Historically, the attribution of biology to race has shaped societies and manifested in innumerable disparities and irreparable harm, especially in communities of color. From the earliest days of the United States to the present day, the dehumanization and "othering" of African Americans have caused deep racial inequities that have been perpetuated and embedded in American culture. The early months of the COVID-19 pandemic underscored the deep racial inequalities in the US, especially in health outcomes for communities of color. Structural racism has played a critical role in exacerbating disparities, with Black, Hispanic, Latinx, and Indigenous populations experiencing higher rates of severe disease and mortality. The interconnectedness of racism with the social determinants of health, concomitant with higher rates of chronic illnesses like diabetes and hypertension, increases vulnerability to severe COVID-19. Health disparities are compounded by implicit biases in the medical field, a lack of diversity among healthcare providers, and historical medical mistrust among marginalized groups. Underrepresentation in the medical field, biomedical sciences, and academia hinders efforts to address health disparities effectively. This essay seeks to raise awareness of how the concepts of race and racism have resulted in racial hierarchies that perpetuate systems of oppression and impede efforts toward racial and health equity. Specifically, this essay covers time periods in American history, including slavery, the Jim Crow Era, the Civil Rights Movement, and the COVID-19 pandemic, and discusses how addressing race and racism and the achievement of racial health equity require targeted efforts to increase diversity in healthcare and biomedical fields, improve cultural competence, and foster trust between medical professionals and communities of color.

This paper tracks trends in COVID-19 case, death, and vaccination rate disparities by race and ethnicity in Virginia during the COVID-19 pandemic. COVID-19 case, death, and vaccination rates were obtained from electronic state health department records from March 2020 to February 2022. Rate ratios were then utilized to quantify racial and ethnic disparities for several time periods during the pandemic. The Hispanic population had the highest COVID-19 case and age-adjusted death rates, and the lowest vaccination rates at the beginning of the pandemic in Virginia. These disparities resolved later in the pandemic. COVID-19 case and death rates among the Black population were also higher than those of the White population and these disparities remained throughout the pandemic. Racial and ethnic disparities changed over time in Virginia as vaccination coverage and public health policies evolved. Year 2 of the analysis saw lower case and death rates, and higher vaccination rates for non-White populations in Virginia. Public health strategies need to be addressed during the pandemic and developed before the next pandemic to ensure that large racial and ethnic disparities are not again present at the outset.

Electronic health records (EHRs) facilitate the accessibility and sharing of patient data among various health care providers, contributing to more coordinated and efficient care.

This study aimed to summarize the evolution of secondary use of EHRs and their interoperability in medical research over the past 25 years.

We conducted an extensive literature search in the PubMed, Scopus, and Web of Science databases using the keywords Electronic health record and Electronic medical record in the title or abstract and Medical research in all fields from 2000 to 2024. Specific terms were applied to different time periods.

The review yielded 2212 studies, all of which were then screened and processed in a structured manner. Of these 2212 studies, 2102 (93.03%) were included in the review analysis, of which 1079 (51.33%) studies were from 2000 to 2009, 582 (27.69%) were from 2010 to 2019, 251 (11.94%) were from 2020 to 2023, and 190 (9.04%) were from 2024.

The evolution of EHRs marks an important milestone in health care's journey toward integrating technology and medicine. From early documentation practices to the sophisticated use of artificial intelligence and big data analytics today, EHRs have become central to improving patient care, enhancing public health surveillance, and advancing medical research.

Disparities in coronavirus disease 2019 mortality are driven by inequalities in group-specific incidence rates (IRs), case fatality rates (CFRs), and their interaction. For emerging infections, such as severe acute respiratory syndrome coronavirus 2, group-specific IRs and CFRs change on different time scales, and inequities in these measures may reflect different social and medical mechanisms. To be useful tools for public health surveillance and policy, analyses of changing mortality rate disparities must independently address changes in IRs and CFRs. However, this is rarely done. In this analysis, we examine the separate contributions of disparities in the timing of infection-reflecting differential infection risk factors such as residential segregation, housing, and participation in essential work-and declining CFRs over time on mortality disparities by race/ethnicity in the US state of Michigan. We used detailed case data to decompose race/ethnicity-specific mortality rates into their age-specific IR and CFR components during each of 3 periods from March to December 2020. We used these estimates in a counterfactual simulation model to estimate that that 35% (95% credible interval, 30%-40%) of deaths in black Michigan residents could have been prevented if these residents were infected along the timeline experienced by white residents, resulting in a 67% (61%-72%) reduction in the mortality rate gap between black and white Michigan residents during 2020. These results clearly illustrate why differential power to "wait out" infection during an infectious disease emergency-a function of structural racism-is a key, underappreciated, driver of inequality in disease and death from emerging infections.

In the future, large language models (LLMs) may enhance the delivery of healthcare, but there are risks of misuse. These methods may be trained to allocate resources via unjust criteria involving multimodal data - financial transactions, internet activity, social behaviors, and healthcare information. This study shows that LLMs may be biased in favor of collective/systemic benefit over the protection of individual rights and could facilitate AI-driven social credit systems.

There is a need to consider COVID-19 a syndemic; which calls for a comprehensive approach to tackle the associated interconnected challenges. The objective of this study is to investigate the potential syndemic nature of COVID-19, with a specific focus on understanding how viral infection, mental health (such as anxiety and depression), and pre-existing comorbidities interact and influence each other.

Retrospective population-based cohort study.

We conducted a population-based retrospective cohort study using linked health administrative data from the Institute for Clinical Evaluative Sciences, Ontario. The study included 2,863,423 Ontario residents from January 2020 to March 2021. We analysed healthcare services utilisation (physician visits, emergency visits, and hospitalisations) for chronic conditions among individuals with both COVID-19 and either anxiety or depression, to understand the syndemic impact of COVID-19 and mental health issues among Ontario population.

Multiple regression models were used to explore the study's objective. In the final adjusted regression model for the sample, it was found that the individuals who were COVID-19 positive and had either anxiety or depression were more likely to utilise health services for chronic conditions of interest during the pandemic than those who were COVID-19-negative with mental health issues (odds ratio [OR]:, 1.33; 95% confidence interval [CI]: 1.12-1.58). A higher risk of morbidity was observed among males (OR: 1.28; CI: 1.16-1.41), as well as in individuals with diverse ethnic backgrounds and low socioeconomic status.

The impact of COVID-19 on mental health, particularly among vulnerable populations with chronic diseases, can be seen as a syndemic. This complex interaction emphasises the need for integrated public health strategies.

Racial disparities in COVID-19 incidence and outcomes have been widely reported. Non-Hispanic Black patients endured worse outcomes disproportionately compared with non-Hispanic White patients, but the epidemiological basis for these observations was complex and multifaceted.

This study aimed to elucidate the potential reasons behind the worse outcomes of COVID-19 experienced by non-Hispanic Black patients compared with non-Hispanic White patients and how these variables interact using an explainable machine learning approach.

In this retrospective cohort study, we examined 28,943 laboratory-confirmed COVID-19 cases from the OneFlorida Research Consortium's data trust of health care recipients in Florida through April 28, 2021. We assessed the prevalence of pre-existing comorbid conditions, geo-socioeconomic factors, and health outcomes in the structured electronic health records of COVID-19 cases. The primary outcome was a composite of hospitalization, intensive care unit admission, and mortality at index admission. We developed and validated a machine learning model using Extreme Gradient Boosting to evaluate predictors of worse outcomes of COVID-19 and rank them by importance.

Compared to non-Hispanic White patients, non-Hispanic Blacks patients were younger, more likely to be uninsured, had a higher prevalence of emergency department and inpatient visits, and were in regions with higher area deprivation index rankings and pollutant concentrations. Non-Hispanic Black patients had the highest burden of comorbidities and rates of the primary outcome. Age was a key predictor in all models, ranking highest in non-Hispanic White patients. However, for non-Hispanic Black patients, congestive heart failure was a primary predictor. Other variables, such as food environment measures and air pollution indicators, also ranked high. By consolidating comorbidities into the Elixhauser Comorbidity Index, this became the top predictor, providing a comprehensive risk measure.

The study reveals that individual and geo-socioeconomic factors significantly influence the outcomes of COVID-19. It also highlights varying risk profiles among different racial groups. While these findings suggest potential disparities, further causal inference and statistical testing are needed to fully substantiate these observations. Recognizing these relationships is vital for creating effective, tailored interventions that reduce disparities and enhance health outcomes across all racial and socioeconomic groups.

Large language models (LLMs) have been proposed to support many healthcare tasks, including disease diagnostics and treatment personalization. While AI may be applied to assist or enhance the delivery of healthcare, there is also a risk of misuse. LLMs could be used to allocate resources via unfair, unjust, or inaccurate criteria. For example, a social credit system uses big data to assess "trustworthiness" in society, penalizing those who score poorly based on evaluation metrics defined only by a power structure (e.g., a corporate entity or governing body). Such a system may be amplified by powerful LLMs which can evaluate individuals based on multimodal data - financial transactions, internet activity, and other behavioral inputs. Healthcare data is perhaps the most sensitive information which can be collected and could potentially be used to violate civil liberty or other rights via a "clinical credit system", which may include limiting access to care. The results of this study show that LLMs may be biased in favor of collective or systemic benefit over protecting individual rights, potentially enabling this type of future misuse. Moreover, experiments in this report simulate how clinical datasets might be exploited with current LLMs, demonstrating the urgency of addressing these ethical dangers. Finally, strategies are proposed to mitigate the risk of developing large AI models for healthcare.

Patients with acute heart failure (AHF) exacerbation are susceptible to complications in the setting of COVID-19 infection. Data regarding the racial/ethnic and sex disparities in patients with AHF and COVID-19 remains limited.

We aim to evaluate the impact of race, ethnicity, and sex on the in-hospital outcomes of AHF with COVID-19 infection using the data from the National Inpatient Sample (NIS).

We extracted data from the NIS (2020) by using ICD-10-CM to identify all hospitalizations with a diagnosis of AHF and COVID-19 in the year 2020. The associations between sex, race/ethnicity, and outcomes were examined using a multivariable logistic regression model.

We identified a total of 158,530 weighted AHF hospitalizations with COVID-19 infection in 2020. The majority were White (63.9 %), 23.3 % were Black race, and 12.8 % were of Hispanic ethnicity, mostly males (n = 84,870 [53.5 %]). After adjustment, the odds of in-hospital mortality were lowest in White females (aOR 0.83, [0.78-0.98]) and highest in Hispanic males (aOR 1.27 [1.13-1.42]) compared with White males. Overall, the odds of cardiac arrest (aOR 1.54 [1.27-1.85]) and AKI (aOR 1.36 [1.26-1.47] were higher, while odds for procedural interventions such as PCI (aOR 0.23 [0.10-0.55]), and placement on a ventilator (aOR 0.85 [0.75-0.97]) were lower among Black males in comparison to White males.

Male sex was associated with a higher risk of in-hospital mortality in white and black racial groups, while no such association was noted in the Hispanic group. Hispanic males had the highest odds of death compared with White males.

Understanding healthcare system resilience has become paramount, particularly in the wake of the COVID-19 pandemic, which imposed unprecedented burdens on healthcare services and severely impacted public health. Resilience is defined as the system's ability to absorb, recover from and adapt to disruptions; however, despite extensive studies on this subject, we still lack empirical evidence and mathematical tools to quantify its adaptability (the ability of the system to adjust to and learn from disruptions). By analyzing millions of patients' electronic medical records across US states, we find that the COVID-19 pandemic caused two successive waves of disruptions within the healthcare systems, enabling natural experiment analysis of the adaptive capacity of each system to adapt to past disruptions. We generalized the quantification framework and found that the US healthcare systems exhibit substantial adaptability (ρ = 0.58) but only a moderate level of resilience (r = 0.70). When considering system responses across racial groups, Black and Hispanic groups were more severely impacted by pandemic disruptions than white and Asian groups. Physician abundance was the key characteristic for determining healthcare system resilience. Our results offer vital guidance in designing resilient and sustainable healthcare systems to prepare for future waves of disruptions akin to COVID-19 pandemics.

In recent years, a range of novel smartphone-derived data streams about human mobility have become available on a near-real-time basis. These data have been used, for example, to perform traffic forecasting and epidemic modeling. During the COVID-19 pandemic in particular, human travel behavior has been considered a key component of epidemiological modeling to provide more reliable estimates about the volumes of the pandemic's importation and transmission routes, or to identify hot spots. However, nearly universally in the literature, the representativeness of these data, how they relate to the underlying real-world human mobility, has been overlooked. This disconnect between data and reality is especially relevant in the case of socially disadvantaged minorities.

The objective of this study is to illustrate the nonrepresentativeness of data on human mobility and the impact of this nonrepresentativeness on modeling dynamics of the epidemic. This study systematically evaluates how real-world travel flows differ from census-based estimations, especially in the case of socially disadvantaged minorities, such as older adults and women, and further measures biases introduced by this difference in epidemiological studies.

To understand the demographic composition of population movements, a nationwide mobility data set from 318 million mobile phone users in China from January 1 to February 29, 2020, was curated. Specifically, we quantified the disparity in the population composition between actual migrations and resident composition according to census data, and shows how this nonrepresentativeness impacts epidemiological modeling by constructing an age-structured SEIR (Susceptible-Exposed-Infected- Recovered) model of COVID-19 transmission.

We found a significant difference in the demographic composition between those who travel and the overall population. In the population flows, 59% (n=20,067,526) of travelers are young and 36% (n=12,210,565) of them are middle-aged (P<.001), which is completely different from the overall adult population composition of China (where 36% of individuals are young and 40% of them are middle-aged). This difference would introduce a striking bias in epidemiological studies: the estimation of maximum daily infections differs nearly 3 times, and the peak time has a large gap of 46 days.

The difference between actual migrations and resident composition strongly impacts outcomes of epidemiological forecasts, which typically assume that flows represent underlying demographics. Our findings imply that it is necessary to measure and quantify the inherent biases related to nonrepresentativeness for accurate epidemiological surveillance and forecasting.

Occupational and residential segregation and other manifestations of social and economic inequity drive of racial and socioeconomic inequities in infection, severe disease, and death from a wide variety of infections including SARS-CoV-2, influenza, HIV, tuberculosis, and many others. Despite a deep and long-standing quantitative and qualitative literature on infectious disease inequity, mathematical models that give equally serious attention to the social and biological dynamics underlying infection inequity remain rare. In this paper, we develop a simple transmission model that accounts for the mechanistic relationship between residential segregation on inequity in infection outcomes. We conceptualize segregation as a high-level, fundamental social cause of infection inequity that impacts both who-contacts-whom (separation or preferential mixing) as well as the risk of infection upon exposure (vulnerability). We show that the basic reproduction number, ℛ 0 , and epidemic dynamics are sensitive to the interaction between these factors. Specifically, our analytical and simulation results and that separation alone is insufficient to explain segregation-associated differences in infection risks, and that increasing separation only results in the concentration of risk in segregated populations when it is accompanied by increasing vulnerability. Overall, this work shows why it is important to carefully consider the causal linkages and correlations between high-level social determinants - like segregation - and more-proximal transmission mechanisms when either crafting or evaluating public health policies. While the framework applied in this analysis is deliberately simple, it lays the groundwork for future, data-driven explorations of the mechanistic impact of residential segregation on infection inequities.

There has been a substantial increase in the number of students with mental health needs, yet significant discrepancies exist in access to timely intervention. Traditional gatekeeping to intervention has been the provenance of single information sources. Multi-informant decision-making is a promising mechanism to improve equitable access. However, critical advancements are necessary to improve decision-making relating to (a) who is identified, (b) what type of need is determined, (c) the type of intervention necessary, and (d) where or under what circumstances to implement the intervention. We review critical components of effective mental health decision-making, contributors to inequities in school mental health services, and offer future directions for research and practice to increase equitable student outcomes.

During COVID-19 epidemic, health protocols limited face-to-face perinatal visits and increased reliance on telehealth. To prevent increased health disparities among BIPOC pregnant patients in health-underserved areas, we used a pre-post survey design to pilot a study assessing (1) feasibility of transferring technology including a blood pressure (BP) cuff (BPC) and a home screening tool, (2) providers' and patients' acceptance and use of technology, and (3) benefits and challenges of using the technology. Specific objectives included (1) increasing contact points between patients and perinatal providers; (2) decreasing barriers to reporting and treating maternal hypertension, stress/depression, and intimate partner violence (IPV)/domestic violence (DV); and (3) bundling to normalize and facilitate mental, emotional, and social health monitoring alongside BP screening. Findings confirm this model is feasible. Patients and providers used this bundling model to improve antenatal screening under COVID quarantine restrictions. More broadly, home-monitoring improved antenatal telehealth communication, provider diagnostics, referral and treatment, and bolstered patient autonomy through authoritative knowledge. Implementation challenges included provider resistance, disagreement with lower than ACOG BP values to initiate clinical contact and fear of service over-utilization, and patient and provider confusion about tool symbols due to limited training. We hypothesize that routinized pathologization and projection of crisis onto BIPOC people, bodies, and communities, especially around reproduction and continuity, may contribute to persistent racial/ethnic health disparities. Further research is needed to examine whether authoritative knowledge increases use of critical and timely perinatal services by strengthening embodied knowledge of marginalized patients and, thus, their autonomy and self-efficacy to enact self-care and self-advocacy.

There are concerns that artificial intelligence (AI) algorithms may create underdiagnosis bias by mislabeling patient individuals with certain attributes (e.g., female and young) as healthy. Addressing this bias is crucial given the urgent need for AI diagnostics facing rapidly spreading infectious diseases like COVID-19. We find the prevalent AI diagnostic models show an underdiagnosis rate among specific patient populations, and the underdiagnosis rate is higher in some intersectional specific patient populations (for example, females aged 20-40 years). Additionally, we find training AI models on heterogeneous datasets (positive and negative samples from different datasets) may lead to poor model generalization. The model's classification performance varies significantly across test sets, with the accuracy of the better performance being over 40% higher than that of the poor performance. In conclusion, we developed an AI bias analysis pipeline to help researchers recognize and address biases that impact medical equality and ethics.

The authors report on their development of a National Advisory Board (NAB) to guide a funded project: Two in One: HIV + COVID-19 Screening and Testing Model. This project aimed to improve primary care practitioners' capacity to routinize HIV, PrEP/PEP, and COVID-19 vaccine screenings for all their patients while relying on culturally responsive communication with their minoritized patients. To approach their monumental research and education tasks, they created a NAB, drawing from the literature on advisory boards to (a) promote board member engagement and (b) progress successfully through the six stages suggested for successful advisory boards. A midpoint survey and final focus groups with NAB members indicated mixed levels of engagement, a sense of time and work being valued, and pride in the media and academic reach of the project. The authors offer considerations for others considering forming a NAB to guide primary care research and interventions.

This study aims to understand the impact of the COVID-19 pandemic on social determinants of health (SDOH) of marginalized racial/ethnic US population groups, specifically African Americans and Asians, by leveraging natural language processing (NLP) and machine learning (ML) techniques on race-related spatiotemporal social media text data. Specifically, this study establishes the extent to which Latent Dirichlet Allocation (LDA) and Gibbs Sampling Dirichlet Multinomial Mixture (GSDMM)-based topic modeling determines social determinants of health (SDOH) categories, and how adequately custom named-entity recognition (NER) detects key SDOH factors from a race/ethnicity-related Reddit data corpus.

In this study, we collected race/ethnicity-specific data from 5 location subreddits including New York City, NY; Los Angeles, CA; Chicago, IL; Philadelphia, PA; and Houston, TX from March to December 2019 (before COVID-19 pandemic) and from March to December 2020 (during COVID-19 pandemic). Next, we applied methods from natural language processing and machine learning to analyze SDOH issues from extracted Reddit comments and conversation threads using feature engineering, topic modeling, and custom named-entity recognition (NER).

Topic modeling identified 35 SDOH-related topics. The SDOH-based custom NER analyses revealed that the COVID-19 pandemic significantly impacted SDOH issues of marginalized Black and Asian communities. On average, the Social and Community Context (SCC) category of SDOH had the highest percent increase (366%) from the pre-pandemic period to the pandemic period across all locations and population groups. Some of the detected SCC issues were racism, protests, arrests, immigration, police brutality, hate crime, white supremacy, and discrimination.

Reddit social media platform can be an alternative source to assess the SDOH issues of marginalized Black and Asian communities during the COVID-19 pandemic. By employing NLP/ML techniques such as LDA/GSDMM-based topic modeling and custom NER on a race/ethnicity-specific Reddit corpus, we uncovered various SDOH issues affecting marginalized Black and Asian communities that were significantly worsened during the COVID-19 pandemic. As a result of conducting this research, we recommend that researchers, healthcare providers, and governments utilize social media and collaboratively formulate responses and policies that will address SDOH issues during public health crises.

"Helicopter research" refers to a practice where researchers from wealthier countries conduct studies in lower-income countries with little involvement of local researchers or community members. This practice also occurs domestically. In this Commentary, we outline strategies to curb domestic helicopter research and to foster equity-centered collaborations.

Generating evidence on health inequalities (HI) is necessary to raise awareness of these issues, describe and monitor their evolution, analyze their causes, and inform interventions aiming to improve health equity. Yet not all cities and countries have the capacity to produce this type of research. Recent research provides new contextual and causal insights into this research production process, and in-depth understanding on why and how this type of research is produced in certain settings. This article aims to analyze two recent case studies that have uniquely explored this process in two high producers of HI research and high-income country settings to identify learning and distil recommendations, which may be insightful for other settings. Expanding and investing in this line of research is critical, particularly in places with lower HI research output and related capacity, in order to identify key contextual conditions and mechanisms that may enable or hinder this process. This new knowledge could guide the development of new HI research capacity strengthening strategies to foster this research in different settings, worldwide. More understanding is also needed on the relationship between HI research, policy, and action in order to tackle HI.

This article examines some of the racist features of nineteenth-century medical school curricula in the United States and the imperial networks necessary to acquire the data and specimens that underpinned this part of medical education, which established hierarchies between human races and their relationship to the natural environment. It shows how, in a world increasingly linked by trade and colonialism, medical schools were founded in the United States and grew as the country developed its own imperial ambitions. Taking advantage of the global reach of empires, a number of medical professors in different states, such as Daniel Drake, Josiah Nott and John Collins Warren, who donated his anatomical collection to Harvard Medical School on his retirement in 1847, began to develop racial theories that naturalised slavery and emerging imperialism as part of their medical teaching.

This study assesses disparities in medications for opioid use disorder in adults with opioid use disorder and examines the associations between state-level COVID-19 lockdown and telehealth policies and medications for opioid use disorder utilization rates during the COVID-19 pandemic.

This retrospective cohort study of 396,872 adults with opioid use disorder analyzed monthly medications for opioid use disorder utilization rates between January 2019 and June 2022 using data from Clinformatics Data Mart Database. Primary outcome measure was monthly medications for opioid use disorder utilization rates. Variables of interest were patients' demographics and state-level characteristics (telehealth policies for controlled substance prescribing, COVID-19 lockdown policy, and registered buprenorphine providers/100,000). In multivariable analyses, time trend was grouped into four time periods: before COVID-19, early COVID-19, early vaccine, and Omicron-related COVID-19 surge and thereafter.

Medications for opioid use disorder rates increased from a 1.2% change in slope monthly on a log scale to 2% monthly from February 2021 to October 2021, after which the utilization rate increased to a lesser degree. Women had 28% lower odds of receiving medications for opioid use disorder than men; Hispanic, Black, and Asian patients had 40%, 34%, and 32% lower odds of receiving medications for opioid use disorder than White patients, respectively. These sex and racial disparities persisted throughout the pandemic. Regional medications for opioid use disorder rate differences, mediated by buprenorphine providers/100,000 state population, decreased during the pandemic. States with telehealth policies for controlled substance prescribing had greater percentages of patients on medications for opioid use disorder (11.7%) than states without such policies (10.4%).

Monthly medications for opioid use disorder rates increased during the pandemic, with higher rates in men, White individuals, and residents of the Northeast region. States with policies permitting telehealth prescribing of controlled substances also had higher medications for opioid use disorder rates, supporting a future expansion of medications for opioid use disorder-related telehealth to improve access to care.

Medical researchers have historically utilized the variable of race uncritically, rarely defining race, rarely acknowledging it as a social construct, and often omitting information about how it was measured. In this study, we use the following definition of race: "a system of structuring opportunity and assigning value based on the social interpretation of how one looks." We examine the influence of racial misclassification, racial discrimination, and racial consciousness on the self-rated health of Native Hawaiian and Pacific Islanders (NHPI) living in the United States of America (USA).

Our analysis used online survey data from a subgroup of NHPI adults living in the USA (n = 252) who were oversampled as part of a larger study of US adults (N = 2022). Respondents were recruited between September 7, 2021 and October 3, 2021, from an online opt-in panel of individuals across the USA. Statistical analyses include weighted and unweighted descriptive statistics for the sample, as well as a weighted logistic regression for poor/fair self-rated health.

Odds of poor/fair self-rated health were greater for women (OR = 2.72; 95% CI [1.19, 6.21]) and those who experienced racial misclassification (OR = 2.90; 95% CI [1.20, 7.05]). No other sociodemographic, healthcare, or race-related variables were significantly associated with self-rated health in the fully adjusted results.

Findings suggest that racial misclassification may be an important correlate of self-rated health among NHPI adults in the US context.

Background: The COVID-19 pandemic has accelerated telehealth usage. This study aims to understand the impact of sociodemographic factors on telehealth usage during COVID-19 among surgical specialties. Methods: Our data contain surgical outpatient visits at an academic center from five periods between 2019 and 2020. A difference-in-differences regression model was used to examine the effect of exposure variables on virtual visit proportions between prepandemic and postpandemic time periods. Results: Compared with white patients, non-Medicare beneficiaries, and English-proficient patients, the rate of uptake in telehealth visits from prepandemic to postpandemic periods was lower for black patients, Medicare beneficiaries, and non-English-speaking patients, respectively. Surgical subspecialties saw varied usage of telehealth. A strong preference for phone visits by black patients, Medicare beneficiaries, and non-English-speaking patients existed. Conclusion: Phone visits are an important resource for marginalized communities. Understanding disparities in telemedicine usage may inform policy that could alleviate inequities in health care access.

Antecedents of racist treatments of Black patients by the psychiatric profession in the United States affect the way they view treatment today. Specifically, in this essay, we explore the enduring consequences of racial science on various treatment practices. We examined a range of primary sources on the history of racial theories about the mind, medical and psychiatric publications, and hospitals. We contextualize this analysis by examining the secondary literature in the history and sociology of psychiatry. Through analyzing racial thinking from the antebellum through the Jim Crow periods, we show how US medicine and psychiatry have roots in antebellum racial science and how carceral logics underpinned the past and present politics of Black mental health. Changing this trajectory requires practitioners to interrogate the historical foundations of racist psychiatric concepts. This essay urges them to reject biological racial realism, which bears reminiscences to 19th-century racial science, and embrace the variable of race as a social construct to study social inequalities in health as a first step toward moving away from the legacies of past injustices in medicine. (Am J Public Health. 2024;114(S3):S250-S257. https://doi.org/10.2105/AJPH.2023.307554).

The unprecedented events of 2020 required a pivot in scientific training to better prepare the biomedical research workforce to address global pandemics, structural racism, and social inequities that devastate human health individually and erode it collectively. Furthermore, this pivot had to be accomplished in the virtual environment given the nation-wide lockdown.

These needs and context led to leveraging of the San Francisco Building Infrastructure Leading to Diversity (SF BUILD) theories of change to innovate a Virtual BUILD Research Collaboratory (VBRC). The purpose of VBRC was to train Black, Indigenous, and people of color (BIPOC) students to apply their unique perspectives to biomedical research. These training activities were evaluated using a pre-post survey design that included both validated and new psychosocial scales. A new scale was piloted to measure culturally relevant pedagogy.

VBRC scholars increased science identity on two items: thinking of myself as a scientist (+1point, p = 0.006) and belonging to a community of scientists (+1point, p = 0.069). Overall, scholars perceived stress also decreased over VBRC (-2.35 points, p = 0.02). Post VBRC, scholars had high agency scores (μ = 11.02, Md = 12, range = 6-12, σ = 1.62) and cultural humility scores (μ = 22.11, Md = 23, range = 12-24, σ = 2.71). No notable race/ethnic differences were found in any measures.

Taken together, our innovative approach to data science training for BIPOC in unprecedented times shows promise for better preparing the workforce critically needed to address the fundamental gaps in knowledge at the intersection of public health, structural racism, and biomedical sciences.

The COVID-19 pandemic disproportionately affected older adults, particularly those with pre-existing chronic health conditions. To address the health disparity and challenges faced by under-resourced African American older adults in South Los Angeles during this period, we implemented a hybrid (virtual/in-person), pre-post, community-based participatory intervention research project utilizing a faith-based lay health advisor model (COVID-19 Health Ambassador Program (CHAP)). We recruited COVID-19 Health Ambassadors (CHAs) and African American older adults (participants) from faith-based organizations who partook in CHA-led meetings and follow-ups that educated and supported the participants. This paper seeks to evaluate this intervention's implementation using the Consolidated Framework for Implementation Research (CFIR) as a reporting tool with an emphasis on fidelity, challenges, and adaptations based on data collected via stakeholder interviews and surveys.

CHAP was delivered to 152 participants by 19 CHAs from 17 faith-based organizations. CHAs assisted with chronic disease management, resolved medication-related challenges, encouraged COVID-19 vaccination, reduced psychological stress and addressed healthcare avoidance behaviors such as COVID-19 vaccine hesitancy among the participants. Challenges encountered include ensuring participant engagement and retention in the virtual format and addressing technological barriers for CHAs and participants. Adaptations made to better suit the needs of participants included providing communication tools and additional training to CHAs to improve their proficiency in using virtual platforms in addition to adapting scientific/educational materials to suit our participants' diverse cultural and linguistic needs.

The community-centered hybrid approach in addition to our partnership with faith-based organizations and their respective COVID-19 health ambassadors proved to be essential in assisting underserved African American older adults manage chronic health conditions and address community-wide health disparities during the COVID-19 pandemic. Adaptability, cultural sensitivity, and teamwork are key to implementing health interventions especially in underserved populations.

Advancing equitable patient-centered care in the Veterans Health Administration (VHA) requires understanding the differential experiences of unique patient groups.

To inform a comprehensive strategy for improving VHA health equity through the comparative qualitative analysis of care experiences at the VHA among veterans of Black and White race and male and female sex.

This qualitative study used a technique termed freelisting, an anthropologic technique eliciting responses in list form, at an urban academic VHA medical center from August 2, 2021, to February 9, 2022. Participants included veterans with chronic hypertension. The length of individual lists, item order in those lists, and item frequency across lists were used to calculate a salience score for each item, allowing comparison of salient words and topics within and across different groups. Participants were asked about current perceptions of VHA care, challenges in the past year, virtual care, suggestions for change, and experiences of racism. Data were analyzed from February 10 through September 30, 2022.

The Smith salience index, which measures the frequency and rank of each word or phrase, was calculated for each group.

Responses from 49 veterans (12 Black men, 12 Black women, 12 White men, and 13 White women) were compared by race (24 Black and 25 White) and sex (24 men and 25 women). The mean (SD) age was 64.5 (9.2) years. Some positive items were salient across race and sex, including "good medical care" and telehealth as a "comfortable/great option," as were some negative items, including "long waits/delays in getting care," "transportation/traffic challenges," and "anxiety/stress/fear." Reporting "no impact" of racism on experiences of VHA health care was salient across race and sex; however, reports of race-related unprofessional treatment and active avoidance of race-related conflict differed by race (present among Black and not White participants). Experiences of interpersonal interactions also diverged. "Impersonal/cursory" telehealth experiences and the need for "more personal/attentive" care were salient among women and Black participants, but not men or White participants, who associated VHA care with courtesy and respect.

In this qualitative freelist study of veteran experiences, divergent experiences of interpersonal care by race and sex provided insights for improving equitable, patient-centered VHA care. Future research and interventions could focus on identifying differences across broader categories both within and beyond race and sex and bolstering efforts to improve respect and personalized care to diverse veteran populations.

We described the oral nirmatrelvir/ritonavir (NMV/r) and molnupiravir (MOV) uptake among a subgroup of highly vaccinated adults in a US national prospective cohort who were infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) between 12/2021 and 10/2022.

We estimate antiviral uptake within 5 days of SARS-CoV-2 infection, as well as age- and gender-adjusted antiviral uptake prevalence ratios by antiviral eligibility (based on age and comorbidities), sociodemographic characteristics, and clinical characteristics including vaccination status and history of long coronavirus disease 2019 (COVID).

NMV/r uptake was 13.6% (95% CI, 11.9%-15.2%) among 1594 participants, and MOV uptake was 1.4% (95% CI, 0.8%-2.1%) among 1398 participants. NMV/r uptake increased over time (1.9%; 95% CI, 1.0%-2.9%; between 12/2021 and 3/2022; 16.5%; 95% CI, 13.0%-20.0%; between 4/2022 and 7/2022; and 25.3%; 95% CI, 21.6%-29.0%; between 8/2022 and 10/2022). Participants age ≥65 and those who had comorbidities for severe COVID-19 had higher NMV/r uptake. There was lower NMV/r uptake among non-Hispanic Black participants (7.2%; 95% CI, 2.4%-12.0%; relative to other racial/ethnic groups) and among individuals in the lowest income groups (10.6%; 95% CI, 7.3%-13.8%; relative to higher income groups). Among a subset of 278 participants with SARS-CoV-2 infection after 12/2021 who also had a history of prior SARS-CoV-2 infection, those with (vs without) a history of long COVID reported greater NMV/r uptake (22.0% vs 7.9%; P = .001). Among those prescribed NMV/r (n = 216), 137 (63%; 95% CI, 57%-70%) reported that NMV/r was helpful for reducing COVID-19 symptoms.

Despite proven effectiveness against severe outcomes, COVID-19 antiviral uptake remains low among those with SARS-CoV-2 infection in the United States. Further outreach to providers and patients to improve awareness of COVID-19 oral antivirals and indications is needed.

Cancer patients are highly vulnerable to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Few studies have examined racial disparities of clinical prognosis among gastrointestinal (GI) cancer patients with COVID-19, especially after the approval of COVID-19 vaccines.

We conducted a retrospective study based on the University of California COVID Research Data Set (UC CORDS). Patients aged ≥ 18 with GI cancer as well as SARS-CoV-2 infection between March 10, 2020, and May 8, 2022, were included. We examined racial disparities using multivariable logistic regression.

Among the 1054 GI cancer cases included, 117 (11.1%) patients were Asian and Pacific Islander, 51 (4.8%) were Black patients, 377 (35.8%) were Hispanic patients, 403 (38.2%) were White patients, and 106 (10.1%) belonged to other or unknown races. Fully adjusted logistic models revealed a significantly increased risk of COVID-19-related hospitalization or emergency room visits among the Black (OR = 2.26, 95% CI = 1.08-4.70), the Hispanic (OR = 2.24, 95% CI = 1.48-3.39), and the patients of other or unknown races (OR = 1.80, 95% CI = 1.00-3.26) compared with the White patients. No significant racial disparities in 30-day all-cause mortality and mechanical ventilation rate were found. Vaccination, age, cancer type, recent cancer diagnoses in UC CORDS, metastatic cancer or secondary malignant neoplasm, and Charlson comorbidity index score were associated with the prognosis of GI cancer patients with COVID-19.

GI cancer patients belonging to racial minorities experience worse COVID-19 outcomes. Vaccination status is a crucial factor associated with GI cancer patients' prognosis among different race/ethnicity groups. Targeted communication in the context of cancer is needed to encourage vaccination uptake in this vulnerable population.

Objective: To create an interdisciplinary curriculum to teach key topics at the intersection of women's health, gender-affirming care, and health disparities to internal medicine (IM) residents. Materials and Methods: A core team of faculty from IM, Obstetrics and Gynecology, and Surgery partnered with faculty and fellows from other disciplines and with community experts to design and deliver the curriculum. The resulting curriculum consisted of themed half-day modules, each consisting of three to four inter-related topics, updated and repeated on an ∼3-year cycle. Health equity was a focus of all topics. Module delivery used diverse interactive learning strategies. Modules have been presented to ∼175 residents annually, beginning in 2015. To assess the curriculum, we used formative evaluation methods, using primarily anonymous, electronic surveys, and collected quantitative and qualitative data. Most surveys assessed resident learning by quantifying residents' self-reported comfort with skills taught in the module pre- and postsession. Results: Of 131 residents who completed an evaluation in 2022/23, 121 (90%) "somewhat" or "strongly" agreed with their readiness to perform a range of skills taught in the module. In all previous years where pre- and postsurveys were used to evaluate modules, we observed a consistent meaningful increase in the proportion of residents reporting high levels of comfort with the material. Residents particularly valued interactive teaching methods, and direct learning from community members and peers. Conclusion: Our interdisciplinary curriculum was feasible, valued by trainees, and increased resident learning. The curriculum provides a template to address equity issues across a spectrum of women's and gender-affirming care conditions that can be used by other institutions in implementing similar curricula.

Although many studies have addressed such disparities caused by COVID-19, to our knowledge, no study has focused on the association of race on outcomes for patients with COVID-19 requiring venovenous extracorporeal membrane oxygenation support. The goal of this study was to assess association of race on death and duration on venovenous extracorporeal membrane oxygenation in both the pre-COVID-19 and COVID-19 eras.

We retrospectively reviewed the Extracorporeal Life Support Organization registry and included adults (≥18 years) who required venovenous extracorporeal membrane oxygenation between January 2019 and April 2021. We performed descriptive statistics and multivariable logistic regression. Our primary outcomes were death and extracorporeal membrane oxygenation duration.

A total of 7477 patients were included after excluding 340 patients (4.3%) who were missing race data. In the COVID-19 era, 1474 of 2777 COVID-19-positive patients (53.1%) died. Our regression model suggested somewhat of a protective effect on death for Black and multiple race patients. Additionally, a diagnosis of COVID-19 and patients in the COVID-19 era in general, irrespective of COVID-19 diagnosis, had higher odds of death. Hispanic patients had the longest average venovenous extracorporeal membrane oxygenation run times.

Our study using data from the international Extracorporeal Life Support Organization Registry provides updated data on patients supported with venovenous extracorporeal membrane oxygenation in the pre-COVID-19 and COVID-19 eras between 2019 and 2021 with a focus on race. Patients in the COVID-19 era group also had higher mortality compared with those in the pre-COVID-19 era even after being adjusted for COVID-19 diagnosis. Black and multiple races appeared somewhat protective in terms of death. Hispanic race was associated with longer venovenous extracorporeal membrane oxygenation duration.

Men who have sex with men and identify as persons of color (MSM of color) are significantly impacted by HIV in the United States. The COVID-19 pandemic may have disproportionately exacerbated HIV-related disparities among MSM of color by affecting sexual networking behaviors and disrupting access to sexual health care. The current study explored the impact of COVID-19 on sexual networking and HIV/sexually transmitted infection (STI) prevention behaviors among MSM of color in Boston, MA. Eighteen semi-structured interviews were conducted via the 2020-2021 Boston sample of the National HIV Behavioral Surveillance (NHBS) project. Eligible participants were at least 18 years old, identified as a man or non-binary person assigned male at birth and as a person of color, and endorsed ever having sex with men. Interviews were coded using inductive and deductive approaches, and themes were extracted using thematic analysis. When participants were asked about the impact of COVID-19 on sexual networking and HIV/STI prevention, the following themes emerged: (1) differing interpretations of COVID-19 public health guidance, (2) behavior change to meet social and sexual needs, (3) limited or changed access to HIV/STI prevention services; and (4) avoidance of healthcare appointments. Overall, the pandemic affected sexual networking and HIV/STI prevention behaviors among MSM of color. Though changes in sexual networking varied, most participants decreased in-person networking, increased dating app use, and prioritized longer-term relationships. Despite loosening of restrictions, these impacts may persist and should inform the adaptation of sexual networking guidance and interventions to mitigate HIV-related disparities in communities of color.

With the onset of COVID-19, there were rapid changes in healthcare delivery as remote access became the norm. The aim of this study was to determine the impact of changes in healthcare delivery during the COVID-19 pandemic on patients with inflammatory bowel disease (IBD), in both well-resourced and vulnerable populations.

Using a mixed methods, observational study design, patients receiving IBD care at a university or a safety-net hospital were identified by the electronic health record. Patient demographics, IBD history, and disease activity were acquired from the electronic health record. IBD-related outcomes were compared from the onset of the pandemic in the United States until December 2020 (COVID-19 pandemic year 1) and compared with outcomes in the previous year. A subset of participants provided their perspective on how changes in healthcare delivery and financial stability impacted their IBD through a standardized questionnaire and semi-structured interview.

Data from a total of 1449 participants were captured, 1324 at the tertiary care university hospital and 125 at the safety-net hospital. During COVID-19, there was a decrease in healthcare utilization at both sites. Race/ethnicity and primary language were not associated with IBD-related hospitalizations or admissions. Patients that were employed and those with insurance had a higher number of IBD-related emergency department visits at both the university and safety-net hospitals (P = .03 and P = .01, respectively). Patients who did not speak English were more likely to report challenges using technology with telehealth and difficulty contacting IBD providers.

For IBD populations, during COVID-19, in both hospital settings, emergency department visits, hospitalizations, outpatient surgery, and clinic visits were reduced compared with the year prior. Patients with lower socioeconomic status and limited English proficiency reported facing more challenges with changes to healthcare delivery, healthcare access, and conveying changes in IBD activity. These results highlight the need for payors and providers to specifically attend to those populations most susceptible to these systemic and lasting changes in care delivery and promote greater equity in healthcare.

The impact of social isolation and loneliness (SIL) was heightened during the COVID-19 pandemic. Although the pandemic disproportionately affected racial/ ethnic minorities, no studies have investigated the ramifications of the pandemic on SIL among these populations. This study aimed to determine the prevalence and pervasiveness of SIL during the COVID-19 pandemic on minority communities.

This was a single center, cross sectional study conducted by scientists from the University of Rochester Medical Center (URMC) working in collaboration with members of the Rochester community. Adult patients presenting to the emergency department at URMC who identified themselves as belonging to minority communities were asked to complete a survey that comprised questions from the Lubben Social Network Scale-6 and questions from the Campaign to End Loneliness Measurement Tool. We analyzed the percentage of SIL and conducted linear regression models to study the association between these outcomes and race/ ethnicity, age, gender, chronic disease status and the frequency of hospitalizations.

A total of 1,029 subjects completed the survey. Social isolation was reported by 375 (37%) persons. Those of Latinx ethnicity had higher prevalence of social isolation (41%) compared to those of Black/African American race (36%) and also had higher degrees of isolation (14.8%) (15.42; p = 0.07). Loneliness was documented by 215 (21%) for the cohort with no differences based on race or ethnicity.

Social isolation was common among minority communities during the pandemic but loneliness was less pervasive. The study highlights the need to address the specific needs of these populations.

Patients with septic shock have the highest risk of death from sepsis, however, racial disparities in mortality outcomes in this cohort have not been rigorously investigated. Our objective was to describe the association between race/ethnicity and mortality in patients with septic shock.

Our study is a retrospective cohort study of adult patients in the OneFlorida Data Trust (Florida, United States of America) admitted with septic shock between January 2012 and July 2018. We identified patients as having septic shock if they received vasopressors during their hospital encounter and had either an explicit International Classification of Disease (ICD) code for sepsis, or had an infection ICD code and received intravenous antibiotics. Our primary outcome was 90-day mortality. Our secondary outcome was in-hospital mortality. Multiple logistic regression with Least Absolute Shrinkage and Selection Operator (LASSO) for variable selection was used to assess associations.

There were 13,932 patients with septic shock in our cohort. The mean age was 61 years (SD 16), 68% of the cohort identified as White (n = 9419), 28% identified as Black (n = 3936), 2% (n = 294) identified as Hispanic ethnicity, and 2% as other races not specified in the previous groups (n = 283). In our logistic regression model for 90-day mortality, patients identified as Black had 1.57 times the odds of mortality (95% CI 1.07-2.29, p = 0.02) compared to White patients. Other significant predictors included mechanical ventilation (OR 3.66, 95% CI 3.35-4.00, p < 0.01), liver disease (OR 1.75, 95% CI 1.59-1.93, p < 0.01), laboratory components of the Sequential Organ Failure Assessment score (OR 1.18, 95% CI 1.16-1.21, p < 0.01), lactate (OR 1.10, 95% CI 1.08-1.12, p < 0.01), congestive heart failure (OR 1.19, 95% CI 1.10-1.30, p < 0.01), human immunodeficiency virus (OR 1.35, 95% CI 1.04-1.75, p = 0.03), age (OR 1.04, 95% CI 1.04-1.04, p < 0.01), and the interaction between age and race (OR 0.99, 95% CI 0.99-1.00, p < 0.01). Among younger patients (<45 years), patients identified as Black accounted for a higher proportion of the deaths. Results were similar in the in-hospital mortality model.

In this retrospective study of septic shock patients, we found that patients identified as Black had higher odds of mortality compared to patients identified as non-Hispanic White. Our findings suggest that the greatest disparities in mortality are among younger Black patients with septic shock.

National Institutes of Health National Center for Advancing Translational Sciences (1KL2TR001429); National Institute of Health National Institute of General Medical Sciences (1K23GM144802).

COVID-19 has had a disproportionate impact on the most disadvantaged members of society, including minorities and those with disabling chronic illnesses such as schizophrenia. We examined the pandemic's impacts among New York State's Medicaid beneficiaries with schizophrenia in the immediate post-pandemic surge period, with a focus on equity of access to critical healthcare. We compared changes in utilization of key behavioral health outpatient services and inpatient services for life-threatening conditions between the pre-pandemic and surge periods for White and non-White beneficiaries. We found racial and ethnic differences across all outcomes, with most differences stable over time. The exception was pneumonia admissions-while no differences existed in the pre-pandemic period, Black and Latinx beneficiaries were less likely than Whites to be hospitalized in the surge period despite minorities' heavier COVID-19 disease burden. The emergence of racial and ethnic differences in access to scarce life-preserving healthcare may hold lessons for future crises.

Faith-based organisations (FBOs) and religious actors increase vaccine confidence and uptake among ethnoracially minoritised communities in low-income and middle-income countries. During the COVID-19 pandemic and the subsequent vaccine rollout, global organisations such as the WHO and UNICEF called for faith-based collaborations with public health agencies (PHAs). As PHA-FBO partnerships emerge to support vaccine uptake, the scoping review aims to: (1) outline intervention typologies and implementation frameworks guiding interventions; (2) describe the roles of PHAs and FBOs in the design, implementation and evaluation of strategies and (3) synthesise outcomes and evaluations of PHA-FBO vaccine uptake initiatives for ethnoracially minoritised communities.

We will perform six library database searches in PROQUEST-Public Health, OVID MEDLINE, Cochrane Library, CINAHL, SCOPUS- all, PROQUEST - Policy File index; three theses repositories, four website searches, five niche journals and 11 document repositories for public health. These databases will be searched for literature that describe partnerships for vaccine confidence and uptake for ethnoracially minoritised populations, involving at least one PHA and one FBO, published in English from January 2011 to October 2023. Two reviewers will pilot-test 20 articles to refine and finalise the inclusion/exclusion criteria and data extraction template. Four reviewers will independently screen and extract the included full-text articles. An implementation science process framework outlining the design, implementation and evaluation of the interventions will be used to capture the array of partnerships and effectiveness of PHA-FBO vaccine uptake initiatives.

This multiphase Canadian Institutes of Health Research (CIHR) project received ethics approval from the University of Toronto. Findings will be translated into a series of written materials for dissemination to CIHR, and collaborating knowledge users (ie, regional and provincial PHAs), and panel presentations at conferences to inform the development of a best-practices framework for increasing vaccine confidence and uptake.

There is a need to assess neighborhood-level factors driving COVID-19 disparities across racial and ethnic groups.

To use census tract-level data to investigate neighborhood-level factors contributing to racial and ethnic group-specific COVID-19 case rates in California.

Quasi-Poisson generalized linear models were used to identify neighborhood-level factors associated with COVID-19 cases. In separate sequential models for Hispanic, Black, and Asian, we characterized the associations between neighborhood factors on neighborhood COVID-19 cases. Subanalyses were conducted on neighborhoods with majority Hispanic, Black, and Asian residents to identify factors that might be unique to these neighborhoods. Geographically weighted regression using a quasi-Poisson model was conducted to identify regional differences.

All COVID-19 cases and tests reported through January 31, 2021, to the California Department of Public Health. Neighborhood-level data from census tracts were obtained from American Community Survey 5-year estimates (2015-2019), United States Census (2010), and United States Department of Housing and Urban Development.

The neighborhood factors associated with COVID-19 case rate were racial and ethnic composition, age, limited English proficiency (LEP), income, household size, and population density. LEP had the largest influence on the positive association between proportion of Hispanic residents and COVID-19 cases (- 2.1% change). This was also true for proportion of Asian residents (- 1.8% change), but not for the proportion of Black residents (- 0.1% change). The influence of LEP was strongest in areas of the Bay Area, Los Angeles, and San Diego.

Neighborhood-level contextual drivers of COVID-19 burden differ across racial and ethnic groups.

Scientifically, there is little genetic variation among humans and race has no biological basis. However, medical school preclinical curricula tend to misrepresent race and reify biologically essentialist explanations for disease. The social construct of race is, therefore, used to inform health care providers' treatment decisions. Use of race-based medicine has been identified as a contributor to racial health disparities, spurring a growing movement to challenge race essentialism and race-based medicine. The current research tested an intervention that educates college students about the historical construction of racial categories in the United States.

Participants who were randomly assigned to the intervention condition read an article highlighting the history of the sociopolitical construction of race. They were then prompted to discuss in dyads how racial categories were created and changed over history, and-in light of all this-the appropriateness of race-based medicine. Those assigned to the control condition advanced directly to the outcome measures.

Participants in the intervention condition reported less race essentialism, less support for race-based medicine, and greater belief that race-based medicine contributes to racial health disparities. Findings were not moderated by premed status.

Our data provide initial evidence that our interactive intervention could effectively reduce biological essentialism and support for race-based medicine in both premed and non-premed students.

This intervention has the potential to shape the way health care providers in-training understand race, their internalization of biologically essentialist explanations for disease, and willingness to adopt race-based treatment plans.

Coronavirus Disease 2019 (COVID-19) has been linked to severe consequences among hospitalized patients diagnosed with pulmonary hypertension (PH), as evidenced by a limited number of studies. Our retrospective study employed the National Inpatient Sample (NIS) database to evaluate in-hospital mortality and various clinical outcomes in COVID-19 patients with and without PH. This study included all patients ages 18 years and above who were hospitalized in the United States from January 1,2020 to December 31, 2020 with a COVID-19 diagnosis. The patients were then divided into 2 cohorts based on their PH status. After multivariate adjustment, we discovered that COVID-19 patients with PH experienced considerably higher in-hospital mortality, longer hospital stays, and higher costs of hospitalization when compared to COVID-19 patients without PH. Moreover, we observed an increased dependence on invasive and noninvasive positive pressure ventilation among COVID-19 patients with PH, indicating more severe respiratory failure. Our findings suggest that COVID-19 patients with PH had a heightened risk of acute pulmonary embolism and myocardial infarction while hospitalized. Lastly, among COVID-19 patients with PH, Hispanic and Native American patients demonstrated a persistently higher risk of in-hospital mortality compared to other racial groups. To our knowledge, this is the most comprehensive study of outcomes for COVID-19 patients with PH. The observed inpatient mortality appears to be driven by in-hospital complications, particularly pulmonary embolism. Given the substantial mortality and complications associated with COVID-19 and PH, we advocate for SARS-CoV-2 vaccination and the implementation of aggressive nonpharmacological preventive measures.

Most respiratory virus surveillance relies on medically attended respiratory illness, but an understanding of the true patterns of infection independent of care-seeking behaviour would enhance clinical and public health responses to respiratory virus outbreaks. We evaluated the potential of decedent surveillance by estimating the burden of respiratory virus infection in decedents in a large, urban medical examiner's office.

Observational.

In 2020-2022, we tested nasopharyngeal swabs from 4121 decedents in Detroit, Michigan for 15 respiratory viruses, including SARS-CoV-2, respiratory syncytial virus, and influenza virus A and B. We analysed infection prevalence over time and by age, sex, race/ethnicity, and manner of death.

Of 4113 valid tests, 30.2% were positive for at least one virus, and 6.1% were positive for multiple viruses. All viruses were detected except for influenza A/H1N1 and influenza B. The most prevalent viruses were SARS-CoV-2 (15.7%), rhinovirus (11.2%), and adenovirus (4.9%), which were detected in all months. Most viruses exhibited decreasing prevalence with age, higher prevalence among Black and Hispanic than among White decedents and lower prevalence among deaths from natural causes; SARS-CoV-2 was a notable exception to the patterns by age and manner of death, instead reflecting community trends in catchment counties.

There was high prevalence and diversity of respiratory viruses in decedents entering a large, urban medical examiner's office. Decedent surveillance could offer a clearer picture of the true underlying burden of infection, motivating public health priorities for intervention and vaccine development, and augmenting data for real-time response to respiratory virus outbreaks.