Pseudo-likelihood based logistic regression for estimating COVID-19 infection and case fatality rates by gender, race, and age in California.
Epidemics
; 33: 100418, 2020 12.
Article
em En
| MEDLINE
| ID: mdl-33221671
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.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Contexto em Saúde:
1_ASSA2030
/
2_ODS3
/
4_TD
/
6_ODS3_enfermedades_notrasmisibles
/
7_ODS3_muertes_prevenibles_nacidos_ninos
Problema de saúde:
1_financiamento_saude
/
2_muertes_prevenibles
/
4_covid_19
/
4_pneumonia
/
6_other_respiratory_diseases
/
7_non_communicable_diseases
Assunto principal:
Modelos Logísticos
/
COVID-19
Tipo de estudo:
Etiology_studies
/
Health_economic_evaluation
/
Prognostic_studies
/
Risk_factors_studies
Aspecto:
Determinantes_sociais_saude
Limite:
Adolescent
/
Adult
/
Aged
/
Aged80
/
Child
/
Female
/
Humans
/
Male
/
Middle aged
País/Região como assunto:
America do norte
Idioma:
En
Revista:
Epidemics
Ano de publicação:
2020
Tipo de documento:
Article
País de afiliação:
Estados Unidos