About: Pseudo-Likelihood Based Logistic Regression for Estimating COVID-19 Infection and Case Fatality Rates by Gender, Race, and Age in California

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About: Pseudo-Likelihood Based Logistic Regression for Estimating COVID-19 Infection and Case Fatality Rates by Gender, Race, and Age in California Goto Sponge NotDistinct Permalink

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Pseudo-Likelihood Based Logistic Regression for Estimating COVID-19 Infection and Case Fatality Rates by Gender, Race, and Age in California
Creator	Zhang, Lu Suchard, Marc Ramirez, Christina Bufford, Teresa Shamshoian, John Sundin, Phillip Watson, Gregory Xiong, Di Zoller, Joseph
Source	MedRxiv
abstract	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 combatting the epidemic. This has been challenging during the coronavirus disease 2019 (COVID-19) pandemic, because governmental agencies typically release aggregate COVID-19 data as marginal 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 overcomes 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 and 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. 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 African American male, Multi-race male, Asian male, African American female, and American Indian or Alaska Native male, indicating that African Americans are an especially vulnerable California subpopulation.
has issue date	2020-07-01(xsd:dateTime)
bibo:doi	10.1101/2020.06.29.20141978
has license	medrxiv
sha1sum (hex)	8e1f5fe0389d622326ad7422cf7ecbfdfd6c8f79
schema:url	https://doi.org/10.1101/2020.06.29.20141978
resource representing a document's title	Pseudo-Likelihood Based Logistic Regression for Estimating COVID-19 Infection and Case Fatality Rates by Gender, Race, and Age in California
resource representing a document's body	covid:8e1f5fe0389d622326ad7422cf7ecbfdfd6c8f79#body_text
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