Outcomes of COVID-19 Admissions in the New York City Public Health System and Variations by Hospitals and Boroughs During the Initial Pandemic Response

Abstract

Introduction: In the initial pandemic regional differences may have existed in COVID-19 hospitalizations and patient outcomes in New York City. Whether these patterns were present in public hospitals is unknown. The aim of this brief study was to investigate COVID-19 hospitalizations and outcomes in the public health system during the initial pandemic response.Methods: A retrospective review was conducted on COVID-19 admissions in New York City public hospitals during the exponential phase of the pandemic. All data were collected from an integrated electronic medical records system (Epic Health Systems, Verona, WI). Overall, 5,422 patients with at least one admission each for COVID-19 were reviewed, with a study of demographic characteristics (including age, gender, race, BMI), pregnancy status, comorbidities, facility activity, and outcomes. Data related to hospitalization and mortality trends were also collected from City of New York website. These data often involved more than one facility and/or service line resulting in more location or treatment facility counts than patients due to utilization of services at more than one location and transfers between locations and facilities.Results: Higher mortality was associated with increasing age with the highest death rate (51.9%) noted in the age group >75 years (OR 7.88, 95%CI 6.32–10.08). Comorbidities with higher mortality included diabetes (OR 1.5, 95% CI 1.33–1.70), hypertension (OR 1.62, 95% CI 1.44–1.83), cardiovascular conditions (OR 1.66, 95% CI 1.47–1.87), COPD (OR 1.86, 95% CI 1.39–2.50). It was deduced that 20% of all New York City COVID-19 positive admissions were in public health system during this timeframe. A high proportion of admissions (21.26%) and deaths (19.93%) were at Elmhurst Hospital in Queens. Bellevue and Metropolitan Hospitals had the lowest number of deaths, both in borough of Manhattan. Mortality in public hospitals in Brooklyn was 29.9%, Queens 28.1%, Manhattan 20.4%.Conclusion: Significant variations existed in COVID-19 hospitalizations and outcomes in the public health system in New York City during the initial pandemic. Although outcomes are worse with older age and those with comorbidities, variations in hospitals and boroughs outside of Manhattan are targets to investigate and strategize efforts.

Unraveling the COVID-19 hospitalization dynamics in Spain using Bayesian inference

“…They found large heterogeneity with higher hospitalization rates in the initial epicenter. Similar results for the first wave were found in Aleta et al BMC Medical Research Methodology (2023) 23:24 Italy [33], Saudi Arabia [34], Scotland [35] or New York City [36]. In terms of LoS, an early systematic review conducted in April 2020 found a median of 14 days in China and 5 days in the rest of the world [37].…”

Revelation of mindlines in the setting of crisis

“…Facing tremendous surge conditions, hospitals across the city assembled teams for the purpose of organizing, updating and redirecting clinical and logistical efforts day‐to‐day. These varied greatly between institutions; some of the variation reflected socioeconomic differences between healthcare systems and even individual sites 17–19 . Some municipal hospitals lacked the ability to make platforms for virtual meetings routinely available to clinical departments or individuals.…”

“…These varied greatly between institutions; some of the variation reflected socioeconomic differences between healthcare systems and even individual sites. [17][18][19] Some municipal hospitals lacked the ability to make platforms for virtual meetings routinely available to clinical departments or individuals. In one such case, the emergency room became a venue for in-person interdepartmental and multidisciplinary meetings at each change of shift.…”

Use of Epic Electronic Health Record System for Health Care Research: Scoping Review

“…Brant et al [200], 2021; Dhar et al [201], 2019; Egede et al [202], 2021; Kukhareva et al [203], 2022; Mahajan et al [204], 2022; Manzar [205], 2022; McCarthy et al [206], 2021; Mehta et al [207], 2018; Nasehi et al [208], 2018; Patil et al [209], 2022; Reddy et al [210], 2021; Renjithlal et al [211], 2022; Torres et al [212], 2023 Adult Dinesh et al [213], 2021; Harbison and Gillan [214], 2018; Khanna et al [215], 2021; McCain et al [216], 2022; Rajamani et al [217], 2015; Simmons et al [218], 2022…”

“…From 2021 onwards, we identified 15 COVID-19-related studies [88,101,112,125,128,137,181,189,195,202,209,213,215,216,244]. These ranged from descriptive studies on disparities due to social determinants of health, the association of the virus with other conditions, severity in specific groups, provision of virtual care, treatment options, and changes in physician behavior.…”