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Supplemental Digital Content is Available in the Text. Background: Persons with HIV (PWH) with high mobility face obstacles to HIV care engagement and viral suppression. We sought to understand whether a patient-centered intervention for mobile PWH would improve viral suppression and retention in care, and if so, which subgroups would benefit most. Methods: In a randomized trial, we evaluated the effect of an intervention designed to address barriers to care among mobile (≥2 weeks out of community in previous year) PWH with viral nonsuppression or recent missed visits in Kenya and Uganda (NCT04810650). The intervention included dynamic choice of a "travel pack" (emergency antiretroviral therapy [ART] supply, discrete ART packaging, and travel checklist), multimonth and offsite refills, facilitated transfer to out-of-community clinics, and hotline access to a mobility coordinator. The primary outcome was viral suppression (<400 copies/mL) at 48 weeks. Secondary outcomes included retention in care and ART possession. Results: From April 2021 to July 2022, 201 participants were enrolled and randomized (102 intervention, 99 control): 109 (54%) were female participants and 101 (50%) from Kenya; median age was 37 years (interquartile range: 29–43). At 48 weeks, there was no significant difference in viral suppression in intervention (85%) vs. control (86%). The intervention improved retention in care (risk ratio: 1.06[1.02–1.1]; P < 0.001) and ART possession (risk ratio: 1.07[1.03–1.11]; P < 0.001), with larger effect sizes among persons with baseline nonsuppression and high mobility (≥2 weeks out of community in previous 3 months). Conclusions: Mobile PWH-centered care should be considered for high-risk mobile populations, including nonsuppressed and highly mobile PWH, to improve retention in care and sustain viral suppression over time. Trial registration: NCT04810650. [ABSTRACT FROM AUTHOR]

Background: We sought to investigate the effect of public masking mandates in US states on COVID-19 at the national level in Fall 2020. Specifically, we aimed to evaluate how the relative growth of COVID-19 cases and deaths would have differed if all states had issued a mandate to mask in public by 1 September 2020 versus if all states had delayed issuing such a mandate. Methods: We applied the Causal Roadmap, a formal framework for causal and statistical inference. We defined the outcome as the state-specific relative increase in cumulative cases and in cumulative deaths 21, 30, 45, and 60 days after 1 September. Despite the natural experiment occurring at the state-level, the causal effect of masking policies on COVID-19 outcomes was not identifiable. Nonetheless, we specified the target statistical parameter as the adjusted rate ratio (aRR): the expected outcome with early implementation divided by the expected outcome with delayed implementation, after adjusting for state-level confounders. To minimize strong estimation assumptions, primary analyses used targeted maximum likelihood estimation with Super Learner. Results: After 60 days and at a national level, early implementation was associated with a 9% reduction in new COVID-19 cases (aRR = 0.91 [95% CI = 0.88, 0.95]) and a 16% reduction in new COVID-19 deaths (aRR = 0.84 [95% CI = 0.76, 0.93]). Conclusions: Although lack of identifiability prohibited causal interpretations, application of the Causal Roadmap facilitated estimation and inference of statistical associations, providing timely answers to pressing questions in the COVID-19 response. [ABSTRACT FROM AUTHOR]

Background: The SEARCH study provided community-based HIV and multidisease testing and antiretroviral therapy (ART) to 32 communities in East Africa and reported no statistically significant difference in 3-year HIV incidence. We used mathematical modeling to estimate the effect of control arm viral suppression and community mixing on SEARCH trial outcomes. Setting: Uganda and Kenya. Methods: Using the individual-based HIV modeling software EMODHIV, we configured a new model of SEARCH communities. The model was parameterized using demographic, HIV prevalence, male circumcision, and viral suppression data and calibrated to HIV prevalence, ART coverage, and population size. Using assumptions about ART scale-up in the control arm, degree of community mixing, and effect of baseline testing, we estimated comparative HIV incidence under multiple scenarios. Results: Before the trial results, we predicted that SEARCH would report a 4%-40% reduction between arms, depending on control arm ART linkage rates and community mixing. With universal baseline testing followed by rapidly expanded ART eligibility and uptake, modeled effect sizes were smaller than the study was powered to detect. Using interim viral suppression data, we estimated 3-year cumulative incidence would have been reduced by up to 27% in the control arm and 43% in the intervention arm compared with a counterfactual without universal baseline testing. Conclusions: Our model suggests that the active control arm substantially reduced expected effect size and power of the SEARCH study. However, compared with a counterfactual "true control" without increased ART linkage because of baseline testing, SEARCH reduced HIV incidence by up to 43%. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: HIV-infected youth in sub-Saharan Africa are less likely to initiate antiretroviral therapy (ART) than older adults. Setting and methods: Adult (≥15 years) residents enumerated during a census in 32 communities in rural Kenya and Uganda named social contacts in 5 domains: health, money, emotional support, food, and free time. Named contacts were matched to other enumerated residents to build social networks among 150,395 adults; 90% were tested for HIV at baseline. Among youth (15-24 years) who were ART naive at baseline (2013-2014), we evaluated whether having ≥1 network contact who was HIV infected predicted ART initiation within 3 years and modification of this association by age and strength of contact, using logistic regression with robust standard errors. Results: Among 1120 HIV-infected youth who were ART naive at baseline, 805 remained alive and community residents after 3 years. Of these, 270 (33.5%) named at least one baseline HIV-infected contact; 70% (569/805) subsequently initiated ART. Youth with ≥1 HIV-infected same-age baseline contact were more likely to initiate ART [adjusted odds ratio (aOR), 2.95; 95% confidence interval (CI): 1.49 to 5.86] than those with no HIV-infected contact, particularly if the contact was a strong tie (named in >1 domain; aOR, 5.33; 95% CI: 3.34 to 8.52). When nonhousehold contacts were excluded, having an HIV-infected same age contact who was a strong tie remained associated with ART initiation (aOR, 2.81; 95% CI: 1.76 to 4.49). Conclusions: Interventions that increase and strengthen existing social connections to other HIV-infected peers at the time of HIV diagnosis may increase ART initiation among HIV-infected youth. [ABSTRACT FROM AUTHOR]

Great care is taken in epidemiologic studies to ensure the internal validity of causal effect estimates; however, external validity has received considerably less attention. When the study sample is not a random sample of the target population, the sample average treatment effect, even if internally valid, cannot usually be expected to equal the average treatment effect in the target population. The utility of an effect estimate for planning purposes and decision making will depend on the degree of departure from the true causal effect in the target population due to problems with both internal and external validity. Herein, we review concepts from recent literature on generalizability, one facet of external validity, using the potential outcomes framework. Identification conditions sufficient for external validity closely parallel identification conditions for internal validity, namely conditional exchangeability; positivity; the same distributions of the versions of treatment; no interference; and no measurement error. We also require correct model specification. Under these conditions, we discuss how a version of direct standardization (the g-formula, adjustment formula, or transport formula) or inverse probability weighting can be used to generalize a causal effect from a study sample to a well-defined target population, and demonstrate their application in an illustrative example. [ABSTRACT FROM AUTHOR]

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., Competing Interests: Disclosure: The authors report no conflicts of interest., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc.)