Race and ethnicity, not just insurance, is associated with biologics initiation in asthma and related conditions.

BACKGROUND: There are pre-existing inequities in asthma care. OBJECTIVES: We sought to evaluate effect modification by race of the effect of insurance on biologic therapy use in patients with asthma and related diseases. METHODS: We conducted inverse probability weighted analyses using electronic health records data from 2011 to 2020 from a large health care system in Boston, Mass. We evaluated the odds of not initiating omalizumab or mepolizumab therapy within 1 year of prescription for an approved indication. RESULTS: We identified 1132 individuals who met study criteria. Twenty-seven percent of these patients had public insurance and 12% belonged to a historically marginalized group (HMG). One-quarter of patients did not initiate the prescribed biologic. Among patients with asthma, individuals belonging to HMG had higher exacerbation rates in the period before initiation compared to non-HMG individuals, regardless of insurance type. Among HMG patients with asthma, those with private insurance were less likely to not initiate therapy compared to those with public insurance (odds ratio [OR]: 0.67, and 95% CI: 0.56-0.79). Among non-HMG with asthma, privately insured and publicly insured individuals had similar rates of not initiating the prescribed biologic (OR: 1.02; 95% CI: 0.95-1.09). Among those publicly insured with asthma, HMGs had higher odds of not initiating therapy compared to non-HMGs (OR: 1.16; 95% CI: 1.03-1.31), but privately insured HMG and non-HMG did not differ significantly (OR: 0.99; 95% CI: 0.91-1.07). CONCLUSIONS: Publicly insured individuals belonging to HMG are less likely to initiate biologics when prescribed despite having more severe asthma, while there are no inequities by insurance in individuals belonging to other groups.

The Importance of Equity Value Judgments and Estimator-Estimand Alignment in Measuring Disparity and Identifying Targets to Reduce Disparity.

The choice of which covariates to adjust for (so-called allowability designation (AD)) in health disparity measurements reflects value judgments about inequitable versus equitable sources of health differences, which is paramount for making inferences about disparity. Yet, many off-the-shelf estimators used in health disparity research are not designed with equity considerations in mind, and they imply different ADs. We demonstrated the practical importance of incorporating equity concerns in disparity measurements through simulations, motivated by the example of reducing racial disparities in hypertension control via interventions on disparities in treatment intensification. Seven causal decomposition estimators, each with a particular AD (with respect to disparities in hypertension control and treatment intensification), were considered to estimate the observed outcome disparity and the reduced/residual disparity under the intervention. We explored the implications for bias of the mismatch between equity concerns and the AD in the estimator under various causal structures (through altering racial differences in covariates or the confounding mechanism). The estimator that correctly reflects equity concerns performed well under all scenarios considered, whereas the other estimators were shown to have the risk of yielding large biases in certain scenarios, depending on the interaction between their ADs and the specific causal structure.

Assessing mental health treatment receipt among Asian adults with limited English proficiency using an intersectional approach.

US Asian adults and people with limited English proficiency (LEP) confront mental health treatment receipt disparities. At the intersection of racial and language injustice, Asian adults with LEP may face even greater disparity, but studies have not assessed this through explicitly intersectional approaches. Using 2019 and 2020 National Survey of Drug Use and Health data, we computed disparities in mental health treatment among those with mental illness comparing: non-Hispanic (NH) Asian adults with LEP to NH White adults without LEP (joint disparity), NH Asian adults without LEP to NH White adults without LEP (referent race disparity), NH Asian adults with LEP to those without LEP (referent LEP disparity), and the joint disparity versus the sum of referent disparities (excess intersectional disparity). In age- and gender-adjusted analyses, excess intersectional disparity was 26.8% (95% CI, -29.8 to 83.4) of the joint disparity in 2019 and 63.0% (95% CI, 29.1-96.8) in 2020. The 2019 joint disparity was 1.37 (95% CI, 0.31-2.42) times that if the race-related disparity did not vary by LEP, and if LEP-related disparity did not vary by race; this figure was 2.70 (95% CI, 0.23-5.17) in 2020. These findings highlight the necessity of considering the intersection of race and LEP in addressing mental health treatment disparities. This article is part of a Special Collection on Mental Health.

Predictive Factors of Apparent Treatment Resistant Hypertension Among Patients With Hypertension Identified Using Electronic Health Records.

BACKGROUND: Early identification of a patient with resistant hypertension (RH) enables quickly intensified treatment, short-interval follow-up, or perhaps case management to bring his or her blood pressure under control and reduce the risk of complications. OBJECTIVE: To identify predictors of RH among individuals with newly diagnosed hypertension (HTN), while comparing different prediction models and techniques for managing missing covariates using electronic health records data. DESIGN: Risk prediction study in a retrospective cohort. PARTICIPANTS: Adult patients with incident HTN treated in any of the primary care clinics of one health system between April 2013 and December 2016. MAIN MEASURES: Predicted risk of RH at the time of HTN identification and candidate predictors for variable selection in future model development. KEY RESULTS: Among 26,953 individuals with incident HTN, 613 (2.3%) met criteria for RH after 4.7 months (interquartile range, 1.2-11.3). Variables selected by the least absolute shrinkage and selection operator (LASSO), included baseline systolic blood pressure (SBP) and its missing indicator (a dummy variable created if baseline SBP is absent), use of antihypertensive medication at the time of cohort entry, body mass index, and atherosclerosis risk. The random forest technique achieved the highest area under the curve (AUC) of 0.893 (95% CI, 0.881-0.904) and the best calibration with a calibration slope of 1.01. Complete case analysis is not a valuable option (AUC = 0.625). CONCLUSIONS: Machine learning techniques and traditional logistic regression exhibited comparable levels of predictive performance after handling the missingness. We suggest that the variables identified by this study may be good candidates for clinical prediction models to alert clinicians to the need for short-interval follow up and more intensive early therapy for HTN.

Evaluating Effects of Multilevel Interventions on Disparity in Health and Healthcare Decisions.

In this paper, we introduce an analytic approach for assessing effects of multilevel interventions on disparity in health outcomes and health-related decision outcomes (i.e., a treatment decision made by a healthcare provider). We outline common challenges that are encountered in interventional health disparity research, including issues of effect scale and interpretation, choice of covariates for adjustment and its impact on effect magnitude, and the methodological challenges involved with studying decision-based outcomes. To address these challenges, we introduce total effects of interventions on disparity for the entire sample and the treated sample, and corresponding direct effects that are relevant for decision-based outcomes. We provide weighting and g-computation estimators in the presence of study attrition and sketch a simulation-based procedure for sample size determinations based on precision (e.g., confidence interval width). We validate our proposed methods through a brief simulation study and apply our approach to evaluate the RICH LIFE intervention, a multilevel healthcare intervention designed to reduce racial and ethnic disparities in hypertension control.

Comparative effectiveness of omalizumab, mepolizumab, and dupilumab in asthma: A target trial emulation.

BACKGROUND: Multiple mAbs are currently approved for the treatment of asthma. However, there is limited evidence on their comparative effectiveness. OBJECTIVE: Our aim was to compare the effectiveness of omalizumab, mepolizumab, and dupilumab in individuals with moderate-to-severe asthma. METHODS: We emulated a hypothetical randomized trial using electronic health records from a large US-based academic health care system. Participants aged 18 years or older with baseline IgE levels between 30 and 700 IU/mL and peripheral eosinophil counts of at least 150 cells/µL were eligible for study inclusion. The study period extended from March 2016 to August 2021. Outcomes included the incidence of asthma-related exacerbations and change in baseline FEV1 value over 12 months of follow-up. RESULTS: In all, 68 individuals receiving dupilumab, 68 receiving omalizumab, and 65 receiving mepolizumab met the inclusion criteria. Over 12 months of follow-up, 31 exacerbations occurred over 68 person years (0.46 exacerbations per person year) in the dupilumab group, 63 over 68 person years (0.93 per person year) in the omalizumab group, and 86 over 65 person years (1.32 per person year) in the mepolizumab group (adjusted incidence rate ratios: dupilumab vs mepolizumab, 0.28 [95% CI = 0.09-0.84]; dupilumab vs omalizumab, 0.36 [95% CI = 0.12-1.09]; and omalizumab vs mepolizumab, 0.78 [95% CI = 0.32-1.91]). The differences in the change in FEV1 comparing patients who received the different biologics were as follows: 0.11 L (95% CI = -0.003 to 0.222 L) for dupilumab versus mepolizumab, 0.082 L (95% CI -0.040 to 0.204 L) for dupilumab versus omalizumab, and 0.026 L (95% CI -0.083 to 0.140 L) for omalizumab versus mepolizumab. CONCLUSIONS: Among patients with asthma and eosinophil counts of at least 150 cells/µL and IgE levels of 30 to 700 kU/L, dupilumab was associated with greater improvements in exacerbation and FEV1 value than omalizumab and mepolizumab.

Critical interactions between race and the highly granular area deprivation index in liver transplant evaluation.

Neighborhood socioeconomic deprivation may have important implications on disparities in liver transplant (LT) evaluation. In this retrospective cohort study, we constructed a novel dataset by linking individual patient-level data with the highly granular Area Deprivation Index (ADI), which is advantageous over other neighborhood measures due to: specificity of Census Block-Group (versus Census Tract, Zip code), scoring, and robust variables. Our cohort included 1377 adults referred to our center for LT evaluation 8/1/2016-12/31/2019. Using modified Poisson regression, we tested for effect measure modification of the association between neighborhood socioeconomic status (nSES) and LT evaluation outcomes (listing, initiating evaluation, and death) by race and ethnicity. Compared to patients with high nSES, those with low nSES were at higher risk of not being listed (aRR = 1.14; 95%CI 1.05-1.22; p < .001), of not initiating evaluation post-referral (aRR = 1.20; 95%CI 1.01-1.42; p = .03) and of dying without initiating evaluation (aRR = 1.55; 95%CI 1.09-2.2; p = .01). While White patients with low nSES had similar rates of listing compared to White patients with high nSES (aRR = 1.06; 95%CI .96-1.17; p = .25), Underrepresented patients from neighborhoods with low nSES incurred 31% higher risk of not being listed compared to Underrepresented patients from neighborhoods with high nSES (aRR = 1.31; 95%CI 1.12-1.5; p < .001). Interventions addressing neighborhood deprivation may not only benefit patients with low nSES but may address racial and ethnic inequities.

An intersectional analysis of historical and contemporary structural racism on non-fatal shootings in Baltimore, Maryland.

Introduction Non-fatal shooting rates vary tremendously within cities in the USA. Factors related to structural racism (both historical and contemporary) could help explain differences in non-fatal shooting rates at the neighbourhood level. Most research assessing the relationship between structural racism and firearm violence only includes one dimension of structural racism. Our study uses an intersectional approach to examine how the interaction of two forms of structural racism is associated with spatial non-fatal shooting disparities in Baltimore, Maryland. Methods We present three additive interaction measures to describe the relationship between historical redlining and contemporary racialized economic segregation on neighbourhood-level non-fatal shootings. Results Our findings revealed that sustained disadvantage census tracts (tracts that experience contemporary socioeconomic disadvantage and were historically redlined) have the highest burden of non-fatal shootings. Sustained disadvantage tracts had on average 24 more non-fatal shootings a year per 10 000 residents compared with similarly populated sustained advantage tracts (tracts that experience contemporary socioeconomic advantage and were not historically redlined). Moreover, we found that between 2015 and 2019, the interaction between redlining and racialized economic segregation explained over one-third of non-fatal shootings (approximately 650 shootings) in sustained disadvantage tracts. Conclusion These findings suggest that the intersection of historical and contemporary structural racism is a fundamental cause of firearm violence inequities in Baltimore. Intersectionality can advance injury prevention research and practice by (1) serving as an analytical tool to expose inequities in injury-related outcomes and (2) informing the development and implementation of injury prevention interventions and policies that prioritise health equity and racial justice.

Comparative efficacy of mepolizumab, benralizumab, and dupilumab in eosinophilic asthma: A Bayesian network meta-analysis.

BACKGROUND: The comparative safety and efficacy of the biologics currently approved for asthma are unclear. OBJECTIVE: We compared the safety and efficacy of mepolizumab, benralizumab, and dupilumab in individuals with severe eosinophilic asthma. METHODS: We performed a systematic review of peer-reviewed literature published 2000 to 2021. We studied Bayesian network meta-analyses of exacerbation rates, prebronchodilator FEV1, the Asthma Control Questionnaire, and serious adverse events in individuals with eosinophilic asthma. RESULTS: Eight randomized clinical trials (n = 6461) were identified. We found in individuals with eosinophils ≥300 cells/µL the following: in reducing exacerbation rates compared to placebo: dupilumab (risk ratio [RR], 0.32; 95% credible interval [CI], 0.23 to 0.45), mepolizumab (RR, 0.37; 95% CI, 0.30 to 0.45), and benralizumab (RR, 0.49; 95% CI, 0.43 to 0.55); in improving FEV1: dupilumab (mean difference in milliliters [MD] 230; 95% CI, 160 to 300), benralizumab (MD, 150; 95% CI, 100 to 200), and mepolizumab (MD, 150; 95% CI, 66 to 220); and in reducing Asthma Control Questionnaire scores: mepolizumab (MD, -0.63; 95% CI, -0.81 to -0.45), dupilumab (MD, -0.48; 95% CI, -0.83 to -0.14), and benralizumab (MD, -0.32; 95% CI, -0.43 to -0.21). In individuals with eosinophils 150-299 cells/µL, benralizumab (RR, 0.62; 95% CI, 0.52 to 0.73) and dupilumab (RR, 0.60; 95% CI, 0.38 to 0.95) were associated with lower exacerbation rates; and only benralizumab (MD, 81; 95% CI, 8 to 150) significantly improved FEV1. These differences were minimal compared to clinically important thresholds. For serious adverse events in the overall population, mepolizumab (odds ratio, 0.67; 95% CI, 0.48 to 0.92) and benralizumab (odds ratio, 0.74; 95% CI, 0.59 to 0.93) were associated with lower odds of a serious adverse event, while dupilumab was not different from placebo (odds ratio, 1.0; 95% CI, 0.74 to 1.4). CONCLUSION: There are minimal differences in the efficacy and safety of mepolizumab, benralizumab, and dupilumab in eosinophilic asthma.

Recommendations for Using Causal Diagrams to Study Racial Health Disparities.

There have been calls for race to be denounced as a biological variable and for a greater focus on racism, instead of solely race, when studying racial health disparities in the United States. These calls are grounded in extensive scholarship and the rationale that race is not a biological variable, but instead socially constructed, and that structural/institutional racism is a root cause of race-related health disparities. However, there remains a lack of clear guidance for how best to incorporate these assertions about race and racism into tools, such as causal diagrams, that are commonly used by epidemiologists to study population health. We provide clear recommendations for using causal diagrams to study racial health disparities that were informed by these calls. These recommendations consider a health disparity to be a difference in a health outcome that is related to social, environmental, or economic disadvantage. We present simplified causal diagrams to illustrate how to implement our recommendations. These diagrams can be modified based on the health outcome and hypotheses, or for other group-based differences in health also rooted in disadvantage (e.g., gender). Implementing our recommendations may lead to the publication of more rigorous and informative studies of racial health disparities.

Multicenter study of racial and ethnic inequities in liver transplantation evaluation: Understanding mechanisms and identifying solutions.

Racial and ethnic disparities persist in access to the liver transplantation (LT) waiting list; however, there is limited knowledge about underlying system-level factors that may be responsible for these disparities. Given the complex nature of LT candidate evaluation, a human factors and systems engineering approach may provide insights. We recruited participants from the LT teams (coordinators, advanced practice providers, physicians, social workers, dieticians, pharmacists, leadership) at two major LT centers. From December 2020 to July 2021, we performed ethnographic observations (participant-patient appointments, committee meetings) and semistructured interviews (N = 54 interviews, 49 observation hours). Based on findings from this multicenter, multimethod qualitative study combined with the Systems Engineering Initiative for Patient Safety 2.0 (a human factors and systems engineering model for health care), we created a conceptual framework describing how transplant work system characteristics and other external factors may improve equity in the LT evaluation process. Participant perceptions about listing disparities described external factors (e.g., structural racism, ambiguous national guidelines, national quality metrics) that permeate the LT evaluation process. Mechanisms identified included minimal transplant team diversity, implicit bias, and interpersonal racism. A lack of resources was a common theme, such as social workers, transportation assistance, non-English-language materials, and time (e.g., more time for education for patients with health literacy concerns). Because of the minimal data collection or center feedback about disparities, participants felt uncomfortable with and unadaptable to unwanted outcomes, which perpetuate disparities. We proposed transplant center-level solutions (i.e., including but not limited to training of staff on health equity) to modifiable barriers in the clinical work system that could help patient navigation, reduce disparities, and improve access to care. Our findings call for an urgent need for transplant centers, national societies, and policy makers to focus efforts on improving equity (tailored, patient-centered resources) using the science of human factors and systems engineering.

Flexible propensity score estimation strategies for clustered data in observational studies.

Existing studies have suggested superior performance of nonparametric machine learning over logistic regression for propensity score estimation. However, it is unclear whether the advantages of nonparametric propensity score modeling are carried to settings where there is clustering of individuals, especially when there is unmeasured cluster-level confounding. In this work we examined the performance of logistic regression (all main effects), Bayesian additive regression trees and generalized boosted modeling for propensity score weighting in clustered settings, with the clustering being accounted for by including either cluster indicators or random intercepts. We simulated data for three hypothetical observational studies of varying sample and cluster sizes. Confounders were generated at both levels, including a cluster-level confounder that is unobserved in the analyses. A binary treatment and a continuous outcome were generated based on seven scenarios with varying relationships between the treatment and confounders (linear and additive, nonlinear/nonadditive, nonadditive with the unobserved cluster-level confounder). Results suggest that when the sample and cluster sizes are large, nonparametric propensity score estimation may provide better covariate balance, bias reduction, and 95% confidence interval coverage, regardless of the degree of nonlinearity or nonadditivity in the true propensity score model. When the sample or cluster sizes are small, however, nonparametric approaches may become more vulnerable to unmeasured cluster-level confounding and thus may not be a better alternative to multilevel logistic regression. We applied the methods to the National Longitudinal Study of Adolescent to Adult Health data, estimating the effect of team sports participation during adolescence on adulthood depressive symptoms.

Scaling Interventions to Manage Chronic Disease: Innovative Methods at the Intersection of Health Policy Research and Implementation Science.

Policy implementation is a key component of scaling effective chronic disease prevention and management interventions. Policy can support scale-up by mandating or incentivizing intervention adoption, but enacting a policy is only the first step. Fully implementing a policy designed to facilitate implementation of health interventions often requires a range of accompanying implementation structures, like health IT systems, and implementation strategies, like training. Decision makers need to know what policies can support intervention adoption and how to implement those policies, but to date research on policy implementation is limited and innovative methodological approaches are needed. In December 2021, the Johns Hopkins ALACRITY Center for Health and Longevity in Mental Illness and the Johns Hopkins Center for Mental Health and Addiction Policy convened a forum of research experts to discuss approaches for studying policy implementation. In this report, we summarize the ideas that came out of the forum. First, we describe a motivating example focused on an Affordable Care Act Medicaid health home waiver policy used by some US states to support scale-up of an evidence-based integrated care model shown in clinical trials to improve cardiovascular care for people with serious mental illness. Second, we define key policy implementation components including structures, strategies, and outcomes. Third, we provide an overview of descriptive, predictive and associational, and causal approaches that can be used to study policy implementation. We conclude with discussion of priorities for methodological innovations in policy implementation research, with three key areas identified by forum experts: effect modification methods for making causal inferences about how policies' effects on outcomes vary based on implementation structures/strategies; causal mediation approaches for studying policy implementation mechanisms; and characterizing uncertainty in systems science models. We conclude with discussion of overarching methods considerations for studying policy implementation, including measurement of policy implementation, strategies for studying the role of context in policy implementation, and the importance of considering when establishing causality is the goal of policy implementation research.

Meaningful Causal Decompositions in Health Equity Research: Definition, Identification, and Estimation Through a Weighting Framework.

Causal decomposition analyses can help build the evidence base for interventions that address health disparities (inequities). They ask how disparities in outcomes may change under hypothetical intervention. Through study design and assumptions, they can rule out alternate explanations such as confounding, selection bias, and measurement error, thereby identifying potential targets for intervention. Unfortunately, the literature on causal decomposition analysis and related methods have largely ignored equity concerns that actual interventionists would respect, limiting their relevance and practical value. This article addresses these concerns by explicitly considering what covariates the outcome disparity and hypothetical intervention adjust for (so-called allowable covariates) and the equity value judgments these choices convey, drawing from the bioethics, biostatistics, epidemiology, and health services research literatures. From this discussion, we generalize decomposition estimands and formulae to incorporate allowable covariate sets (and thereby reflect equity choices) while still allowing for adjustment of non-allowable covariates needed to satisfy causal assumptions. For these general formulae, we provide weighting-based estimators based on adaptations of ratio-of-mediator-probability and inverse-odds-ratio weighting. We discuss when these estimators reduce to already used estimators under certain equity value judgments, and a novel adaptation under other judgments.

Meta-analysis of Total Effect Decomposition in the Presence of Multiple Mediators: The Example of Schizophrenia Treatment.

BACKGROUND: Causal mediation analysis addresses mechanistic questions by decomposing and quantifying effects operating through different pathways. Because most individual studies are underpowered to detect mediating effects, we outlined a parametric approach to meta-analyzing causal mediation and interaction analyses with multiple mediators, compared it with a bootstrap-based alternative, and discussed its limitations. METHODS: We employed fixed- and random-effects multivariate meta-analyses to integrate evidence on treatment-mediators and mediators-outcome associations across trials. We estimated path-specific effects as functions of meta-analyzed regression coefficients; we obtained standard errors using the delta method. We evaluated the performance of this approach in simulations and applied it to assess the mediating roles of positive symptoms of schizophrenia and weight gain in the treatment effect of paliperidone ER on negative symptoms across four efficacy trials. RESULTS: Both simulations and the application showed that the meta-analytic approaches increased statistical power. In the application, we observed substantial mediating effects of positive symptoms (proportions mediated from fixed-effects meta-analysis: (Equation is included in full-text article.)). Weight gain may have beneficial mediating effects; however, such benefit may disappear at high doses when metabolic side effects were excessive. CONCLUSIONS: Meta-analyzing causal mediation analysis combines evidence from multiple sources and improves power. Targeting positive symptoms may be an effective way to reduce negative symptoms that are challenging to treat. Future work should focus on extending the existing methods to allow for more flexible modeling of mediation.

Invited Commentary: Making Causal Inference More Social and (Social) Epidemiology More Causal.

A society's social structure and the interactions of its members determine when key drivers of health occur, for how long they last, and how they operate. Yet, it has been unclear whether causal inference methods can help us find meaningful interventions on these fundamental social drivers of health. Galea and Hernán propose we place hypothetical interventions on a spectrum and estimate their effects by emulating trials, either through individual-level data analysis or systems science modeling (Am J Epidemiol. 2020;189(3):167-170). In this commentary, by way of example in health disparities research, we probe this "closer engagement of social epidemiology with formal causal inference approaches." The formidable, but not insurmountable, tensions call for causal reasoning and effect estimation in social epidemiology that should always be enveloped by a thorough understanding of how systems and the social exposome shape risk factor and health distributions. We argue that one way toward progress is a true partnership of social epidemiology and causal inference with bilateral feedback aimed at integrating social epidemiologic theory, causal identification and modeling methods, systems thinking, and improved study design and data. To produce consequential work, we must make social epidemiology more causal and causal inference more social.

Diagnosing Covariate Balance Across Levels of Right-Censoring Before and After Application of Inverse-Probability-of-Censoring Weights.

Covariate balance is a central concept in the potential outcomes literature. With selected populations or missing data, balance across treatment groups can be insufficient for estimating marginal treatment effects. Recently, a framework for using covariate balance to describe measured confounding and selection bias for time-varying and other multivariate exposures in the presence of right-censoring has been proposed. Here, we revisit this framework to consider balance across levels of right-censoring over time in more depth. Specifically, we develop measures of covariate balance that can describe what is known as "dependent censoring" in the literature, along with its associated selection bias, under multiple mechanisms for right censoring. Such measures are interesting because they substantively describe the evolution of dependent censoring mechanisms. Furthermore, we provide weighted versions that can depict how well such dependent censoring has been eliminated when inverse-probability-of-censoring weights are applied. These results provide a conceptually grounded way to inspect covariate balance across levels of right-censoring as a validity check. As a motivating example, we applied these measures to a study of hypothetical "static" and "dynamic" treatment protocols in a sequential multiple-assignment randomized trial of antipsychotics with high dropout rates.

From Epidemiologic Knowledge to Improved Health: A Vision for Translational Epidemiology.

Epidemiology should aim to improve population health; however, no consensus exists regarding the activities and skills that should be prioritized to achieve this goal. We performed a scoping review of articles addressing the translation of epidemiologic knowledge into improved population health outcomes. We identified 5 themes in the translational epidemiology literature: foundations of epidemiologic thinking, evidence-based public health or medicine, epidemiologic education, implementation science, and community-engaged research (including literature on community-based participatory research). We then identified 5 priority areas for advancing translational epidemiology: 1) scientific engagement with public health; 2) public health communication; 3) epidemiologic education; 4) epidemiology and implementation; and 5) community involvement. Using these priority areas as a starting point, we developed a conceptual framework of translational epidemiology that emphasizes interconnectedness and feedback among epidemiology, foundational science, and public health stakeholders. We also identified 2-5 representative principles in each priority area that could serve as the basis for advancing a vision of translational epidemiology. We believe an emphasis on translational epidemiology can help the broader field to increase the efficiency of translating epidemiologic knowledge into improved health outcomes and to achieve its goal of improving population health.

Decomposition Analysis to Identify Intervention Targets for Reducing Disparities.

There has been considerable interest in using decomposition methods in epidemiology (mediation analysis) and economics (Oaxaca-Blinder decomposition) to understand how health disparities arise and how they might change upon intervention. It has not been clear when estimates from the Oaxaca-Blinder decomposition can be interpreted causally because its implementation does not explicitly address potential confounding of target variables. While mediation analysis does explicitly adjust for confounders of target variables, it typically does so in a way that effectively entails equalizing confounders across racial groups, which may not reflect the intended intervention. Revisiting prior analyses in the National Longitudinal Survey of Youth on disparities in wages, unemployment, incarceration, and overall health with test scores, taken as a proxy for educational attainment, as a target intervention, we propose and demonstrate a novel decomposition that controls for confounders of test scores (e.g., measures of childhood socioeconomic status [SES]) while leaving their association with race intact. We compare this decomposition with others that use standardization (to equalize childhood SES [the confounders] alone), mediation analysis (to equalize test scores within levels of childhood SES), and one that equalizes both childhood SES and test scores. We also show how these decompositions, including our novel proposals, are equivalent to implementations of the Oaxaca-Blinder decomposition but provide a more formal causal interpretation for these decompositions.