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ObjectivesDetermine whether patient-centered, streamlined HIV care achieves higher antiretroviral therapy (ART) uptake and viral suppression than the standard treatment model for people with HIV (PWH) reporting hazardous alcohol use.DesignCommunity cluster-randomized trial.MethodsThe Sustainable East Africa Research in Community Health trial (NCT01864603) compared an intervention of annual population HIV testing, universal ART, and patient-centered care with a control of baseline population testing with ART by country standard in 32 Kenyan and Ugandan communities. Adults (15 years or older) completed a baseline Alcohol Use Disorders Identification Test-Consumption (AUDIT-C) and were classified as no/nonhazardous (AUDIT-C 0-2 women/0-3 men) or hazardous alcohol use (≥3 women/≥4 men). We compared year 3 ART uptake and viral suppression of PWH reporting hazardous use between intervention and control arms. We compared alcohol use as a predictor of year 3 ART uptake and viral suppression among PWH, by arm.ResultsOf 11,070 PWH with AUDIT-C measured, 1723 (16%) reported any alcohol use and 893 (8%) reported hazardous use. Among PWH reporting hazardous use, the intervention arm had higher ART uptake (96%) and suppression (87%) compared with control (74%, adjusted risk ratio [aRR] = 1.28, 95% CI: 1.19 to 1.38; and 72%, aRR = 1.20, 95% CI: 1.10 to 1.31, respectively). Within arm, hazardous alcohol use predicted lower ART uptake in control (aRR = 0.86, 95% CI: 0.78 to 0.96), but not intervention (aRR = 1.02, 95% CI: 1.00 to 1.04); use was not predictive of suppression in either arm.ConclusionsThe Sustainable East Africa Research in Community Health intervention improved ART uptake and viral suppression among PWH reporting hazardous alcohol use and eliminated gaps in ART uptake between PWH with hazardous and no/nonhazardous use. Patient-centered HIV care may decrease barriers to HIV care for PWH with hazardous alcohol use.

Personalized intervention strategies, in particular those that modify treatment based on a participants 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.

San Francisco implemented one of the most intensive, comprehensive, multipronged COVID-19 pandemic responses in the United States using 4 core strategies: (1) aggressive mitigation measures to protect populations at risk for severe disease, (2) prioritization of resources in neighborhoods highly affected by COVID-19, (3) timely and adaptive data-driven policy making, and (4) leveraging of partnerships and public trust. We collected data to describe programmatic and population-level outcomes. The excess all-cause mortality rate in 2020 in San Francisco was half that seen in 2019 in California as a whole (8% vs 16%). In almost all age and race and ethnicity groups, excess mortality from COVID-19 was lower in San Francisco than in California overall, with markedly diminished excess mortality among people aged >65 years. The COVID-19 response in San Francisco highlights crucial lessons, particularly the importance of community responsiveness, joint planning, and collective action, to inform future pandemic response and advance health equity.

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.

BackgroundSequencing of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral genome from patient samples is an important epidemiological tool for monitoring and responding to the pandemic, including the emergence of new mutations in specific communities.MethodsSARS-CoV-2 genomic sequences were generated from positive samples collected, along with epidemiological metadata, at a walk-up, rapid testing site in the Mission District of San Francisco, California during 22 November to 1 December, 2020, and 10-29 January 2021. Secondary household attack rates and mean sample viral load were estimated and compared across observed variants.ResultsA total of 12 124 tests were performed yielding 1099 positives. From these, 928 high-quality genomes were generated. Certain viral lineages bearing spike mutations, defined in part by L452R, S13I, and W152C, comprised 54.4% of the total sequences from January, compared to 15.7% in November. Household contacts exposed to the "California" or "West Coast" variants (B.1.427 and B.1.429) were at higher risk of infection compared to household contacts exposed to lineages lacking these variants (0.36 vs 0.29, risk ratio [RR] = 1.28; 95% confidence interval [CI]: 1.00-1.64). The reproductive number was estimated to be modestly higher than other lineages spreading in California during the second half of 2020. Viral loads were similar among persons infected with West Coast versus non-West Coast strains, as was the proportion of individuals with symptoms (60.9% vs 64.3%).ConclusionsThe increase in prevalence, relative household attack rates, and reproductive number are consistent with a modest transmissibility increase of the West Coast variants. Summary: We observed a growing prevalence and modestly elevated attack rate for "West Coast" severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants in a community testing setting in San Francisco during January 2021, suggesting its modestly higher transmissibility.

BackgroundWe 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.MethodsIn 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.ResultsAmong 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

We present two novel approaches to variance estimation of semi-parametric efficient point estimators of the treatment-specific mean: (i) a robust approach that directly targets the variance of the influence function (IF) as a counterfactual mean outcome and (ii) a modified non-parametric bootstrap-based approach. The performance of these approaches to variance estimation is compared to variance estimation based on the sample variance of the empirical IF in simulations across different levels of positivity violations and treatment effect sizes. In this article, we focus on estimation of the nuisance parameters using correctly specified parametric models for the treatment mechanism in order to highlight the challenges posed by violation of positivity assumptions (distinct from the challenges posed by non-parametric estimation of the nuisance parameters). Results demonstrate that (1) variance estimation based on the empirical IF may provide highly anti-conservative confidence interval coverage (as reported previously), (2) the proposed robust approach to variance estimation in this setting provides conservative coverage, and (3) the proposed modified bootstrap maintains close to nominal coverage and improves power. In the appendix, we (a) generalize the robust approach of estimating variance to marginal structural working models and (b) provide a proof of the consistency of the targeted minimum loss-based estimation bootstrap.

BackgroundThere is an urgent need to understand the dynamics and risk factors driving ongoing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission during shelter-in-place mandates.MethodsWe offered SARS-CoV-2 reverse-transcription polymerase chain reaction (PCR) and antibody (Abbott ARCHITECT IgG) testing, regardless of symptoms, to all residents (aged ≥4 years) and workers in a San Francisco census tract (population: 5174) at outdoor, community-mobilized events over 4 days. We estimated SARS-CoV-2 point prevalence (PCR positive) and cumulative incidence (antibody or PCR positive) in the census tract and evaluated risk factors for recent (PCR positive/antibody negative) vs prior infection (antibody positive/PCR negative). SARS-CoV-2 genome recovery and phylogenetics were used to measure viral strain diversity, establish viral lineages present, and estimate number of introductions.ResultsWe tested 3953 persons (40% Latinx; 41% White; 9% Asian/Pacific Islander; and 2% Black). Overall, 2.1% (83/3871) tested PCR positive: 95% were Latinx and 52% were asymptomatic when tested; 1.7% of census tract residents and 6.0% of workers (non-census tract residents) were PCR positive. Among 2598 tract residents, estimated point prevalence of PCR positives was 2.3% (95% confidence interval [CI], 1.2%-3.8%): 3.9% (95% CI, 2.0%-6.4%) among Latinx persons vs 0.2% (95% CI, .0-.4%) among non-Latinx persons. Estimated cumulative incidence among residents was 6.1% (95% CI, 4.0%-8.6%). Prior infections were 67% Latinx, 16% White, and 17% other ethnicities. Among recent infections, 96% were Latinx. Risk factors for recent infection were Latinx ethnicity, inability to shelter in place and maintain income, frontline service work, unemployment, and household income

BackgroundSequencing of the SARS-CoV-2 viral genome from patient samples is an important epidemiological tool for monitoring and responding to the pandemic, including the emergence of new mutations in specific communities.MethodsSARS-CoV-2 genomic sequences were generated from positive samples collected, along with epidemiological metadata, at a walk-up, rapid testing site in the Mission District of San Francisco, California during November 22-December 1, 2020 and January 10-29, 2021. Secondary household attack rates and mean sample viral load were estimated and compared across observed variants.ResultsA total of 12,124 tests were performed yielding 1,099 positives. From these, 928 high quality genomes were generated. Certain viral lineages bearing spike mutations, defined in part by L452R, S13I, and W152C, comprised 54.4% of the total sequences from January, compared to 15.7% in November. Household contacts exposed to the "California" or "West Coast" variants (B.1.427 and B.1.429) were at higher risk of infection compared to household contacts exposed to lineages lacking these variants (0.36 vs 0.29, RR=1.28; 95% CI:1.00-1.64). The reproductive number was estimated to be modestly higher than other lineages spreading in California during the second half of 2020. Viral loads were similar among persons infected with West Coast versus non-West Coast strains, as was the proportion of individuals with symptoms (60.9% vs 64.3%).ConclusionsThe increase in prevalence, relative household attack rates, and reproductive number are consistent with a modest transmissibility increase of the West Coast variants.

BackgroundAdditional progress towards HIV epidemic control requires understanding who remains at risk of HIV infection in the context of high uptake of universal testing and treatment (UTT). We sought to characterize seroconverters and risk factors in the SEARCH UTT trial (NCT01864603), which achieved high uptake of universal HIV testing and ART coverage in 32 communities of adults (≥15 years) in rural Uganda and Kenya.MethodsIn a pooled cohort of 117,114 individuals with baseline HIV negative test results, we described those who seroconverted within 3 years, calculated gender-specific HIV incidence rates, evaluated adjusted risk ratios (aRR) for seroconversion using multivariable targeted maximum likelihood estimation, and assessed potential infection sources based on self-report.ResultsOf 704 seroconverters, 63% were women. Young (15-24 years) men comprised a larger proportion of seroconverters in Western Uganda (18%) than Eastern Uganda (6%) or Kenya (10%). After adjustment for other risk factors, men who were mobile [≥1 month of prior year living outside community] (aRR:1.68; 95%CI:1.09,2.60) or who HIV tested at home vs. health fair (aRR:2.44; 95%CI:1.89,3.23) were more likely to seroconvert. Women who were aged ≤24 years (aRR:1.91; 95%CI:1.27,2.90), mobile (aRR:1.49; 95%CI:1.04,2.11), or reported a prior HIV test (aRR:1.34; 95%CI:1.06,1.70), or alcohol use (aRR:2.07; 95%CI:1.34,3.22) were more likely to seroconvert. Among survey responders (N = 607, 86%), suspected infection source was more likely for women than men to be ≥10 years older (28% versus 8%) or a spouse (51% vs. 31%) and less likely to be transactional sex (10% versus 16%).ConclusionIn the context of universal testing and treatment, additional strategies tailored to regional variability are needed to address HIV infection risks of young women, alcohol users, mobile populations, and those engaged in transactional sex to further reduce HIV incidence rates.

We have a new and unprecedented opportunity to mitigate the suffering, death and inequities of COVID-19 in the United States with vaccine boosters-if we deploy them effectively, rapidly, and widely with simplified messaging to all eligible adults.

BackgroundPopulation-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.MethodsWe 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.ResultsDespite 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.ConclusionsEstimation 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.

BackgroundUniversal antiretroviral therapy (ART) with annual population testing and a multidisease, patient-centered strategy could reduce new human immunodeficiency virus (HIV) infections and improve community health.MethodsWe randomly assigned 32 rural communities in Uganda and Kenya to baseline HIV and multidisease testing and national guideline-restricted ART (control group) or to baseline testing plus annual testing, eligibility for universal ART, and patient-centered care (intervention group). The primary end point was the cumulative incidence of HIV infection at 3 years. Secondary end points included viral suppression, death, tuberculosis, hypertension control, and the change in the annual incidence of HIV infection (which was evaluated in the intervention group only).ResultsA total of 150,395 persons were included in the analyses. Population-level viral suppression among 15,399 HIV-infected persons was 42% at baseline and was higher in the intervention group than in the control group at 3 years (79% vs. 68%; relative prevalence, 1.15; 95% confidence interval [CI], 1.11 to 1.20). The annual incidence of HIV infection in the intervention group decreased by 32% over 3 years (from 0.43 to 0.31 cases per 100 person-years; relative rate, 0.68; 95% CI, 0.56 to 0.84). However, the 3-year cumulative incidence (704 incident HIV infections) did not differ significantly between the intervention group and the control group (0.77% and 0.81%, respectively; relative risk, 0.95; 95% CI, 0.77 to 1.17). Among HIV-infected persons, the risk of death by year 3 was 3% in the intervention group and 4% in the control group (0.99 vs. 1.29 deaths per 100 person-years; relative risk, 0.77; 95% CI, 0.64 to 0.93). The risk of HIV-associated tuberculosis or death by year 3 among HIV-infected persons was 4% in the intervention group and 5% in the control group (1.19 vs. 1.50 events per 100 person-years; relative risk, 0.79; 95% CI, 0.67 to 0.94). At 3 years, 47% of adults with hypertension in the intervention group and 37% in the control group had hypertension control (relative prevalence, 1.26; 95% CI, 1.15 to 1.39).ConclusionsUniversal HIV treatment did not result in a significantly lower incidence of HIV infection than standard care, probably owing to the availability of comprehensive baseline HIV testing and the rapid expansion of ART eligibility in the control group. (Funded by the National Institutes of Health and others; SEARCH ClinicalTrials.gov number, NCT01864603.).

ObjectiveRegular HIV RNA testing for all HIV-positive patients on antiretroviral therapy (ART) is expensive and has low yield since most tests are undetectable. Selective testing of those at higher risk of failure may improve efficiency. We investigated whether a novel analysis of adherence data could correctly classify virological failure and potentially inform a selective testing strategy.DesignMultisite prospective cohort consortium.MethodsWe evaluated longitudinal data on 1478 adult patients treated with ART and monitored using the Medication Event Monitoring System (MEMS) in 16 US cohorts contributing to the MACH14 consortium. Because the relationship between adherence and virological failure is complex and heterogeneous, we applied a machine-learning algorithm (Super Learner) to build a model for classifying failure and evaluated its performance using cross-validation.ResultsApplication of the Super Learner algorithm to MEMS data, combined with data on CD4 T-cell counts and ART regimen, significantly improved classification of virological failure over a single MEMS adherence measure. Area under the receiver operating characteristic curve, evaluated on data not used in model fitting, was 0.78 (95% confidence interval: 0.75 to 0.80) and 0.79 (95% confidence interval: 0.76 to 0.81) for failure defined as single HIV RNA level >1000 copies per milliliter or >400 copies per milliliter, respectively. Our results suggest that 25%-31% of viral load tests could be avoided while maintaining sensitivity for failure detection at or above 95%, for a cost savings of $16-$29 per person-month.ConclusionsOur findings provide initial proof of concept for the potential use of electronic medication adherence data to reduce costs through behavior-driven HIV RNA testing.

This paper describes a targeted maximum likelihood estimator (TMLE) for the parameters of longitudinal static and dynamic marginal structural models. We consider a longitudinal data structure consisting of baseline covariates, time-dependent intervention nodes, intermediate time-dependent covariates, and a possibly time-dependent outcome. The intervention nodes at each time point can include a binary treatment as well as a right-censoring indicator. Given a class of dynamic or static interventions, a marginal structural model is used to model the mean of the intervention-specific counterfactual outcome as a function of the intervention, time point, and possibly a subset of baseline covariates. Because the true shape of this function is rarely known, the marginal structural model is used as a working model. The causal quantity of interest is defined as the projection of the true function onto this working model. Iterated conditional expectation double robust estimators for marginal structural model parameters were previously proposed by Robins (2000, 2002) and Bang and Robins (2005). Here we build on this work and present a pooled TMLE for the parameters of marginal structural working models. We compare this pooled estimator to a stratified TMLE (Schnitzer et al. 2014) that is based on estimating the intervention-specific mean separately for each intervention of interest. The performance of the pooled TMLE is compared to the performance of the stratified TMLE and the performance of inverse probability weighted (IPW) estimators using simulations. Concepts are illustrated using an example in which the aim is to estimate the causal effect of delayed switch following immunological failure of first line antiretroviral therapy among HIV-infected patients. Data from the International Epidemiological Databases to Evaluate AIDS, Southern Africa are analyzed to investigate this question using both TML and IPW estimators. Our results demonstrate practical advantages of the pooled TMLE over an IPW estimator for working marginal structural models for survival, as well as cases in which the pooled TMLE is superior to its stratified counterpart.

The positivity assumption, or the experimental treatment assignment (ETA) assumption, is important for identifiability in causal inference. Even if the positivity assumption holds, practical violations of this assumption may jeopardize the finite sample performance of the causal estimator. One of the consequences of practical violations of the positivity assumption is extreme values in the estimated propensity score (PS). A common practice to address this issue is truncating the PS estimate when constructing PS-based estimators. In this study, we propose a novel adaptive truncation method, Positivity-C-TMLE, based on the collaborative targeted maximum likelihood estimation (C-TMLE) methodology. We demonstrate the outstanding performance of our novel approach in a variety of simulations by comparing it with other commonly studied estimators. Results show that by adaptively truncating the estimated PS with a more targeted objective function, the Positivity-C-TMLE estimator achieves the best performance for both point estimation and confidence interval coverage among all estimators considered. [ABSTRACT FROM AUTHOR]

Objectives: Determine whether patient-centered, streamlined HIV care achieves higher antiretroviral therapy (ART) uptake and viral suppression than the standard treatment model for people with HIV (PWH) reporting hazardous alcohol use., Design: Community cluster-randomized trial., Methods: The Sustainable East Africa Research in Community Health trial (NCT01864603) compared an intervention of annual population HIV testing, universal ART, and patient-centered care with a control of baseline population testing with ART by country standard in 32 Kenyan and Ugandan communities. Adults (15 years or older) completed a baseline Alcohol Use Disorders Identification Test-Consumption (AUDIT-C) and were classified as no/nonhazardous (AUDIT-C 0-2 women/0-3 men) or hazardous alcohol use (≥3 women/≥4 men). We compared year 3 ART uptake and viral suppression of PWH reporting hazardous use between intervention and control arms. We compared alcohol use as a predictor of year 3 ART uptake and viral suppression among PWH, by arm., Results: Of 11,070 PWH with AUDIT-C measured, 1723 (16%) reported any alcohol use and 893 (8%) reported hazardous use. Among PWH reporting hazardous use, the intervention arm had higher ART uptake (96%) and suppression (87%) compared with control (74%, adjusted risk ratio [aRR] = 1.28, 95% CI: 1.19 to 1.38; and 72%, aRR = 1.20, 95% CI: 1.10 to 1.31, respectively). Within arm, hazardous alcohol use predicted lower ART uptake in control (aRR = 0.86, 95% CI: 0.78 to 0.96), but not intervention (aRR = 1.02, 95% CI: 1.00 to 1.04); use was not predictive of suppression in either arm., Conclusions: The Sustainable East Africa Research in Community Health intervention improved ART uptake and viral suppression among PWH reporting hazardous alcohol use and eliminated gaps in ART uptake between PWH with hazardous and no/nonhazardous use. Patient-centered HIV care may decrease barriers to HIV care for PWH with hazardous alcohol use., Competing Interests: The authors have no funding or conflicts of interest to disclose., (Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)