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.
The ongoing pandemic caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), necessitates strategies to identify prophylactic and therapeutic drug candidates for rapid ...clinical deployment. Here, we describe a screening pipeline for the discovery of efficacious SARS-CoV-2 inhibitors. We screen a best-in-class drug repurposing library, ReFRAME, against two high-throughput, high-content imaging infection assays: one using HeLa cells expressing SARS-CoV-2 receptor ACE2 and the other using lung epithelial Calu-3 cells. From nearly 12,000 compounds, we identify 49 (in HeLa-ACE2) and 41 (in Calu-3) compounds capable of selectively inhibiting SARS-CoV-2 replication. Notably, most screen hits are cell-line specific, likely due to different virus entry mechanisms or host cell-specific sensitivities to modulators. Among these promising hits, the antivirals nelfinavir and the parent of prodrug MK-4482 possess desirable in vitro activity, pharmacokinetic and human safety profiles, and both reduce SARS-CoV-2 replication in an orthogonal human differentiated primary cell model. Furthermore, MK-4482 effectively blocks SARS-CoV-2 infection in a hamster model. Overall, we identify direct-acting antivirals as the most promising compounds for drug repurposing, additional compounds that may have value in combination therapies, and tool compounds for identification of viral host cell targets.
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.
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.
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%).
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.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Reduction of mitochondrial oxidative stress-mediated diseases is an important pharmaceutical objective in recent biomedical research. In this context, a series of novel pyrrolobenzoxazines (PyBs) ...framework with enormous diversity (compounds 5a–w) was synthesized by employing a low-temperature greener pathway, and antioxidant property of the synthesized compounds was successfully demonstrated on preclinical model goat heart mitochondria, in vitro. Copper–ascorbate (Cu–As) was utilized as an oxidative stress generator. Out of screened PyBs, the compound possessing −OH and −OMe groups on benzene nucleus along with pyrrolobenzoxazine core moiety (compound 5w) displayed magnificent antioxidant property with a minimum effective dose of 66 μM during the biochemical assessment. The ameliorative effect of synthesized pyrrolobenzoxazine moiety on levels of biomarkers of oxidative stress, antioxidant enzyme, activities of Krebs cycle and respiratory chain enzymes, mitochondrial morphology, and Ca2+ permeability of mitochondrial membrane was investigated in the presence of Cu–As. Furthermore, the binding mode of Cu–As by compound 5w was explored successfully using isothermal titration calorimetry (ITC) analysis.
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.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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.
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.
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.
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).
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.
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.
Celotno besedilo
Dostopno za:
CEKLJ, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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.
Retrospective cohort study.
Conterminous US from 2008 and 2019.
2% random sample of all Medicare fee-for-service beneficiaries eligible for Parts A, B, and D.
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.
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.
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.
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