A multifaceted approach to global health.

World Health Day underscores the scientific community's commitment to achieving health equity for all. It is paramount to eliminate bias in research that has traditionally focused on men, neglecting the specific needs of diverse populations. Innovative clinical trial designs are being developed with more inclusive enrollment. Ensuring equitable access to essential antibiotics, coupled with robust infection prevention and control measures, is vital to safeguarding public health. The pursuit of health equity extends beyond the realm of medicine. Investments in local food production and robust social safety nets are critical for mitigating the effects of climate change on access to healthy diets. Additionally, in times of polycrisis, prioritizing the unique needs of children and empowering community-led healthcare initiatives in conflict zones are essential steps. By taking these actions, we can move closer to realizing everyone's fundamental right to health.

Obstetric Clinical Trial Diversity: Analysis of Trial Focus and Representation From 2007 to 2020.

Inclusive clinical trials are necessary to improve maternal health equity. We aimed to analyze the current state of race and ethnicity reporting and representation in obstetric trials and the association with trial focus for all U.S.-based obstetric trials between 2007 and 2020. In this cross-sectional, multivariable regression analysis, the exposure variable was clinical trial focus (eg, prematurity), and the outcomes were race and ethnicity reporting and representation of diverse cohorts. Obstetric anesthesia trials reported race and ethnicity the least frequently of all trial foci (adjusted odds ratio 0.2, 95% CI 0.08-0.48). Hypertension and obstetric anesthesia trials enrolled the lowest proportion of Black participants, and prematurity trials enrolled the lowest proportion of Latinx and Asian participants. All researchers should strive to improve measurement and reporting of demographic data as well participation of diverse cohorts.

Race and Ethnicity Reporting and Representation in Pediatric Clinical Trials.

BACKGROUND AND OBJECTIVES: Representative enrollment of racial and ethnic minoritized populations in biomedical research ensures the generalizability of results and equitable access to novel therapies. Previous studies on pediatric clinical trial diversity are limited to subsets of journals or disciplines. We aimed to evaluate race and ethnicity reporting and representation in all US pediatric clinical trials on ClinicalTrials.gov. METHODS: We performed a cross-sectional study of US-based clinical trials registered on ClinicalTrials.gov that enrolled participants aged <18 years old between October 2007 and March 2020. We used descriptive statistics, compound annual growth rates, and multivariable logistic regression for data analysis. Estimates of US population statistics and disease burden were calculated with the US Census, Kids' Inpatient Database, and National Survey of Children's Health. RESULTS: Among 1183 trials encompassing 405 376 participants, race and ethnicity reporting significantly increased from 27% in 2007 to 87% in 2018 (P < .001). The median proportional enrollment of Asian American children was 0.6% (interquartile range [IQR], 0%-3.7%); American Indian, 0% (IQR, 0%-0%); Black, 12% (IQR, 2.9%-28.4%); Hispanic, 7.1% (IQR, 0%-18.6%); and white 66.4% (IQR, 41.5%-81.6%). Asian American, Black, and Hispanic participants were underrepresented relative to US population demographics. Compared with expected proportions based on disease prevalence and hospitalizations, Asian American and Hispanic participants were most consistently underrepresented across diagnoses. CONCLUSIONS: While race and ethnicity reporting in pediatric clinical trials has improved, the representative enrollment of minoritized participants remains an ongoing challenge. Evidence-based and policy solutions are needed to address these disparities to advance biomedical innovation for all children.

Race and Ethnicity Reporting and Representation in Obstetrics and Gynecology Clinical Trials and Publications From 2007-2020.

Importance: Clinical trials guide evidence-based obstetrics and gynecology (OB-GYN) but often enroll nonrepresentative participants. Objective: To characterize race and ethnicity reporting and representation in US OB-GYN clinical trials and their subsequent publications and to analyze the association of subspecialty and funding with diverse representation. Design and Setting: Cross-sectional analysis of all OB-GYN studies registered on ClinicalTrials.gov (2007-2020) and publications from PubMed and Google Scholar (2007-2021). Analyses included logistic regression controlling for year, subspecialty, phase, funding, and site number. Data from 332 417 studies were downloaded. Studies with a noninterventional design, with a registration date before October 1, 2007, without relevance to OB-GYN, with no reported results, and with no US-based study site were excluded. Exposures: OB-GYN subspecialty and funder. Main Outcomes and Measures: Reporting of race and ethnicity data and racial and ethnic representation (the proportion of enrollees of American Indian or Alaskan Native, Asian, Black, Latinx, or White identity and odds of representation above US Census estimates by race and ethnicity). Results: Among trials with ClinicalTrials.gov results (1287 trials with 591 196 participants) and publications (1147 trials with 821 111 participants), 662 (50.9%) and 856 (74.6%) reported race and ethnicity data, respectively. Among publications, gynecology studies were significantly less likely to report race and ethnicity than obstetrics (adjusted odds ratio [aOR], 0.54; 95% CI, 0.38-0.75). Reproductive endocrinology and infertility trials had the lowest odds of reporting race and ethnicity (aOR, 0.14; 95% CI, 0.07-0.27; reference category, obstetrics). Obstetrics and family planning demonstrated the most diverse clinical trial cohorts. Compared with obstetric trials, gynecologic oncology had the lowest odds of Black representation (ClinicalTrials.gov: aOR, 0.04; 95% CI, 0.02-0.09; publications: aOR, 0.06; 95% CI, 0.03-0.11) and Latinx representation (ClinicalTrials.gov: aOR, 0.05; 95% CI, 0.02-0.14; publications: aOR, 0.23; 95% CI, 0.10-0.48), followed by urogynecology and reproductive endocrinology and infertility. Urogynecology (ClinicalTrials.gov: aOR, 0.15; 95% CI, 0.05-0.39; publications: aOR, 0.24; 95% CI, 0.09-0.58) had the lowest odds of Asian representation. Conclusions and Relevance: Race and ethnicity reporting and representation in OB-GYN trials are suboptimal. Obstetrics and family planning trials demonstrate improved representation is achievable. Nonetheless, all subspecialties should strive for more equitably representative research.

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.

Recognizing the Role of Language in the Hidden Curriculum of Undergraduate Medical Education: Implications for Equity in Medical Training.

Medical education involves a transition from "outsider" to "insider" status, which entails both rigorous formal training and an inculturation of values and norms via a hidden curriculum. Within this transition, the ability to "talk the talk" designates an individual as an insider, and learning to talk this talk is a key component of professional socialization. This Article uses the framework of "patterns of medical language" to explore the role of language in the hidden curriculum of medical education, exploring how students must learn to recognize and participate fluently within patterns of medical language to be acknowledged and evaluated as competent trainees. The authors illustrate this by reframing the Association of American Medical Colleges' Core Entrustable Professional Activities for Entering Residency as a series of overlapping patterns of medical language that students are expected to master before residency. The authors propose that many of these patterns of medical language are learned through trial and error, taught via a hidden curriculum rather than through explicit instruction. Medical students come from increasingly diverse backgrounds and therefore begin medical training further from or closer to insider status. Thus, evaluative practices based on patterns of medical language, which are not explicitly taught, may exacerbate and perpetuate existing inequities in medical education. This Article aims to bring awareness to the importance of medical language within the hidden curriculum of medical education, to the role of medical language as a marker of insider status, and to the centrality of medical language in evaluative practices. The authors conclude by offering possible approaches to ameliorate the inequities that may exist due to current evaluative practices.

Hierarchies improve individual assessment of temporal discounting behavior.

Delay discounting behavior has proven useful in assessing impulsivity across a wide range of populations. As such, accurate estimation of the shape of each individual's temporal discounting profile is paramount when drawing conclusions about how impulsivity relates to clinical and health outcomes such as gambling, addiction, and obesity. Here, we identify an estimation problem with current methods of assessing temporal discounting behavior, and propose a simple solution. First, through a simulation study we identify types of temporal discounting profiles that cannot reliably be estimated. Second, we show how imposing constraints through hierarchical modeling ameliorates these recovery problems. Finally, we apply our solution to a large data set from a temporal discounting task, and illustrate the importance of reliable estimation within patient populations. We conclude with a brief discussion on how hierarchical Bayesian methods can aid in model estimation, compensate for small samples, and improve predictions of externalizing psychopathology.

Characteristics of Ophthalmology Trials Registered in ClinicalTrials.gov, 2007-2018.

PURPOSE: To perform a comprehensive analysis of characteristics of ophthalmology trials registered in ClinicalTrials.gov. DESIGN: Cross-sectional study. METHODS: All 4,203 ophthalmologic clinical trials registered on ClinicalTrials.gov between October 1, 2007, and April 30, 2018, were identified by using medical subject headings (MeSH). Disease condition terms were verified by manual review. Trial characteristics were assessed through frequency calculations. Hazard ratios and 95% confidence intervals were determined for characteristics associated with early discontinuation. RESULTS: The majority of trials were multiarmed (73.6%), single-site (69.4%), randomized (64.8%), and had <100 enrollees (66.3%). A total of 33% used a data-monitoring committee (DMC), and 50.6% incorporated blinding. Other groups (51.6%) were funded by industry, whereas 2.6% were funded by the US National Institutes of Health (NIH). NIH trials were significantly more likely to address oncologic (NIH = 15.5%, Other = 3%, Industry = 1.5%; P < 0.001) or pediatric disease (NIH = 20.9%, Other = 5.9%, Industry = 1.4%; P < 0.001). Industry-sponsored trials (69.6% of phase 3 trials) were significantly more likely to be randomized (Industry = 68.7%, NIH = 58.9%, Other = 60.8%; P < 0.001) and blinded (Industry = 57.2%, NIH = 42.7%, Other = 43.5%; P < 0.001). A total of 359 trials (8.5%) were discontinued early, and 530 trials (12.6%) had unknown status. Trials were less likely to be discontinued if funded by sources other than industry (hazard ratio [HR], 0.72; 95% confidence interval [CI], 0.55-0.95; P = 0.021) and/or had a DMC (HR, 0.71; 95% CI, 0.55-0.92; P = 0.010). CONCLUSIONS: Ophthalmology trials in the past decade reveal heterogeneity across study funding sources. NIH trials were more likely to support historically underfunded subspecialties, whereas Industry trials were more likely to face early discontinuation. These trends emphasize the importance of carefully monitored and methodologically sound trials with deliberate funding allocation.

A method for efficiently sampling from distributions with correlated dimensions.

Bayesian estimation has played a pivotal role in the understanding of individual differences. However, for many models in psychology, Bayesian estimation of model parameters can be difficult. One reason for this difficulty is that conventional sampling algorithms, such as Markov chain Monte Carlo (MCMC), can be inefficient and impractical when little is known about the target distribution--particularly the target distribution's covariance structure. In this article, we highlight some reasons for this inefficiency and advocate the use of a population MCMC algorithm, called differential evolution Markov chain Monte Carlo (DE-MCMC), as a means of efficient proposal generation. We demonstrate in a simulation study that the performance of the DE-MCMC algorithm is unaffected by the correlation of the target distribution, whereas conventional MCMC performs substantially worse as the correlation increases. We then show that the DE-MCMC algorithm can be used to efficiently fit a hierarchical version of the linear ballistic accumulator model to response time data, which has proven to be a difficult task when conventional MCMC is used.