Marshall H Chin’s research while affiliated with University of Illinois Chicago and other places

A difficult challenge in health equity training is conducting honest and safe discussions about differences in lived experience based on social identity, and how racism and other systems of oppression impact health care. To evaluate a Theatre of the Oppressed workshop for medical students that examines systems of oppression as related to lived health care experiences. Mixed-methods cross-sectional survey and interviews. Forty randomly assigned early first-year medical students. A 90-min virtual workshop with three clinical scenes created by students where a character is being discriminated against or oppressed. During performance, students can stop scene, replace oppressed character, and role play how they would address harm, marginalization, and power imbalance. Participants discuss what they have witnessed and experienced. Likert-scale questions assessing workshop’s impact. Open-ended survey questions and interviews about workshop. Thirty-one (78%) of 40 participants completed the survey. Fifty-three percent were female. Thirty-seven percent were White, 33% Asian American, 15% Black, 11% Latinx, and 4% multiracial. Ninety percent thought this training could help them take better care of patients with lived experiences different from their own. Most agreed or strongly agreed the workshop helped them develop listening (23, 77%) and observation (26, 84%) skills. Twelve (39%) students felt stressed, while 29 (94%) felt safe. Twenty-five (81%) students agreed or strongly agreed there were meaningful discussions about systemic inequities. Students reported the workshop helped them step into others’ shoes, understand intersectional experiences of multiple identities, and discuss navigating and addressing bias, discrimination, social drivers of health, hierarchy, power structures, and systems of oppression. Some thought it was difficult to have open discussions because of fear of being poorly perceived by peers. Theatre of the Oppressed enabled medical students to engage in meaningful discussions about racism and other systems of oppression.

Background Two propensity score (PS) based balancing covariate methods, the overlap weighting method (OW) and the fine stratification method (FS), produce superb covariate balance. OW has been compared with various weighting methods while FS has been compared with the traditional stratification method and various matching methods. However, no study has yet compared OW and FS. In addition, OW has not yet been evaluated in large claims data with low prevalence exposure and with low frequency outcomes, a context in which optimal use of balancing methods is critical. In the study, we aimed to compare OW and FS using real-world data and simulations with low prevalence exposure and with low frequency outcomes. Methods We used the Texas State Medicaid claims data on adult beneficiaries with diabetes in 2012 as an empirical example (N = 42,628). Based on its real-world research question, we estimated an average treatment effect of health center vs. non-health center attendance in the total population. We also performed simulations to evaluate their relative performance. To preserve associations between covariates, we used the plasmode approach to simulate outcomes and/or exposures with N = 4,000. We simulated both homogeneous and heterogeneous treatment effects with various outcome risks (1-30% or observed: 27.75%) and/or exposure prevalence (2.5-30% or observed:10.55%). We used a weighted generalized linear model to estimate the exposure effect and the cluster-robust standard error (SE) method to estimate its SE. Results In the empirical example, we found that OW had smaller standardized mean differences in all covariates (range: OW: 0.0–0.02 vs. FS: 0.22–3.26) and Mahalanobis balance distance (MB) (< 0.001 vs. > 0.049) than FS. In simulations, OW also achieved smaller MB (homogeneity: <0.04 vs. > 0.04; heterogeneity: 0.0-0.11 vs. 0.07–0.29), relative bias (homogeneity: 4.04–56.20 vs. 20–61.63; heterogeneity: 7.85–57.6 vs. 15.0-60.4), square root of mean squared error (homogeneity: 0.332–1.308 vs. 0.385–1.365; heterogeneity: 0.263-0.526 vs 0.313-0.620), and coverage probability (homogeneity: 0.0–80.4% vs. 0.0-69.8%; heterogeneity: 0.0-97.6% vs. 0.0-92.8%), than FS, in most cases. Conclusions These findings suggest that OW can yield nearly perfect covariate balance and therefore enhance the accuracy of average treatment effect estimation in the total population.

Background Limited research has explored the performance of health centers (HCs) compared to other primary care settings among children in the United States. We evaluated utilization, quality, and expenditures for pediatric Medicaid enrollees receiving care in HCs versus non-HCs. Methods This national cross-sectional study utilized 2012 Medicaid Analytic eXtract (MAX) claims to examine children 0–17 years with a primary care visit, stratified by whether majority (> 50%) of primary care visits were at HCs or non-HCs. Outcome measures include utilization (primary care visits, non-primary care outpatient visits, prescription claims, Emergency Department (ED) visits, hospitalizations) and quality (well-child visits, avoidable ED visits, avoidable hospitalizations). For children enrolled in fee-for-service Medicaid, we also measured expenditures. Propensity score-based overlap weighting was used to balance covariates. Results A total of 2,383,270 Medicaid-enrolled children received the majority of their primary care at HCs, while 18,540,743 did at non-HCs. In adjusted analyses, HC patients had 20% more primary care visits, 15% less non-primary care outpatient visits, and 21% less prescription claims than non-HC patients. ED visits were similar across the two groups, while HC patients had 7% lower chance of hospitalization than non-HC. Quality of care outcomes favored HC patients in main analyses, but results were less robust when excluding managed care beneficiaries. Total expenditures among the fee-for-service subpopulation were lower by $239 (8%) for HC patients. Conclusions In this study of nationwide claims data to evaluate healthcare utilization, quality, and spending among Medicaid-enrolled children who receive primary care at HCs versus non-HCs, findings suggest primary care delivery in HCs may be associated with a more cost-effective model of healthcare for children.

Importance Health care algorithms are used for diagnosis, treatment, prognosis, risk stratification, and allocation of resources. Bias in the development and use of algorithms can lead to worse outcomes for racial and ethnic minoritized groups and other historically marginalized populations such as individuals with lower income. Objective To provide a conceptual framework and guiding principles for mitigating and preventing bias in health care algorithms to promote health and health care equity. Evidence Review The Agency for Healthcare Research and Quality and the National Institute for Minority Health and Health Disparities convened a diverse panel of experts to review evidence, hear from stakeholders, and receive community feedback. Findings The panel developed a conceptual framework to apply guiding principles across an algorithm’s life cycle, centering health and health care equity for patients and communities as the goal, within the wider context of structural racism and discrimination. Multiple stakeholders can mitigate and prevent bias at each phase of the algorithm life cycle, including problem formulation (phase 1); data selection, assessment, and management (phase 2); algorithm development, training, and validation (phase 3); deployment and integration of algorithms in intended settings (phase 4); and algorithm monitoring, maintenance, updating, or deimplementation (phase 5). Five principles should guide these efforts: (1) promote health and health care equity during all phases of the health care algorithm life cycle; (2) ensure health care algorithms and their use are transparent and explainable; (3) authentically engage patients and communities during all phases of the health care algorithm life cycle and earn trustworthiness; (4) explicitly identify health care algorithmic fairness issues and trade-offs; and (5) establish accountability for equity and fairness in outcomes from health care algorithms. Conclusions and Relevance Multiple stakeholders must partner to create systems, processes, regulations, incentives, standards, and policies to mitigate and prevent algorithmic bias. Reforms should implement guiding principles that support promotion of health and health care equity in all phases of the algorithm life cycle as well as transparency and explainability, authentic community engagement and ethical partnerships, explicit identification of fairness issues and trade-offs, and accountability for equity and fairness.