SARS-CoV-2 Sequelae and Postdischarge Health Care Visits Over 5 Months Follow-up Among Children Hospitalized for COVID-19 or MIS-C.

Although post-acute sequelae of COVID-19 among adult survivors has gained significant attention, data in children hospitalized for severe acute respiratory syndrome coronavirus 2 is limited. This study of commercially insured US children shows that those hospitalized with COVID-19 or multisystem inflammatory syndrome in children have a substantial burden of severe acute respiratory syndrome coronavirus 2 sequelae and associated health care visits postdischarge.

Is there an association between hospital staffing levels and inpatient-COVID-19 mortality rates?

OBJECTIVE: This study aims to investigate the relationship between RNs and hospital-based medical specialties staffing levels with inpatient COVID-19 mortality rates. METHODS: We relied on data from AHA Annual Survey Database, Area Health Resource File, and UnitedHealth Group Clinical Discovery Database. In phase 1 of the analysis, we estimated the risk-standardized event rates (RSERs) based on 95,915 patients in the UnitedHealth Group Database 1,398 hospitals. We then used beta regression to analyze the association between hospital- and county- level factors with risk-standardized inpatient COVID-19 mortality rates from March 1, 2020, through December 31, 2020. RESULTS: Higher staffing levels of RNs and emergency medicine physicians were associated with lower COVID-19 mortality rates. Moreover, larger teaching hospitals located in urban settings had higher COVID-19 mortality rates. Finally, counties with greater social vulnerability, specifically in terms of housing type and transportation, and those with high infection rates had the worst patient mortality rates. CONCLUSION: Higher staffing levels are associated with lower inpatient mortality rates for COVID-19 patients. More research is needed to determine appropriate staffing levels and how staffing levels interact with other factors such as teams, leadership, and culture to impact patient care during pandemics.

Distributed learning for heterogeneous clinical data with application to integrating COVID-19 data across 230 sites.

Integrating real-world data (RWD) from several clinical sites offers great opportunities to improve estimation with a more general population compared to analyses based on a single clinical site. However, sharing patient-level data across sites is practically challenging due to concerns about maintaining patient privacy. We develop a distributed algorithm to integrate heterogeneous RWD from multiple clinical sites without sharing patient-level data. The proposed distributed conditional logistic regression (dCLR) algorithm can effectively account for between-site heterogeneity and requires only one round of communication. Our simulation study and data application with the data of 14,215 COVID-19 patients from 230 clinical sites in the UnitedHealth Group Clinical Research Database demonstrate that the proposed distributed algorithm provides an estimator that is robust to heterogeneity in event rates when efficiently integrating data from multiple clinical sites. Our algorithm is therefore a practical alternative to both meta-analysis and existing distributed algorithms for modeling heterogeneous multi-site binary outcomes.

Distributed Quasi-Poisson regression algorithm for modeling multi-site count outcomes in distributed data networks.

BACKGROUND: Observational studies incorporating real-world data from multiple institutions facilitate study of rare outcomes or exposures and improve generalizability of results. Due to privacy concerns surrounding patient-level data sharing across institutions, methods for performing regression analyses distributively are desirable. Meta-analysis of institution-specific estimates is commonly used, but has been shown to produce biased estimates in certain settings. While distributed regression methods are increasingly available, methods for analyzing count outcomes are currently limited. Count data in practice are commonly subject to overdispersion, exhibiting greater variability than expected under a given statistical model. OBJECTIVE: We propose a novel computational method, a one-shot distributed algorithm for quasi-Poisson regression (ODAP), to distributively model count outcomes while accounting for overdispersion. METHODS: ODAP incorporates a surrogate likelihood approach to perform distributed quasi-Poisson regression without requiring patient-level data sharing, only requiring sharing of aggregate data from each participating institution. ODAP requires at most three rounds of non-iterative communication among institutions to generate coefficient estimates and corresponding standard errors. In simulations, we evaluate ODAP under several data scenarios possible in multi-site analyses, comparing ODAP and meta-analysis estimates in terms of error relative to pooled regression estimates, considered the gold standard. In a proof-of-concept real-world data analysis, we similarly compare ODAP and meta-analysis in terms of relative error to pooled estimatation using data from the OneFlorida Clinical Research Consortium, modeling length of stay in COVID-19 patients as a function of various patient characteristics. In a second proof-of-concept analysis, using the same outcome and covariates, we incorporate data from the UnitedHealth Group Clinical Discovery Database together with the OneFlorida data in a distributed analysis to compare estimates produced by ODAP and meta-analysis. RESULTS: In simulations, ODAP exhibited negligible error relative to pooled regression estimates across all settings explored. Meta-analysis estimates, while largely unbiased, were increasingly variable as heterogeneity in the outcome increased across institutions. When baseline expected count was 0.2, relative error for meta-analysis was above 5% in 25% of iterations (250/1000), while the largest relative error for ODAP in any iteration was 3.59%. In our proof-of-concept analysis using only OneFlorida data, ODAP estimates were closer to pooled regression estimates than those produced by meta-analysis for all 15 covariates. In our distributed analysis incorporating data from both OneFlorida and the UnitedHealth Group Clinical Discovery Database, ODAP and meta-analysis estimates were largely similar, while some differences in estimates (as large as 13.8%) could be indicative of bias in meta-analytic estimates. CONCLUSIONS: ODAP performs privacy-preserving, communication-efficient distributed quasi-Poisson regression to analyze count outcomes using data stored within multiple institutions. Our method produces estimates nearly matching pooled regression estimates and sometimes more accurate than meta-analysis estimates, most notably in settings with relatively low counts and high outcome heterogeneity across institutions.

DLMM as a lossless one-shot algorithm for collaborative multi-site distributed linear mixed models.

Linear mixed models are commonly used in healthcare-based association analyses for analyzing multi-site data with heterogeneous site-specific random effects. Due to regulations for protecting patients' privacy, sensitive individual patient data (IPD) typically cannot be shared across sites. We propose an algorithm for fitting distributed linear mixed models (DLMMs) without sharing IPD across sites. This algorithm achieves results identical to those achieved using pooled IPD from multiple sites (i.e., the same effect size and standard error estimates), hence demonstrating the lossless property. The algorithm requires each site to contribute minimal aggregated data in only one round of communication. We demonstrate the lossless property of the proposed DLMM algorithm by investigating the associations between demographic and clinical characteristics and length of hospital stay in COVID-19 patients using administrative claims from the UnitedHealth Group Clinical Discovery Database. We extend this association study by incorporating 120,609 COVID-19 patients from 11 collaborative data sources worldwide.

Associations between psychiatric morbidity and COVID-19 vaccine hesitancy: An analysis of electronic health records and patient survey.

Psychiatric illness confers significant risk for severe COVID-19 morbidity and mortality; identifying psychiatric risk factors for vaccine hesitancy is critical to mitigating risk in this population. This study examined the prevalence of vaccine hesitancy among those with psychiatric illness and the associations between psychiatric morbidity and vaccine hesitancy. Data came from electronic health records and a patient survey obtained from 14,365 patients at a group medical practice between February and May 2021. Logistic regression was used to calculate odds for vaccine hesitancy adjusted for sociodemographic characteristics and physical comorbidity. Of 14,365 participants 1,761 (12.3%) participants reported vaccine hesitancy. Vaccine hesitancy was significantly more prevalent among participants with substance use (29.6%), attention deficit and hyperactivity (23.3%), posttraumatic stress (23.1%), bipolar (18.0%), generalized anxiety (16.5%), major depressive (16.1%), and other anxiety (15.5%) disorders, tobacco use (18.6%), and those previously infected with COVID-19 (19.8%) compared to participants without . After adjusting for sociodemographic characteristics and physical comorbidities, substance use disorders and tobacco use were significantly associated with increased odds for vaccine hesitancy and bipolar disorder was significantly inversely associated with vaccine hesitancy. Interventions to improve uptake in these populations may be warranted.

Patient and Hospital Factors Associated With Differences in Mortality Rates Among Black and White US Medicare Beneficiaries Hospitalized With COVID-19 Infection.

Importance: Black patients hospitalized with COVID-19 may have worse outcomes than White patients because of excess individual risk or because Black patients are disproportionately cared for in hospitals with worse outcomes for all. Objectives: To examine differences in COVID-19 hospital mortality rates between Black and White patients and to assess whether the mortality rates reflect differences in patient characteristics by race or by the hospitals to which Black and White patients are admitted. Design, Setting, and Participants: This cohort study assessed Medicare beneficiaries admitted with a diagnosis of COVID-19 to 1188 US hospitals from January 1, 2020, through September 21, 2020. Exposure: Hospital admission for a diagnosis of COVID-19. Main Outcomes and Measures: The primary composite outcome was inpatient death or discharge to hospice within 30 days of admission. We estimated the association of patient-level characteristics (including age, sex, zip code-level income, comorbidities, admission from a nursing facility, and days since January 1, 2020) with differences in mortality or discharge to hospice among Black and White patients. To examine the association with the hospital itself, we adjusted for the specific hospitals to which patients were admitted. We used simulation modeling to estimate the mortality among Black patients had they instead been admitted to the hospitals where White patients were admitted. Results: Of the 44 217 Medicare beneficiaries included in the study, 24 281 (55%) were women; mean (SD) age was 76.3 (10.5) years; 33 459 participants (76%) were White, and 10 758 (24%) were Black. Overall, 2634 (8%) White patients and 1100 (10%) Black patients died as inpatients, and 1670 (5%) White patients and 350 (3%) Black patients were discharged to hospice within 30 days of hospitalization, for a total mortality-equivalent rate of 12.86% for White patients and 13.48% for Black patients. Black patients had similar odds of dying or being discharged to hospice (odds ratio [OR], 1.06; 95% CI, 0.99-1.12) in an unadjusted comparison with White patients. After adjustment for clinical and sociodemographic patient characteristics, Black patients were more likely to die or be discharged to hospice (OR, 1.11; 95% CI, 1.03-1.19). This difference became indistinguishable when adjustment was made for the hospitals where care was delivered (odds ratio, 1.02; 95% CI, 0.94-1.10). In simulations, if Black patients in this sample were instead admitted to the same hospitals as White patients in the same distribution, their rate of mortality or discharge to hospice would decline from the observed rate of 13.48% to the simulated rate of 12.23% (95% CI for difference, 1.20%-1.30%). Conclusions and Relevance: This cohort study found that Black patients hospitalized with COVID-19 had higher rates of hospital mortality or discharge to hospice than White patients after adjustment for the personal characteristics of those patients. However, those differences were explained by differences in the hospitals to which Black and White patients were admitted.

Variation in US Hospital Mortality Rates for Patients Admitted With COVID-19 During the First 6 Months of the Pandemic.

Importance: It is unknown how much the mortality of patients with coronavirus disease 2019 (COVID-19) depends on the hospital that cares for them, and whether COVID-19 hospital mortality rates are improving. Objective: To identify variation in COVID-19 mortality rates and how those rates have changed over the first months of the pandemic. Design, Setting, and Participants: This cohort study assessed 38 517 adults who were admitted with COVID-19 to 955 US hospitals from January 1, 2020, to June 30, 2020, and a subset of 27 801 adults (72.2%) who were admitted to 398 of these hospitals that treated at least 10 patients with COVID-19 during 2 periods (January 1 to April 30, 2020, and May 1 to June 30, 2020). Exposures: Hospital characteristics, including size, the number of intensive care unit beds, academic and profit status, hospital setting, and regional characteristics, including COVID-19 case burden. Main Outcomes and Measures: The primary outcome was the hospital's risk-standardized event rate (RSER) of 30-day in-hospital mortality or referral to hospice adjusted for patient-level characteristics, including demographic data, comorbidities, community or nursing facility admission source, and time since January 1, 2020. We examined whether hospital characteristics were associated with RSERs or their change over time. Results: The mean (SD) age among participants (18 888 men [49.0%]) was 70.2 (15.5) years. The mean (SD) hospital-level RSER for the 955 hospitals was 11.8% (2.5%). The mean RSER in the worst-performing quintile of hospitals was 15.65% compared with 9.06% in the best-performing quintile (absolute difference, 6.59 percentage points; 95% CI, 6.38%-6.80%; P < .001). Mean RSERs in all but 1 of the 398 hospitals improved; 376 (94%) improved by at least 25%. The overall mean (SD) RSER declined from 16.6% (4.0%) to 9.3% (2.1%). The absolute difference in rates of mortality or referral to hospice between the worst- and best-performing quintiles of hospitals decreased from 10.54 percentage points (95% CI, 10.03%-11.05%; P < .001) to 5.59 percentage points (95% CI, 5.33%-5.86%; P < .001). Higher county-level COVID-19 case rates were associated with worse RSERs, and case rate declines were associated with improvement in RSERs. Conclusions and Relevance: Over the first months of the pandemic, COVID-19 mortality rates in this cohort of US hospitals declined. Hospitals did better when the prevalence of COVID-19 in their surrounding communities was lower.