Trends in Patient Transfers From Overall and Caseload-Strained US Hospitals During the COVID-19 Pandemic.

Importance: Transferring patients to other hospitals because of inpatient saturation or need for higher levels of care was often challenging during the early waves of the COVID-19 pandemic. Understanding how transfer patterns evolved over time and amid hospital overcrowding could inform future care delivery and load balancing efforts. Objective: To evaluate trends in outgoing transfers at overall and caseload-strained hospitals during the COVID-19 pandemic vs prepandemic times. Design, Setting, and Participants: This retrospective cohort study used data for adult patients at continuously reporting US hospitals in the PINC-AI Healthcare Database. Data analysis was performed from February to July 2023. Exposures: Pandemic wave, defined as wave 1 (March 1, 2020, to May 31, 2020), wave 2 (June 1, 2020, to September 30, 2020), wave 3 (October 1, 2020, to June 19, 2021), Delta (June 20, 2021, to December 18, 2021), and Omicron (December 19, 2021, to February 28, 2022). Main Outcomes and Measures: Weekly trends in cumulative mean daily acute care transfers from all hospitals were assessed by COVID-19 status, hospital urbanicity, and census index (calculated as daily inpatient census divided by nominal bed capacity). At each hospital, the mean difference in transfer counts was calculated using pairwise comparisons of pandemic (vs prepandemic) weeks in the same census index decile and averaged across decile hospitals in each wave. For top decile (ie, high-surge) hospitals, fold changes (and 95% CI) in transfers were adjusted for hospital-level factors and seasonality. Results: At 681 hospitals (205 rural [30.1%] and 476 urban [69.9%]; 360 [52.9%] small with <200 beds and 321 [47.1%] large with ≥200 beds), the mean (SD) weekly outgoing transfers per hospital remained lower than the prepandemic mean of 12.1 (10.4) transfers per week for most of the pandemic, ranging from 8.5 (8.3) transfers per week during wave 1 to 11.9 (10.7) transfers per week during the Delta wave. Despite more COVID-19 transfers, overall transfers at study hospitals cumulatively decreased during each high national surge period. At 99 high-surge hospitals, compared with a prepandemic baseline, outgoing acute care transfers decreased in wave 1 (fold change -15.0%; 95% CI, -22.3% to -7.0%; P < .001), returned to baseline during wave 2 (2.2%; 95% CI, -4.3% to 9.2%; P = .52), and displayed a sustained increase in subsequent waves: 19.8% (95% CI, 14.3% to 25.4%; P < .001) in wave 3, 19.2% (95% CI, 13.4% to 25.4%; P < .001) in the Delta wave, and 15.4% (95% CI, 7.8% to 23.5%; P < .001) in the Omicron wave. Observed increases were predominantly limited to small urban hospitals, where transfers peaked (48.0%; 95% CI, 36.3% to 60.8%; P < .001) in wave 3, whereas large urban and small rural hospitals displayed little to no increases in transfers from baseline throughout the pandemic. Conclusions and Relevance: Throughout the COVID-19 pandemic, study hospitals reported paradoxical decreases in overall patient transfers during each high-surge period. Caseload-strained rural (vs urban) hospitals with fewer than 200 beds were unable to proportionally increase transfers. Prevailing vulnerabilities in flexing transfer capabilities for care or capacity reasons warrant urgent attention.

Undiagnosed metabolic dysfunction and sudden infant death syndrome--a case-control study.

BACKGROUND: Decades of research has yielded few clues about causes of sudden infant death syndrome (SIDS). While some studies have shown a link to inborn errors of metabolism (IEMs), few have examined the link in a large population-based sample. This population-based case-control study assessed the association between undiagnosed IEMs and SIDS. METHODS: Children born in California during 2005-08 who died from SIDS were obtained from death records and linked to the newborn screening, birth certificate, and hospital discharge databases. Individuals with known chromosomal and neural tube defects, genetic disorders, and non-singleton births were excluded. Five controls were matched to each case on tandem mass spectrometry testing date and lab code. Rates of undiagnosed IEMs were compared between cases and controls using conditional logistic regression adjusting for known confounding factors. RESULTS: After adjusting for known confounding factors, SIDS cases had similar risk of having IEMs as controls (adjusted hazard ratio [HR] 1.3, 95% confidence interval [CI] 0.3, 5.5). Infants who were male, Black, and born preterm had higher risk of SIDS with the highest risk observed for those born preterm [adjusted HR = 1.7, 95% CI 1.3, 2.2]. Younger maternal age at delivery, mother being born in the US, parity after current birth >3, and delayed prenatal care were also significantly associated with higher risk of SIDS. CONCLUSIONS: While many maternal and infant factors are associated with an increased risk of SIDS, there is no evidence that undiagnosed IEMs are associated with increased risk.