ABSTRACT
Environmental exposures have been linked to COVID-19 severity. Previous studies examined very few environmental factors, and often only separately without considering the totality of the environment, or the exposome. In addition, existing risk prediction models of severe COVID-19 predominantly rely on demographic and clinical factors. To address these gaps, we conducted a spatial and contextual exposome-wide association study (ExWAS) and developed polyexposomic scores (PES) of COVID-19 hospitalization leveraging rich information from individuals' spatial and contextual exposome. Individual-level electronic health records of 50 368 patients aged 18 years and older with a positive SARS-CoV-2 PCR/Antigen lab test or a COVID-19 diagnosis between March 2020 and October 2021 were obtained from the OneFlorida+ Clinical Research Network. A total of 194 spatial and contextual exposome factors from 10 data sources were spatiotemporally linked to each patient based on geocoded residential histories. We used a standard two-phase procedure in the ExWAS and developed and validated PES using gradient boosting decision trees models. Four exposome measures significantly associated with COVID-19 hospitalization were identified, including 2-chloroacetophenone, low food access, neighborhood deprivation, and reduced access to fitness centers. The initial prediction model in all patients without considering exposome factors had a testing-area under the curve (AUC) of 0.778. Incorporation of exposome data increased the testing-AUC to 0.787. Similar findings were observed in subgroup analyses focusing on populations without comorbidities and aged 18-24 years old. This spatial and contextual exposome study of COVID-19 hospitalization confirmed previously reported risk factor but also generated novel predictors that warrant more focused evaluation.
ABSTRACT
INTRODUCTION: During the COVID-19 pandemic, many emergency medical services (EMS) agencies modified treatment guidelines for clinical care and standard operating procedures. For the prehospital care of pediatric asthma exacerbations, modifications included changes to bronchodilator administration, systemic corticosteroid administration, and introduction of alternative medications. Since timely administration of bronchodilators and systemic corticosteroids has been shown to improve pediatric asthma clinical outcomes, we investigated the association of COVID-19 protocol modifications in the prehospital management of pediatric asthma on hospital admission rates and emergency department (ED) length-of-stay. METHODS: This is a multicenter, retrospective, observational cohort study comparing prehospital pediatric asthma patients treated by EMS clinicians from four EMS systems before and after implementation of COVID-19 interim EMS protocol modifications. We included children ages 2-18 years who were treated and transported by ground EMS for respiratory-related prehospital primary complaints, and who also had asthma-related ED discharge diagnoses. Patient data and outcomes were compared from 12 months prior to and 12 months after the implementation of interim COVID-19 prehospital protocol modifications using univariate and multivariable statistics. RESULTS: A total of 430 patients met inclusion criteria with a median age of 8 years. There was a slight male predominance (57.9%) and the majority of patients were African American (78.4%). There were twice as many patients treated prior to the COVID-19 protocol modifications (N = 287) compared to after (N = 143). There was a significant decrease in EMS bronchodilator administration from 76% to 59.4% of patients after COVID-19 protocol guidelines were implemented (p < 0.0001). Mixed effects models for hospital admission (to both pediatric inpatient units and pediatric intensive care units) as well as ED length-of-stay did not show any significant effect after the COVID-19 protocol change period (p = 0.18 and p = 0.55, respectively). CONCLUSIONS: Despite a decrease in prehospital bronchodilator administration after COVID-19 changes to prehospital pediatric asthma management protocols, hospital admission rates and ED length-of-stay did not significantly increase. However, this finding is tempered by the marked decrease in study patients treated after COVID-19 prehospital protocol modifications. Given the potential for future waves of COVID-19 variants, further studies with larger patient populations are warranted.
ABSTRACT
Objective. The COVID-19 pandemic created an unprecedented strain on the health care system, and administrators had to make many critical decisions to respond appropriately. This study sought to understand how health care administrators used data and information for decision making during the first 6 mo of the COVID-19 pandemic. Materials and Methods. We conducted semistructured interviews with administrators across University of Florida (UF) Health. We performed an inductive thematic analysis of the transcripts. Results. Four themes emerged from the interviews: 1) common types of health systems or hospital operations data; 2) public health and other external data sources; 3) data interaction, integration, and exchange; and 4) novelty and evolution in data, information, or tools used over time. Participants illustrated the organizational, public health, and regional information they considered essential (e.g., hospital census, community positivity rate, etc.). Participants named specific challenges they faced due to data quality and timeliness. Participants elaborated on the necessity of data integration, validation, and coordination across different boundaries (e.g., different hospital systems in the same metro areas, public health agencies at the local, state, and federal level, etc.). Participants indicated that even within the first 6 mo of the COVID-19 pandemic, the data and tools used for making critical decisions changed. Discussion. While existing medical informatics infrastructure can facilitate decision making in pandemic response, data may not always be readily available in a usable format. Interoperable infrastructure and data standardization across multiple health systems would help provide more reliable and timely information for decision making. Conclusion. Our findings contribute to future discussions of improving data infrastructure and developing harmonized data standards needed to facilitate critical decisions at multiple health care system levels. Highlights: The study revealed common health systems or hospital operations data and information used in decision making during the first 6 mo of the COVID-19 pandemic.Participants described commonly used internal data sources, such as resource and financial reports and dashboards, and external data sources, such as federal, state, and local public health data.Participants described challenges including poor timeliness and limited local relevance of external data as well as poor integration of data sources within and across organizational boundaries.Results suggest the need for continued integration and standardization of health data to support health care administrative decision making during pandemics or other emergencies.
ABSTRACT
The risk factors for severe COVID-19 beyond older age and certain underlying health conditions are largely unknown. Recent studies suggested that long-term environmental exposures may be important determinants of severe COVID-19. However, very few environmental factors have been studied, often separately, without considering the totality of the external environment (i.e., the external exposome). We conducted an external exposome-wide association study (ExWAS) using the nationwide county-level COVID-19 mortality data in the contiguous US. A total of 337 variables characterizing the external exposome from 8 data sources were integrated, harmonized, and spatiotemporally linked to each county. A two-phase procedure was used: (1) in Phase 1, a random 50:50 split divided the data into a discovery set and a replication set, and associations between COVID-19 mortality and individual factors were examined using mixed-effect negative binomial regression models, with multiple comparisons addressed, and (2) in Phase 2, a multivariable regression model including all variables that are significant from both the discovery and replication sets in Phase 1 was fitted. A total of 13 and 22 variables were significant in the discovery and replication sets in Phase 1, respectively. All the 4 variables that were significant in both sets in Phase 1 remained statistically significant in Phase 2, including two air toxicants (i.e., nitrogen dioxide or NO2, and benzidine), one vacant land measure, and one food environment measure. This is the first external exposome study of COVID-19 mortality. It confirmed some of the previously reported environmental factors associated with COVID-19 mortality, but also generated unexpected predictors that may warrant more focused evaluation.