Disparities by race/ethnicity and socioeconomic status (SES) exist in rehospitalization rates and inpatient mortality rates. Few studies have examined how length of stay (LOS, a measure of hospital efficiency/quality) differs by race/ethnicity and SES. This study's objective was to determine whether differences in risk-adjusted LOS exist by race/ethnicity and SES Using a retrospective cohort of 1,432,683 medical and surgical discharges, we compared risk-adjusted LOS, in days, by race/ ethnicity and SES (median household income by patient ZIP code in quartiles), using generalized linear models controlling for demographic and clinical factors, and differences between hospitals and between diagnoses. White patients were on average older than both Black and Hispanic patients, had more chronic conditions, and had a higher inpatient mortality risk. In adjusted analyses, Black patients had a significantly longer LOS than White patients (0.25-day difference when discharged to home and 0.23-day difference when discharged to non-home destinations, both P<.001); there was no difference between Hispanic and White patients. Wealthier patients had a shorter LOS than poorer patients (0.16-day difference when discharged to home and 0.06-day difference when discharged to nonhome destinations, both P<.001). These differences by race/ethnicity reversed for Medicaid patients. Disparities in LOS exist based on a patient's race/ethnicity and SES. Black and poorer patients, but not Hispanic patients, have longer LOS compared to White and wealthier patients. In aggregate, these differences may be related to trust and implicit bias and have implications for use of LOS as a quality metric. Future research should examine the drivers of these disparities.

Machine learning (ML) algorithms may play a role in predicting the adverse health impacts of climate-sensitive extreme weather events because accurate prediction of such effects can guide proactive clinical and policy decisions. To systematically review the literature that describe ML algorithms that predict health outcomes from climate-sensitive extreme weather events. A comprehensive literature search was performed in the following databases from inception–October 2022: Ovid MEDLINE, Ovid EMBASE, The Cochrane Library, Web of Science, bioRxiv, medRxiv, Institute of Electrical and Electronic Engineers, Google Scholar, and Engineering Village. The retrieved studies were then screened for eligibility against predefined inclusion/exclusion criteria. The studies were then qualitatively synthesized based on the type of extreme weather event. Gaps in the literature were identified based on this synthesis. Of the 6096 records screened, seven studies met the inclusion criteria. Six of the studies predicted health outcomes from heat waves, and one for flooding. Health outcomes described included 1) all-cause non-age standardized mortality rates, 2) heat-related conditions and 3) post-traumatic stress disorder. Prediction models were developed using six validated ML techniques including non-linear exponential regression, logistic regression, spatiotemporal Integrated Laplace Approximation (INLA), random forest and decision tree methods (DT), and support vector machines (SVM). Use of ML algorithms to assess adverse health impacts from climate-sensitive extreme weather events is possible. However, to fully utilize these ML techniques, better quality data suitable for use is desirable. Development of data standards for climate change and health may help ensure model robustness and comparison across space and time. Future research should also consider health equity implications.

Importance: Clinician specialization in the care of nursing home (NH) residents or patients in skilled nursing facilities (SNFs) has become increasingly common. It is not known whether clinicians focused on NH care, often referred to as SNFists (ie, physicians, nurse practitioners, and physician assistants concentrating their practice in the NH or SNF setting), are associated with a reduced likelihood of burdensome transitions in the last 90 days of life for residents, which are a marker of poor-quality end-of-life (EOL) care.Objective: To quantify the association between receipt of care from an SNFist and quality of EOL care for NH residents.Design, Setting, and Participants: This cohort study analyzed Medicare fee-for-service claims for a nationally representative 20% sample of beneficiaries to examine burdensome transitions among NH decedents at the EOL from January 1, 2013, through December 31, 2019. Statistical analyses were conducted from December 2022 to June 2023.Exposure: Receipt of care from an SNFist, defined as physicians and advanced practitioners who provided 80% or more of their evaluation and management visits in NHs annually.Main Outcomes and Measures: This study used augmented inverse probability weighting in analyses of Medicare fee-for-service claims for a nationally representative 20% sample of beneficiaries. Main outcomes included 4 measures of burdensome transitions: (1) hospital transfer in the last 3 days of life; (2) lack of continuity in NHs after hospitalization in the last 90 days of life; (3) multiple hospitalizations in the last 90 days of life for any reason or any hospitalization for pneumonia, urinary tract infection, dehydration, or sepsis; and (4) any hospitalization in the last 90 days of life for an ambulatory care–sensitive condition.Results: Of the 2 091 954 NH decedents studied (mean [SD] age, 85.4 [8.5] years; 1 470 724 women [70.3%]), 953 722 (45.6%) received care from SNFists and 1 138 232 (54.4%) received care from non-SNFists; 422 575 of all decedents (20.2%) experienced a burdensome transition at the EOL. Receipt of care by an SNFist was associated with a reduced risk of (1) hospital transfer in the last 3 days of life (−1.6% [95% CI, −2.5% to −0.8%]), (2) lack of continuity in NHs after hospitalization (−4.8% [95% CI, −6.7% to −3.0%]), and (3) decedents experiencing multiple hospitalizations for any reason or any hospitalization for pneumonia, urinary tract infection, dehydration, or sepsis (−5.8% [95% CI, −10.1% to −1.7%]). There was not a statistically significant association with the risk of hospitalization for an ambulatory care–sensitive condition in the last 90 days of life (0.0% [95% CI, −14.7% to 131.7%]).Conclusions and Relevance: This study suggests that SNFists may be an important resource to improve the quality of EOL care for NH residents.