Causal fairness assessment of treatment allocation with electronic health records.

OBJECTIVE: Healthcare continues to grapple with the persistent issue of treatment disparities, sparking concerns regarding the equitable allocation of treatments in clinical practice. While various fairness metrics have emerged to assess fairness in decision-making processes, a growing focus has been on causality-based fairness concepts due to their capacity to mitigate confounding effects and reason about bias. However, the application of causal fairness notions in evaluating the fairness of clinical decision-making with electronic health record (EHR) data remains an understudied domain. This study aims to address the methodological gap in assessing causal fairness of treatment allocation with electronic health records data. In addition, we investigate the impact of social determinants of health on the assessment of causal fairness of treatment allocation. METHODS: We propose a causal fairness algorithm to assess fairness in clinical decision-making. Our algorithm accounts for the heterogeneity of patient populations and identifies potential unfairness in treatment allocation by conditioning on patients who have the same likelihood to benefit from the treatment. We apply this framework to a patient cohort with coronary artery disease derived from an EHR database to evaluate the fairness of treatment decisions. RESULTS: Our analysis reveals notable disparities in coronary artery bypass grafting (CABG) allocation among different patient groups. Women were found to be 4.4%-7.7% less likely to receive CABG than men in two out of four treatment response strata. Similarly, Black or African American patients were 5.4%-8.7% less likely to receive CABG than others in three out of four response strata. These results were similar when social determinants of health (insurance and area deprivation index) were dropped from the algorithm. These findings highlight the presence of disparities in treatment allocation among similar patients, suggesting potential unfairness in the clinical decision-making process. CONCLUSION: This study introduces a novel approach for assessing the fairness of treatment allocation in healthcare. By incorporating responses to treatment into fairness framework, our method explores the potential of quantifying fairness from a causal perspective using EHR data. Our research advances the methodological development of fairness assessment in healthcare and highlight the importance of causality in determining treatment fairness.

Comparative Effectiveness of Second-line Antihyperglycemic Agents for Cardiovascular Outcomes: A Large-scale, Multinational, Federated Analysis of the LEGEND-T2DM Study.

Background: SGLT2 inhibitors (SGLT2is) and GLP-1 receptor agonists (GLP1-RAs) reduce major adverse cardiovascular events (MACE) in patients with type 2 diabetes mellitus (T2DM). However, their effectiveness relative to each other and other second-line antihyperglycemic agents is unknown, without any major ongoing head-to-head trials. Methods: Across the LEGEND-T2DM network, we included ten federated international data sources, spanning 1992-2021. We identified 1,492,855 patients with T2DM and established cardiovascular disease (CVD) on metformin monotherapy who initiated one of four second-line agents (SGLT2is, GLP1-RAs, dipeptidyl peptidase 4 inhibitor [DPP4is], sulfonylureas [SUs]). We used large-scale propensity score models to conduct an active comparator, target trial emulation for pairwise comparisons. After evaluating empirical equipoise and population generalizability, we fit on-treatment Cox proportional hazard models for 3-point MACE (myocardial infarction, stroke, death) and 4-point MACE (3-point MACE + heart failure hospitalization) risk, and combined hazard ratio (HR) estimates in a random-effects meta-analysis. Findings: Across cohorts, 16·4%, 8·3%, 27·7%, and 47·6% of individuals with T2DM initiated SGLT2is, GLP1-RAs, DPP4is, and SUs, respectively. Over 5·2 million patient-years of follow-up and 489 million patient-days of time at-risk, there were 25,982 3-point MACE and 41,447 4-point MACE events. SGLT2is and GLP1-RAs were associated with a lower risk for 3-point MACE compared with DPP4is (HR 0·89 [95% CI, 0·79-1·00] and 0·83 [0·70-0·98]), and SUs (HR 0·76 [0·65-0·89] and 0·71 [0·59-0·86]). DPP4is were associated with a lower 3-point MACE risk versus SUs (HR 0·87 [0·79-0·95]). The pattern was consistent for 4-point MACE for the comparisons above. There were no significant differences between SGLT2is and GLP1-RAs for 3-point or 4-point MACE (HR 1·06 [0·96-1·17] and 1·05 [0·97-1·13]). Interpretation: In patients with T2DM and established CVD, we found comparable cardiovascular risk reduction with SGLT2is and GLP1-RAs, with both agents more effective than DPP4is, which in turn were more effective than SUs. These findings suggest that the use of GLP1-RAs and SGLT2is should be prioritized as second-line agents in those with established CVD. Funding: National Institutes of Health, United States Department of Veterans Affairs.

Evaluating and Improving the Performance and Racial Fairness of Algorithms for GFR Estimation.

Data-driven clinical prediction algorithms are used widely by clinicians. Understanding what factors can impact the performance and fairness of data-driven algorithms is an important step towards achieving equitable healthcare. To investigate the impact of modeling choices on the algorithmic performance and fairness, we make use of a case study to build a prediction algorithm for estimating glomerular filtration rate (GFR) based on the patient's electronic health record (EHR). We compare three distinct approaches for estimating GFR: CKD-EPI equations, epidemiological models, and EHR-based models. For epidemiological models and EHR-based models, four machine learning models of varying computational complexity (i.e., linear regression, support vector machine, random forest regression, and neural network) were compared. Performance metrics included root mean squared error (RMSE), median difference, and the proportion of GFR estimates within 30% of the measured GFR value (P30). Differential performance between non-African American and African American group was used to assess algorithmic fairness with respect to race. Our study showed that the variable race had a negligible effect on error, accuracy, and differential performance. Furthermore, including more relevant clinical features (e.g., common comorbidities of chronic kidney disease) and using more complex machine learning models, namely random forest regression, significantly lowered the estimation error of GFR. However, the difference in performance between African American and non-African American patients did not decrease, where the estimation error for African American patients remained consistently higher than non-African American patients, indicating that more objective patient characteristics should be discovered and included to improve algorithm performance.

Scalable and interpretable alternative to chart review for phenotype evaluation using standardized structured data from electronic health records.

OBJECTIVES: Chart review as the current gold standard for phenotype evaluation cannot support observational research on electronic health records and claims data sources at scale. We aimed to evaluate the ability of structured data to support efficient and interpretable phenotype evaluation as an alternative to chart review. MATERIALS AND METHODS: We developed Knowledge-Enhanced Electronic Profile Review (KEEPER) as a phenotype evaluation tool that extracts patient's structured data elements relevant to a phenotype and presents them in a standardized fashion following clinical reasoning principles. We evaluated its performance (interrater agreement, intermethod agreement, accuracy, and review time) compared to manual chart review for 4 conditions using randomized 2-period, 2-sequence crossover design. RESULTS: Case ascertainment with KEEPER was twice as fast compared to manual chart review. 88.1% of the patients were classified concordantly using charts and KEEPER, but agreement varied depending on the condition. Missing data and differences in interpretation accounted for most of the discrepancies. Pairs of clinicians agreed in case ascertainment in 91.2% of the cases when using KEEPER compared to 76.3% when using charts. Patient classification aligned with the gold standard in 88.1% and 86.9% of the cases respectively. CONCLUSION: Structured data can be used for efficient and interpretable phenotype evaluation if they are limited to relevant subset and organized according to the clinical reasoning principles. A system that implements these principles can achieve noninferior performance compared to chart review at a fraction of time.

A metadata framework for computational phenotypes.

With the burgeoning development of computational phenotypes, it is increasingly difficult to identify the right phenotype for the right tasks. This study uses a mixed-methods approach to develop and evaluate a novel metadata framework for retrieval of and reusing computational phenotypes. Twenty active phenotyping researchers from 2 large research networks, Electronic Medical Records and Genomics and Observational Health Data Sciences and Informatics, were recruited to suggest metadata elements. Once consensus was reached on 39 metadata elements, 47 new researchers were surveyed to evaluate the utility of the metadata framework. The survey consisted of 5-Likert multiple-choice questions and open-ended questions. Two more researchers were asked to use the metadata framework to annotate 8 type-2 diabetes mellitus phenotypes. More than 90% of the survey respondents rated metadata elements regarding phenotype definition and validation methods and metrics positively with a score of 4 or 5. Both researchers completed annotation of each phenotype within 60 min. Our thematic analysis of the narrative feedback indicates that the metadata framework was effective in capturing rich and explicit descriptions and enabling the search for phenotypes, compliance with data standards, and comprehensive validation metrics. Current limitations were its complexity for data collection and the entailed human costs.

Optimizing Chemicals Management in the United States and Canada through the Essential-Use Approach.

Chemicals have improved the functionality and convenience of industrial and consumer products, but sometimes at the expense of human or ecological health. Existing regulatory systems have proven to be inadequate for assessing and managing the tens of thousands of chemicals in commerce. A different approach is urgently needed to minimize ongoing production, use, and exposures to hazardous chemicals. The premise of the essential-use approach is that chemicals of concern should be used only in cases in which their function in specific products is necessary for health, safety, or the functioning of society and when feasible alternatives are unavailable. To optimize the essential-use approach for broader implementation in the United States and Canada, we recommend that governments and businesses (1) identify chemicals of concern for essentiality assessments based on a broad range of hazard traits, going beyond toxicity; (2) expedite decision-making by avoiding unnecessary assessments and strategically asking up to three questions to determine whether the use of the chemical in the product is essential; (3) apply the essential-use approach as early as possible in the process of developing and assessing chemicals; and (4) engage diverse experts in identifying chemical uses and functions, assessing alternatives, and making essentiality determinations and share such information broadly. If optimized and expanded into regulatory systems in the United States and Canada, other policymaking bodies, and businesses, the essential-use approach can improve chemicals management and shift the market toward safer chemistries that benefit human and ecological health.

Data assimilation on mechanistic models of glucose metabolism predicts glycemic states in adolescents following bariatric surgery.

Type 2 diabetes mellitus is a complex and under-treated disorder closely intertwined with obesity. Adolescents with severe obesity and type 2 diabetes have a more aggressive disease compared to adults, with a rapid decline in pancreatic ß cell function and increased incidence of comorbidities. Given the relative paucity of pharmacotherapies, bariatric surgery has become increasingly used as a therapeutic option. However, subsets of this population have sub-optimal outcomes with either inadequate weight loss or little improvement in disease. Predicting which patients will benefit from surgery is a difficult task and detailed physiological characteristics of patients who do not respond to treatment are generally unknown. Identifying physiological predictors of surgical response therefore has the potential to reveal both novel phenotypes of disease as well as therapeutic targets. We leverage data assimilation paired with mechanistic models of glucose metabolism to estimate pre-operative physiological states of bariatric surgery patients, thereby identifying latent phenotypes of impaired glucose metabolism. Specifically, maximal insulin secretion capacity, σ, and insulin sensitivity, SI, differentiate aberrations in glucose metabolism underlying an individual's disease. Using multivariable logistic regression, we combine clinical data with data assimilation to predict post-operative glycemic outcomes at 12 months. Models using data assimilation sans insulin had comparable performance to models using oral glucose tolerance test glucose and insulin. Our best performing models used data assimilation and had an area under the receiver operating characteristic curve of 0.77 (95% confidence interval 0.7665, 0.7734) and mean average precision of 0.6258 (0.6206, 0.6311). We show that data assimilation extracts knowledge from mechanistic models of glucose metabolism to infer future glycemic states from limited clinical data. This method can provide a pathway to predict long-term, post-surgical glycemic states by estimating the contributions of insulin resistance and limitations of insulin secretion to pre-operative glucose metabolism.

Targeted Delivery of Notch Inhibitor Attenuates Obesity-Induced Glucose Intolerance and Liver Fibrosis.

As the prevalence of obesity-induced type 2 diabetes mellitus (T2DM) and nonalcoholic steatohepatitis (NASH) continue to increase, the need for pharmacologic therapies becomes urgent. However, endeavors to identify and develop novel therapeutic strategies for these chronic conditions are balanced by the need for safety, impeding clinical translation. One shared pathology of these two diseases is a maladaptive reactivation of the Notch signaling pathway in liver. Notch antagonism with γ-secretase inhibitors effectively suppresses hepatic glucose production and reduces liver fibrosis in NASH, but its extrahepatic side effects, particularly goblet cell metaplasia, limit therapeutic utility. To overcome this barrier, we developed a nanoparticle-mediated delivery system to target γ-secretase inhibitor to liver (GSI NPs). GSI NP application reduced hepatic glucose production in diet-induced obese mice and reduced hepatic fibrosis and inflammation in mice fed a NASH-provoking diet, without apparent gastrointestinal toxicity. By changing the delivery method, these results provide proof-of-concept for the repurposing of a previously intolerable medication to address unmet needs in the clinical landscape for obesity-induced T2DM and NASH.

Correction: Guideline levels for PFOA and PFOS in drinking water: the role of scientific uncertainty, risk assessment decisions, and social factors.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Challenging dominant breast cancer research agendas: perspectives on the outcomes of the interagency breast cancer and environment research coordinating committee.

BACKGROUND: As breast cancer rates increase globally, there is growing scientific consensus that greater understanding of the causes of breast cancer is needed to better prevent its occurrence. Genetics accounts for a small percentage of cases, thus environmental factors and epigenetics are increasingly suspect in breast cancer etiology. Within the breast cancer and environmental breast cancer social movements, there are longstanding calls for research and policy aimed toward the prevention of breast cancer. To better understand the opportunities and barriers to addressing environmental contributors to breast cancer, this article investigates both outcomes and perceptions of stakeholders involved in the Interagency Breast Cancer and Environment Research Coordinating Committee (IBCERCC). The IBCERCC was mandated by the 2008 U.S. Breast Cancer and Environmental Research Act, a law representing years of advocate and researcher efforts to produce national strategies and federal funding for breast cancer prevention research. METHODS: To understand the meaning and impact of the IBCERCC advisory committee and final report, Prioritizing Prevention, I draw on fifteen confidential semi-structured interviews with members of the twenty-five person IBCERCC, in addition to six confidential semi-structured interviews with key breast cancer funders, advocates, and researchers affiliated with national reports on environmental contributors to cancer. I examine media coverage, congressional hearing transcripts, and official responses to the release of the IBCERCC report by governmental and non-governmental organizations. RESULTS: Interviews and publicly available documents reveal a set of direct and indirect outcomes of the 2013 IBCERCC report. Interviewees in government positions perceived the 2014 renewal of the Breast Cancer and the Environment Research Program to result from IBCERCC efforts, notable in the context of declining U.S. federal research funding. Interviews also revealed a suite of potential barriers to the implementation of report recommendations including: distinct interpretations of the federal mandate, disparate assessments of scientific evidence, government funding crises, and lack of specificity around responsibility for implementation of report findings. CONCLUSION: This article examines efforts to shift institutional research and funding priorities in cancer research towards prevention. Social science research can support efforts to shift institutional priorities by identifying broader social contexts and underlying values typically unnamed in scientific discourse.

Correction: Guideline levels for PFOA and PFOS in drinking water: the role of scientific uncertainty, risk assessment decisions, and social factors.

This paper was originally published under a standard licence. This has now been amended to a CC BY licence in the PDF and HTML.

Guideline levels for PFOA and PFOS in drinking water: the role of scientific uncertainty, risk assessment decisions, and social factors.

Communities across the U.S. are discovering drinking water contaminated by perfluoroalkyl and polyfluoroalkyl substances (PFAS) and determining appropriate actions. There are currently no federal PFAS drinking water standards despite widespread drinking water contamination, ubiquitous population-level exposure, and toxicological and epidemiological evidence of adverse health effects. Absent federal PFAS standards, multiple U.S. states have developed their own health-based water guideline levels to guide decisions about contaminated site cleanup and drinking water surveillance and treatment. We examined perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS) water guideline levels developed by the U.S. Environmental Protection Agency (EPA) and state agencies to protect people drinking the water, and summarized how and why these levels differ. We referenced documents and tables released in June 2018 by the Interstate Technology and Regulatory Council (ITRC) to identify states that have drinking water and groundwater guideline levels for PFOA and/or PFOS that differ from EPA's health advisories (HAs). We also gathered assessment documents from state websites and contacted state environmental and health agencies to identify and confirm current guidelines. Seven states have developed their own water guideline levels for PFOA and/or PFOS ranging from 13 to 1000 ng/L, compared to EPA's HA of 70 ng/L for both compounds individually or combined. We find that the development of PFAS guideline levels via exposure and hazard assessment decisions is influenced by multiple scientific, technical, and social factors, including managing scientific uncertainty, technical decisions and capacity, and social, political, and economic influences from involved stakeholders. Assessments by multiple states and academic scientists suggest that EPA's HA is not sufficiently protective. The ability of states to develop their own guideline levels and standards provides diverse risk assessment approaches as models for other state and federal regulators, while a sufficiently protective, scientifically sound, and enforceable federal standard would provide more consistent protection.

Environmental Chemicals and Public Sociology: Engaged Scholarship on Highly Fluorinated Compounds.

We report here on a multifaceted body of research on per- and polyfluoroalkyl substances (PFAS), chemicals that have become a well-known group of 'emerging contaminants' in recent years. Our PFAS Project team of over 10 researchers - faculty, postdocs, graduate students, and undergraduates - has been working since 2015 to study the extent and health effects of PFAS contamination through a broad model of engaged public sociology. Our model of research combines organic public sociology with elements of community-based participatory research, a related but distinct research form most widely used in the environmental health sciences. Based on long-term, place-based relationships, our engaged public sociology has led to numerous academic, regulatory, and social movement effects. We argue that this form of engaged, intervention-oriented public sociology is appropriate and beneficial for research in many areas of environmental sociology given the social and ecological stakes in the current moment. Engaged public sociology involves collaborative, reflexive research with broadly-conceived communities or publics. It facilitates the creation of previously undone science by addressing research topics of interest to community members, and allows researchers to directly contribute to environmental and social justice movements by acting as reflexive, observant participants.

Non-stick science: Sixty years of research and (in)action on fluorinated compounds.

Understandings of environmental governance both assume and challenge the relationship between expert knowledge and corresponding action. We explore this interplay by examining the context of knowledge production pertaining to a contested class of chemicals. Per-and polyfluorinated alkyl substances (PFASs) are widely used industrial compounds containing chemical chains of carbon and fluorine that are persistent, bioaccumulative and toxic. Although industry and regulatory scientists have studied the exposure and toxicity concerns of these compounds for decades, and several contaminated communities have documented health concerns as a result of their high levels of exposure, PFAS use remains ubiquitous in a large range of consumer and industrial products. Despite this significant history of industry knowledge production documenting exposure and toxicity concerns, the regulatory approach to PFASs has been limited. This is largely due to a regulatory framework that privileges industry incentives for rapid market entry and trade secret protection over substantive public health protection, creating areas of unseen science, research that is conducted but never shared outside of institutional boundaries. In particular, the risks of PFASs have been both structurally hidden and unexamined by existing regulatory and industry practice. This reveals the uneven pathways that construct issues of social and scientific concern.

History of US Presidential Assaults on Modern Environmental Health Protection.

The Trump administration has undertaken an assault on the Environmental Protection Agency (EPA), an agency critical to environmental health. This assault has precedents in the administrations of Ronald Reagan and George W. Bush. The early Reagan administration (1981-1983) launched an overt attack on the EPA, combining deregulation with budget and staff cuts, whereas the George W. Bush administration (2001-2008) adopted a subtler approach, undermining science-based policy. The current administration combines both these strategies and operates in a political context more favorable to its designs on the EPA. The Republican Party has shifted right and now controls the executive branch and both chambers of Congress. Wealthy donors, think tanks, and fossil fuel and chemical industries have become more influential in pushing deregulation. Among the public, political polarization has increased, the environment has become a partisan issue, and science and the mainstream media are distrusted. For these reasons, the effects of today's ongoing regulatory delays, rollbacks, and staff cuts may well surpass those of the administrations of Reagan and Bush, whose impacts on environmental health were considerable.

Can Chemical Class Approaches Replace Chemical-by-Chemical Strategies? Lessons from Recent U.S. FDA Regulatory Action on Per- And Polyfluoroalkyl Substances.

Concern about the toxicity and exposure of per- and polyfluoroalkyl substances (PFASs) is growing among scientists, regulators, and residents of contaminated communities. In 2016, the United States Food and Drug Administration (FDA) removed three food contact substances (FCSs) containing perfluorinated chemicals from the list of approved FCSs due to concerns regarding chemical safety. To investigate the significance and limitations of the FDA's regulatory action for environmental health research, advocacy, and regulation, we conducted a media analysis and qualitative interviews with a range of involved stakeholders. We find that the FDA's regulatory action represents a potential shift from chemical-by-chemical regulation toward class-based regulation, where groups of chemicals can be identified as sharing properties and risks, and are thus evaluated and regulated together. The FDA decision sets an important precedent of using a petition process to delist chemicals based on a safety standard. However, the narrow reach of this action also highlights the need for more comprehensive, precautionary chemical regulation capable of thoroughly evaluating classes of chemicals, and raises important questions about how classes of chemicals are delimited in environmental health science and regulation.