Incorporating Acute Conditions into Risk-Adjustment for Provider Profiling: The Case of the US News and World Report Best Hospitals Rankings Methodology.

Several years ago, the US News and World Report changed their risk-adjustment methodology, now relying almost exclusively on chronic conditions for risk adjustment. The impacts of adding selected acute conditions like pneumonia, sepsis, and electrolyte disorders ("augmented") to their current risk models ("base") for 4 specialties-cardiology, neurology, oncology, and pulmonology-on estimates of hospital performance are reported here. In the augmented models, many acute conditions were associated with substantial risks of mortality. Compared to the base models, the discrimination and calibration of the augmented models for all specialties were improved. While estimated hospital performance was highly correlated between the 2 models, the inclusion of acute conditions in risk-adjustment models meaningfully improved the predictive ability of those models and had noticeable effects on hospital performance estimates. Measures or conditions that address disease severity should always be included when risk-adjusting hospitalization outcomes, especially if the goal is provider profiling.

Drug development for major chronic health conditions-aligning with growing public health needs: Proceedings from a multistakeholder think tank.

The global pharmaceutical industry portfolio is skewed towards cancer and rare diseases due to more predictable development pathways and financial incentives. In contrast, drug development for major chronic health conditions that are responsible for a large part of mortality and disability worldwide is stalled. To examine the processes of novel drug development for common chronic health conditions, a multistakeholder Think Tank meeting, including thought leaders from academia, clinical practice, non-profit healthcare organizations, the pharmaceutical industry, the Food and Drug Administration (FDA), payors as well as investors, was convened in July 2022. Herein, we summarize the proceedings of this meeting, including an overview of the current state of drug development for chronic health conditions and key barriers that were identified. Six major action items were formulated to accelerate drug development for chronic diseases, with a focus on improving the efficiency of clinical trials and rapid implementation of evidence into clinical practice.

"Click and mortar" opportunities for digitization and consumerism in trials.

BACKGROUND: Digitization (using novel digital tools and strategies) and consumerism (taking a consumer-oriented approach) are increasingly commonplace in clinical trials, but the implications of these changes are not well described. METHODS: We assembled a group of trial experts from academia, industry, non-profit, and government to discuss implications of this changing trial landscape and provide guidance. RESULTS: Digitization and consumerism can increase the volume and diversity of trial participants and expedite recruitment. However, downstream bottlenecks, challenges with retention, and serious issues with equity, ethics, and security can result. A "click and mortar" approach, combining approaches from novel and traditional trials with the thoughtful use of technology, may optimally balance opportunities and challenges facing many trials. CONCLUSION: We offer expert guidance and three "click and mortar" approaches to digital, consumer-oriented trials. More guidance and research are needed to navigate the associated opportunities and challenges.

Lessons From COVID-19 for Pandemic Preparedness: Proceedings From a Multistakeholder Think Tank.

While the coronavirus disease 2019 (COVID-19) pandemic continues to present global challenges, sufficient time has passed to reflect on lessons learned and use those insights to inform policy and approaches to prepare for the next pandemic. In May 2022, the Duke Clinical Research Institute convened a think tank with thought leaders from academia, clinical practice, the pharmaceutical industry, patient advocacy, the National Institutes of Health, the US Food and Drug Administration, and the Centers for Disease Control and Prevention to share, firsthand, expert knowledge of the insights gained from the COVID-19 pandemic and how this acquired knowledge can help inform the next pandemic response. The think tank focused on pandemic preparedness, therapeutics, vaccines, and challenges related to clinical trial design and scale-up during the early phase of a pandemic. Based on the multi-faceted discussions, we outline 10 key steps to an improved and equitable pandemic response.

Regulatory and HTA Considerations for Development of Real-World Data Derived External Controls.

Regulators and Health Technology Assessment (HTA) bodies are increasingly familiar with, and publishing guidance on, external controls derived from real-world data (RWD) to generate real-world evidence (RWE). We recently conducted a systematic literature review (SLR) evaluating publicly available information on the use of RWD-derived external controls to contextualize outcomes from uncontrolled trials submitted to the European Medicines Agency (EMA), the US Food and Drug Administration (FDA), and/or select HTA bodies. The review identified several key operational and methodological aspects for which more detailed guidance and alignment within and between regulatory agencies and HTA bodies is necessary. This paper builds on the SLR findings by delineating a set of key takeaways for the responsible generation of fit-for-purpose RWE. Practical methodological and operational guidelines for designing, conducting, and reporting RWD-derived external control studies are explored and discussed. These considerations include: (i) early engagement with regulators and HTA bodies during the study planning phase; (ii) consideration of the appropriateness and comparability of external controls across multiple dimensions, including eligibility criteria, temporality, population representation, and clinical evaluation; (iii) ensuring adequate sample sizes, including hypothesis testing considerations; (iv) implementation of a clear and transparent strategy for assessing and addressing data quality, including data missingness across trials and RWD; (v) selection of comparable and meaningful endpoints that are operationalized and analyzed using appropriate analytic methods; and (vi) conduct of sensitivity analyses to assess the robustness of findings in the context of uncertainty and sources of potential bias.

Effectively Leveraging RWD for External Controls: A Systematic Literature Review of Regulatory and HTA Decisions.

Real-world data (RWD)-derived external controls can be used to contextualize efficacy findings for investigational therapies evaluated in uncontrolled trials. As the number of submissions to regulatory and health technology assessment (HTA) bodies using external controls rises, and in light of recent regulatory and HTA guidance on the appropriate use of RWD, there is a need to address the operational and methodological challenges impeding the quality of real-world evidence (RWE) generation and the consistency in evaluation of RWE across agencies. This systematic review summarizes publicly available information on the use of external controls to contextualize outcomes from uncontrolled trials for all indications from January 1, 2015, through August 20, 2021, that were submitted to the European Medicines Agency, the US Food and Drug Administration, and/or select major HTA bodies (National Institute for Health and Care Excellence (NICE), Haute Autorité de Santé (HAS), Institut für Qualität und Wirtschaftlichkeit im Gesundheitswesen (IQWiG), and Gemeinsamer Bundesausschuss (G-BA)). By systematically reviewing submissions to regulatory and HTA bodies in the context of recent guidance, this study provides quantitative and qualitative insights into how external control design and analytic choices may be viewed by different agencies in practice. The primary operational and methodological aspects identified for discussion include, but are not limited to, engagement of regulators and HTA bodies, approaches to handling missing data (a component of data quality), and selection of real-world endpoints. Continued collaboration and guidance to address these and other aspects will inform and assist stakeholders attempting to generate evidence using external controls.

Needles in a Haystack: Finding Qualitative and Quantitative Collaborators in Academic Medical Centers.

Translational research is a data-driven process that involves transforming scientific laboratory- and clinic-based discoveries into products and activities with real-world impact to improve individual and population health. Successful execution of translational research requires collaboration between clinical and translational science researchers, who have expertise in a wide variety of domains across the field of medicine, and qualitative and quantitative scientists, who have specialized methodologic expertise across diverse methodologic domains. While many institutions are working to build networks of these specialists, a formalized process is needed to help researchers navigate the network to find the best match and to track the navigation process to evaluate an institution's unmet collaborative needs. In 2018, a novel analytic resource navigation process was developed at Duke University to connect potential collaborators, leverage resources, and foster a community of researchers and scientists. This analytic resource navigation process can be readily adopted by other academic medical centers. The process relies on navigators with broad qualitative and quantitative methodologic knowledge, strong communication and leadership skills, and extensive collaborative experience. The essential elements of the analytic resource navigation process are as follows: (1) strong institutional knowledge of methodologic expertise and access to analytic resources, (2) deep understanding of research needs and methodologic expertise, (3) education of researchers on the role of qualitative and quantitative scientists in the research project, and (4) ongoing evaluation of the analytic resource navigation process to inform improvements. Navigators help researchers determine the type of expertise needed, search the institution to find potential collaborators with that expertise, and document the process to evaluate unmet needs. Although the navigation process can create a basis for an effective solution, some challenges remain, such as having resources to train navigators, comprehensively identifying all potential collaborators, and keeping updated information about resources as methodologists join and leave the institution.

Concordance Between Patient-Reported Health Data and Electronic Health Data in the ADAPTABLE Trial.

Importance: Patient-reported health data can facilitate clinical event capture in pragmatic clinical trials. However, few data are available on the fitness for use of patient-reported data in large-scale health research. Objective: To evaluate the concordance of a set of variables reported by patients and available in the electronic health record as part of a pragmatic clinical trial. Design, Setting, and Participants: Data from ADAPTABLE (Aspirin Dosing: A Patient-Centric Trial Assessing Benefits and Long-term Effectiveness), a pragmatic clinical trial, were used in a concordance substudy of a comparative effectiveness research trial. The trial randomized 15 076 patients with existing atherosclerotic cardiovascular disease in a 1:1 ratio to low- or high-dose aspirin from April 2016 through June 30, 2019. Main Outcomes and Measures: Concordance of data was evaluated from 4 domains (demographic characteristics, encounters, diagnoses, and procedures) present in 2 data sources: patient-reported data captured through an online portal and data from electronic sources (electronic health record data). Overall agreement, sensitivity, specificity, positive predictive value, negative predictive value, and κ statistics with 95% CIs were calculated using patient report as the criterion standard for demographic characteristics and the electronic health record as the criterion standard for clinical outcomes. Results: Of 15 076 patients with complete information, the median age was 67.6 years (range, 21-99 years), and 68.7% were male. With the use of patient-reported data as the criterion standard, agreement (κ) was high for Black and White race and ethnicity but only moderate for current smoking status. Electronic health record data were highly specific (99.6%) but less sensitive (82.5%) for Hispanic ethnicity. Compared with electronic health record data, patient report of clinical end points had low sensitivity for myocardial infarction (33.0%), stroke (34.2%), and major bleeding (36.6%). Positive predictive value was similarly low for myocardial infarction (40.7%), stroke (38.8%), and major bleeding (21.9%). Coronary revascularization was the most concordant event by data source, with only moderate agreement (κ = 0.54) and positive predictive value. Agreement metrics varied by site for all demographic characteristics and several clinical events. Conclusions and Relevance: In a concordance substudy of a large, pragmatic comparative effectiveness research trial, sensitivity and chance-corrected agreement of patient-reported data captured through an online portal for cardiovascular events were low to moderate. Findings suggest that additional work is needed to optimize integration of patient-reported health data into pragmatic research studies. Trial Registration: ClinicalTrials.gov Identifier: NCT02697916.

Fitness of real-world data for clinical trial data collection: Results and lessons from a HARMONY Outcomes ancillary study.

BACKGROUND: Despite the extensive use of real-world data for retrospective, observational clinical research, our understanding of how real-world data might increase the efficiency of data collection in patient-level randomized clinical trials is largely unknown. The structure of real-world data is inherently heterogeneous, with each source electronic health record and claims database different from the next. Their fitness-for-use as data sources for multisite trials in the United States has not been established. METHODS: For a subset of participants in the HARMONY Outcomes Trial, we obtained electronic health record data from recruiting sites or Medicare claims data from the Centers for Medicare & Medicaid Services. For baseline characteristics and follow-up events, we assessed the level of agreement between these real-world data and data documented in the trial database. RESULTS: Real-world data-derived demographic information tended to agree with trial-reported demographic information, although real-world data were less accurate in identifying medical history. The ability of real-world data to identify baseline medication usage differed by real-world data source, with claims data demonstrating substantially better performance than electronic health record data. The limited number of lab results in the collected electronic health record data matched closely with values in the trial database. There were not enough follow-up events in the ancillary study population to draw meaningful conclusions about the performance of real-world data for identification of events. Based on the conduct of this ancillary study, the challenges and opportunities of using real-world data within clinical trials are discussed. CONCLUSION: Based on a subset of participants from the HARMONY Outcomes Trial, our results suggest that electronic health record or claims data, as currently available, are unlikely to be a complete substitute for trial data collection of medical history or baseline lab results, but that Medicare claims were able to identify most medications. The limited size of the study population prevents us from drawing strong conclusions based on these results, and other studies are clearly needed to confirm or refute these findings.

The impact of COVID-19 on pragmatic clinical trials: lessons learned from the NIH Health Care Systems Research Collaboratory.

BACKGROUND: The COVID-19 pandemic has considerably disrupted nearly all aspects of daily life, including healthcare delivery and clinical research. Because pragmatic clinical trials are often embedded within healthcare delivery systems, they may be at high risk of disruption due to the dual impacts on the conduct of both care and research. METHODS: We collected qualitative data using multiple methods to characterize the impact of COVID-19 on the research activities of 14 active pragmatic clinical trials in the National Institutes of Health (NIH) Health Care Systems Research Collaboratory. A COVID-19 impact questionnaire was administered electronically to principal investigators in June 2020. Text responses were analyzed thematically, and qualitative summaries were subsequently reviewed by five independent reviewers, who made iterative revisions. Additional COVID-19-related impacts were identified during virtual meetings with trial teams during April-July 2020 and combined with questionnaire responses for analysis. RESULTS: Impacts of the pandemic were broadly classified into two main types: healthcare operations and social distancing. In some instances, trial delays created statistical challenges, particularly with trials using stepped-wedge designs, and necessitated changing data collection strategies or modifying interventions. The majority of projects used existing stakeholder-driven approaches to adapt interventions. Several benefits of these adaptions were identified, including expanded outreach capabilities and ability to study virtual intervention delivery. All trial teams were able to adapt to pandemic-related modifications. CONCLUSION: In a group of 14 ongoing pragmatic clinical trials, there was significant impact of COVID-19 on trial activities. Engaging appropriate stakeholders was critical to designing and implementing trial modifications and making continued safe progress toward meeting research objectives.

Inclusion and diversity in clinical trials: Actionable steps to drive lasting change.

BACKGROUND: Improving diversity in clinical trials is essential in order to produce generalizable results. Although the importance of representation has become increasingly recognized, identifying strategies to approach this work remains elusive. This article reviews the proceedings of a multi-stakeholder conference about the current state of diversity in clinical trials and outlines actionable steps for improvement. METHODS: Conference attendees included representatives from the United States Food and Drug Administration (FDA), National Institutes of Health (NIH), practicing clinical investigators, pharmaceutical and device companies, community-based organizations, data analytics companies, and patient advocacy groups. At this virtual event, attendees were asked to consider key questions around best practices for engagement of underrepresented populations. RESULTS: Community engagement is an integral part of recruitment and retention of underrepresented groups. Decentralization of sites and use of digital tools can enhance the accessibility of clinical research. Finally, improving representation among investigators and clinical research staff may translate to diverse clinical trial participants. CONCLUSION: Improving diversity in clinical trials is an ethical and scientific imperative, which requires a multifaceted approach.

Evaluating fitness-for-use of electronic health records in pragmatic clinical trials: reported practices and recommendations.

OBJECTIVE: To empirically explore how pragmatic clinical trials (PCTs) that used real-world data (RWD) assessed study-specific fitness-for-use. METHODS: We conducted interviews and surveys with PCT teams who used electronic health record (EHR) data to ascertain endpoints. The survey cataloged key concerns about RWD, activities used to assess data fitness-for-use, and related barriers encountered by study teams. Patterns and commonalities across trials were used to develop recommendations for study-specific fitness-for-use assessments. RESULTS: Of 15 invited trial teams, 7 interviews were conducted. Of 31 invited trials, 15 responded to the survey. Most respondents had prior experience using RWD (93%). Major concerns about EHR data were data reliability, missingness or incompleteness of EHR elements, variation in data quality across study sites, and presence of implausible or incorrect values. Although many PCTs conducted fitness-for-use activities (eg, data quality assessments, 11/14, 79%), less than a quarter did so before choosing a data source. Fitness-for-use activities, findings, and resulting study design changes were not often publically documented. Overall costs and personnel costs were barriers to fitness-for-use assessments. DISCUSSION: These results support three recommendations for PCTs that use EHR data for endpoint ascertainment. Trials should detail the rationale and plan for study-specific fitness-for-use activities, conduct study-specific fitness-for-use assessments early in the prestudy phase to inform study design changes before the trial begins, and share results of fitness-for-use assessments and description of relevant challenges and facilitators. CONCLUSION: These recommendations can help researchers and end-users of real-world evidence improve characterization of RWD reliability and relevance in the PCT-specific context.

Clinician Specialty, Access to Care, and Outcomes Among Patients with Peripheral Artery Disease.

BACKGROUND: Understanding the relationship between patterns of peripheral artery disease and outcomes is an essential step toward improving care and outcomes. We hypothesized that clinician specialty would be associated with occurrence of major adverse vascular events (MAVE). METHODS: Patients with at least 1 peripheral artery disease-related encounter in our health system and fee-for-service Medicare were divided into groups based on the specialty of the clinician (ie, cardiologist, surgeon, podiatrist, primary care, or other) providing a plurality of peripheral artery disease-coded care in the year prior to index encounter. The primary outcome was MAVE (a composite of all-cause mortality, myocardial infarction, stroke, lower extremity revascularization, and lower extremity amputation). RESULTS: The cohort included 1768 patients, of whom 30.0% were Black, 23.9% were Medicaid dual-enrollment eligible, and 31.1% lived in rural areas. Patients receiving a plurality of their care from podiatrists had the highest 1-year rates of MAVE (34.4%, P <.001), hospitalization (65.9%, P <.001), and amputations (22.6%, P <.001). Clinician specialty was not associated with outcomes after adjustment. Patients who were Medicaid dual-eligible had higher adjusted risks of mortality (adjusted hazard ratio [HRadj] 1.54, 95% confidence interval [CI] 1.11-2.14) and all-cause hospitalization (HRadj 1.20, 95% CI 1.03-1.40) and patients who were Black had a higher adjusted risk of amputation (HRadj 1.49, 95% CI 1.03-2.15). CONCLUSIONS: Clinician specialty was not associated with worse outcomes after adjustment, but certain socioeconomic factors were. The effects of clinician specialty and socioeconomic status were likely attenuated by the fact that all patients in this study had health insurance; these analyses require confirmation in a more representative cohort.

Broadening the reach of the FDA Sentinel system: A roadmap for integrating electronic health record data in a causal analysis framework.

The Sentinel System is a major component of the United States Food and Drug Administration's (FDA) approach to active medical product safety surveillance. While Sentinel has historically relied on large quantities of health insurance claims data, leveraging longitudinal electronic health records (EHRs) that contain more detailed clinical information, as structured and unstructured features, may address some of the current gaps in capabilities. We identify key challenges when using EHR data to investigate medical product safety in a scalable and accelerated way, outline potential solutions, and describe the Sentinel Innovation Center's initiatives to put solutions into practice by expanding and strengthening the existing system with a query-ready, large-scale data infrastructure of linked EHR and claims data. We describe our initiatives in four strategic priority areas: (1) data infrastructure, (2) feature engineering, (3) causal inference, and (4) detection analytics, with the goal of incorporating emerging data science innovations to maximize the utility of EHR data for medical product safety surveillance.

Psychosocial determinants of cardiovascular events among black Americans with chronic kidney disease or associated risk factors in the Jackson heart study.

BACKGROUND: Individuals with chronic kidney disease (CKD), hypertension (HTN), or diabetes mellitus (DM) are at increased risk for cardiovascular disease (CVD). The extent to which psychosocial factors are associated with increased CVD risk within these individuals is unclear. Black individuals experience a high degree of psychosocial stressors due to socioeconomic factors, environment, racism, and discrimination. We examined the association between psychosocial factors and risk of CVD events among Black men and women with CKD and CKD risk factors in the Jackson Heart Study. METHODS AND RESULTS: We identified 1919 participants with prevalent CKD or CKD risk factors at baseline. We used rotated principal component analysis - a form of unsupervised machine learning that may identify constructs not intuitively identified by a person - to describe five groups of psychosocial components (including negative moods, religiosity, discrimination, negative outlooks, and negative coping resources) based on a battery of questionnaires. Multiple imputation by chained equation (MICE) was used to impute missing covariate data. Cox models were used to quantify the association between psychosocial components and incident CVD, defined as a fatal coronary heart disease event, myocardial infarction, cardiac procedure (angiography or revascularization procedure), or stroke. Of the 929 participants in the analysis, 67% were female, 28% were current/former smokers with mean age of 56 years and mean BMI of 33 kg/m2. Over a median follow-up of 8 years, 6% had an incident CVD event. In multivariable models, each standard deviation (SD) increase in the religiosity component was associated with an increased hazard for CVD event (hazard ratio [HR] = 1.52, 95% CI: 1.09-2.13). CONCLUSIONS: Religiosity was associated with CVD among participants with prevalent CKD or CKD risk factors. Studies to better understand the mechanisms of this relationship are needed.

Clinical Effectiveness of Sacubitril/Valsartan Among Patients Hospitalized for Heart Failure With Reduced Ejection Fraction.

Background Sacubitril/Valsartan has been highly efficacious in randomized trials of heart failure with reduced ejection fraction (HFrEF). However, the effectiveness of sacubitril/valsartan in older patients hospitalized for HFrEF in real-world US practice is unclear. Methods and Results This study included Medicare beneficiaries age ≥65 years who were hospitalized for HFrEF ≤40% in the Get With The Guidelines-Heart Failure registry between October 2015 and December 2018, and eligible for sacubitril/valsartan. Associations between discharge prescription of sacubitril/valsartan and clinical outcomes were assessed after inverse probability of treatment weighting and adjustment for other HFrEF medications. Overall, 1551 (10.9%) patients were discharged on sacubitril/valsartan. Of those not prescribed sacubitril/valsartan, 7857 (62.0%) were prescribed an angiotensin-converting enzyme inhibitor/angiotensin II receptor blocker. Over 12-month follow-up, compared with a discharge prescription of angiotensin-converting enzyme inhibitor/angiotensin II receptor blocker, sacubitril/valsartan was independently associated with lower all-cause mortality (adjusted hazard ratio [HR], 0.82; 95% CI, 0.72-0.94; P=0.004) but not all-cause hospitalization (adjusted HR, 0.97; 95% CI, 0.89-1.07; P=0.55) or heart failure hospitalization (adjusted HR, 1.04; 95% CI, 0.91-1.18; P=0.59). Patients prescribed sacubitril/valsartan versus those without a prescription had lower risk of all-cause mortality (adjusted HR, 0.69; 95% CI, 0.60-0.79; P<0.001), all-cause hospitalization (adjusted HR, 0.90; 95% CI, 0.82-0.98; P=0.02), but not heart failure hospitalization (adjusted HR, 0.94; 95% CI, 0.82-1.08; P=0.40). Conclusions Among patients hospitalized for HFrEF, prescription of sacubitril/valsartan at discharge was independently associated with reduced postdischarge mortality compared with angiotensin-converting enzyme inhibitor/angiotensin II receptor blocker, and reduced mortality and all-cause hospitalization compared with no sacubitril/valsartan. These findings support the use of sacubitril/valsartan to improve postdischarge outcomes among older patients hospitalized for HFrEF in routine US clinical practice.

The role of machine learning in clinical research: transforming the future of evidence generation.

BACKGROUND: Interest in the application of machine learning (ML) to the design, conduct, and analysis of clinical trials has grown, but the evidence base for such applications has not been surveyed. This manuscript reviews the proceedings of a multi-stakeholder conference to discuss the current and future state of ML for clinical research. Key areas of clinical trial methodology in which ML holds particular promise and priority areas for further investigation are presented alongside a narrative review of evidence supporting the use of ML across the clinical trial spectrum. RESULTS: Conference attendees included stakeholders, such as biomedical and ML researchers, representatives from the US Food and Drug Administration (FDA), artificial intelligence technology and data analytics companies, non-profit organizations, patient advocacy groups, and pharmaceutical companies. ML contributions to clinical research were highlighted in the pre-trial phase, cohort selection and participant management, and data collection and analysis. A particular focus was paid to the operational and philosophical barriers to ML in clinical research. Peer-reviewed evidence was noted to be lacking in several areas. CONCLUSIONS: ML holds great promise for improving the efficiency and quality of clinical research, but substantial barriers remain, the surmounting of which will require addressing significant gaps in evidence.

The use of electronic health records for recruitment in clinical trials: a mixed methods analysis of the Harmony Outcomes Electronic Health Record Ancillary Study.

BACKGROUND: The electronic health record (EHR) contains a wealth of clinical data that may be used to streamline the identification of potential clinical trial participants. However, there is little empirical information on site-level facilitators of and barriers to optimal use of EHR systems with respect to trial recruitment. METHODS: We conducted qualitative focus groups and quantitative surveys as part of the EHR Ancillary Study, which is being conducted alongside the multicenter, global, Harmony Outcomes Trial comparing albiglutide to standard care for the prevention of cardiovascular events in type 2 diabetes. Subject matter experts used findings from focus groups to draft a 20-question survey examining the use of the EHR for participant identification, common site recruitment strategies, and variation in perceived barriers to optimal use of the EHR. The final survey was fielded with 446 site investigators actively enrolling participants in the main trial. RESULTS: Nearly two-thirds of respondents were study coordinators (63.2%), 23.1% were principal investigators, and 13.7% held other research roles. Approximately half of the respondents reported using the EHR to find potential trial participants. Of these, 79.4% reported using EHR searches in conjunction with other recruitment methods, including reviewing of upcoming clinic schedules (75.3%) and contacting past trial participants (71.2%). Important barriers to optimal use of the EHR included the lack of availability of certain research-focused EHR modules and limitations on the ability to contact patients cared for by other providers. Of survey respondents who did not use the EHR to find potential participants, one-quarter reported that the EHR was not accessible in their country; this finding varied from 2.6% of respondents in North America to 50% of respondents in the Asia Pacific. CONCLUSIONS: While EHR screening was commonly used for recruitment in a cardiovascular outcomes trial, important technical, governance, and regulatory barriers persist. Multifaceted, scalable, and customizable strategies are needed to support the optimal use of the EHR for trial participant identification. TRIAL REGISTRATION: ClinicalTrials.gov NCT02465515. Registered on 8 June 2015.

Life expectancy and voting patterns in the 2020 U.S. presidential election.

INTRODUCTION: In the 2016 U.S. Presidential election, voters in communities with recent stagnation or decline in life expectancy were more likely to vote for the Republican candidate than in prior Presidential elections. We aimed to assess the association between change in life expectancy and voting patterns in the 2020 Presidential election. METHODS: With data on county-level life expectancy from the Institute for Health Metrics and Evaluation and voting data from a GitHub repository of results scraped from news outlets, we used weighted multivariable linear regression to estimate the association between the change in life expectancy from 1980 to 2014 and the proportion of votes for the Republican candidate and change in the proportion of votes cast for the Republican candidate in the 2020 Presidential election. RESULTS: Among 3110 U.S counties and Washington, D.C., change in life expectancy at the county level was negatively associated with Republican share of the vote in the 2020 Presidential election (parameter estimate -7.2, 95% confidence interval, -7.8 to -6.6). With the inclusion of state, sociodemographic, and economic variables in the model, the association was attenuated (parameter estimate -0.8; 95% CI, -1.5 to -0.2). County-level change in life expectancy was positively associated with change in Republican vote share 0.29 percentage points (95% CI, 0.23 to 0.36). The association was attenuated when state, sociodemographic, and economic variables were added (parameter estimate 0.24; 95% CI, 0.15 to 0.33). CONCLUSION: Counties with a less positive trajectory in life expectancy were more likely to vote for the Republican candidate in the 2020 U.S. Presidential election, but the Republican candidate's share improved in some counties that experienced marked gains in life expectancy. Associations were moderated by demographic, social and economic factors.