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Tytuł pozycji:

Pseudo-likelihood based logistic regression for estimating COVID-19 infection and case fatality rates by gender, race, and age in California

Tytuł :
Pseudo-likelihood based logistic regression for estimating COVID-19 infection and case fatality rates by gender, race, and age in California
Autorzy :
Di Xiong
Lu Zhang
Gregory L. Watson
Phillip Sundin
Teresa Bufford
Joseph A. Zoller
John Shamshoian
Marc A. Suchard
Christina M. Ramirez
Pokaż więcej
Temat :
COVID-19
Infection rate
Case fatality rate
California Health Interview Survey
Logistic regression
Infectious and parasitic diseases
RC109-216
Źródło :
Epidemics, Vol 33, Iss , Pp 100418- (2020)
Wydawca :
Elsevier, 2020.
Rok publikacji :
2020
Kolekcja :
LCC:Infectious and parasitic diseases
Typ dokumentu :
article
Opis pliku :
electronic resource
Język :
English
ISSN :
1755-4365
Relacje :
http://www.sciencedirect.com/science/article/pii/S1755436520300396; https://doaj.org/toc/1755-4365
DOI :
10.1016/j.epidem.2020.100418
Dostęp URL :
https://doaj.org/article/1df5a44a830b4ba3ab5e40f96c8c9910
Numer akcesji :
edsdoj.1df5a44a830b4ba3ab5e40f96c8c9910
Czasopismo naukowe
In emerging epidemics, early estimates of key epidemiological characteristics of the disease are critical for guiding public policy. In particular, identifying high-risk population subgroups aids policymakers and health officials in combating the epidemic. This has been challenging during the coronavirus disease 2019 (COVID-19) pandemic because governmental agencies typically release aggregate COVID-19 data as summary statistics of patient demographics. These data may identify disparities in COVID-19 outcomes between broad population subgroups, but do not provide comparisons between more granular population subgroups defined by combinations of multiple demographics.We introduce a method that helps to overcome the limitations of aggregated summary statistics and yields estimates of COVID-19 infection and case fatality rates — key quantities for guiding public policy related to the control and prevention of COVID-19 — for population subgroups across combinations of demographic characteristics. Our approach uses pseudo-likelihood based logistic regression to combine aggregate COVID-19 case and fatality data with population-level demographic survey data to estimate infection and case fatality rates for population subgroups across combinations of demographic characteristics.We illustrate our method on California COVID-19 data to estimate test-based infection and case fatality rates for population subgroups defined by gender, age, and race/ethnicity. Our analysis indicates that in California, males have higher test-based infection rates and test-based case fatality rates across age and race/ethnicity groups, with the gender gap widening with increasing age. Although elderly infected with COVID-19 are at an elevated risk of mortality, the test-based infection rates do not increase monotonically with age. The workforce population, especially, has a higher test-based infection rate than children, adolescents, and other elderly people in their 60–80. LatinX and African Americans have higher test-based infection rates than other race/ethnicity groups. The subgroups with the highest 5 test-based case fatality rates are all-male groups with race as African American, Asian, Multi-race, LatinX, and White, followed by African American females, indicating that African Americans are an especially vulnerable California subpopulation.

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