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Coronavirus Disease 2019 Planning and Response: A Tale of 2 Health Workforce Estimator Tools

BACKGROUND: As coronavirus disease 2019 (COVID-19) rapidly progressed throughout the United States, increased demand for health workers required health workforce data and tools to aid planning and response at local, state, and national levels. OBJECTIVE: We describe the development of 2 estimator to...

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Detalles Bibliográficos
Autores principales: Chen, Candice, Luo, Qian, Chong, Nicholas, Westergaard, Sara, Brantley, Erin, Salsberg, Edward, Erikson, Clese, Pillai, Drishti, Green, Katherine, Pittman, Patricia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8428849/
https://www.ncbi.nlm.nih.gov/pubmed/34524238
http://dx.doi.org/10.1097/MLR.0000000000001606
Descripción
Sumario:BACKGROUND: As coronavirus disease 2019 (COVID-19) rapidly progressed throughout the United States, increased demand for health workers required health workforce data and tools to aid planning and response at local, state, and national levels. OBJECTIVE: We describe the development of 2 estimator tools designed to inform health workforce planning for COVID-19. RESEARCH DESIGN: We estimated supply and demand for intensivists, critical care nurses, hospitalists, respiratory therapists, and pharmacists, using Institute for Health Metrics and Evaluation projections for COVID-19 hospital care and National Plan and Provider Enumeration System, Provider Enrollment Chain and Ownership System, American Hospital Association, and Bureau of Labor Statistics Occupation Employment Statistics for workforce supply. We estimated contact tracing workforce needs using Johns Hopkins University COVID-19 case counts and workload parameters based on expert advice. RESULTS: The State Hospital Workforce Deficit Estimator estimated the sufficiency of state hospital-based clinicians to meet projected COVID-19 demand. The Contact Tracing Workforce Estimator calculated the workforce needed based on the 14-day COVID-19 caseload at county, state, and the national level, allowing users to adjust workload parameters to reflect local contexts. CONCLUSIONS: The 2 estimators illustrate the value of integrating health workforce data and analysis with pandemic response planning. The many unknowns associated with COVID-19 required tools to be flexible, allowing users to change assumptions on number of contacts and work capacity. Data limitations were a challenge for both estimators, highlighting the need to invest in health workforce data and data infrastructure as part of future emergency preparedness planning.