Incident Tuberculosis Infection is Associated with Alcohol use in Adults in Rural Uganda.

Data on alcohol use and incident Tuberculosis (TB) infection are needed. In adults aged 15+ in rural Uganda (N=49,585), estimated risk of incident TB infection was 29.2% with alcohol use vs. 19.2% without (RR: 1.49; 95%CI: 1.40-1.60). There is potential for interventions to interrupt transmission among people who drink alcohol.

Two-Stage TMLE to reduce bias and improve efficiency in cluster randomized trials.

Cluster randomized trials (CRTs) randomly assign an intervention to groups of individuals (e.g., clinics or communities) and measure outcomes on individuals in those groups. While offering many advantages, this experimental design introduces challenges that are only partially addressed by existing analytic approaches. First, outcomes are often missing for some individuals within clusters. Failing to appropriately adjust for differential outcome measurement can result in biased estimates and inference. Second, CRTs often randomize limited numbers of clusters, resulting in chance imbalances on baseline outcome predictors between arms. Failing to adaptively adjust for these imbalances and other predictive covariates can result in efficiency losses. To address these methodological gaps, we propose and evaluate a novel two-stage targeted minimum loss-based estimator to adjust for baseline covariates in a manner that optimizes precision, after controlling for baseline and postbaseline causes of missing outcomes. Finite sample simulations illustrate that our approach can nearly eliminate bias due to differential outcome measurement, while existing CRT estimators yield misleading results and inferences. Application to real data from the SEARCH community randomized trial demonstrates the gains in efficiency afforded through adaptive adjustment for baseline covariates, after controlling for missingness on individual-level outcomes.

The Causal Roadmap and Simulations to Improve the Rigor and Reproducibility of Real-data Applications.

The Causal Roadmap outlines a systematic approach to asking and answering questions of cause and effect: define the quantity of interest, evaluate needed assumptions, conduct statistical estimation, and carefully interpret results. To protect research integrity, it is essential that the algorithm for statistical estimation and inference be prespecified prior to conducting any effectiveness analyses. However, it is often unclear which algorithm will perform optimally for the real-data application. Instead, there is a temptation to simply implement one's favorite algorithm, recycling prior code or relying on the default settings of a computing package. Here, we call for the use of simulations that realistically reflect the application, including key characteristics such as strong confounding and dependent or missing outcomes, to objectively compare candidate estimators and facilitate full specification of the statistical analysis plan. Such simulations are informed by the Causal Roadmap and conducted after data collection but prior to effect estimation. We illustrate with two worked examples. First, in an observational longitudinal study, we use outcome-blind simulations to inform nuisance parameter estimation and variance estimation for longitudinal targeted minimum loss-based estimation. Second, in a cluster randomized trial with missing outcomes, we use treatment-blind simulations to examine type-I error control in two-stage targeted minimum loss-based estimation. In both examples, realistic simulations empower us to prespecify an estimation approach that is expected to have strong finite sample performance and also yield quality-controlled computing code for the actual analysis. Together, this process helps to improve the rigor and reproducibility of our research.

Adaptive sequential surveillance with network and temporal dependence.

Strategic test allocation is important for control of both emerging and existing pandemics (eg, COVID-19, HIV). It supports effective epidemic control by (1) reducing transmission via identifying cases and (2) tracking outbreak dynamics to inform targeted interventions. However, infectious disease surveillance presents unique statistical challenges. For instance, the true outcome of interest (positive infection status) is often a latent variable. In addition, presence of both network and temporal dependence reduces data to a single observation. In this work, we study an adaptive sequential design, which allows for unspecified dependence among individuals and across time. Our causal parameter is the mean latent outcome we would have obtained, if, starting at time t given the observed past, we had carried out a stochastic intervention that maximizes the outcome under a resource constraint. The key strength of the method is that we do not have to model network and time dependence: a short-term performance Online Super Learner is used to select among dependence models and randomization schemes. The proposed strategy learns the optimal choice of testing over time while adapting to the current state of the outbreak and learning across samples, through time, or both. We demonstrate the superior performance of the proposed strategy in an agent-based simulation modeling a residential university environment during the COVID-19 pandemic.

The Association Between Social Network Characteristics and Tuberculosis Infection Among Adults in 9 Rural Ugandan Communities.

BACKGROUND: Social network analysis can elucidate tuberculosis transmission dynamics outside the home and may inform novel network-based case-finding strategies. METHODS: We assessed the association between social network characteristics and prevalent tuberculosis infection among residents (aged ≥15 years) of 9 rural communities in Eastern Uganda. Social contacts named during a census were used to create community-specific nonhousehold social networks. We evaluated whether social network structure and characteristics of first-degree contacts (sex, human immunodeficiency virus [HIV] status, tuberculosis infection) were associated with revalent tuberculosis infection (positive tuberculin skin test [TST] result) after adjusting for individual-level risk factors (age, sex, HIV status, tuberculosis contact, wealth, occupation, and Bacillus Calmette-Guérin [BCG] vaccination) with targeted maximum likelihood estimation. RESULTS: Among 3 335 residents sampled for TST, 32% had a positive TST results and 4% reported a tuberculosis contact. The social network contained 15 328 first-degree contacts. Persons with the most network centrality (top 10%) (adjusted risk ratio, 1.3 [95% confidence interval, 1.1-1.1]) and the most (top 10%) male contacts (1.5 [1.3-1.9]) had a higher risk of prevalent tuberculosis, than those in the remaining 90%. People with ≥1 contact with HIV (adjusted risk ratio, 1.3 [95% confidence interval, 1.1-1.6]) and ≥2 contacts with tuberculosis infection were more likely to have tuberculosis themselves (2.6 [ 95% confidence interval: 2.2-2.9]). CONCLUSIONS: Social networks with higher centrality, more men, contacts with HIV, and tuberculosis infection were positively associated with tuberculosis infection. Tuberculosis transmission within measurable social networks may explain prevalent tuberculosis not associated with a household contact. Further study on network-informed tuberculosis case finding interventions is warranted.

Efficient and robust approaches for analysis of sequential multiple assignment randomized trials: Illustration using the ADAPT-R trial.

Personalized intervention strategies, in particular those that modify treatment based on a participant's own response, are a core component of precision medicine approaches. Sequential multiple assignment randomized trials (SMARTs) are growing in popularity and are specifically designed to facilitate the evaluation of sequential adaptive strategies, in particular those embedded within the SMART. Advances in efficient estimation approaches that are able to incorporate machine learning while retaining valid inference can allow for more precise estimates of the effectiveness of these embedded regimes. However, to the best of our knowledge, such approaches have not yet been applied as the primary analysis in SMART trials. In this paper, we present a robust and efficient approach using targeted maximum likelihood estimation (TMLE) for estimating and contrasting expected outcomes under the dynamic regimes embedded in a SMART, together with generating simultaneous confidence intervals for the resulting estimates. We contrast this method with two alternatives (G-computation and inverse probability weighting estimators). The precision gains and robust inference achievable through the use of TMLE to evaluate the effects of embedded regimes are illustrated using both outcome-blind simulations and a real-data analysis from the Adaptive Strategies for Preventing and Treating Lapses of Retention in Human Immunodeficiency Virus (HIV) Care (ADAPT-R) trial (NCT02338739), a SMART with a primary aim of identifying strategies to improve retention in HIV care among people living with HIV in sub-Saharan Africa.

Defining and estimating effects in cluster randomized trials: A methods comparison.

Across research disciplines, cluster randomized trials (CRTs) are commonly implemented to evaluate interventions delivered to groups of participants, such as communities and clinics. Despite advances in the design and analysis of CRTs, several challenges remain. First, there are many possible ways to specify the causal effect of interest (eg, at the individual-level or at the cluster-level). Second, the theoretical and practical performance of common methods for CRT analysis remain poorly understood. Here, we present a general framework to formally define an array of causal effects in terms of summary measures of counterfactual outcomes. Next, we provide a comprehensive overview of CRT estimators, including the t-test, generalized estimating equations (GEE), augmented-GEE, and targeted maximum likelihood estimation (TMLE). Using finite sample simulations, we illustrate the practical performance of these estimators for different causal effects and when, as commonly occurs, there are limited numbers of clusters of different sizes. Finally, our application to data from the Preterm Birth Initiative (PTBi) study demonstrates the real-world impact of varying cluster sizes and targeting effects at the cluster-level or at the individual-level. Specifically, the relative effect of the PTBi intervention was 0.81 at the cluster-level, corresponding to a 19% reduction in outcome incidence, and was 0.66 at the individual-level, corresponding to a 34% reduction in outcome risk. Given its flexibility to estimate a variety of user-specified effects and ability to adaptively adjust for covariates for precision gains while maintaining Type-I error control, we conclude TMLE is a promising tool for CRT analysis.

Two or more significant life-events in 6-months are associated with lower rates of HIV treatment and virologic suppression among youth with HIV in Uganda and Kenya.

Youth living with HIV in sub-Saharan Africa have poor HIV care outcomes. We determined the association of recent significant life-events with HIV antiretroviral treatment (ART) initiation and HIV viral suppression in youth aged 15-24 years living with HIV in rural Kenya and Uganda. This was a cross-sectional analysis of 995 youth enrolled in the SEARCH Youth study. At baseline, providers assessed recent (within 6 months) life-events, defined as changes in schooling/employment, residence, partnerships, sickness, incarceration status, family strife or death, and birth/pregnancy, self-reported alcohol use, being a parent, and HIV-status disclosure. We examined the frequencies of events and their association with ART status and HIV viral suppression (<400 copies/ul). Recent significant life-events were prevalent (57.7%). Having >2 significant life-events (aOR = 0.61, 95% CI:0.45-0.85) and consuming alcohol (aOR = 0.61, 95% CI:0.43-0.87) were associated with a lower odds of HIV viral suppression, while disclosure of HIV-status to partner (aOR = 2.39, 95% CI:1.6-3.5) or to family (aOR = 1.86, 95% CI:1.3-2.7), being a parent (aOR = 1.8, 95% CI:1.2-2.5), and being single (aOR = 1.6, 95% CI:1.3-2.1) had a higher odds. This suggest that two or more recent life-events and alcohol use are key barriers to ART initiation and achievement of viral suppression among youth living with HIV in rural East Africa.Trial registration: ClinicalTrials.gov identifier: NCT03848728..

SEARCH Human Immunodeficiency Virus (HIV) Streamlined Treatment Intervention Reduces Mortality at a Population Level in Men With Low CD4 Counts.

BACKGROUND: We tested the hypothesis that patient-centered, streamlined human immunodeficiency virus (HIV) care would achieve lower mortality than the standard treatment model for persons with HIV and CD4 ≤ 350/uL in the setting of population-wide HIV testing. METHODS: In the SEARCH (Sustainable East Africa Research in Community Health) Study (NCT01864603), 32 communities in rural Uganda and Kenya were randomized to country-guided antiretroviral therapy (ART) versus streamlined ART care that included rapid ART start, visit spacing, flexible clinic hours, and welcoming environment. We assessed persons with HIV and CD4 ≤ 350/uL, ART eligible in both arms, and estimated the effect of streamlined care on ART initiation and mortality at 3 years. Comparisons between study arms used a cluster-level analysis with survival estimates from Kaplan-Meier; estimates of ART start among ART-naive persons treated death as a competing risk. RESULTS: Among 13 266 adults with HIV, 2973 (22.4%) had CD4 ≤ 350/uL. Of these, 33% were new diagnoses, and 10% were diagnosed but ART-naive. Men with HIV were almost twice as likely as women with HIV to have CD4 ≤ 350/uL and be untreated (15% vs 8%, respectively). Streamlined care reduced mortality by 28% versus control (risk ratio [RR] = 0.72; 95% confidence interval [CI]: .56, .93; P = .02). Despite eligibility in both arms, persons with CD4 ≤ 350/uL started ART faster under streamlined care versus control (76% vs 43% by 12 months, respectively; P < .001). Mortality was reduced substantially more among men (RR = 0.61; 95% CI: .43, .86; P = .01) than among women (RR = 0.90; 95% CI: .62, 1.32; P = .58). CONCLUSIONS: After population-based HIV testing, streamlined care reduced population-level mortality among persons with HIV and CD4 ≤ 350/uL, particularly among men. Streamlined HIV care models may play a key role in global efforts to reduce AIDS deaths.

Effect of a patient-centered hypertension delivery strategy on all-cause mortality: Secondary analysis of SEARCH, a community-randomized trial in rural Kenya and Uganda.

BACKGROUND: Hypertension treatment reduces morbidity and mortality yet has not been broadly implemented in many low-resource settings, including sub-Saharan Africa (SSA). We hypothesized that a patient-centered integrated chronic disease model that included hypertension treatment and leveraged the HIV care system would reduce mortality among adults with uncontrolled hypertension in rural Kenya and Uganda. METHODS AND FINDINGS: This is a secondary analysis of the SEARCH trial (NCT:01864603), in which 32 communities underwent baseline population-based multidisease testing, including hypertension screening, and were randomized to standard country-guided treatment or to a patient-centered integrated chronic care model including treatment for hypertension, diabetes, and HIV. Patient-centered care included on-site introduction to clinic staff at screening, nursing triage to expedite visits, reduced visit frequency, flexible clinic hours, and a welcoming clinic environment. The analytic population included nonpregnant adults (≥18 years) with baseline uncontrolled hypertension (blood pressure ≥140/90 mm Hg). The primary outcome was 3-year all-cause mortality with comprehensive population-level assessment. Secondary outcomes included hypertension control assessed at a population level at year 3 (defined per country guidelines as at least 1 blood pressure measure <140/90 mm Hg on 3 repeated measures). Between-arm comparisons used cluster-level targeted maximum likelihood estimation. Among 86,078 adults screened at study baseline (June 2013 to July 2014), 10,928 (13%) had uncontrolled hypertension. Median age was 53 years (25th to 75th percentile 40 to 66); 6,058 (55%) were female; 677 (6%) were HIV infected; and 477 (4%) had diabetes mellitus. Overall, 174 participants (3.2%) in the intervention group and 225 participants (4.1%) in the control group died during 3 years of follow-up (adjusted relative risk (aRR) 0.79, 95% confidence interval (CI) 0.64 to 0.97, p = 0.028). Among those with baseline grade 3 hypertension (≥180/110 mm Hg), 22 (4.9%) in the intervention group and 42 (7.9%) in the control group died during 3 years of follow-up (aRR 0.62, 95% CI 0.39 to 0.97, p = 0.038). Estimated population-level hypertension control at year 3 was 53% in intervention and 44% in control communities (aRR 1.22, 95% CI 1.12 to 1.33, p < 0.001). Study limitations include inability to identify specific causes of death and control conditions that exceeded current standard hypertension care. CONCLUSIONS: In this cluster randomized comparison where both arms received population-level hypertension screening, implementation of a patient-centered hypertension care model was associated with a 21% reduction in all-cause mortality and a 22% improvement in hypertension control compared to standard care among adults with baseline uncontrolled hypertension. Patient-centered chronic care programs for HIV can be leveraged to reduce the overall burden of cardiovascular mortality in SSA. TRIAL REGISTRATION: ClinicalTrials.gov NCT01864603.

HIV incidence after pre-exposure prophylaxis initiation among women and men at elevated HIV risk: A population-based study in rural Kenya and Uganda.

BACKGROUND: Oral pre-exposure prophylaxis (PrEP) is highly effective for HIV prevention, but data are limited on HIV incidence among PrEP users in generalized epidemic settings, particularly outside of selected risk groups. We performed a population-based PrEP study in rural Kenya and Uganda and sought to evaluate both changes in HIV incidence and clinical and virologic outcomes following seroconversion on PrEP. METHODS AND FINDINGS: During population-level HIV testing of individuals ≥15 years in 16 communities in the Sustainable East Africa Research in Community Health (SEARCH) study (NCT01864603), we offered universal access to PrEP with enhanced counseling for persons at elevated HIV risk (based on serodifferent partnership, machine learning-based risk score, or self-identified HIV risk). We offered rapid or same-day PrEP initiation and flexible service delivery with follow-up visits at facilities or community-based sites at 4, 12, and every 12 weeks up to week 144. Among participants with incident HIV infection after PrEP initiation, we offered same-day antiretroviral therapy (ART) initiation and analyzed HIV RNA, tenofovir hair concentrations, drug resistance, and viral suppression (<1,000 c/ml based on available assays) after ART start. Using Poisson regression with cluster-robust standard errors, we compared HIV incidence among PrEP initiators to incidence among propensity score-matched recent historical controls (from the year before PrEP availability) in 8 of the 16 communities, adjusted for risk group. Among 74,541 individuals who tested negative for HIV, 15,632/74,541 (21%) were assessed to be at elevated HIV risk; 5,447/15,632 (35%) initiated PrEP (49% female; 29% 15-24 years; 19% in serodifferent partnerships), of whom 79% engaged in ≥1 follow-up visit and 61% self-reported PrEP adherence at ≥1 visit. Over 7,150 person-years of follow-up, HIV incidence was 0.35 per 100 person-years (95% confidence interval [CI] 0.22-0.49) among PrEP initiators. Among matched controls, HIV incidence was 0.92 per 100 person-years (95% CI 0.49-1.41), corresponding to 74% lower incidence among PrEP initiators compared to matched controls (adjusted incidence rate ratio [aIRR] 0.26, 95% CI 0.09-0.75; p = 0.013). Among women, HIV incidence was 76% lower among PrEP initiators versus matched controls (aIRR 0.24, 95% CI 0.07-0.79; p = 0.019); among men, HIV incidence was 40% lower, but not significantly so (aIRR 0.60, 95% CI 0.12-3.05; p = 0.54). Of 25 participants with incident HIV infection (68% women), 7/25 (28%) reported taking PrEP ≤30 days before HIV diagnosis, and 24/25 (96%) started ART. Of those with repeat HIV RNA after ART start, 18/19 (95%) had <1,000 c/ml. One participant with viral non-suppression was found to have transmitted viral resistance, as well as emtricitabine resistance possibly related to PrEP use. Limitations include the lack of contemporaneous controls to assess HIV incidence without PrEP and that plasma samples were not archived to assess for baseline acute infection. CONCLUSIONS: Population-level offer of PrEP with rapid start and flexible service delivery was associated with 74% lower HIV incidence among PrEP initiators compared to matched recent controls prior to PrEP availability. HIV infections were significantly lower among women who started PrEP. Universal HIV testing with linkage to treatment and prevention, including PrEP, is a promising approach to accelerate reductions in new infections in generalized epidemic settings. TRIAL REGISTRATION: ClinicalTrials.gov NCT01864603.

Invited Commentary: Machine Learning in Causal Inference-How Do I Love Thee? Let Me Count the Ways.

In this issue of the Journal, Mooney et al. (Am J Epidemiol. 2021;190(8):1476-1482) discuss machine learning as a tool for causal research in the style of Internet headlines. Here we comment by adapting famous literary quotations, including the one in our title (from "Sonnet 43" by Elizabeth Barrett Browning (Sonnets From the Portuguese, Adelaide Hanscom Leeson, 1850)). We emphasize that any use of machine learning to answer causal questions must be founded on a formal framework for both causal and statistical inference. We illustrate the pitfalls that can occur without such a foundation. We conclude with some practical recommendations for integrating machine learning into causal analyses in a principled way and highlight important areas of ongoing work.

Social Networks and HIV Care Outcomes in Rural Kenya and Uganda.

BACKGROUND: Social isolation among HIV-positive persons might be an important barrier to care. Using data from the SEARCH Study in rural Kenya and Uganda, we constructed 32 community-wide, sociocentric networks and evaluated whether less socially connected HIV-positive persons were less likely to know their status, have initiated treatment, and be virally suppressed. METHODS: Between 2013 and 2014, 168,720 adult residents in the SEARCH Study were census-enumerated, offered HIV testing, and asked to name social contacts. Social networks were constructed by matching named contacts to other residents. We characterized the resulting networks and estimated risk ratios (aRR) associated with poor HIV care outcomes, adjusting for sociodemographic factors and clustering by community with generalized estimating equations. RESULTS: The sociocentric networks contained 170,028 residents (nodes) and 362,965 social connections (edges). Among 11,239 HIV-positive persons who named ≥1 contact, 30.9% were previously undiagnosed, 43.7% had not initiated treatment, and 49.4% had viral nonsuppression. Lower social connectedness, measured by the number of persons naming an HIV-positive individual as a contact (in-degree), was associated with poorer outcomes in Uganda, but not Kenya. Specifically, HIV-positive persons in the lowest connectedness tercile were less likely to be previously diagnosed (Uganda-West aRR: 0.89 [95% confidence interval (CI): 0.83, 0.96]; Uganda-East aRR: 0.85 [95% CI: 0.76, 0.96]); on treatment (Uganda-West aRR: 0.88 [95% CI: 0.80, 0.98]; Uganda-East aRR: 0.81 [0.72, 0.92]), and suppressed (Uganda-West aRR: 0.84 [95% CI: 0.73, 0.96]; Uganda-East aRR: 0.74 [95% CI: 0.58, 0.94]) than those in the highest connectedness tercile. CONCLUSIONS: HIV-positive persons named as a contact by fewer people may be at higher risk for poor HIV care outcomes, suggesting opportunities for targeted interventions.

Machine Learning to Identify Persons at High-Risk of Human Immunodeficiency Virus Acquisition in Rural Kenya and Uganda.

BACKGROUND: In generalized epidemic settings, strategies are needed to prioritize individuals at higher risk of human immunodeficiency virus (HIV) acquisition for prevention services. We used population-level HIV testing data from rural Kenya and Uganda to construct HIV risk scores and assessed their ability to identify seroconversions. METHODS: During 2013-2017, >75% of residents in 16 communities in the SEARCH study were tested annually for HIV. In this population, we evaluated 3 strategies for using demographic factors to predict the 1-year risk of HIV seroconversion: membership in ≥1 known "risk group" (eg, having a spouse living with HIV), a "model-based" risk score constructed with logistic regression, and a "machine learning" risk score constructed with the Super Learner algorithm. We hypothesized machine learning would identify high-risk individuals more efficiently (fewer persons targeted for a fixed sensitivity) and with higher sensitivity (for a fixed number targeted) than either other approach. RESULTS: A total of 75 558 persons contributed 166 723 person-years of follow-up; 519 seroconverted. Machine learning improved efficiency. To achieve a fixed sensitivity of 50%, the risk-group strategy targeted 42% of the population, the model-based strategy targeted 27%, and machine learning targeted 18%. Machine learning also improved sensitivity. With an upper limit of 45% targeted, the risk-group strategy correctly classified 58% of seroconversions, the model-based strategy 68%, and machine learning 78%. CONCLUSIONS: Machine learning improved classification of individuals at risk of HIV acquisition compared with a model-based approach or reliance on known risk groups and could inform targeting of prevention strategies in generalized epidemic settings. CLINICAL TRIALS REGISTRATION: NCT01864603.

Far from MCAR: Obtaining Population-level Estimates of HIV Viral Suppression.

BACKGROUND: Population-level estimates of disease prevalence and control are needed to assess prevention and treatment strategies. However, available data often suffer from differential missingness. For example, population-level HIV viral suppression is the proportion of all HIV-positive persons with suppressed viral replication. Individuals with measured HIV status, and among HIV-positive individuals those with measured viral suppression, likely differ from those without such measurements. METHODS: We discuss three sets of assumptions to identify population-level suppression in the intervention arm of the SEARCH Study (NCT01864603), a community randomized trial in rural Kenya and Uganda (2013-2017). Using data on nearly 100,000 participants, we compare estimates from (1) an unadjusted approach assuming data are missing-completely-at-random (MCAR); (2) stratification on age group, sex, and community; and (3) targeted maximum likelihood estimation to adjust for a larger set of baseline and time-updated variables. RESULTS: Despite high measurement coverage, estimates of population-level viral suppression varied by identification assumption. Unadjusted estimates were most optimistic: 50% (95% confidence interval [CI] = 46%, 54%) of HIV-positive persons suppressed at baseline, 80% (95% CI = 78%, 82%) at year 1, 85% (95% CI = 83%, 86%) at year 2, and 85% (95% CI = 83%, 87%) at year 3. Stratifying on baseline predictors yielded slightly lower estimates, and full adjustment reduced estimates meaningfully: 42% (95% CI = 37%, 46%) of HIV-positive persons suppressed at baseline, 71% (95% CI = 69%, 73%) at year 1, 76% (95% CI = 74%, 78%) at year 2, and 79% (95% CI = 77%, 81%) at year 3. CONCLUSIONS: Estimation of population-level disease burden and control requires appropriate adjustment for missing data. Even in large studies with limited missingness, estimates relying on the MCAR assumption or baseline stratification should be interpreted cautiously.

Understanding Demand for PrEP and Early Experiences of PrEP Use Among Young Adults in Rural Kenya and Uganda: A Qualitative Study.

Few studies have sought to understand factors influencing uptake and continuation of pre-exposure prophylaxis (PrEP) among young adults in sub-Saharan Africa in the context of population-based delivery of open-label PrEP. To address this gap, this qualitative study was implemented within the SEARCH study (NCT#01864603) in Kenya and Uganda, which achieved near-universal HIV testing, and offered PrEP in 16 intervention communities beginning in 2016-2017. Focus group discussions (8 groups, n = 88 participants) and in-depth interviews (n = 23) with young adults who initiated or declined PrEP were conducted in five communities, to explore PrEP-related beliefs and attitudes, HIV risk perceptions, motivations for uptake and continuation, and experiences. Grounded theoretical methods were used to analyze data. Young people felt personally vulnerable to HIV, but perceived the severity of HIV to be low, due to the success of antiretroviral therapy (ART): daily pill-taking was more threatening than the disease itself. Motivations for PrEP were highly gendered: young men viewed PrEP as a vehicle for safely pursuing multiple partners, while young women saw PrEP as a means to control risks in the context of engagement in transactional sex and limited agency to negotiate condom use and partner testing. Rumors, HIV/ART-related stigma, and desire for "proof" of efficacy militated against uptake, and many women required partners' permission to take PrEP. Uptake was motivated by high perceived HIV risk, and beliefs that PrEP use supported life goals. PrEP was often discontinued due to dissolution of partnerships/changing risk, unsupportive partners/peers, or early side effects/pill burden. Despite high perceived risks and interest, PrEP was received with moral ambivalence because of its associations with HIV/ART and stigmatized behaviors. Delivery models that promote youth access, frame messaging on wellness and goals, and foster partner and peer support, may facilitate uptake among young people.

Predictors of isoniazid preventive therapy completion among HIV-infected patients receiving differentiated and non-differentiated HIV care in rural Uganda.

Rates of Isoniazid Preventive Therapy (IPT) completion remain low in programmatic settings in sub-Saharan Africa. Differentiated HIV care models may improve IPT completion by addressing joint barriers to IPT and HIV treatment. However, the impact of differentiated care on IPT completion remains unknown. In a cross-sectional study of people with HIV on antiretroviral therapy in 5 communities in rural Uganda, we compared IPT completion between patients receiving HIV care via a differentiated care model versus a standard HIV care model and assessed multi-level predictors of IPT completion. A total of 103/144 (72%) patients received differentiated care and 85/161 (53%) received standard care completed IPT (p < 0.01). Adjusting for age, gender and community, patients receiving differentiated care had higher odds of completing IPT (aOR: 2.6, 95% CI: 1.5-4.5, p < 0.01). Predictors of IPT completion varied by the care model, and differentiated care modified the positive association between treatment completion and the belief in the efficacy of IPT and the negative association with side-effects. Patients receiving a multi-component differentiated care model had a higher odds of IPT completion than standard care, and the model's impact on health beliefs, social support, and perceived side effects to IPT may underlie this positive association.

Early Adopters of Human Immunodeficiency Virus Preexposure Prophylaxis in a Population-based Combination Prevention Study in Rural Kenya and Uganda.

Background: Global guidelines recommend preexposure prophylaxis (PrEP) for individuals with substantial human immunodeficiency virus (HIV) risk. Data on PrEP uptake in sub-Saharan Africa outside of clinical trials are limited. We report on "early adopters" of PrEP in the Sustainable East Africa Research in Community Health (SEARCH) study in rural Uganda and Kenya. Methods: After community mobilization and PrEP education, population-based HIV testing was conducted. HIV-uninfected adults were offered PrEP based on an empirically derived HIV risk score or self-identified HIV risk (if not identified by score). Using logistic regression, we analyzed predictors of early PrEP adoption (starting PrEP within 30 days vs delayed/no start) among adults identified for PrEP. Results: Of 21212 HIV-uninfected adults in 5 communities, 4064 were identified for PrEP (2991 by empiric risk score, 1073 by self-identified risk). Seven hundred and thirty nine individuals started PrEP within 30 days (11% of those identified by risk score; 39% of self-identified); 77% on the same day. Among adults identified by risk score, predictors of early adoption included male sex (adjusted odds ratio 1.53; 95% confidence interval, 1.09-2.15), polygamy (1.92; 1.27-2.90), serodiscordant spouse (3.89; 1.18-12.76), self-perceived HIV risk (1.66; 1.28-2.14), and testing at health campaign versus home (5.24; 3.33-8.26). Among individuals who self-identified for PrEP, predictors of early adoption included older age (2.30; 1.29-4.08) and serodiscordance (2.61; 1.01-6.76). Conclusions: Implementation of PrEP incorporating a population-based empiric risk score, self-identified risk, and rapid initiation, is feasible in rural East Africa. Strategies are needed to overcome barriers to PrEP uptake, particularly among women and youth. Clinical Trials Registration: NCT01864603.

Adaptive pre-specification in randomized trials with and without pair-matching.

In randomized trials, adjustment for measured covariates during the analysis can reduce variance and increase power. To avoid misleading inference, the analysis plan must be pre-specified. However, it is often unclear a priori which baseline covariates (if any) should be adjusted for in the analysis. Consider, for example, the Sustainable East Africa Research in Community Health (SEARCH) trial for HIV prevention and treatment. There are 16 matched pairs of communities and many potential adjustment variables, including region, HIV prevalence, male circumcision coverage, and measures of community-level viral load. In this paper, we propose a rigorous procedure to data-adaptively select the adjustment set, which maximizes the efficiency of the analysis. Specifically, we use cross-validation to select from a pre-specified library the candidate targeted maximum likelihood estimator (TMLE) that minimizes the estimated variance. For further gains in precision, we also propose a collaborative procedure for estimating the known exposure mechanism. Our small sample simulations demonstrate the promise of the methodology to maximize study power, while maintaining nominal confidence interval coverage. We show how our procedure can be tailored to the scientific question (intervention effect for the study sample vs. for the target population) and study design (pair-matched or not). Copyright © 2016 John Wiley & Sons, Ltd.

Targeted estimation and inference for the sample average treatment effect in trials with and without pair-matching.

In cluster randomized trials, the study units usually are not a simple random sample from some clearly defined target population. Instead, the target population tends to be hypothetical or ill-defined, and the selection of study units tends to be systematic, driven by logistical and practical considerations. As a result, the population average treatment effect (PATE) may be neither well defined nor easily interpretable. In contrast, the sample average treatment effect (SATE) is the mean difference in the counterfactual outcomes for the study units. The sample parameter is easily interpretable and arguably the most relevant when the study units are not sampled from some specific super-population of interest. Furthermore, in most settings, the sample parameter will be estimated more efficiently than the population parameter. To the best of our knowledge, this is the first paper to propose using targeted maximum likelihood estimation (TMLE) for estimation and inference of the sample effect in trials with and without pair-matching. We study the asymptotic and finite sample properties of the TMLE for the sample effect and provide a conservative variance estimator. Finite sample simulations illustrate the potential gains in precision and power from selecting the sample effect as the target of inference. This work is motivated by the Sustainable East Africa Research in Community Health (SEARCH) study, a pair-matched, community randomized trial to estimate the effect of population-based HIV testing and streamlined ART on the 5-year cumulative HIV incidence (NCT01864603). The proposed methodology will be used in the primary analysis for the SEARCH trial. Copyright © 2016 John Wiley & Sons, Ltd.