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Clinical Science

Implementation and Operational Research

Pulling the Network Together

Quasiexperimental Trial of a Patient-Defined Support Network Intervention for Promoting Engagement in HIV Care and Medication Adherence on Mfangano Island, Kenya

Hickey, Matthew D. MD*,†,‡; Salmen, Charles R. MD, MPhil†,‡,§; Omollo, Dan BSc; Mattah, Brian; Fiorella, Kathryn J. MPH†,‖; Geng, Elvin H. MD, MPH; Bacchetti, Peter PhD#; Blat, Cinthia MPH*; Ouma, Gor B.; Zoughbie, Daniel MSc, DPhil; Tessler, Robert A. MD†,**; Salmen, Marcus R. MD; Campbell, Harold PhD†,‡; Gandhi, Monica MD, MPH; Shade, Starley MS, PhD††; Njoroge, Betty MBChB, MPH‡‡; Bukusi, Elizabeth A. MBChB, MMed, MPH, PhD, PGD‡‡; Cohen, Craig R. MD, MPH§§

Author Information
JAIDS Journal of Acquired Immune Deficiency Syndromes: August 1, 2015 - Volume 69 - Issue 4 - p e127-e134
doi: 10.1097/QAI.0000000000000664



As HIV treatment programs scale-up across resource-limited settings, unprecedented numbers of patients are newly initiating antiretroviral therapy (ART) each year. In 2012, nearly 1.3 million patients started ART in sub-Saharan Africa alone.1 Despite this substantial progress, consistent and long-lasting engagement in HIV care remains a major challenge. Applying best- and worst-case 3-year retention scenarios, an estimated 200,000–450,000 of those newly initiated on therapy in sub-Saharan Africa during 2012 will have discontinued treatment by 2015.2,3

Given the magnitude of the retention challenge, there is considerable interest in understanding factors that help patients maintain consistent engagement in care over time.4 One large ethnographic study across 3 sub-Saharan African countries identified access to social capital as a key facilitator of adherence to therapy.5 Findings from that study, and others, indicate that patient support networks provide necessary psychosocial and material resources for maintaining engagement in HIV care and adherence to therapy.4,6 In return, supporters expect “good adherence,” providing positive peer pressure for health-sustaining behaviors.

However, social capital can be difficult for HIV-infected individuals to access when seeking support for HIV treatment.4,7 Status disclosure is often avoided because of fear of the real and perceived ways that disclosure can affect social standing, livelihoods, and relationships.4,8,9 Consequently, many people living with HIV navigate treatment in secret,10–12 leading to diverse negative consequences on maintenance of therapy over time.4,13

Social interventions to promote the exchange of social capital have been previously developed to improve retention in HIV care and adherence to medications. Some ART programs encourage patients to identify a “treatment supporter”—a trusted individual who can provide psychosocial support and assistance with clinic appointments and medication taking.14–20 Patient support groups, another common intervention, allow patients to exchange knowledge and experiences with fellow patients.21,22 Evidence suggests that these interventions may reduce stigma and facilitate disclosure.23 However, by focusing exclusively on a single treatment supporter or a group of patient peers, these interventions may not fully use the preexisting social infrastructure that patients engage with throughout daily life.

To address this gap, we adapted a social network-based intervention known as “microclinics” that has previously been applied to address diabetes and other chronic diseases in other low-resource settings.24,25 Microclinics are informal social networks empowered to support chronic disease management and prevention. Randomized trials of the microclinic model have demonstrated reductions in hemoglobin A1C levels and body mass indices for patients with diabetes in Jordan26,27 and in rural Kentucky.24 Hypothesizing that a combined stigma reduction and social network empowerment intervention would result in improved HIV treatment outcomes,28 we developed a novel adaptation of microclinics to encompass groups of mixed HIV-infected and HIV-uninfected individuals in rural Kenya. We conducted a quasiexperimental trial to evaluate the impact of microclinics on engagement in HIV care and medication adherence among patients in this setting.


Study Population and Setting

This study was conducted at Sena Health Center, the largest of 6 public-sector health facilities and dispensaries on Lake Victoria's Mfangano Island. Mfangano is located within Homa Bay County, the most HIV-affected county in Kenya, with an estimated adult prevalence of 27%.29 Mfangano has a population of approximately 21,000 and is divided into 4 administrative sublocations of roughly equal size. The Sena Health Center is located on the boundary between the East and North sublocations, and over 90% of patients at Sena reside in one of these 2 locations. Adult patients at the Sena Health Center were eligible to participate if they were Mfangano residents and had initiated ART before or during the study enrollment period from November 2011 to February 2012. The study was approved by the Kenya Medical Research Institute Ethical Review Committee and the University of California, San Francisco, Committee for Human Subjects Research. The study protocol is registered at (NCT01912521). Written informed consent was obtained before study enrollment.

Design and Intervention

We conducted a quasiexperimental study with the intervention administered within the Mfangano East sublocation, and the remaining 3 sublocations serving as control. For this pilot study, Mfangano East was selected as the intervention community out of convenience because the implementing organization, the Organic Health Response, is located within Mfangano East. Thus, Sena Health Center patients who lived in East comprised the intervention group and those residing in the remaining 3 neighboring sublocations comprised the control group. We used an intention-to-treat analysis with treatment assignment based on sublocation of residence rather than intervention uptake. As secondary analysis, we also conducted as-treated analyses based on intervention participation.

After enrolling patients on ART at the Sena Health Center in the study, those living in the intervention community were invited to form “microclinic” groups. These microclinic groups were intended to contain 5–15 close family, friends, or other members of the patient's social support system, irrespective of these individuals' HIV status. Community Health Workers (CHWs) and study staff worked with “seed” individuals (ie, study participants on ART) to identify microclinic group members. In some cases, several seed individuals and their networks were combined into 1 microclinic group, based on CHW catchment area. Additionally, preexisting community groups were also invited to form microclinic groups and participate in the intervention. At the time of group formation, all microclinic participants underwent confidential individual HIV counseling and testing.

Once formed, microclinics were assigned a CHW coordinator and facilitator and were guided through a series of 10 discussion sessions over a period of 5 months (see Figure S1, Supplemental Digital Content, Sessions were scheduled every 2 weeks at a time and location of each group's choosing and lasted 2–3 hours each. CHWs participated in a 3–4 hour “train-the-trainer” workshop before each session to learn the games, role plays, and didactic components of each session, ask questions, and discuss with fellow CHWs before delivering the material to microclinic groups. CHWs were paid a stipend to compensate their role in microclinic coordination.

Over the course of the 10 group discussion sessions, major intervention components included (1) health education to promote knowledge of HIV prevention and treatment; (2) promotion of group support through discussions of confidentiality, HIV status disclosure, and encouragement of group support for adherence and clinic attendance; and (3) outreach to promote HIV testing and clinic enrollment within the community. At the conclusion of the 10 sessions, groups were invited to participate in voluntary group HIV testing, allowing microclinic members to disclose their HIV status to one another. Participants were followed for 18 months after initiation of the intervention to ascertain treatment outcomes.


Study staff conducted surveys and chart review to measure baseline demographic and clinical characteristics (Table 1). At baseline and immediately after intervention, we measured perceived community (attributable) stigma,30 HIV-related knowledge,31 and social support.32 Study staff also collected small hair samples for measurement of ART concentration, using previously described procedures.33 Hair samples were shipped at room temperature to a UCSF laboratory (the Drug Studies Unit) in San Francisco for analysis by liquid chromatography/tandem mass spectrometry.34,35

Baseline Characteristics of the 369 Participants Enrolled in the MIHNIS Study

We also collected clinic visit dates and corresponding next scheduled appointment dates from clinic records. For participants who were lost to follow-up, we conducted active patient tracing at the end of study follow-up and review of records at other clinics on Mfangano to ascertain whether the patient had transferred, died, or simply discontinued clinical care. We assumed that patients who could not be located and were not in care at another clinic within Mfangano were disengaged from care. For patients in care at another Mfangano facility, we continued chart review at those facilities after the transfer.

Statistical Analysis

Primary outcomes were engagement in HIV care and change in antiretroviral drug concentration in hair from baseline to immediately after intervention. We evaluated engagement in care in 2 different ways, namely (1) time to first 90-day clinic absence after a missed visit and (2) time spent adhering to clinic visit schedules (termed “time in care”). Secondary outcomes included changes in HIV-related stigma, HIV knowledge, and reports of social support.

We used logistic regression, with a test for overall effect for categorical variables with more than 2 categories, to compare distribution of baseline characteristics between study arms. Because nevirapine (NVP) was the most prevalent drug taken by study participants (88% at baseline and 84% at after intervention) and because the mean values and ranges of hair concentrations differ for each drug, we restricted our hair sample analysis to NVP users.36 We computed the difference in hair NVP concentrations from baseline to immediately after completion of the intervention. Patients who were not taking NVP or who did not donate hair for analysis at one or both time points were excluded from analysis. We used univariable and multivariable linear regression to compare changes in NVP hair levels between study arms.

We calculated gaps in care by determining the number of days between a missed visit and the date of return to any clinic on Mfangano; participants were censored on the date of death or transfer to a health facility outside Mfangano Island. Thus, 90-day disengagement indicates missing an appointment by ≥90 days and not known to have first transferred or died. “Time in care” constituted the proportion of time participants spent adhering to their scheduled appointment dates and was calculated as follows:

Total time eligible for care was calculated from the date of study enrollment until the date of censoring or study closure. We compared time to 90-day disengagement between study arms using Cox proportional hazards. We evaluated the proportional hazards assumption both graphically and using formal testing with Schoenfeld residuals. We also computed the cumulative incidence function using death as a competing event and displayed differences between groups graphically.37 We used linear regression to compare differences in time in care between study arms. To enhance interpretability, we converted model-derived estimates to days per person-year by multiplying by 365.25. To address potential nonnormality of the residuals, we used bootstrapping with 10,000 replications and cluster resampling to evaluate the degree to which potential nonnormality of residuals impacted standard errors.

Although primary analysis was conducted using intention-to-treat analysis, we also performed sensitivity analyses excluding individuals in the intervention arm who did not join a microclinic group. For each model, we used robust standard errors, which accounted for nonindependence resulting from the clustered nature of the intervention. In multivariate models, we adjusted for baseline factors reasonably thought to confound the relationship between community of residence and study outcomes. These included age, sex, monthly household income, walking distance to the Sena Health Center, stigma score, HIV-related knowledge, social support, CD4 count, WHO stage, and time since ART initiation. Predictors with P values <0.1 were retained in an intermediate model, and each predictor was readded and included in the final model only if the addition changed the estimated intervention effect by ≥±10%. In addition to the primary study outcomes, we used univariable linear regression to evaluate intervention impact on changes in perceived stigma, HIV-related knowledge, and social support.


Of 426 eligible clinic patients, 369 (87%) enrolled in the study (Fig. 1). Baseline characteristics were similar between communities, although intervention community participants tended to live closer to the clinic and have higher baseline CD4 cell counts (Table 1). Within the intervention community, 44 microclinic groups were formed. The median (range) microclinic group was 13 members in size (4–18), 78% female (0%–100%), and 33% HIV infected (0%–86%). Thirty-four groups contained a study participant on ART, 9 groups contained members who were HIV infected but not yet on ART, and 1 group was composed entirely of HIV-uninfected individuals (note, some groups did not contain a study participant on ART because we allowed preexisting groups to also form microclinics). In total, 113 (74%) of the 153 intervention community study participants on ART and 423 members of their social support networks participated in a microclinic. Four control community study participants also participated in a microclinic group. Thus, standard errors for all models were adjusted for 286 clusters, namely 212 control arm participants who did not join a microclinic, 40 intervention arm participants who did not join a microclinic, and 34 microclinic groups containing 117 study participants on ART from both intervention and control study arms. Microclinic participation was excellent; 110 of the 113 (97%) intervention arm study participants remained active group members at the end of the 10 sessions, based on CHW report, with study staff verification. Furthermore, 86% of both patients on ART (97/113) and their social support network members (364/423) attended voluntary group counseling, testing, and disclosure. Twenty-one percent (75/364) of support network members who participated in the group disclosure were HIV infected but had not yet started ART. Clinic data were not available for these participants, and thus, we were not able to determine whether they were enrolled in clinical care. HIV status of group members who did not participate in group testing and disclosure was not available.

Participant flow.

Medication Adherence

The acceptability of hair collection was 95% (350/369) at study baseline and 99% (338/340 remaining in the study) at 6-month follow-up. One hundred eleven (73%) intervention arm participants and 162 (75%) control arm participants were taking NVP and had hair samples collected at both baseline and 6-month study visits. Mean NVP levels decreased in both cohorts, from 82.9 to 77.4 ng/mg (change: −5.5, SD: 42.4) in the intervention community and 93.4–81.0 ng/mg (change: −12.4, SD: 38.8) in the control community (n = 273). In univariable linear regression, the decline in NVP hair concentrations over the course of the intervention was 6.9 ng/mg less in the intervention arm compared with the control arm [95% confidence interval (CI): −2.5 to 16.2]. Because both groups experienced decreases in NVP hair concentrations, this represented a nonstatistically significant smaller decrease in the intervention arm in comparison with the control. In multivariable modeling, only age was retained as a potential confounder, and estimates remained similar (effect size: 6.7 ng/mg; 95% CI: −2.7 to 16.1).

In as-treated analysis, comparing those who joined a microclinic group in the intervention arm with all participants in the control arm, decrease in NVP hair concentration was 11.1 ng/mg (95% CI: 1.3 to 21.0) less in the intervention group than the control. Multivariable analysis, including age, yielded similar results (effect size: 11.3 ng/mg; 95% CI: 1.4 to 21.1).

Disengagement From Care

After study enrollment, participants were followed for 22 months or until the date of death or transfer to a health facility outside Mfangano Island. Most participants were retained in care by the end of follow-up (Fig. 1); however, over the course of follow-up, 11% of the participants in the intervention arm and 20% of those in the control arm experienced a clinic absence of ≥90 days. Incidence rates of 90-day disengagement were 6.8 per 100 person-years in the intervention group (95% CI: 4.2 to 10.9) and 12.9 (95% CI: 9.6 to 17.3) in the control. Using an unadjusted Cox proportional hazard model, participants in the intervention arm had one-half the rate of ≥90-day clinic absence as those in the control arm (hazard ratio: 0.53; 95% CI: 0.28 to 1.02) (Table 2). Adjusted analysis, including time since ART initiation and distance to the health center, yielded similar results (adjusted hazard ratio: 0.48; 95% CI: 0.25 to 0.92). We plotted the cumulative incidence of 90-day disengagement, treating death as a competing event, to visually represent disengagement occurrence over the study period (Fig. 2). Notably, the first 4 months of follow-up were contemporaneous with group formation and the intervention itself did not begin until month 5. Cumulative incidence curves suggest a difference in disengagement that begins approximately 2 months after initiation of the intervention.

Disengagement From Care
Cumulative incidence of 90-day disengagement from care. The intervention commenced at month 5 and ran through month 9. A, Intention-to-treat analysis. B, As-treated analysis, with green line representing intervention arm participants who joined microclinics and orange line representing intervention arm participants who did not join a microclinic.

Time in Care

To further characterize engagement in care over time, we measured the proportion of time participants spent adhering to clinic appointment schedules (time in care). During study follow-up, the average time in care was 86.2% in the intervention community and 81.6% in the control community, an absolute difference of 4.6% (95% CI: 1.3% to 8.5%) (Table 3). This is equivalent to an increase of 17 days “in care” per patient-year (95% CI: 3 to 31 days) among patients in the intervention arm. In multivariable linear regression, adjusting for time since ART initiation, distance from clinic, and baseline stigma, the intervention community experienced a 6.0% absolute increase in time in care (95% CI: 3.4% to 8.6%), an increase of 22 days in care per patient-year (95% CI: 10 to 34 days). CIs were not substantively changed when recalculated using the bootstrap method (data not shown).

Time in Care

Stigma decreased by 25% relative to baseline in the intervention community and was unchanged in the control community, with a difference in change scores between groups of −1.6 units on a 17-unit scale (95% CI: −2.4 to −0.8; see Table S1, Supplemental Digital Content, There was no difference in change in HIV-related knowledge between groups. Social support increased slightly in the intervention community, although the change within the intervention arm represented only a 2% relative increase from baseline.


Microclinics improved community-wide engagement in HIV care among patients on ART. Patients residing in the intervention community had one-half the rate of 90-day gaps in care as control participants. Those in the intervention community also spent a larger proportion of time adherent to clinic schedules. The observed 6% increase in time in care in the multivariable model is equivalent to a 3-week reduction, per patient-year, in the delay between missed visits and subsequent return to clinic.

We also observed increases in hair NVP concentrations in intervention community participants relative to controls, although this improvement was not statistically significant. The CI of our observed estimates for change in NVP hair concentrations was wide and included the possibility of either no true effect or an effect large enough to be beneficial for many patients, based on comparison of NVP changes with virologic suppression in another study.36 As-treated results suggested that the intervention might exert a protective effect on declining hair drug levels over time. However, this analysis is subject to potentially substantial selection bias and should be regarded with caution. Absolute NVP hair concentrations are difficult to interpret clinically, especially because this rural Kenyan cohort had baseline mean concentrations that were over 2 times higher than the United States-based cohorts.33,36 However, the within-individual differences over a relatively short period likely reflect changes in adherence rather than alterations in pharmacokinetics.38

We propose that the microclinic intervention impacts the above clinical processes by reducing HIV-related stigma and thus lowering the “activation energy” required for engaging social networks in the treatment process. The resulting increase in access to social capital for HIV treatment support could explain our observed improvements in clinic appointment adherence and possible medication adherence.5 Our observation that HIV-related stigma decreased, whereas overall social support and HIV-related knowledge remained relatively unchanged, may support this hypothesis.

Microclinics build on key strengths of existing social interventions for promoting engagement in HIV care, including treatment supporters and patient support groups. Whereas treatment supporters promote status disclosure and reduce stigma through a single supportive relationship,23 microclinics provide this degree of support by means of patient's broader social network. In addition, microclinics also promote the role of “expert patients” commonly found in patient support group interventions.39 By encouraging group members to be both supported by and supporters of other group members, the microclinic model facilitates group empowerment and may represent a more socially relevant approach to chronic disease management than more individual-oriented approaches.4

Although most participants who met our definition of disengagement eventually returned to care, we observed a substantial reduction in long gaps in care in the intervention community. Recent work by Ware et al40 highlights a pathway from missing a clinic visit to “disengaging” from care that includes, as intermediary steps, developing a “reluctance to return” and subsequent feelings of decreased connectedness to care. In our study, missed visits were very common, with over 90% of participants missing at least 1 visit by more than 3 days over the course of follow-up and no significant difference between study arms (data not shown). It is possible that microclinic participation either prevented the development of “reluctance to return” after a missed visit or prevented this reluctance from eroding ultimate feelings of connection to care, although further study is needed to understand how the microclinic intervention interacts with these concepts.

These results bolster empiric support for microclinics as an effective model for chronic disease management. Microclinic interventions to address diabetes have demonstrated beneficial effects not only for “index” diabetes patients but also for members of their social networks—arguably individuals who are also at high risk for developing diabetes because of shared genetic, environmental, and behavioral risk factors.24,26 Similarly, this intervention holds potential for improving care not only for individuals who are on ART but also for improving engagement in care by those who have not yet sought HIV care. Still, other HIV-uninfected group members may benefit from increased knowledge, motivation, and group support for preventing HIV. This multilevel social network effect may be especially important among high prevalence populations.

This study has several limitations, including the quasiexperimental design and our inability to assess impact on downstream health outcomes. Although treatment assignment was not randomized, we compared outcomes among populations that were qualitatively and quantitatively highly similar at baseline. Additionally, our intention-to-treat analysis eliminated the confounding that occurs when patients with lower risk of poor outcomes are also more likely to participate in a social intervention of this type. Viral load was cost prohibitive in this early-phase trial, and our study design was not intended to evaluate impact on mortality. However, others have shown that gaps in clinical HIV care predict subsequent virologic failure, morbidity, and mortality.41 Our successful efforts to ascertain outcomes for nearly all study participants through active tracing also increase our confidence that observed gaps are reflective of true treatment interruptions. Our time in care measure, the proportion of time patients adhered to their clinic appointment schedules, further supports our observation that patients attended appointments more regularly and with less delay in the intervention community.


The microclinic intervention holds promise as a feasible community-based strategy to improve long-term engagement in HIV care. The success of a social network approach on reducing treatment interruptions and improving engagement in care has important implications for improving virologic suppression and subsequently decreasing morbidity, mortality, and HIV transmission. Because of the way in which social networks are woven directly into the fabric of daily life, particularly in poor communities in resource-limited settings, this strategy may result in a more sustained and amplified effect than previously evaluated approaches and warrants further study.


The authors thank the research team and staff at the Ekialo Kiona Center for their tireless work, including Lister Amondi Ngare, Millicent Nyauke, Sylvanus Agong, Ruth Omondi, Pamela Mohamed, Joyce Obanda, Martabel Okeyo, Elsa Awino Odero, Clifford Omondi Maginga, Reagan Okoth, Odhiambo Mourline Atieno, Abdi Odhiambo Kitewega, Vallary Achieng Migawi, Janet Awino Ooko, Mary Janet Achieng, Theresa Alingo, Jane Akinyi Odindo, Robinson Okeyo, and Richard Magerenge. We would also like to recognize and appreciate Michael Nguyen for putting together the supplemental figure and the rest of the staff at Microclinic International for their ongoing support. We would also like to thank the faculty in UCSF's Training in Clinical Research program for their second-to-none teaching, the staff at the Sena Health Center for their collaborative spirit, the Director, KEMRI, for his support of the study, and most importantly, the study participants for believing in the value of kanyaklas and dedicating their time toward making this project a success.


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HIV/AIDS; Kenya; social network; retention in care; adherence; stigma

Supplemental Digital Content

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