optimum retention. An important treatment attrition preventive strategy we implemented was keeping in touch with patients from time of treatment initiation until the next refill period by frequent calls or SMS messages to provide adherence couseling,patient education, and support. Early retention data after 3 months suggests that some patients may not have initiated treatment even though they took the drugs or may have started and then stopped in a short period of time and therefore, more research is needed to determine the best interventions for newly identified PLHIV to support durable linkage and early retention as this group has been shown to be 2–3 times more likely to experience an interruption in treatment than those who have been on ART >3 months [26]. Studies have shown that bringing treatment closer to PLHIV substantially improves linkage and treatment initiation among linkage-resistant PLHIV [27–29], therefore the community treatment initiation strategy deployed to hard-to-reach areas might have also contributed to higher linkage rates in the Surge, even though the proportion of PLHIV who were initiated on treatment in the community was low.

The use of USSD code and dashboards by LC and CBO as part of the NAIIS LTC strategy (not used in ART Surge) served as a solution to the third category of barriers to linkage to care (problems documenting linkage to care). Unfortunately, this strategy was not used in the Surge due to time and funding constraints. Manual linkage to care management using registers and daily meetings for records reconcilliation in the Surge was found to be cumbersome and inefficient. Daily and weekly meetings to reconcile LTC records between CBOs, community teams and sites takes several man-hours which could have been used to find and link more PLHIV to lifesaving ART. Similarly, dealing with hardcopy records put data security and confidentiality at higher risk when compared with the mHealth strategy. Data quality challenges, including transcription errors, are common such as age and sex miscategorization and various inconsistencies which may affect quality of evaluations. Additionally, these errors result in challenges tracking and tracing individuals for linkage and retention efforts and determining which patients are lost in the system. Following the lessons learned from NAIIS in using the mHealth technology for LTC documentation, and the inefficiencies experienced in manual LTC documentation in the Surge, there are plans to deploy mHealth technology for the Surge going forward to enhance efficiencies in Surge LTC and field operations. Using mHealth, NAIIS staff ensured that unique individuals were counted at sites after referral and patients linked passively or by CBOs did not register in more than one healthcare facility. Several other benefits of using mHealth have been reported in efforts at improving efficiencies in Early Infant Diagnosis (EID) turnaroud time in some developing countries [30], improving accuracy, completeness, and timeliness of reporting from healthcare facilities to district health ministry data repositories [31].

The generalization of our experience in optimizing LTC through active linkage with CBOs was subject to at least three limitations. First, this strategy was designed as a program and not as an experiment. Thus, the strategy was not designed to evaluate the superiority of our package in comparison to existing practice. The absence of a rigorous analysis to control for potential socio-economic and socio-behavioural confounders that would have been in place, if this was designed as an experiment, may affect the replicability of our experience elsewhere. Second, which is closely related to the first limitation, is that we were unable to ascertain the impact of individual strategies deployed by the CBOs on linkage to care and treatment initiation rates and outcomes. Third, categorization into known PLHIV and newly identified PLHIV was based on participant self-report and may be misleading. Several studies, including NAIIS, have reported low disclosure rates among PLHIV during surveys or in healthcare settings [1,32–34]. Therefore it may be possible that some of the newly identified PLHIV might actually be known PLHIV and on treatment hence refused linkage to care.

Conclusions

Active linkage to care using CBOs was associated with high levels of community linkage to care and ART initiation among PLHIVs in the NAIIS and the Nigeria ART Surge. In addition, the use of mHealth technology in the NAIIS linkage to care strategy was associated with timely documentation of results with high accuracy. By deploying active LTC strategies with mHealth technology, CBOs and mobile community teams may be able to effectively and efficiently scale-up ART with real-time documentation of client level outcomes across disparate service delivery points in low-resource settings like Nigeria.

Supporting information

Acknowledgments

The authors acknowledge the enormous contributions from the NAIIS consortium (Center for International Health, Education and Biosecurity [CIHEB] at the University of Maryland, African Field Epidemiology Network [AFENET], University of Washington [UW] and ICF International), the nine state ministries of health (Benue, Delta, Enugu, FCT, Gombe, Imo, Lagos, Nasarawa, Rivers) and the four PEPFAR implementing partners (APIN Public Health Initiative [APIN], Center for Integrated Health Program in Nigeria [CIHP], Catholic Caritas Foundation Nigeria [CCFN], Institute of Human Virology Nigeria [IHVN]).