Ambient heat exposure patterns and emergency department visits and hospitalizations among medicare beneficiaries 2008-2019.

OBJECTIVE: To assess the association between ambient heat and all-cause and cause-specific emergency department (ED) visits and acute hospitalizations among Medicare beneficiaries in the conterminous United States. DESIGN: Retrospective cohort study. SETTING: Conterminous US from 2008 and 2019. PARTICIPANTS: 2% random sample of all Medicare fee-for-service beneficiaries eligible for Parts A, B, and D. MAIN OUTCOME MEASURES: All-cause and cause-specific (cardiovascular, renal, and heat-related) ED visits and unplanned hospitalizations were identified using primary ICD-9 or ICD-10 diagnosis codes. We measured the association between ambient temperature - defined as daily mean temperature percentile of summer (June through September) - and the outcomes. Hazard ratios and their associated 95% confidence intervals were estimated using multivariable Cox proportional hazards regression, adjusting for individual level demographics, comorbidities, healthcare utilization factors and zip-code level social factors. RESULTS: Among 809,636 Medicare beneficiaries (58% female, 81% non-Hispanic White, 24% <65), older beneficiaries (aged ≥65) exposed to >95th percentile temperature had a 64% elevated adjusted risk of heat-related ED visits (HR [95% CI], 1.64 [1.46,1.85]) and a 4% higher risk of all-cause acute hospitalization (1.04 [1.01,1.06]) relative to <25th temperature percentile. Younger beneficiaries (aged <65) showed increased risk of heat-related ED visits (2.69 [2.23,3.23]) and all-cause ED visits (1.03 [1.01,1.05]). The associations with heat related events were stronger in males and individuals dually eligible for Medicare and Medicaid. No significant differences were observed by climatic region. We observed no significant relationship between temperature percentile and risk of CV-related ED visits or renal-related ED visits. CONCLUSIONS: Among Medicare beneficiaries from 2008 to 2019, exposure to daily mean temperature ≥ 95th percentile was associated with increased risk of heat-related ED visits, with stronger associations seen among beneficiaries <65, males, and patients with low socioeconomic position. Further longitudinal studies are needed to understand the impact of heat duration, intensity, and frequency on cause-specific hospitalization outcomes.

Clinicians Who Practice Primarily in Nursing Homes and the Quality of End-of-Life Care Among Residents.

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.

Building Climate Change into Medical Education: A Society of General Internal Medicine Position Statement.

Building expertise in climate and planetary health among healthcare professionals cannot come with greater urgency as the threats from climate change become increasingly apparent. Current and future healthcare professionals-particularly internists-will increasingly need to understand the interconnectedness of natural systems and human health to better serve their patients longitudinally. Despite this, few national medical societies and accreditation bodies espouse frameworks for climate change and planetary health-related education at the undergraduate (UME), graduate (GME), and continuing (CME) medical education level. As a community of medical educators with an enduring interest in climate change and planetary health, the Society of General Internal Medicine (SGIM) recognizes the need to explicitly define structured educational opportunities and core competencies in both UME and GME as well as pathways for faculty development. In this position statement, we build from the related SGIM Climate and Health position statement, and review and synthesize existing position statements made by US-based medical societies and accreditation bodies that focus on climate change and planetary health-related medical education, identify gaps using Bloom's Hierarchy, and provide recommendations on behalf of SGIM regarding the development of climate and planetary health curricula development. Identified gaps include (1) limited systematic approach to climate and planetary health medical education at all levels; (2) minimal emphasis on learner-driven approaches; (3) limited focus on physician and learner well-being; and (4) limited role for health equity and climate justice. Recommendations include a call to relevant accreditation bodies to explicitly include climate change and planetary health as a competency, extend the structural competency framework to climate change and planetary health to build climate justice, proactively include learners in curricular development and teaching, and ensure resources and support to design and implement climate and planetary health-focused education that includes well-being and resiliency.

Ambient Heat and Risk of Serious Hypoglycemia in Older Adults With Diabetes Using Insulin in the U.S. and Taiwan: A Cross-National Case-Crossover Study.

OBJECTIVE: To measure the association between ambient heat and hypoglycemia-related emergency department visit or hospitalization in insulin users. RESEARCH DESIGN AND METHODS: We identified cases of serious hypoglycemia among adults using insulin aged ≥65 in the U.S. (via Medicare Part A/B/D-eligible beneficiaries) and Taiwan (via National Health Insurance Database) from June to September, 2016-2019. We then estimated odds of hypoglycemia by heat index (HI) percentile categories using conditional logistic regression with a time-stratified case-crossover design. RESULTS: Among ∼2 million insulin users in the U.S. (32,461 hypoglycemia case subjects), odds ratios of hypoglycemia for HI >99th, 95-98th, 85-94th, and 75-84th percentiles compared with the 25-74th percentile were 1.38 (95% CI, 1.28-1.48), 1.14 (1.08-1.20), 1.12 (1.08-1.17), and 1.09 (1.04-1.13) respectively. Overall patterns of associations were similar for insulin users in the Taiwan sample (∼283,000 insulin users, 10,162 hypoglycemia case subjects). CONCLUSIONS: In two national samples of older insulin users, higher ambient temperature was associated with increased hypoglycemia risk.

Risk Prediction for Atherosclerotic Cardiovascular Disease With and Without Race Stratification.

Importance: Use of race-specific risk prediction in clinical medicine is being questioned. Yet, the most commonly used prediction tool for atherosclerotic cardiovascular disease (ASCVD)-pooled cohort risk equations (PCEs)-uses race stratification. Objective: To quantify the incremental value of race-specific PCEs and determine whether adding social determinants of health (SDOH) instead of race improves model performance. Design, Setting, and Participants: Included in this analysis were participants from the biracial Reasons for Geographic and Racial Differences in Stroke (REGARDS) prospective cohort study. Participants were aged 45 to 79 years, without ASCVD, and with low-density lipoprotein cholesterol level of 70 to 189 mg/dL or non-high-density lipoprotein cholesterol level of 100 to 219 mg/dL at baseline during the period of 2003 to 2007. Participants were followed up to 10 years for incident ASCVD, including myocardial infarction, coronary heart disease death, and fatal and nonfatal stroke. Study data were analyzed from July 2022 to February 2023. Main outcome/measures: Discrimination (C statistic, Net Reclassification Index [NRI]), and calibration (plots, Nam D'Agostino test statistic comparing observed to predicted events) were assessed for the original PCE, then for a set of best-fit, race-stratified equations including the same variables as in the PCE (model C), best-fit equations without race stratification (model D), and best-fit equations without race stratification but including SDOH as covariates (model E). Results: This study included 11 638 participants (mean [SD] age, 61.8 [8.3] years; 6764 female [58.1%]) from the REGARDS cohort. Across all strata (Black female, Black male, White female, and White male participants), C statistics did not change substantively compared with model C (Black female, 0.71; 95% CI, 0.68-0.75; Black male, 0.68; 95% CI, 0.64-0.73; White female, 0.77; 95% CI, 0.74-0.81; White male, 0.68; 95% CI, 0.64-0.71), in model D (Black female, 0.71; 95% CI, 0.67-0.75; Black male, 0.68; 95% CI, 0.63-0.72; White female, 0.76; 95% CI, 0.73-0.80; White male, 0.68; 95% CI, 0.65-0.71), or in model E (Black female, 0.72; 95% CI, 0.68-0.76; Black male, 0.68; 95% CI, 0.64-0.72; White female, 0.77; 95% CI, 0.74-0.80; White male, 0.68; 95% CI, 0.65-0.71). Comparing model D with E using the NRI showed a net percentage decline in the correct assignment to higher risk for male but not female individuals. The Nam D'Agostino test was not significant for all race-sex strata in each model series, indicating good calibration in all groups. Conclusions: Results of this cohort study suggest that PCE performed well overall but had poorer performance in both BM and WM participants compared with female participants regardless of race in the REGARDS cohort. Removal of race or the addition of SDOH did not improve model performance in any subgroup.

Risk stratification models for predicting preventable hospitalization in commercially insured late middle-aged adults with depression.

BACKGROUND: A significant number of late middle-aged adults with depression have a high illness burden resulting from chronic conditions which put them at high risk of hospitalization. Many late middle-aged adults are covered by commercial health insurance, but such insurance claims have not been used to identify the risk of hospitalization in individuals with depression. In the present study, we developed and validated a non-proprietary model to identify late middle-aged adults with depression at risk for hospitalization, using machine learning methods. METHODS: This retrospective cohort study involved 71,682 commercially insured older adults aged 55-64 years diagnosed with depression. National health insurance claims were used to capture demographics, health care utilization, and health status during the base year. Health status was captured using 70 chronic health conditions, and 46 mental health conditions. The outcomes were 1- and 2-year preventable hospitalization. For each of our two outcomes, we evaluated seven modelling approaches: four prediction models utilized logistic regression with different combinations of predictors to evaluate the relative contribution of each group of variables, and three prediction models utilized machine learning approaches - logistic regression with LASSO penalty, random forests (RF), and gradient boosting machine (GBM). RESULTS: Our predictive model for 1-year hospitalization achieved an AUC of 0.803, with a sensitivity of 72% and a specificity of 76% under the optimum threshold of 0.463, and our predictive model for 2-year hospitalization achieved an AUC of 0.793, with a sensitivity of 76% and a specificity of 71% under the optimum threshold of 0.452. For predicting both 1-year and 2-year risk of preventable hospitalization, our best performing models utilized the machine learning approach of logistic regression with LASSO penalty which outperformed more black-box machine learning models like RF and GBM. CONCLUSIONS: Our study demonstrates the feasibility of identifying depressed middle-aged adults at higher risk of future hospitalization due to burden of chronic illnesses using basic demographic information and diagnosis codes recorded in health insurance claims. Identifying this population may assist health care planners in developing effective screening strategies and management approaches and in efficient allocation of public healthcare resources as this population transitions to publicly funded healthcare programs, e.g., Medicare in the US.

The impact of commercial health datasets on medical research and health-care algorithms.

As the health-care industry emerges into a new era of digital health driven by cloud data storage, distributed computing, and machine learning, health-care data have become a premium commodity with value for private and public entities. Current frameworks of health data collection and distribution, whether from industry, academia, or government institutions, are imperfect and do not allow researchers to leverage the full potential of downstream analytical efforts. In this Health Policy paper, we review the current landscape of commercial health data vendors, with special emphasis on the sources of their data, challenges associated with data reproducibility and generalisability, and ethical considerations for data vending. We argue for sustainable approaches to curating open-source health data to enable global populations to be included in the biomedical research community. However, to fully implement these approaches, key stakeholders should come together to make health-care datasets increasingly accessible, inclusive, and representative, while balancing the privacy and rights of individuals whose data are being collected.

Comparing Hospital Length of Stay Risk-Adjustment Models in US Value-Based Physician Payments.

BACKGROUNG AND OBJECTIVES: Under the Affordable Care Act, the US Centers for Medicare & Medicaid Services created the Physician Value-Based Payment Modifier Program and its successor, the Merit-Based Incentive Payment System, to tie physician payments to quality and cost. The addition of hospital length of stay (LOS) to these value-based physician payment models reflects its increasing importance as a metric of health care cost and efficiency and its association with adverse health outcomes. This study compared the Centers for Medicare & Medicaid Services-endorsed LOS risk-adjustment methodology with a novel methodology that accounts for pre-hospitalization clinical, socioeconomic status (SES), and admission-related factors as influential factors of hospital LOS. METHODS: Using the 2014 New York, Florida, and New Jersey State Inpatient Database, we compared the observed-to-expected LOS of 2373102 adult admissions for 742 medical and surgical diagnosis-related groups (DRGs) by 3 models: ( a ) current risk-adjustment model (CRM), which adjusted for age, sex, number of chronic conditions, Elixhauser comorbidity score, and DRG severity weight, ( b ) CRM but modeling LOS using a generalized linear model (C-GLM), and (c) novel risk-adjustment model (NRM), which added to the C-GLM covariates for race/ethnicity, SES, discharge destination, weekend admission, and individual intercepts for DRGs instead of severity weights. RESULTS: The NRM disadvantaged physicians for fewer medical and surgical DRGs, compared with both the C-GLM and CRM models (medical DRGs: 0.49% vs 13.17% and 10.89%, respectively; surgical DRGs: 0.30% vs 13.17% and 10.98%, respectively). In subgroup analysis, the NRM reduced the proportion of physician-penalizing DRGs across all racial/ethnic and socioeconomic groups, with the highest reduction among Whites, followed by low SES patients, and the lowest reduction among Hispanic patients. CONCLUSIONS: After accounting for pre-hospitalization socioeconomic and clinical factors, the adjusted LOS using the NRM was lower than estimates from the current Centers for Medicare & Medicaid Services-endorsed model. The current model may disadvantage physicians serving communities with higher socioeconomic risks.

Impact of Hurricanes and Associated Extreme Weather Events on Cardiovascular Health: A Scoping Review.

BACKGROUND: The frequency and destructiveness of hurricanes and related extreme weather events (e.g., cyclones, severe storms) have been increasing due to climate change. A growing body of evidence suggests that victims of hurricanes have increased incidence of cardiovascular disease (CVD), likely due to increased stressors around time of the hurricane and in their aftermath. OBJECTIVES: The objective was to systematically examine the evidence of the association between hurricanes (and related extreme weather events) and adverse CVD outcomes with the goal of understanding the gaps in the literature. METHODS: A comprehensive literature search of population-level and cohort studies focused on CVD outcomes (i.e., myocardial infarction, stroke, and heart failure) related to hurricanes, cyclones, and severe storms was performed in the following databases from inception to December 2021: Ovid MEDLINE, Ovid EMBASE, Web of Science, and The Cochrane Library. Studies retrieved were then screened for eligibility against predefined inclusion/exclusion criteria. Studies were then qualitatively synthesized based on the time frame of the CVD outcomes studied and special populations that were studied. Gaps in the literature were identified based on this synthesis. RESULTS: Of the 1,103 citations identified, 48 met our overall inclusion criteria. We identified articles describing the relationship between CVD and extreme weather, primarily hurricanes, based on data from the United States (42), Taiwan (3), Japan (2), and France (1). Outcomes included CVD and myocardial infarction-related hospitalizations (30 studies) and CVVD-related mortality (7 studies). Most studies used a retrospective study design, including one case-control study, 39 cohort studies, and 4 time-series studies. DISCUSSION: Although we identified a number of papers that reported evaluations of extreme weather events and short-term adverse CVD outcomes, there were important gaps in the literature. These gaps included a) a lack of rigorous long-term evaluation of hurricane exposure, b) lack of investigation of hurricane exposure on vulnerable populations regarding issues related to environmental justice, c) absence of research on the exposure of multiple hurricanes on populations, and d) absence of an exploration of mechanisms leading to worsened CVD outcomes. Future research should attempt to fill these gaps, thus providing an important evidence base for future disaster-related policy. https://doi.org/10.1289/EHP11252.

Closing the Knowledge Gap in the Long-Term Health Effects of Natural Disasters: A Research Agenda for Improving Environmental Justice in the Age of Climate Change.

Natural disasters continue to worsen in both number and intensity globally, but our understanding of their long-term consequences on individual and community health remains limited. As climate-focused researchers, we argue that a publicly funded research agenda that supports the comprehensive exploration of these risks, particularly among vulnerable groups, is urgently needed. This exploration must focus on the following three critical components of the research agenda to promote environmental justice in the age of climate change: (1) a commitment to long term surveillance and care to examine the health impacts of climate change over their life course; (2) an emphasis on interventions using implementation science frameworks; (3) the employment of a transdisciplinary approach to study, address, and intervene on structural disadvantage among vulnerable populations. Without doing so, we risk addressing these consequences in a reactive way at greater expense, limiting the opportunity to safeguard communities and vulnerable populations in the era of climate change.

Association between city-wide lockdown and COVID-19 hospitalization rates in multigenerational households in New York City.

BACKGROUND: City-wide lockdowns and school closures have demonstrably impacted COVID-19 transmission. However, simulation studies have suggested an increased risk of COVID-19 related morbidity for older individuals inoculated by house-bound children. This study examines whether the March 2020 lockdown in New York City (NYC) was associated with higher COVID-19 hospitalization rates in neighborhoods with larger proportions of multigenerational households. METHODS: We obtained daily age-segmented COVID-19 hospitalization counts in each of 166 ZIP code tabulation areas (ZCTAs) in NYC. Using Bayesian Poisson regression models that account for spatiotemporal dependencies between ZCTAs, as well as socioeconomic risk factors, we conducted a difference-in-differences study amongst ZCTA-level hospitalization rates from February 23 to May 2, 2020. We compared ZCTAs in the lowest quartile of multigenerational housing to other quartiles before and after the lockdown. FINDINGS: Among individuals over 55 years, the lockdown was associated with higher COVID-19 hospitalization rates in ZCTAs with more multigenerational households. The greatest difference occurred three weeks after lockdown: Q2 vs. Q1: 54% increase (95% Bayesian credible intervals: 22-96%); Q3 vs. Q1: 48% (17-89%); Q4 vs. Q1: 66% (30-211%). After accounting for pandemic-related population shifts, a significant difference was observed only in Q4 ZCTAs: 37% (7-76%). INTERPRETATION: By increasing house-bound mixing across older and younger age groups, city-wide lockdown mandates imposed during the growth of COVID-19 cases may have inadvertently, but transiently, contributed to increased transmission in multigenerational households.

Predictors of life-threatening complications in relatively lower-risk patients hospitalized with COVID-19.

Older individuals with chronic health conditions are at highest risk of adverse clinical outcomes from COVID-19, but there is widespread belief that risk to younger, relatively lower-risk individuals is negligible. We assessed the rate and predictors of life-threatening complications among relatively lower-risk adults hospitalized with COVID-19. Of 3766 adults hospitalized with COVID-19 to three hospitals in New York City from March to May 2020, 963 were relatively lower-risk based on absence of preexisting health conditions. Multivariable logistic regression models examined in-hospital development of life-threatening complications (major medical events, intubation, or death). Covariates included age, sex, race/ethnicity, hypertension, weight, insurance type, and area-level sociodemographic factors (poverty, crowdedness, and limited English proficiency). In individuals ≥55 years old (n = 522), 33.3% experienced a life-threatening complication, 17.4% were intubated, and 22.6% died. Among those <55 years (n = 441), 15.0% experienced a life-threatening complication, 11.1% were intubated, and 5.9% died. In multivariable analyses among those ≥55 years, age (OR 1.03 [95%CI 1.01-1.06]), male sex (OR 1.72 [95%CI 1.14-2.64]), being publicly insured (versus commercial insurance: Medicare, OR 2.02 [95%CI 1.22-3.38], Medicaid, OR 1.87 [95%CI 1.10-3.20]) and living in areas with relatively high limited English proficiency (highest versus lowest quartile: OR 3.50 [95%CI 1.74-7.13]) predicted life-threatening complications. In those <55 years, no sociodemographic factors significantly predicted life-threatening complications. A substantial proportion of relatively lower-risk patients hospitalized with COVID-19 experienced life-threatening complications and more than 1 in 20 died. Public messaging needs to effectively convey that relatively lower-risk individuals are still at risk of serious complications.

Association Between Patient Diversity in Hospitals and Racial/Ethnic Differences in Patient Length of Stay.

BACKGROUND: Hospitals serving a disproportionate share of racial/ethnic minorities have been shown to have poorer quality outcomes. It is unknown whether efficiencies in inpatient care, measured by length of stay (LOS), differ based on the proportion patients served by a hospital who are minorities. OBJECTIVE: To examine the association between the racial/ethnic diversity of a hospital's patients and disparities in LOS. DESIGN: Retrospective cross-sectional study. PARTICIPANTS: One million five hundred forty-six thousand nine hundred fifty-five admissions using the 2017 New York State Inpatient Database from the Healthcare Cost and Utilization Project. MAIN MEASURE: Differences in mean adjusted LOS (ALOS) between White and Black, Hispanic, and Other (Asian, Pacific Islander, Native American, and Other) admissions by Racial/Ethnic Diversity Index (proportion of non-White patients admitted to total patients admitted to that same hospital) in quintiles (Q1 to Q5), stratified by discharge destination. Mean LOS was adjusted for patient demographic, clinical, and admission characteristics and for individual intercepts for each hospital. KEY RESULTS: In both unadjusted and adjusted analysis, Black-White and Other-White mean LOS differences were smallest in the most diverse hospitals (Black-White: unadjusted, -0.07 days [-0.1 to -0.04], and adjusted, 0.16 days [95% CI: 0.16 to 0.16]; Other-White: unadjusted, -0.74 days [95% CI: -0.77 to -0.71], and adjusted, 0.01 days [95% CI: 0.01 to 0.02]). For Hispanic patients, in unadjusted analysis, the mean LOS difference was greatest in the most diverse hospitals (-0.92 days, 95% CI: -0.95 to -0.89) but after adjustment, this was no longer the case. Similar patterns across all racial/ethnic groups were observed after analyses were stratified by discharge destination. CONCLUSION: Mean adjusted LOS differences between White and Black patients, and White and patients of Other race was smallest in most diverse hospitals, but not differences between Hispanic and White patients. These findings may reflect specific structural factors which affect racial/ethnic differences in patient LOS.

Association Between Racial Disparities in Hospital Length of Stay and the Hospital Readmission Reduction Program.

BACKGROUND: On average Black patients have longer LOS than comparable White patients. Longer hospital length of stay (LOS) may be associated with higher readmission risk. However, evidence suggests that the Hospital Readmission Reduction Program (HRRP) reduced overall racial differences in 30-day adjusted readmission risk. Yet, it is unclear whether the HRRP narrowed these LOS racial differences. OBJECTIVE: We examined the relationship between Medicare-insured Black-White differences in average, adjusted LOS (ALOS) and the HRRP's implementation and evaluation periods. METHODS: Using 2009-2017 data from State Inpatient Dataset from New York, New Jersey, and Florida, we employed an interrupted time series analysis with multivariate generalized regression models controlling for patient, disease, and hospital characteristics. Results are reported per 100 admissions. RESULTS: We found that for those discharged home, Black-White ALOS differences significantly widened by 4.15 days per 100 admissions (95% CI: 1.19 to 7.11, P < 0.001) for targeted conditions from before to after the HRRP implementation period, but narrowed in the HRRP evaluation period by 1.84 days per 100 admissions for every year-quarter (95% CI: -2.86 to -0.82, P < 0.001); for those discharged to non-home destinations, there was no significant change between HRRP periods, but ALOS differences widened over the study period. Black-White ALOS differences for non-targeted conditions remained unchanged regardless of HRRP phase and discharge destination. CONCLUSION: Increased LOS for Black patients may have played a role in reducing Black-White disparities in 30-day readmission risks for targeted conditions among patients discharged to home.

Trends in Medical and Surgical Admission Length of Stay by Race/Ethnicity and Socioeconomic Status: A Time Series Analysis.

BACKGROUND: Length of stay (LOS), a metric of hospital efficiency, differs by race/ethnicity and socioeconomic status (SES) and longer LOS is associated with adverse health outcomes. Historically, projects to improve LOS efficiency have yielded LOS reductions by 0.3 to 0.7 days per admission. OBJECTIVE: To assess differences in average adjusted length of stay (aALOS) over time by race/ethnicity, and SES stratified by discharge destination (home or non-home). METHOD: Data were obtained from 2009-2014 Healthcare Cost and Utilization Project State Inpatient Datasets for New York, New Jersey, and Florida. Multivariate generalized linear models were used to examine trends in aALOS differences by race/ethnicity, and by high vs low SES patients (defined first vs fourth quartile of median income by zip code) controlling for patient, disease and hospital characteristics. RESULTS: For those discharged home, racial/ethnic and SES aALOS differences remained stable from 2009 to 2014. However, among those discharged to non-home destinations, Black vs White aALOS differences increased from 0.21 days in Q1 2009, (95% confidence interval (CI): 0.13 to 0.30) to 0.32 days in Q3 2013, (95% CI: 0.23 to 0.40), and for low vs high SES patients from 0.03 days in Q1 2009 (95% CI: -0.04 to 0.1) to 0.26 days, (95% CI: 0.19 to 0.34). Notably, for patients not discharged home, racial/ethnic and SES aALOS differences increased and persisted after Q3 2011, coinciding with the introduction of the Affordable Care Act (ACA). CONCLUSION: Further research to understand the ACA's policy impact on hospital efficiencies, and relationship to racial/ethnic and SES differences in LOS is warranted.

Association between overcrowded households, multigenerational households, and COVID-19: a cohort study.

INTRODUCTION: The role of overcrowded and multigenerational households as a risk factor for COVID-19 remains unmeasured. The objective of this study is to examine and quantify the association between overcrowded and multigenerational households, and COVID-19 in New York City (NYC). METHODS: We conducted a Bayesian ecological time series analysis at the ZIP Code Tabulation Area (ZCTA) level in NYC to assess whether ZCTAs with higher proportions of overcrowded (defined as proportion of estimated number of housing units with more than one occupant per room) and multigenerational households (defined as the estimated percentage of residences occupied by a grandparent and a grandchild less than 18 years of age) were independently associated with higher suspected COVID-19 case rates (from NYC Department of Health Syndromic Surveillance data for March 1 to 30, 2020). Our main measure was adjusted incidence rate ratio (IRR) of suspected COVID-19 cases per 10,000 population. Our final model controlled for ZCTA-level sociodemographic factors (median income, poverty status, White race, essential workers), prevalence of clinical conditions related to COVID-19 severity (obesity, hypertension, coronary heart disease, diabetes, asthma, smoking status, and chronic obstructive pulmonary disease), and spatial clustering. RESULTS: 39,923 suspected COVID-19 cases presented to emergency departments across 173 ZCTAs in NYC. Adjusted COVID-19 case rates increased by 67% (IRR 1.67, 95% CI = 1.12, 2.52) in ZCTAs in quartile four (versus one) for percent overcrowdedness and increased by 77% (IRR 1.77, 95% CI = 1.11, 2.79) in quartile four (versus one) for percent living in multigenerational housing. Interaction between both exposures was not significant (ß interaction = 0.99, 95% CI: 0.99-1.00). CONCLUSIONS: Over-crowdedness and multigenerational housing are independent risk factors for suspected COVID-19. In the early phase of surge in COVID cases, social distancing measures that increase house-bound populations may inadvertently but temporarily increase SARS-CoV-2 transmission risk and COVID-19 disease in these populations.

Racial/ethnic and socioeconomic variations in hospital length of stay: A state-based analysis.

ABSTRACT: 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 SESUsing 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.

Health Care Needs in the Aftermath of Hurricane Maria in Puerto Rico: A Perspective from Federal Medical Shelter Manatí.

INTRODUCTION: On September 20, 2017, Hurricane Maria, a Category 4 hurricane, swept across Puerto Rico (PR), wreaking devastation to PR's power, water, and health care infrastructure. To address the imminent humanitarian crisis, the US government mobilized Federal Medical Shelters (FMS) to serve the needs of hurricane victims. This study's objective was to provide a description of the patients seeking emergency care at FMS and the changes in their needs over time. METHODS: This retrospective, cross-sectional study included all patients presenting to the FMS Manatí from October 6, two weeks after Hurricane Maria's landfall, to November 2, 2017. Categories were created to catalogue the nature of new acute medical issues by patients presenting to the Shelter. Descriptive, graphical analyses were performed to assess changes to presenting complaints over time, and by age groups defined as infant (age ≤1 years), child (1 year < age ≤10 years), adolescent (10 years < age ≤ 25 years), and adult (age > 25 years). RESULTS: Over the 30-day period, 5,268 patients were seen in the FMS seeking medical care (average 188.1 patients per day), spending less than five hours in the facility. The distribution of patients' age was bimodal: the first peak at one year and the second at age 50. The most common patient complaint was infection (38.8%), then musculoskeletal (MSK) complaints (11.8%) and management of chronic medical conditions (11.8%). The proportion of patients presenting with chronic disease complaints declined over the course of the period of observation (21.4% on Day 4 to 8.0% on Day 30) while the proportion of patients presenting with infection increased (31.0% on Day 4 to 48.6% on Day 30). Infection complaints were highest in all age groups, but most in infxants (80.2%), while MSK and chronic disease complaints were highest in adults (14.9% and 14.9%, respectively). CONCLUSION: Infection treatment and chronic disease management were important medical needs facing patients seeking care at FMS Manatí after Hurricane Maria. These findings suggest that basic needs related to sanitation and shelter remained important weeks after the hurricane, and a focus on access to medications, infection control, and injury prevention/management after a disaster needs to be prioritized during disaster response.

Beta-estradiol protects against copper-ascorbate induced oxidative damage in goat liver mitochondria in vitro by binding with ascorbic acid.

AIMS: ß-Estradiol (ß-E), one of the chemical forms of female gonad hormone exhibited antioxidant efficacy in biochemical system, in vitro. The aim of the study was to investigate whether any other mechanism of protection by ß-E to hepatic mitochondria in presence of stressor agent i.e.,a combination of Cu2+ and ascorbic acid is involved. MAIN METHODS: Freshly prepared goat liver mitochondria was incubated with stressors and 1 µM ß-E and post incubated with the same concentration at 37 °C at pH 7.4. Mitochondrial viability, biomarkers of oxidative stress, activities of Krebs cycle enzymes, mitochondrial membrane potential, Ca2+ permeability were measured. Mitochondrial morphology and binding pattern of ß-E with stressors were also studied. KEY FINDINGS: Upon incubation of mitochondria with Cu, ascorbic acid and their combination there is a significant decline in activities of four of Krebs cycle enzymes in an uncompetitive manner with a concomitant increase in Ca2+ permeability and membrane potential of inner mitochondrial membrane, which is withdrawn during co-incubation with ß-E, but was not reversed during post incubation with the ß-E. The final studies on mitochondrial membrane morphology using scanning electron microscope also exhibited damage. Isothermal titration calorimetry data also showed the negative heat change in the mixture of ß-E with ascorbic acid and also its combination with Cu2+. SIGNIFICANCE: Our results for the first time demonstrated that ß-E protects againstCu2+-ascorbate induced oxidative stress by binding with ascorbic acid. The new mechanism of binding of ß-E with stress agents may have a future therapeutic relevance.