David D. Chen, MD, MPH

Physician Scientist; Physician, Internal Medicine

Publications

A Social Network Analysis Approach for Contact Tracing in the Hospital Setting
A Social Network Analysis Approach for Contact Tracing in the Hospital Setting
Ostovari M, Jurkovitz C, Pachter L, Chen D

Dela J Public Health 2020 Aug;6(3):22-25

PMID: 34467124

Abstract

Since the beginning of the COVID-19 pandemic, the State of Delaware has implemented various strategies including a stay-at-home order, mask-wearing requirements in public places, and community-based testing to control the spread of the disease. Health systems across the U.S. have taken actions including symptom monitoring and screening for visitors and healthcare workers, providing personal protection equipment (PPE), and contact tracing of confirmed infected individuals to provide maximum possible protection for healthcare workers. Despite such efforts, there remains a significant risk of intra-hospital transmission of COVID-19. Healthcare workers who contact patients with COVID-19 or were exposed to the disease in the community may transmit the infection to coworkers in the inpatient setting. In addition to universal and case-based precautions to prevent exposure and disease transmission, contact tracing is essential to minimizing the impact of outbreaks among healthcare workers and the community. A rapid increase in cases can quickly diminish hospital infection control and prevention program capacity to perform high-quality contact tracing. This article will describe an approach using the application of social network analysis (SNA) and Electronic Medical Records (EMR) to enhance the current efforts in COVID-19 contact tracings.
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Hospital length of stay among COVID-19-positive patients
Hospital length of stay among COVID-19-positive patients
Chiam T, Subedi K, Chen D, Best E, Bianco FB, Dobler G, Papas M

J Clin Transl Res 2021 Jun;7(3):377-385

PMID: 34239994

Abstract

Background and Aim: This study aims to determine COVID-19 patient demographics and comorbidities associated with their hospital length of stay (LOS).

Methods: Design: Single-site, retrospective study. Setting: A suburban 700-bed community hospital in Newark, Delaware, USA. Patients: Patients admitted to the hospital from March 11, 2020, to August 11, 2020, with a positive COVID-19 status. We followed a time-to-event analysis approach and used Kaplan-Meir curves and log-rank tests for bivariate analyses, and an accelerated failure time model for a multivariable model of hospital LOS.

Results: Six hundred and eighty-seven patients discharged alive (mean [SD] age, 60.94 [18.10] years; 339 men [49.34%]; 307 Black/African-American [44.69%]; and 267 White [38.86%]) were included in the investigation. Bivariate analysis using Kaplan-Meir curves showed that patients' age, sex, ethnicity, insurance type, comorbidity of fluid and electrolyte disorder, hypertension, renal failure, diabetes, coagulopathy, congestive heart failure, peripheral vascular disease, neurological disorder, coronary artery disease, and cardiac arrhythmias to be significantly associated with LOS (<0.05). In the multivariable analysis, patients' age, sex, ethnicity, number of Elixhauser comorbidities, and number of weeks since onset of the pandemic was significantly associated with LOS (<0.05). Fluid and electrolyte disorder is the only comorbidity independently associated with LOS after adjusting for patients' age, sex, race, ethnicity, number of Elixhauser comorbidities, and weeks since onset of pandemic.

Conclusion: COVID-19 patients LOS vary based on multiple factors. Understanding these factors are crucial to improving the prediction accuracy of COVID-19 patient census in hospitals for resource planning and care delivery.

Relevance for patients: Understanding of the factors associated with LOS of the COVID-19 patients may help the care providers and the patients to better anticipate the LOS, optimize the resources and processes, and prevent protracted stays.

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