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Underserved populations experience higher levels of pain. These disparities persist even after controlling for the objective severity of diseases like osteoarthritis, as graded by human physicians using medical images, raising the possibility that underserved patients’ pain stems from factors external to the knee, such as stress. Here we use a deep learning approach to measure the severity of osteoarthritis, by using knee X-rays to predict patients’ experienced pain. We show that this approach dramatically reduces unexplained racial disparities in pain. Relative to standard measures of severity graded by radiologists, which accounted for only 9% (95% confidence interval (CI), 3–16%) of racial disparities in pain, algorithmic predictions accounted for 43% of disparities, or 4.7× more (95% CI, 3.2–11.8×), with similar results for lower-income and less-educated patients. This suggests that much of underserved patients’ pain stems from factors within the knee not reflected in standard radiographic measures of severity. We show that the algorithm’s ability to reduce unexplained disparities is rooted in the racial and socioeconomic diversity of the training set. Because algorithmic severity measures better capture underserved patients’ pain, and severity measures influence treatment decisions, algorithmic predictions could potentially redress disparities in access to treatments like arthroplasty. An algorithmic, machine-learning approach to measuring severe pain from osteoarthritis applied to X-ray images of knees suggests that reported disparities in knee pain in underserved populations can be reduced by comparison with use of standard radiographic measures of disease severity.

This cross-sectional study examines trends in the suicide rate among racial and ethnic subgroups in the United States from 1999 to 2019.

Racial and ethnic minority groups, such as Black, Hispanic, American Indian or Alaska Native, and Asian or Pacific Islander persons, often experience higher rates of severe influenza disease. To describe rates of influenza-associated hospitalization, intensive care unit (ICU) admission, and in-hospital death by race and ethnicity over 10 influenza seasons. This cross-sectional study used data from the Influenza-Associated Hospitalization Surveillance Network (FluSurv-NET), which conducts population-based surveillance for laboratory-confirmed influenza-associated hospitalizations in selected counties, representing approximately 9% of the US population. Influenza hospitalizations from the 2009 to 2010 season to the 2018 to 2019 season were analyzed. Data were analyzed from October 2020 to July 2021. The main outcomes were age-adjusted and age-stratified rates of influenza-associated hospitalization, ICU admission, and in-hospital death by race and ethnicity overall and by influenza season. Among 113 352 persons with an influenza-associated hospitalization (34 436 persons [32.0%] aged ≥75 years; 61 009 [53.8%] women), 70 225 persons (62.3%) were non-Hispanic White (White), 24 850 persons (21.6%) were non-Hispanic Black (Black), 11 903 persons (10.3%) were Hispanic, 5517 persons (5.1%) were non-Hispanic Asian or Pacific Islander, and 857 persons (0.7%) were non-Hispanic American Indian or Alaska Native. Among persons aged younger than 75 years and compared with White persons of the same ages, Black persons were more likely to be hospitalized (eg, age 50-64 years: rate ratio [RR], 2.50 95% CI, 2.43-2.57) and to be admitted to an ICU (eg, age 50-64 years: RR, 2.09; 95% CI, 1.96-2.23). Among persons aged younger than 50 years and compared with White persons of the same ages, American Indian or Alaska Native persons were more likely to be hospitalized (eg, age 18-49 years: RR, 1.72; 95% CI, 1.51-1.96) and to be admitted to an ICU (eg, age 18-49 years: RR, 1.84; 95% CI, 1.40-2.42). Among children aged 4 years or younger and compared with White children, hospitalization rates were higher in Black children (RR, 2.21; 95% CI, 2.10-2.33), Hispanic children (RR, 1.87; 95% CI, 1.77-1.97), American Indian or Alaska Native children (RR, 3.00; 95% CI, 2.55-3.53), and Asian or Pacific Islander children (RR, 1.26; 95% CI, 1.16-1.38), as were rates of ICU admission (Black children: RR, 2.74; 95% CI, 2.43-3.09; Hispanic children: RR, 1.96; 95% CI, 1.73-2.23; American Indian and Alaska Native children: RR, 3.51; 95% CI, 2.45-5.05). In this age group and compared with White children, in-hospital death rates were higher among Hispanic children (RR, 2.98; 95% CI, 1.23-7.19), Black children (RR, 3.39; 95% CI, 1.40-8.18), and Asian or Pacific Islander children (RR, 4.35; 95% CI, 1.55-12.22). Few differences were observed in rates of severe influenza-associated outcomes by race and ethnicity among adults aged 75 years or older. For example, in this age group, compared with White adults, hospitalization rates were slightly higher only among Black adults (RR, 1.05; 95% CI 1.02-1.09). Overall, Black persons had the highest age-adjusted hospitalization rate (68.8 [95% CI, 68.0-69.7] hospitalizations per 100 000 population) and ICU admission rate (11.6 [95% CI, 11.2-11.9] admissions per 100 000 population). This cross-sectional study found racial and ethnic disparities in rates of severe influenza-associated disease. These data identified subgroups for whom improvements in influenza prevention efforts could be targeted.

This cross-sectional study evaluates changes in reporting practices for race, sex, and socioeconomic status in randomized clinical trials in 2015 vs 2019.

Background The Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) has been shown to have positive effects in promoting healthy birth outcomes in the United States. We explored whether such effects held prior to and during the most recent Great Recession to improve birth outcomes and reduce differences among key socio-demographic groups. Methods We used a pooled cross-sectional time series design to study pregnant women and their infants with birth certificate data. We included Medicaid and uninsured births from Washington State and Florida ( n  = 226,835) before (01/2005–03/2007) and during (12/2007–06/2009) the Great Recession. Interactions between WIC enrollment and key socio-demographic groupings were analyzed for binary and continuous birth weight outcomes. Results Our study found beneficial WIC interaction effects on birth weight. For race, prenatal care, and maternal age we found significantly better birth weight outcomes in the presence of WIC compared to those without WIC. For example, being Black with WIC was associated with an increase in infant birth weight of 53.5 g (baseline) (95% CI = 32.4, 74.5) and 58.0 g (recession) (95% CI = 27.8, 88.3). For most groups this beneficial relationship was stable over time. Conclusions This paper supports previous research linking maternal utilization of WIC services during pregnancy to improved birth weight (both reducing LBW and increasing infant birth weight in grams) among some high-disadvantage groups. WIC appears to have been beneficial at decreasing disparity gaps in infant birth weight among the very young, Black, and late/no prenatal care enrollees in this high-need population, both before and during the Great Recession. Gaps are still present among other social and demographic characteristic groups (e.g., for unmarried mothers) for whom we did not find WIC to be associated with any detectable value in promoting better birth weight outcomes. Future research needs to examine how WIC (and/or other maternal and child health programs) could be made to work better and reach farther to address persistent disparities in birth weight outcomes. Additionally, in preparation for future economic downturns it will be important to determine how to preserve and, if possible, expand WIC services during times of increased need. Trial registration Not applicable, this article reports only on secondary retrospective data (no health interventions with human participants were carried out).

Vaccine hesitancy could become a significant impediment to addressing the COVID-19 pandemic. The current study examined the prevalence of COVID-19 vaccine hesitancy and factors associated with vaccine intentions. A national panel survey by the National Opinion Research Center (NORC) was designed to be representative of the US household population. Sampled respondents were invited to complete the survey between May 14 and 18, 2020 in English or Spanish. 1,056 respondents completed the survey—942 via the web and 114 via telephone. The dependent variable was assessed by the item “If a vaccine against the coronavirus becomes available, do you plan to get vaccinated, or not?” Approximately half (53.6%) reported intending to be vaccinated, 16.7% did not intend, and 29.7% were unsure. In the adjusted stepwise multinominal logistic regression, Black and Hispanic respondents were significantly less likely to report intending to be vaccinated as were respondents who were females, younger, and those who were more politically conservative. Compared to those who reported positive vaccine intentions, respondents with negative vaccine intentions were significantly less likely to report that they engaged in the COVID-19 prevention behaviors of wearing masks (aOR = 0.53, CI = 0.37–0.76) and social distancing (aOR = 0.22, CI = 0.12–0.42). In a sub-analysis of reasons not to be vaccinated, significant race/ethnic differences were observed. This national survey indicated a modest level of COVID-19 vaccine intention. These data suggest that public health campaigns for vaccine uptake should assess in greater detail the vaccine concerns of Blacks, Hispanics, and women to tailor programs.

Recent work has emphasized the benefits of patient–physician concordance on clinical care outcomes for underrepresented minorities, arguing it can ameliorate outgroup biases, boost communication, and increase trust. We explore concordance in a setting where racial disparities are particularly severe: childbirth. In the United States, Black newborns die at three times the rate of White newborns. Results examining 1.8 million hospital births in the state of Florida between 1992 and 2015 suggest that newborn–physician racial concordance is associated with a significant improvement in mortality for Black infants. Results further suggest that these benefits manifest during more challenging births and in hospitals that deliver more Black babies. We find no significant improvement in maternal mortality when birthing mothers share race with their physician.

In this large study, the mortality benefits of reducing levels of fine particulate matter air pollution were greater for low-income and higher-income Black persons and for low-income White persons than for higher-income White persons.

Automated speech recognition (ASR) systems, which use sophisticated machine-learning algorithms to convert spoken language to text, have become increasingly widespread, powering popular virtual assistants, facilitating automated closed captioning, and enabling digital dictation platforms for health care. Over the last several years, the quality of these systems has dramatically improved, due both to advances in deep learning and to the collection of large-scale datasets used to train the systems. There is concern, however, that these tools do not work equally well for all subgroups of the population. Here, we examine the ability of five state-of-the-art ASR systems—developed by Amazon, Apple, Google, IBM, and Microsoft—to transcribe structured interviews conducted with 42 white speakers and 73 black speakers. In total, this corpus spans five US cities and consists of 19.8 h of audio matched on the age and gender of the speaker. We found that all five ASR systems exhibited substantial racial disparities, with an average word error rate (WER) of 0.35 for black speakers compared with 0.19 for white speakers. We trace these disparities to the underlying acoustic models used by the ASR systems as the race gap was equally large on a subset of identical phrases spoken by black and white individuals in our corpus. We conclude by proposing strategies—such as using more diverse training datasets that include African American Vernacular English—to reduce these performance differences and ensure speech recognition technology is inclusive.