Forecasting spatial, socioeconomic and demographic variation in COVID-19 health care demand in England and Wales

Autor: Melinda Mills, Jennifer Beam Dowd, David M. Brazel, Ilya Kashnitsky, Mark D. Verhagen
Rok vydání: 2020
Předmět:
Male
Deprivation
Hospital bed
Project commissioning
0211 other engineering and technologies
Ethnic group
lcsh:Medicine
02 engineering and technology
0302 clinical medicine
Health care
Pandemic
Ethnicity
030212 general & internal medicine
Child
Wales/epidemiology
Aged
80 and over
1. No poverty
021107 urban & regional planning
General Medicine
Middle Aged
Regional
3. Good health
Europe
Hospitalization
Social deprivation
England
Local
Child
Preschool
Female
Coronavirus Infections
Research Article
Adult
medicine.medical_specialty
Adolescent
Pneumonia
Viral
Young Adult
Betacoronavirus
03 medical and health sciences
Age
NHS
Environmental health
medicine
Humans
England/epidemiology
Pandemics
Pneumonia
Viral/epidemiology
Socioeconomic status
021101 geological & geomatics engineering
Demography
Aged
Health Services Needs and Demand
Wales
SARS-CoV-2
business.industry
Public health
lcsh:R
Infant
Newborn
Hospital capacity
Infant
COVID-19
Socioeconomic Factors
Hospital Bed Capacity
Population density
Coronavirus Infections/epidemiology
business
Delivery of Health Care
030217 neurology & neurosurgery
Forecasting
Zdroj: BMC Medicine, Vol 18, Iss 1, Pp 1-11 (2020)
Verhagen, M D, Brazel, D M, Dowd, J B, Kashnitsky, I & Mills, M C 2020, ' Forecasting spatial, socioeconomic and demographic variation in COVID-19 health care demand in England and Wales ', BMC Medicine, vol. 18, 203 . https://doi.org/10.1186/s12916-020-01646-2
BMC Medicine
ISSN: 1741-7015
DOI: 10.1186/s12916-020-01646-2
Popis: Background COVID-19 poses one of the most profound public health crises for a hundred years. As of mid-May 2020, across the world, almost 300,000 deaths and over 4 million confirmed cases were registered. Reaching over 30,000 deaths by early May, the UK had the highest number of recorded deaths in Europe, second in the world only to the USA. Hospitalization and death from COVID-19 have been linked to demographic and socioeconomic variation. Since this varies strongly by location, there is an urgent need to analyse the mismatch between health care demand and supply at the local level. As lockdown measures ease, reinfection may vary by area, necessitating a real-time tool for local and regional authorities to anticipate demand. Methods Combining census estimates and hospital capacity data from ONS and NHS at the Administrative Region, Ceremonial County (CC), Clinical Commissioning Group (CCG) and Lower Layer Super Output Area (LSOA) level from England and Wales, we calculate the number of individuals at risk of COVID-19 hospitalization. Combining multiple sources, we produce geospatial risk maps on an online dashboard that dynamically illustrate how the pre-crisis health system capacity matches local variations in hospitalization risk related to age, social deprivation, population density and ethnicity, also adjusting for the overall infection rate and hospital capacity. Results By providing fine-grained estimates of expected hospitalization, we identify areas that face higher disproportionate health care burdens due to COVID-19, with respect to pre-crisis levels of hospital bed capacity. Including additional risks beyond age-composition of the area such as social deprivation, race/ethnic composition and population density offers a further nuanced identification of areas with disproportionate health care demands. Conclusions Areas face disproportionate risks for COVID-19 hospitalization pressures due to their socioeconomic differences and the demographic composition of their populations. Our flexible online dashboard allows policy-makers and health officials to monitor and evaluate potential health care demand at a granular level as the infection rate and hospital capacity changes throughout the course of this pandemic. This agile knowledge is invaluable to tackle the enormous logistical challenges to re-allocate resources and target susceptible areas for aggressive testing and tracing to mitigate transmission.
Databáze: OpenAIRE
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