tetano
Editor, Senior Moderator
Epidemics
. 2020 Nov 9;33:100418.
doi: 10.1016/j.epidem.2020.100418. Online ahead of print.
Pseudo-likelihood based logistic regression for estimating COVID-19 infection and case fatality rates by gender, race, and age in California
Di Xiong[SUP] 1 [/SUP], Lu Zhang[SUP] 1 [/SUP], Gregory L Watson[SUP] 1 [/SUP], Phillip Sundin[SUP] 1 [/SUP], Teresa Bufford[SUP] 1 [/SUP], Joseph A Zoller[SUP] 1 [/SUP], John Shamshoian[SUP] 1 [/SUP], Marc A Suchard[SUP] 2 [/SUP], Christina M Ramirez[SUP] 3 [/SUP]
Affiliations
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 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.
Keywords: COVID-19; California Health Interview Survey; Case fatality rate; Infection rate; Logistic regression.
. 2020 Nov 9;33:100418.
doi: 10.1016/j.epidem.2020.100418. Online ahead of print.
Pseudo-likelihood based logistic regression for estimating COVID-19 infection and case fatality rates by gender, race, and age in California
Di Xiong[SUP] 1 [/SUP], Lu Zhang[SUP] 1 [/SUP], Gregory L Watson[SUP] 1 [/SUP], Phillip Sundin[SUP] 1 [/SUP], Teresa Bufford[SUP] 1 [/SUP], Joseph A Zoller[SUP] 1 [/SUP], John Shamshoian[SUP] 1 [/SUP], Marc A Suchard[SUP] 2 [/SUP], Christina M Ramirez[SUP] 3 [/SUP]
Affiliations
- PMID: 33221671
- DOI: 10.1016/j.epidem.2020.100418
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 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.
Keywords: COVID-19; California Health Interview Survey; Case fatality rate; Infection rate; Logistic regression.