Integrating HIV Cluster Analysis in Everyday Public Health Practice: Lessons Learned From a Public Health--Academic Partnership.

BACKGROUND: The use of molecular HIV cluster analysis to supplement public health contact tracing has shown promise in addressing HIV outbreaks. However, the potential of HIV cluster analysis as an adjunct to daily, person-by-person HIV prevention efforts remains unknown. We documented lessons learned within a unique public health-academic partnership while guiding workaday HIV prevention efforts with near-real-time molecular cluster analysis. SETTING: A public health-academic partnership in the State of Rhode Island, the United States. METHODS: We recorded perceptions of our team of academicians and public health practitioners that were encountered in an 18-month study evaluating the integration of molecular cluster analysis with HIV contact tracing for public health benefit. The focus was on monthly conferences where molecular clustering of each new statewide diagnosis was discussed to facilitate targeted interventions and on attempted reinterviews of all newly HIV-diagnosed persons statewide whose HIV sequences clustered to increase partner naming. RESULTS: Three main themes emerged: First, multidisciplinary conferences are substantially beneficial for gleaning actionable inferences from integrating molecular cluster analysis and public health data. Second, universal reinterviews were perceived to potentially have negative consequences but may be selectively beneficial. Third, the translation of cluster analysis into public health action is hampered by jurisdictional surveillance boundaries and within-jurisdictional data silos, across which data sharing is problematic. CONCLUSIONS: Insights from a statewide public health-academic partnership support integration of molecular HIV cluster analyses with public health efforts, which can guide public health activities to prevent transmission while identifying substantial barriers to integration, informing continued research.

Acquired Human Immunodeficiency Virus Type 1 Drug Resistance in Rhode Island, USA, 2004-2021.

BACKGROUND: Human immunodeficiency virus type 1 (HIV-1) acquired drug resistance (ADR) compromises antiretroviral therapy (ART). METHODS: We aggregated all HIV-1 protease-reverse transcriptase-integrase sequences over 2004-2021 at the largest HIV center in Rhode Island and evaluated ADR extent, trends, and impact using Stanford Database tools. Trends were measured with Mann-Kendall statistic, and multivariable regressions evaluated resistance predictors. RESULTS: Sequences were available for 914 ART-experienced persons. Overall ADR to any drug decreased from 77% to 49% (-0.66 Mann-Kendall statistic); nucleoside reverse transcriptase inhibitors 65% to 32%, nonnucleoside reverse transcriptase inhibitors 53% to 43%, and protease inhibitors 28% to 7% (2004-2021), and integrase strand transfer inhibitors 16% to 13% (2017-2021). Multiclass resistance decreased from 44% to 12% (2-class) and 12% to 6% (3-class). In 2021, 94% had at least one 3-drug or 2-drug one-pill-once-daily (OPOD) option. Males and those exposed to more ART regimens were more likely to have ≥2-class resistance, and higher regimen exposure was also associated with fewer OPOD options. CONCLUSIONS: Comprehensive analyses within a densely-sampled HIV epidemic over 2004-2021 demonstrated decreasing ADR. Continued ADR monitoring is important to maintain ART success, particularly with rising INSTI use in all lines of therapy and 2-drug and long-acting formulations.

Learning about treatment effects in a new target population under transportability assumptions for relative effect measures.

Investigators often believe that relative effect measures conditional on covariates, such as risk ratios and mean ratios, are "transportable" across populations. Here, we examine the identification of causal effects in a target population using an assumption that conditional relative effect measures are transportable from a trial to the target population. We show that transportability for relative effect measures is largely incompatible with transportability for difference effect measures, unless the treatment has no effect on average or one is willing to make even stronger transportability assumptions that imply the transportability of both relative and difference effect measures. We then describe how marginal (population-averaged) causal estimands in a target population can be identified under the assumption of transportability of relative effect measures, when we are interested in the effectiveness of a new experimental treatment in a target population where the only treatment in use is the control treatment evaluated in the trial. We extend these results to consider cases where the control treatment evaluated in the trial is only one of the treatments in use in the target population, under an additional partial exchangeability assumption in the target population (i.e., an assumption of no unmeasured confounding in the target population with respect to potential outcomes under the control treatment in the trial). We also develop identification results that allow for the covariates needed for transportability of relative effect measures to be only a small subset of the covariates needed to control confounding in the target population. Last, we propose estimators that can be easily implemented in standard statistical software and illustrate their use using data from a comprehensive cohort study of stable ischemic heart disease.

Breast Multiparametric MRI for Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer: The BMMR2 Challenge.

Purpose To describe the design, conduct, and results of the Breast Multiparametric MRI for prediction of neoadjuvant chemotherapy Response (BMMR2) challenge. Materials and Methods The BMMR2 computational challenge opened on May 28, 2021, and closed on December 21, 2021. The goal of the challenge was to identify image-based markers derived from multiparametric breast MRI, including diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) MRI, along with clinical data for predicting pathologic complete response (pCR) following neoadjuvant treatment. Data included 573 breast MRI studies from 191 women (mean age [±SD], 48.9 years ± 10.56) in the I-SPY 2/American College of Radiology Imaging Network (ACRIN) 6698 trial (ClinicalTrials.gov: NCT01042379). The challenge cohort was split into training (60%) and test (40%) sets, with teams blinded to test set pCR outcomes. Prediction performance was evaluated by area under the receiver operating characteristic curve (AUC) and compared with the benchmark established from the ACRIN 6698 primary analysis. Results Eight teams submitted final predictions. Entries from three teams had point estimators of AUC that were higher than the benchmark performance (AUC, 0.782 [95% CI: 0.670, 0.893], with AUCs of 0.803 [95% CI: 0.702, 0.904], 0.838 [95% CI: 0.748, 0.928], and 0.840 [95% CI: 0.748, 0.932]). A variety of approaches were used, ranging from extraction of individual features to deep learning and artificial intelligence methods, incorporating DCE and DWI alone or in combination. Conclusion The BMMR2 challenge identified several models with high predictive performance, which may further expand the value of multiparametric breast MRI as an early marker of treatment response. Clinical trial registration no. NCT01042379 Keywords: MRI, Breast, Tumor Response Supplemental material is available for this article. © RSNA, 2024.

Comparing Lung Cancer Screening Strategies in a Nationally Representative US Population Using Transportability Methods for the National Lung Cancer Screening Trial.

Importance: The National Lung Screening Trial (NLST) found that screening for lung cancer with low-dose computed tomography (CT) reduced lung cancer-specific and all-cause mortality compared with chest radiography. It is uncertain whether these results apply to a nationally representative target population. Objective: To extend inferences about the effects of lung cancer screening strategies from the NLST to a nationally representative target population of NLST-eligible US adults. Design, Setting, and Participants: This comparative effectiveness study included NLST data from US adults at 33 participating centers enrolled between August 2002 and April 2004 with follow-up through 2009 along with National Health Interview Survey (NHIS) cross-sectional household interview survey data from 2010. Eligible participants were adults aged 55 to 74 years, and were current or former smokers with at least 30 pack-years of smoking (former smokers were required to have quit within the last 15 years). Transportability analyses combined baseline covariate, treatment, and outcome data from the NLST with covariate data from the NHIS and reweighted the trial data to the target population. Data were analyzed from March 2020 to May 2023. Interventions: Low-dose CT or chest radiography screening with a screening assessment at baseline, then yearly for 2 more years. Main Outcomes and Measures: For the outcomes of lung-cancer specific and all-cause death, mortality rates, rate differences, and ratios were calculated at a median (25th percentile and 75th percentile) follow-up of 5.5 (5.2-5.9) years for lung cancer-specific mortality and 6.5 (6.1-6.9) years for all-cause mortality. Results: The transportability analysis included 51 274 NLST participants and 685 NHIS participants representing the target population (of approximately 5 700 000 individuals after survey-weighting). Compared with the target population, NLST participants were younger (median [25th percentile and 75th percentile] age, 60 [57 to 65] years vs 63 [58 to 67] years), had fewer comorbidities (eg, heart disease, 6551 of 51 274 [12.8%] vs 1 025 951 of 5 739 532 [17.9%]), and were more educated (bachelor's degree or higher, 16 349 of 51 274 [31.9%] vs 859 812 of 5 739 532 [15.0%]). In the target population, for lung cancer-specific mortality, the estimated relative rate reduction was 18% (95% CI, 1% to 33%) and the estimated absolute rate reduction with low-dose CT vs chest radiography was 71 deaths per 100 000 person-years (95% CI, 4 to 138 deaths per 100 000 person-years); for all-cause mortality the estimated relative rate reduction was 6% (95% CI, -2% to 12%). In the NLST, for lung cancer-specific mortality, the estimated relative rate reduction was 21% (95% CI, 9% to 32%) and the estimated absolute rate reduction was 67 deaths per 100 000 person-years (95% CI, 27 to 106 deaths per 100 000 person-years); for all-cause mortality, the estimated relative rate reduction was 7% (95% CI, 0% to 12%). Conclusions and Relevance: Estimates of the comparative effectiveness of low-dose CT screening compared with chest radiography in a nationally representative target population were similar to those from unweighted NLST analyses, particularly on the relative scale. Increased uncertainty around effect estimates for the target population reflects large differences in the observed characteristics of trial participants and the target population.

Cluster Randomized Trials Designed to Support Generalizable Inferences.

When planning a cluster randomized trial, evaluators often have access to an enumerated cohort representing the target population of clusters. Practicalities of conducting the trial, such as the need to oversample clusters with certain characteristics in order to improve trial economy or support inferences about subgroups of clusters, may preclude simple random sampling from the cohort into the trial, and thus interfere with the goal of producing generalizable inferences about the target population. We describe a nested trial design where the randomized clusters are embedded within a cohort of trial-eligible clusters from the target population and where clusters are selected for inclusion in the trial with known sampling probabilities that may depend on cluster characteristics (e.g., allowing clusters to be chosen to facilitate trial conduct or to examine hypotheses related to their characteristics). We develop and evaluate methods for analyzing data from this design to generalize causal inferences to the target population underlying the cohort. We present identification and estimation results for the expectation of the average potential outcome and for the average treatment effect, in the entire target population of clusters and in its non-randomized subset. In simulation studies, we show that all the estimators have low bias but markedly different precision. Cluster randomized trials where clusters are selected for inclusion with known sampling probabilities that depend on cluster characteristics, combined with efficient estimation methods, can precisely quantify treatment effects in the target population, while addressing objectives of trial conduct that require oversampling clusters on the basis of their characteristics.

Tree-based subgroup discovery using electronic health record data: heterogeneity of treatment effects for DTG-containing therapies.

The rich longitudinal individual level data available from electronic health records (EHRs) can be used to examine treatment effect heterogeneity. However, estimating treatment effects using EHR data poses several challenges, including time-varying confounding, repeated and temporally non-aligned measurements of covariates, treatment assignments and outcomes, and loss-to-follow-up due to dropout. Here, we develop the subgroup discovery for longitudinal data algorithm, a tree-based algorithm for discovering subgroups with heterogeneous treatment effects using longitudinal data by combining the generalized interaction tree algorithm, a general data-driven method for subgroup discovery, with longitudinal targeted maximum likelihood estimation. We apply the algorithm to EHR data to discover subgroups of people living with human immunodeficiency virus who are at higher risk of weight gain when receiving dolutegravir (DTG)-containing antiretroviral therapies (ARTs) versus when receiving non-DTG-containing ARTs.

Systematically missing data in causally interpretable meta-analysis.

Causally interpretable meta-analysis combines information from a collection of randomized controlled trials to estimate treatment effects in a target population in which experimentation may not be possible but from which covariate information can be obtained. In such analyses, a key practical challenge is the presence of systematically missing data when some trials have collected data on one or more baseline covariates, but other trials have not, such that the covariate information is missing for all participants in the latter. In this article, we provide identification results for potential (counterfactual) outcome means and average treatment effects in the target population when covariate data are systematically missing from some of the trials in the meta-analysis. We propose three estimators for the average treatment effect in the target population, examine their asymptotic properties, and show that they have good finite-sample performance in simulation studies. We use the estimators to analyze data from two large lung cancer screening trials and target population data from the National Health and Nutrition Examination Survey (NHANES). To accommodate the complex survey design of the NHANES, we modify the methods to incorporate survey sampling weights and allow for clustering.

Addressing Systematic Missing Data in the Context of Causally Interpretable Meta-analysis.

Evidence synthesis involves drawing conclusions from trial samples that may differ from the target population of interest, and there is often heterogeneity among trials in sample characteristics, treatment implementation, study design, and assessment of covariates. Stitching together this patchwork of evidence requires subject-matter knowledge, a clearly defined target population, and guidance on how to weigh evidence from different trials. Transportability analysis has provided formal identifiability conditions required to make unbiased causal inference in the target population. In this manuscript, we review these conditions along with an additional assumption required to address systematic missing data. The identifiability conditions highlight the importance of accounting for differences in treatment effect modifiers between the populations underlying the trials and the target population. We perform simulations to evaluate the bias of conventional random effect models and multiply imputed estimates using the pooled trials sample and describe causal estimators that explicitly address trial-to-target differences in key covariates in the context of systematic missing data. Results indicate that the causal transportability estimators are unbiased when treatment effect modifiers are accounted for in the analyses. Results also highlight the importance of carefully evaluating identifiability conditions for each trial to reduce bias due to differences in participant characteristics between trials and the target population. Bias can be limited by adjusting for covariates that are strongly correlated with missing treatment effect modifiers, including data from trials that do not differ from the target on treatment modifiers, and removing trials that do differ from the target and did not assess a modifier.

Survival analysis using deep learning with medical imaging.

There is widespread interest in using deep learning to build prediction models for medical imaging data. These deep learning methods capture the local structure of the image and require no manual feature extraction. Despite the importance of modeling survival in the context of medical data analysis, research on deep learning methods for modeling the relationship of imaging and time-to-event data is still under-developed. We provide an overview of deep learning methods for time-to-event outcomes and compare several deep learning methods to Cox model based methods through the analysis of a histology dataset of gliomas.

Predicting Seasonal Influenza Hospitalizations Using an Ensemble Super Learner: A Simulation Study.

Accurate forecasts can inform response to outbreaks. Most efforts in influenza forecasting have focused on predicting influenza-like activity, with fewer on influenza-related hospitalizations. We conducted a simulation study to evaluate a super learner's predictions of 3 seasonal measures of influenza hospitalizations in the United States: peak hospitalization rate, peak hospitalization week, and cumulative hospitalization rate. We trained an ensemble machine learning algorithm on 15,000 simulated hospitalization curves and generated weekly predictions. We compared the performance of the ensemble (weighted combination of predictions from multiple prediction algorithms), the best-performing individual prediction algorithm, and a naive prediction (median of a simulated outcome distribution). Ensemble predictions performed similarly to the naive predictions early in the season but consistently improved as the season progressed for all prediction targets. The best-performing prediction algorithm in each week typically had similar predictive accuracy compared with the ensemble, but the specific prediction algorithm selected varied by week. An ensemble super learner improved predictions of influenza-related hospitalizations, relative to a naive prediction. Future work should examine the super learner's performance using additional empirical data on influenza-related predictors (e.g., influenza-like illness). The algorithm should also be tailored to produce prospective probabilistic forecasts of selected prediction targets.

Role of calibration in uncertainty-based referral for deep learning.

The uncertainty in predictions from deep neural network analysis of medical imaging is challenging to assess but potentially important to include in subsequent decision-making. Using data from diabetic retinopathy detection, we present an empirical evaluation of the role of model calibration in uncertainty-based referral, an approach that prioritizes referral of observations based on the magnitude of a measure of uncertainty. We consider several configurations of network architecture, methods for uncertainty estimation, and training data size. We identify a strong relationship between the effectiveness of uncertainty-based referral and having a well-calibrated model. This is especially relevant as complex deep neural networks tend to have high calibration errors. Finally, we show that post-calibration of the neural network helps uncertainty-based referral with identifying hard-to-classify observations.

An Automated Bioinformatics Pipeline Informing Near-Real-Time Public Health Responses to New HIV Diagnoses in a Statewide HIV Epidemic.

Molecular HIV cluster data can guide public health responses towards ending the HIV epidemic. Currently, real-time data integration, analysis, and interpretation are challenging, leading to a delayed public health response. We present a comprehensive methodology for addressing these challenges through data integration, analysis, and reporting. We integrated heterogeneous data sources across systems and developed an open-source, automatic bioinformatics pipeline that provides molecular HIV cluster data to inform public health responses to new statewide HIV-1 diagnoses, overcoming data management, computational, and analytical challenges. We demonstrate implementation of this pipeline in a statewide HIV epidemic and use it to compare the impact of specific phylogenetic and distance-only methods and datasets on molecular HIV cluster analyses. The pipeline was applied to 18 monthly datasets generated between January 2020 and June 2022 in Rhode Island, USA, that provide statewide molecular HIV data to support routine public health case management by a multi-disciplinary team. The resulting cluster analyses and near-real-time reporting guided public health actions in 37 phylogenetically clustered cases out of 57 new HIV-1 diagnoses. Of the 37, only 21 (57%) clustered by distance-only methods. Through a unique academic-public health partnership, an automated open-source pipeline was developed and applied to prospective, routine analysis of statewide molecular HIV data in near-real-time. This collaboration informed public health actions to optimize disruption of HIV transmission.

Using Trial and Observational Data to Assess Effectiveness: Trial Emulation, Transportability, Benchmarking, and Joint Analysis.

Comparisons between randomized trial analyses and observational analyses that attempt to address similar research questions have generated many controversies in epidemiology and the social sciences. There has been little consensus on when such comparisons are reasonable, what their implications are for the validity of observational analyses, or whether trial and observational analyses can be integrated to address effectiveness questions. Here, we consider methods for using observational analyses to complement trial analyses when assessing treatment effectiveness. First, we review the framework for designing observational analyses that emulate target trials and present an evidence map of its recent applications. We then review approaches for estimating the average treatment effect in the target population underlying the emulation: using observational analyses of the emulation data alone; and using transportability analyses to extend inferences from a trial to the target population. We explain how comparing treatment effect estimates from the emulation against those from the trial can provide evidence on whether observational analyses can be trusted to deliver valid estimates of effectiveness - a process we refer to as benchmarking - and, in some cases, allow the joint analysis of the trial and observational data. We illustrate different approaches using a simplified example of a pragmatic trial and its emulation in registry data. We conclude that synthesizing trial and observational data - in transportability, benchmarking, or joint analyses - can leverage their complementary strengths to enhance learning about comparative effectiveness, through a process combining quantitative methods and epidemiological judgements.

Regression-based estimation of heterogeneous treatment effects when extending inferences from a randomized trial to a target population.

Most work on extending (generalizing or transporting) inferences from a randomized trial to a target population has focused on estimating average treatment effects (i.e., averaged over the target population's covariate distribution). Yet, in the presence of strong effect modification by baseline covariates, the average treatment effect in the target population may be less relevant for guiding treatment decisions. Instead, the conditional average treatment effect (CATE) as a function of key effect modifiers may be a more useful estimand. Recent work on estimating target population CATEs using baseline covariate, treatment, and outcome data from the trial and covariate data from the target population only allows for the examination of heterogeneity over distinct subgroups. We describe flexible pseudo-outcome regression modeling methods for estimating target population CATEs conditional on discrete or continuous baseline covariates when the trial is embedded in a sample from the target population (i.e., in nested trial designs). We construct pointwise confidence intervals for the CATE at a specific value of the effect modifiers and uniform confidence bands for the CATE function. Last, we illustrate the methods using data from the Coronary Artery Surgery Study (CASS) to estimate CATEs given history of myocardial infarction and baseline ejection fraction value in the target population of all trial-eligible patients with stable ischemic heart disease.

Not all clusters are equal: dynamics of molecular HIV-1 clusters in a statewide Rhode Island epidemic.

OBJECTIVES: Molecular epidemiology is a powerful tool to characterize HIV epidemics and prioritize public health interventions. Typically, HIV clusters are assumed to have uniform patterns over time. We hypothesized that assessment of cluster evolution would reveal distinct cluster behavior, possibly improving molecular epidemic characterization, towards disrupting HIV transmission. DESIGN: Retrospective cohort. METHODS: Annual phylogenies were inferred by cumulative aggregation of all available HIV-1 pol sequences of individuals with HIV-1 in Rhode Island (RI) between 1990 and 2020, representing a statewide epidemic. Molecular clusters were detected in annual phylogenies by strict and relaxed cluster definition criteria, and the impact of annual newly-diagnosed HIV-1 cases to the structure of individual clusters was examined over time. RESULTS: Of 2153 individuals, 31% (strict criteria) - 47% (relaxed criteria) clustered. Longitudinal tracking of individual clusters identified three cluster types: normal, semi-normal and abnormal. Normal clusters (83-87% of all identified clusters) showed predicted growing/plateauing dynamics, with approximately three-fold higher growth rates in large (15-18%) vs. small (∼5%) clusters. Semi-normal clusters (1-2% of all clusters) temporarily fluctuated in size and composition. Abnormal clusters (11-16% of all clusters) demonstrated collapses and re-arrangements over time. Borderline values of cluster-defining parameters explained dynamics of non-normal clusters. CONCLUSIONS: Comprehensive tracing of molecular HIV clusters over time in a statewide epidemic identified distinct cluster types, likely missed in cross-sectional analyses, demonstrating that not all clusters are equal. This knowledge challenges current perceptions of consistent cluster behavior over time and could improve molecular surveillance of local HIV epidemics to better inform public health strategies.

Efficient and robust methods for causally interpretable meta-analysis: Transporting inferences from multiple randomized trials to a target population.

We present methods for causally interpretable meta-analyses that combine information from multiple randomized trials to draw causal inferences for a target population of substantive interest. We consider identifiability conditions, derive implications of the conditions for the law of the observed data, and obtain identification results for transporting causal inferences from a collection of independent randomized trials to a new target population in which experimental data may not be available. We propose an estimator for the potential outcome mean in the target population under each treatment studied in the trials. The estimator uses covariate, treatment, and outcome data from the collection of trials, but only covariate data from the target population sample. We show that it is doubly robust in the sense that it is consistent and asymptotically normal when at least one of the models it relies on is correctly specified. We study the finite sample properties of the estimator in simulation studies and demonstrate its implementation using data from a multicenter randomized trial.

Estimating the area under the ROC curve when transporting a prediction model to a target population.

We propose methods for estimating the area under the receiver operating characteristic (ROC) curve (AUC) of a prediction model in a target population that differs from the source population that provided the data used for original model development. If covariates that are associated with model performance, as measured by the AUC, have a different distribution in the source and target populations, then AUC estimators that only use data from the source population will not reflect model performance in the target population. Here, we provide identification results for the AUC in the target population when outcome and covariate data are available from the sample of the source population, but only covariate data are available from the sample of the target population. In this setting, we propose three estimators for the AUC in the target population and show that they are consistent and asymptotically normal. We evaluate the finite-sample performance of the estimators using simulations and use them to estimate the AUC in a nationally representative target population from the National Health and Nutrition Examination Survey for a lung cancer risk prediction model developed using source population data from the National Lung Screening Trial.

Extending prediction models for use in a new target population with failure time outcomes.

Prediction models are often built and evaluated using data from a population that differs from the target population where model-derived predictions are intended to be used in. In this article, we present methods for evaluating model performance in the target population when some observations are right censored. The methods assume that outcome and covariate data are available from a source population used for model development and covariates, but no outcome data, are available from the target population. We evaluate the finite sample performance of the proposed estimators using simulations and apply the methods to transport a prediction model built using data from a lung cancer screening trial to a nationally representative population of participants eligible for lung cancer screening.

Comparison of Low-Value Care Among Commercial and Medicaid Enrollees.

BACKGROUND: Low-value healthcare is costly and inefficient and may adversely affect patient outcomes. Despite increases in low-value service use, little is known about how the receipt of low-value care differs across payers. OBJECTIVE: To evaluate differences in the use of low-value care between patients with commercial versus Medicaid coverage. DESIGN: Retrospective observational analysis of the 2017 Rhode Island All-payer Claims Database, estimating the probability of receiving each of 14 low-value services between commercial and Medicaid enrollees, adjusting for patient sociodemographic and clinical characteristics. Ensemble machine learning minimized the possibility of model misspecification. PARTICIPANTS: Medicaid and commercial enrollees aged 18-64 with continuous coverage and an encounter at which they were at risk of receiving a low-value service. INTERVENTION: Enrollment in Medicaid or Commercial insurance. MAIN MEASURES: Use of one of 14 validated measures of low-value care. KEY RESULTS: Among 110,609 patients, Medicaid enrollees were younger, had more comorbidities, and were more likely to be female than commercial enrollees. Medicaid enrollees had higher rates of use for 7 low-value care measures, and those with commercial coverage had higher rates for 5 measures. Across all measures of low-value care, commercial enrollees received more (risk difference [RD] 6.8 percentage points; CI: 6.6 to 7.0) low-value services than their counterparts with Medicaid. Commercial enrollees were also more likely to receive low-value services typically performed in the emergency room (RD 11.4 percentage points; CI: 10.7 to 12.2) and services that were less expensive (RD 15.3 percentage points; CI 14.6 to 16.0). CONCLUSION: Differences in the provision of low-value care varied across measures, though average use was slightly higher among commercial than Medicaid enrollees. This difference was more pronounced for less expensive services indicating that financial incentives may not be the sole driver of low-value care.