Development of a Novel Clinical Risk Score for COVID-19 Infections.

OBJECTIVE: The ongoing emergence of novel severe acute respiratory syndrome coronavirus 2 strains such as the Omicron variant amplifies the need for precision in predicting severe COVID-19 outcomes. This study presents a machine learning model, tailored to the evolving COVID-19 landscape, emphasizing novel risk factors and refining the definition of severe outcomes to predict the risk of a patient experiencing severe disease more accurately. METHODS: Utilizing electronic health records from the Healthjump database, this retrospective study examined over 1 million US COVID-19 diagnoses from March 2020 to September 2022. Our model predicts severe outcomes, including acute respiratory failure, intensive care unit admission, or ventilator use, circumventing biases associated with hospitalization, which exhibited ∼4× geographical variance of the new outcome. RESULTS: The model exceeded similar predictors with an area under the curve of 0.83 without lab data to predict patient risk. It identifies new risk factors, including acute care history, health care encounters, and distinct medication use. An increase in severe outcomes, typically 2-3× higher than subsequent months, was observed at the onset of each new strain era, followed by a plateau phase, but the risk factors remain consistent across strain eras. CONCLUSION: We offer an improved machine learning model and risk score for predicting severe outcomes during changing COVID-19 strain eras. By emphasizing a more clinically precise definition of severe outcomes, the study provides insights for resource allocation and intervention strategies, aiming to better patient outcomes and reduce health care strain. The necessity for regular model updates is highlighted to maintain relevance amidst the rapidly evolving COVID-19 epidemic.

A framework for setting enrollment goals to ensure participant diversity in sponsored clinical trials in the United States.

BACKGROUND: Diversity in clinical trials (CTs) has the potential to improve health equity and close health disparities. Underrepresentation of historically underserved groups compromises the generalizability of trial findings to the target population, hinders innovation, and contributes to low accrual. The aim of this study was to establish a transparent and reproducible process for setting trial diversity enrollment goals informed by the disease epidemiology. METHOD: An advisory board of epidemiologists with expertise in health disparities, equity, diversity, and social determinants of health was convened to evaluate and strengthen the initial goal-setting framework. Data sources used were the epidemiologic literature, US Census, and real-world data (RWD); limitations were considered and addressed where appropriate. A framework was designed to safeguard against the underrepresentation of historically medically underserved groups. A stepwise approach was created with Y/N decisions based on empirical data. RESULTS: We compared race and ethnicity distributions in the RWD of six diseases from Pfizer's portfolio chosen to represent different therapeutic areas (multiple myeloma, fungal infections, Crohn's disease, Gaucher disease, COVID-19, and Lyme disease) to the distributions in the US Census and established trial enrollment goals. Enrollment goals for potential CTs were based on RWD for multiple myeloma, Gaucher disease, and COVID-19; enrollment goals were based on the Census for fungal infections, Crohn's disease, and Lyme disease. CONCLUSIONS: We developed a transparent and reproducible framework for setting CT diversity enrollment goals. We note how limitations due to data sources can be mitigated and consider several ethical decisions in setting equitable enrollment goals.

Disparities in job characteristics by race and sex in a Southern aluminum smelting facility.

BACKGROUND: Former workers at a Southern aluminum smelting facility raised concerns that the most hazardous jobs were assigned to Black workers, but the role of workplace segregation had not been quantified or examined in the company town. Prior studies discuss race and gender disparities in working conditions, but few have documented them in the aluminum industry. METHODS: We obtained workers' company records for 1985-2007 and characterized four job metrics: prestige (sociologic rankings), worker-defined danger (worker assessments), annual wage (1985 dollars), and estimated total particulate matter (TPM) exposure (job exposure matrix). Characteristics of job at hire and trajectories were compared by race and sex using linear binomial models. RESULTS: Non-White males had the highest percentage of workers in low prestige and high danger jobs at hire and up to 20 years after. After 20 years tenure, 100% of White workers were in higher prestige and lower danger jobs. Most female workers, regardless of race, entered and remained in low-wage jobs, while 50% of all male workers maintained their initial higher-wage jobs. Non-White females had the highest prevalence of workers in low-wage jobs at hire and after 20 years-increasing from 63% (95% CI: 59-67) to 100% (95% CI: 78-100). All female workers were less likely to be in high TPM exposure jobs. Non-White males were most likely to be hired into high TPM exposure jobs, and this exposure prevalence increased as time accrued, while staying constant for other race-sex groups. CONCLUSIONS: There is evidence of job segregation by race and sex in this cohort of aluminum smelting workers. Documentation of disparities in occupational hazards is important for informing health interventions and research.

Distribution of working hour characteristics by race, age, gender, and shift schedule among U.S. manufacturing workers.

Shift work is a common occupational exposure, however, few studies have examined aspects of shift work beyond night work and long hours, such as rotational patterns or weekend work, which may contribute to poor health through disruption of the body's circadian rhythms. In this manuscript, we calculated the prevalence of working hour characteristics using algorithms for type (e.g., day), duration, intensity, rotational direction, and social aspects (e.g., weekend work) in a nationwide cohort of American manufacturing workers (N = 23,044) between 2003 and 2014. Distributions of working hour characteristics were examined by schedules (e.g., permanent day, day/night) and demographics, and were cross-classified in a matrix to examine co-occurrence. Approximately 55% of shifts may cause circadian rhythm disruption as they were non-day shifts or day shifts with a quick return or rotation, or were 13 h or longer. Older workers, female workers, and White workers worked permanent day shifts most often, while workers of color worked more day/night schedules. Night and evening shifts had more frequent shift rotations, quick returns, and longer hours than day shifts. Yet, day shifts, which are presumed to have little negative circadian impact, may cause circadian rhythm disruption as long hours, quick returns and rotations also occurred within day shifts.

Defining Essential Surgery in the US During the COVID-19 Pandemic Response.

This cohort study compares the volume of performed surgical procedures classified as essential, urgent, and nonurgent before and after elective surgeries were restricted during the COVID-19 pandemic in the US.

Risk of primary haematologic cancers following incident non-metastatic breast cancer: A Danish population-based cohort study.

BACKGROUND: Breast cancer survivors may have increased risk of subsequent haematologic cancer. We compared their risk of haematologic cancers with the general population during 38 years of follow-up. METHODS: Using population-based Danish medical registries, we assembled a nationwide cohort of women diagnosed with incident non-metastatic breast cancer during 1980-2017, with follow-up through 2018. We compared breast cancer survivors with the general population by computing standardised incidence ratios (SIR) and 95% confidence intervals (CI). RESULTS: Among 101,117 breast cancer survivors, we observed 815 incident haematologic cancers (median follow-up: 7.9 years). We observed excess risk of acute myeloid leukaemia (AML) (SIR: 1.65, 95%CI: 1.33-2.01), particularly in women who received chemotherapy (SIR: 3.33, 95%CI: 2.24-4.75) and premenopausal women (SIR: 3.23, 95%CI: 2.41-4.25). The risk of acute lymphoid leukaemia (ALL) was increased (SIR: 2.25, 95%CI: 1.29-3.66), whereas the risk of chronic lymphoid leukaemia (CLL) was decreased (SIR: 0.66, 95%CI: 0.53-0.82). An additional analysis showed elevated risk of CLL 0-6 months after breast cancer diagnosis (SIR: 3.00 95%CI: 1.75-4.80). CONCLUSION: Compared to the general population, breast cancer survivors had elevated risk of AML, particularly when treated with chemotherapy. The risk of ALL was elevated, whereas the risk of CLL was lower. The higher risk of CLL in the first six months after diagnosis likely reflects surveillance bias-due to intensified diagnostic efforts at breast cancer diagnosis and treatment-prompting earlier detection. This has likely reduced the long-term risk of CLL in breast cancer survivors.

Race/ethnicity reporting and representation in US clinical trials: a cohort study.

Background: Systemic progress in improving trial representation is uncertain, and previous analyses of minority trial participation have been limited to small cohorts with limited exploration of driving factors. Methods: We analyzed detailed trial records from all US clinical trials registered in ClinicalTrials.gov from March 2000 to March 2020. Minority enrollment was compared to 2010 US Census demographic estimates using Wilcoxon test. We utilized logistic regression and generalized linear regression with a logit link to assess the association of possible drivers (including trials' funding source, size, phase, and design) with trials' disclosure of and amount of minority enrollment respectively. Findings: Among 20,692 US-based trials with reported results (representing ~4·76 million enrollees), only 43% (8,871/20,692) reported any race/ethnicity data. The majority of enrollees were White (median 79·7%; interquartile range [IQR] 61·9-90·0%), followed by Black (10·0%; IQR 2·5-23·5%), Hispanic/Latino (6·0%; IQR 0·43-15·4%), Asian (1·0%; IQR 0·0-4·1%), and American Indian (0·0%; IQR 0·0-0·2%). Median combined enrollment of minority race/ethnicity groups (Black, Hispanic/Latino, Asian, American Indian, Other/Multi) was below census estimates (27·6%) (p<0·001) however increased at an annual rate of 1·7%. Industry and Academic funding were negatively associated with race/ethnicity reporting (Industry adjusted odds ratio [aOR]: 0·42, 95% confidence interval [CI]: 0·38 to 0·46, p<0.0001; Academic aOR: 0·45, CI: 0·41 to 0·50, p<0.0001). Industry also had a negative association with the proportion of minority ethnicity enrollees (aOR: 0·69, CI: 0·60 to 0·79) compared to US Government-funded trials. Interpretation: Over the past two decades, the majority of US trials in ClinicalTrials.gov do not report race/ethnicity enrollment data, and minorities are underrepresented in trials with modest improvement over time. Funding: Stanford Medical Scholars Research Funding, the National Heart, Lung, and Blood Institute, NIH (1K01HL144607) and the American Heart Association/Robert Wood Johnson Medical Faculty Development Program.

Disease-Specific Health Disparities: A Targeted Review Focusing on Race and Ethnicity.

BACKGROUND: Wide disparities in health status exist in the United States across race and ethnicity, broadly driven by social determinants of health-most notably race and ethnic group differences in income, education, and occupational status. However, disparities in disease frequency or severity remain underappreciated for many individual diseases whose distribution in the population varies. Such information is not readily accessible, nor emphasized in treatment guidelines or reviews used by practitioners. Specifically, a summary on disease-specific evidence of disparities from population-based studies is lacking. Our goal was to summarize the published evidence for specific disease disparities in the United States so that this knowledge becomes more widely available "at the bedside". We hope this summary stimulates health equity research at the disease level so that these disparities can be addressed effectively. METHODS: A targeted literature review of disorders in Pfizer's current pipeline was conducted. The 38 diseases included metabolic disorders, cancers, inflammatory conditions, dermatologic disorders, rare diseases, and infectious targets of vaccines under development. Online searches in Ovid and Google were performed to identify sources focused on differences in disease rates and severity between non-Hispanic Whites and Black/African Americans, and between non-Hispanic Whites and Hispanics. As a model for how this might be accomplished for all disorders, disparities in disease rates and disease severity were scored to make the results of our review most readily accessible. After primary review of each condition by one author, another undertook an independent review. Differences between reviewers were resolved through discussion. RESULTS: For Black/African Americans, 29 of the 38 disorders revealed a robust excess in incidence, prevalence, or severity. After sickle cell anemia, the largest excesses in frequency were identified for multiple myeloma and hidradenitis suppurativa. For Hispanics, there was evidence of disparity in 19 diseases. Most notable were metabolic disorders, including non-alcoholic steatohepatitis (NASH). CONCLUSIONS: This review summarized recent disease-specific evidence of disparities based on race and ethnicity across multiple diseases, to inform clinicians and health equity research. Our findings may be well known to researchers and specialists in their respective fields but may not be common knowledge to health care providers or public health and policy institutions. Our hope is that this effort spurs research into the causes of the many disease disparities that exist in the United States.

Long COVID and Medicine's Two Cultures.

Medicine has separated the two cultures of biological science and social science in research, even though they are intimately connected in the lives of our patients. To understand the cause, progression, and treatment of long COVID , biology and biography, the patient's lived experience, must be studied together.

Population health science as a unifying foundation for translational clinical and public health research.

Separated both in academics and practice since the Rockefeller Foundation effort to "liberate" public health from perceived subservience to clinical medicine a century ago, research in public health and clinical medicine have evolved separately. Today, translational research in population health science offers a means of fostering their convergence, with potentially great benefit to both domains. Although evidence that the two fields need not and should not be entirely distinct in their methods and goals has been accumulating for over a decade, the prodigious efforts of biomedical and social sciences over the past year to address the COVID-19 pandemic has placed this unifying approach to translational research in both fields in a new light. Specifically, the coalescence of clinical and population-level strategies to control disease and novel uses of population-level data and tools in research relating to the pandemic have illuminated a promising future for translational research. We exploit this unique window to re-examine how translational research is conducted and where it may be going. We first discuss the transformation that has transpired in the research firmament over the past two decades and the opportunities these changes afford. Next, we present some of the challenges-technical, cultural, legal, and ethical- that need attention if these opportunities are to be successfully exploited. Finally, we present some recommendations for addressing these challenges.

Cardiovascular outcomes associated with prescription of sodium-glucose co-transporter-2 inhibitors versus dipeptidyl peptidase-4 inhibitors in patients with diabetes and chronic kidney disease.

AIM: To determine the association with cardiovascular (CV) outcomes of sodium-glucose co-transporter-2 (SGLT-2) inhibitors compared with dipeptidyl peptidase-4 (DPP-4) inhibitors in patients with type 2 diabetes (T2D) and chronic kidney disease (CKD). MATERIALS AND METHODS: We conducted a population-based cohort study of new users of SGLT-2 inhibitors and DPP-4 inhibitors with T2D and CKD using data from Optum Clinformatics DataMart. We assembled three cohorts: T2D/no CKD, T2D/CKD 1-2, and T2D/CKD 3a. The study outcomes were (a) time to first heart failure (HF) hospitalization and (b) time to a composite CV endpoint comprised of non-fatal myocardial infarction (MI) or stroke. After inverse probability of treatment weighting, we used proportional hazards regression to estimate hazard ratios (HR) and 95% confidence intervals (CI). RESULTS: New users of SGLT-2 inhibitors versus DPP-4 inhibitors had lower risks of HF hospitalization in the T2D/no CKD (HR, 0.76; 95% CI, 0.70, 0.82) and T2D/CKD 1-2 (HR, 0.63; 95% CI, 0.48, 0.84) cohorts, but no significant association was present in the T2D/CKD 3a cohort. Compared with prescription of DPP-4 inhibitors, SGLT-2 inhibitors were associated with lower risks of non-fatal MI or stroke of 23% (HR, 0.77; 95% CI, 0.70, 0.85) in the T2D/no CKD cohort, but no significant associations were present in the T2D/CKD 1-2 and T2D/CKD 3a cohorts. CONCLUSIONS: Incident prescription of SGLT-2 inhibitors was associated with lower risks of HF hospitalization but not with non-fatal MI or stroke despite suggesting benefit, relative to prescription of DPP-4 inhibitor across different stages of CKD.

Upstream oil and gas production and ambient air pollution in California.

BACKGROUND: Prior studies have found that residential proximity to upstream oil and gas production is associated with increased risk of adverse health outcomes. Emissions of ambient air pollutants from oil and gas wells in the preproduction and production stages have been proposed as conferring risk of adverse health effects, but the extent of air pollutant emissions and resulting nearby pollution concentrations from wells is not clear. OBJECTIVES: We examined the effects of upstream oil and gas preproduction (count of drilling sites) and production (total volume of oil and gas) activities on concentrations of five ambient air pollutants in California. METHODS: We obtained data on approximately 1 million daily observations from 314 monitors in the EPA Air Quality System, 2006-2019, including daily concentrations of five routinely monitored ambient air pollutants: PM2.5, CO, NO2, O3, and VOCs. We obtained data on preproduction and production operations from Enverus and the California Geographic Energy Management Division (CalGEM) for all wells in the state. For each monitor and each day, we assessed exposure to upwind preproduction wells and total oil and gas production volume within 10 km. We used a panel regression approach in the analysis and fit adjusted fixed effects linear regression models for each pollutant, controlling for geographic, seasonal, temporal, and meteorological factors. RESULTS: We observed higher concentrations of PM2.5 and CO at monitors within 3 km of preproduction wells, NO2 at monitors at 1-2 km, and O3 at 2-4 km from the wells. Monitors with proximity to increased production volume observed higher concentrations of PM2.5, NO2, and VOCs within 1 km and higher O3 concentrations at 1-2 km. Results were robust to sensitivity analyses. CONCLUSION: Adjusting for geographic, meteorological, seasonal, and time-trending factors, we observed higher concentrations of ambient air pollutants at air quality monitors in proximity to preproduction wells within 4 km and producing wells within 2 km.

Rethinking Table 1.

Clinical and translational medicine studies of disease risk or treatment response typically include a table 1 comparing groups on age, sex, and race and/or ethnicity. Although customarily treated as biological variables, each denote biography, elements of a person's lived experience. Capturing these biographical features is essential to achieving the ambition of personalized medicine.

Trends in US Surgical Procedures and Health Care System Response to Policies Curtailing Elective Surgical Operations During the COVID-19 Pandemic.

Importance: The COVID-19 pandemic has affected every aspect of medical care, including surgical treatment. It is critical to understand the association of government policies and infection burden with surgical access across the United States. Objective: To describe the change in surgical procedure volume in the US after the government-suggested shutdown and subsequent peak surge in volume of patients with COVID-19. Design, Setting, and Participants: This retrospective cohort study was conducted using administrative claims from a nationwide health care technology clearinghouse. Claims from pediatric and adult patients undergoing surgical procedures in 49 US states within the Change Healthcare network of health care institutions were used. Surgical procedure volume during the 2020 initial COVID-19-related shutdown and subsequent fall and winter infection surge were compared with volume in 2019. Data were analyzed from November 2020 through July 2021. Exposures: 2020 policies to curtail elective surgical procedures and the incidence rate of patients with COVID-19. Main Outcomes and Measures: Incidence rate ratios (IRRs) were estimated from a Poisson regression comparing total procedure counts during the initial shutdown (March 15 to May 2, 2020) and subsequent COVID-19 surge (October 22, 2020-January 31, 2021) with corresponding 2019 dates. Surgical procedures were analyzed by 11 major procedure categories, 25 subcategories, and 12 exemplar operative procedures along a spectrum of elective to emergency indications. Results: A total of 13 108 567 surgical procedures were identified from January 1, 2019, through January 30, 2021, based on 3498 Current Procedural Terminology (CPT) codes. This included 6 651 921 procedures in 2019 (3 516 569 procedures among women [52.9%]; 613 192 procedures among children [9.2%]; and 1 987 397 procedures among patients aged ≥65 years [29.9%]) and 5 973 573 procedures in 2020 (3 156 240 procedures among women [52.8%]; 482 637 procedures among children [8.1%]; and 1 806 074 procedures among patients aged ≥65 years [30.2%]). The total number of procedures during the initial shutdown period and its corresponding period in 2019 (ie, epidemiological weeks 12-18) decreased from 905 444 procedures in 2019 to 458 469 procedures in 2020, for an IRR of 0.52 (95% CI, 0.44 to 0.60; P < .001) with a decrease of 48.0%. There was a decrease in surgical procedure volume across all major categories compared with corresponding weeks in 2019. During the initial shutdown, otolaryngology (ENT) procedures (IRR, 0.30; 95% CI, 0.13 to 0.46; P < .001) and cataract procedures (IRR, 0.11; 95% CI, -0.11 to 0.32; P = .03) decreased the most among major categories. Organ transplants and cesarean deliveries did not differ from the 2019 baseline. After the initial shutdown, during the ensuing COVID-19 surge, surgical procedure volumes rebounded to 2019 levels (IRR, 0.97; 95% CI, 0.95 to 1.00; P = .10) except for ENT procedures (IRR, 0.70; 95% CI, 0.65 to 0.75; P < .001). There was a correlation between state volumes of patients with COVID-19 and surgical procedure volume during the initial shutdown (r = -0.00025; 95% CI, -0.0042 to -0.0009; P = .003), but there was no correlation during the COVID-19 surge (r = -0.00034; 95% CI, -0.0075 to 0.00007; P = .11). Conclusions and Relevance: This study found that the initial shutdown period in March through April 2020, was associated with a decrease in surgical procedure volume to nearly half of baseline rates. After the reopening, the rate of surgical procedures rebounded to 2019 levels, and this trend was maintained throughout the peak burden of patients with COVID-19 in fall and winter; these findings suggest that after initial adaptation, health systems appeared to be able to self-regulate and function at prepandemic capacity.

Characterizing patient flow after an academic hospital merger and acquisition.

OBJECTIVES: Hospital mergers and acquisitions are increasingly used as a strategy to facilitate value-based care. However, no studies have assessed health care utilization (HCU) and patient flow across merged institutions. We aim to evaluate patient population distribution, HCU, and patient flow across a recent hospital merger of an academic medical center (AMC), a primary and specialty care alliance (PSC), and a community-based medical center (CMC). STUDY DESIGN: This was a retrospective observational study. METHODS: The study used 2018 adult demographic and encounter data from electronic health records. Patients' parent health care institution was determined by the most frequently visited site of face-to-face visits. Differences in patient demographics and HCU (ie, emergency department [ED] visits, hospitalizations, primary care visits) were compared. Independent factors associated with utilization were identified using adjusted logistic regression models. RESULTS: A total of 406,303 adult patients were identified in the cohort. The PSC setting, compared with the AMC and the CMC, had significantly more female (62.7% vs 54.4% and 58.5%, respectively), older (mean [SD] age, 52.0 [18.1] vs 51.1 [17.8] and 49.2 [17.8] years), and privately insured (63.6% vs 51.3% and 56.0%) patients. A higher proportion of patients at the CMC (27.5%) visited the ED compared with patients at the AMC (10.8%). Approximately 1645 primary care patients (7%) at the CMC setting went to the AMC for specialized care such as oncology, surgery, and neurology. CONCLUSIONS: Hospital mergers are increasing across the United States, allowing AMCs to expand their reach. These findings suggest that patients mainly sought care at their parent health care institution, yet appropriately received specialized care at the AMC. These results provide insights for future mergers and guide resource allocation and opportunities for improving care delivery.

Biosocial medicine: Biology, biography, and the tailored care of the patient.

Biosocial Medicine, with its emphasis on the full integration of the person's biology and biography, proposes a strategy for clinical research and the practice of medicine that is transformative for the care of individual patients. In this paper, we argue that Biology is one component of what makes a person unique, but it does not do so alone. Biography, the lived experience of the person, integrates with biology to create a unique signature for each individual and is the foundational concept on which Biosocial Medicine is based. Biosocial Medicine starts with the premise that the individual patient is the focus of clinical care, and that average results for "ideal" patients in population level research cannot substitute for the "real" patient for whom clinical decisions are needed. The paper begins with a description of the case-based method of clinical reasoning, considers the strengths and limitations of Randomized Controlled Trials and Evidence Based Medicine, reviews the increasing focus on precision medicine and then explores the neglected role of biography as part of a new approach to the tailored care of patients. After a review of the analytical challenges in Biosocial Medicine, the paper concludes by linking the physician's commitment to understanding the patient's biography as a critical element in developing trust with the patient.

Analysis of Female Enrollment and Participant Sex by Burden of Disease in US Clinical Trials Between 2000 and 2020.

Importance: Although female representation has increased in clinical trials, little is known about how clinical trial representation compares with burden of disease or is associated with clinical trial features, including disease category. Objective: To describe the rate of sex reporting (ie, the presence of clinical trial data according to sex), compare the female burden of disease with the female proportion of clinical trial enrollees, and investigate the associations of disease category and clinical trial features with the female proportion of clinical trial enrollees. Design, Setting, and Participants: This cross-sectional study included descriptive analyses and logistic and generalized linear regression analyses with a logit link. Data were downloaded from the Aggregate Analysis of ClinicalTrials.gov database for all studies registered between March 1, 2000, and March 9, 2020. Enrollment was compared with data from the 2016 Global Burden of Disease database. Of 328 452 clinical trials, 70 095 were excluded because they had noninterventional designs, 167 936 because they had recruitment sites outside the US, 69 084 because they had no reported results, 1003 because they received primary funding from the US military, and 314 because they had unclear sex categories. A total of 20 020 interventional studies enrolling approximately 5.11 million participants met inclusion criteria and were divided into those with and without data on participant sex. Exposures: The primary exposure variable was clinical trial disease category. Secondary exposure variables included funding, study design, and study phase. Main Outcomes and Measures: Sex reporting and female proportion of participants in clinical trials. Results: Among 20 020 clinical trials from 2000 to 2020, 19 866 studies (99.2%) reported sex, and 154 studies (0.8%) did not. Clinical trials in the fields of oncology (46% of disability-adjusted life-years [DALYs]; 43% of participants), neurology (56% of DALYs; 53% of participants), immunology (49% of DALYs; 46% of participants), and nephrology (45% of DALYs; 42% of participants) had the lowest female representation relative to corresponding DALYs. Male participants were underrepresented in 8 disease categories, with the greatest disparity in clinical trials of musculoskeletal disease and trauma (11.3% difference between representation and proportion of DALYs). Clinical trials of preventive interventions were associated with greater female enrollment (adjusted relative difference, 8.48%; 95% CI, 3.77%-13.00%). Clinical trials in cardiology (adjusted relative difference, -18.68%; 95% CI, -22.87% to -14.47%) and pediatrics (adjusted relative difference, -20.47%; 95% CI, -25.77% to -15.16%) had the greatest negative association with female enrollment. Conclusions and Relevance: In this study, sex differences in clinical trials varied by clinical trial disease category, with male and female participants underrepresented in different medical fields. Although sex equity has progressed, these findings suggest that sex bias in clinical trials persists within medical fields, with negative consequences for the health of all individuals.