Beyond the 405 and the 5: Geographic Variations and Factors Associated With Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Positivity Rates in Los Angeles County

Abstract

Objectives To highlight geographic differences and the socio-structural determinants of SARS-CoV-2 test positivity within Los Angeles County (LAC). Methods A geographic information system was used to integrate, map, and analyze SARS-CoV-2 testing data reported by LAC DPH, and data from the American Community Survey. Structural determinants included race/ethnicity, poverty, insurance status, education, population and household density. We examined which factors were associated with positivity rates, using a 5% test positivity threshold, with spatial analysis and spatial regression. Results Between 1 March and 30 June 2020 there were 843,440 SARS-CoV-2 tests and 86,383 diagnoses reported, for an overall positivity rate of 10.2% within the study area. Communities with high proportions of Latino/a residents, those living below the federal poverty line and with high household densities had higher crude positivity rates. Age- adjusted diagnosis rates were significantly associated with the proportion of Latino/as, individuals living below the poverty line, population, and household density. Conclusions There are significant local variations in test positivity within LAC and several socio-structural determinants contribute to ongoing disparities. Public health interventions, beyond shelter in place, are needed to address and target such disparities.

COVID-19 mortality in California based on death certificates: disproportionate impacts across racial/ethnic groups and nativity

“…Younger populations should be included among targets in public health interventions. Disparities in COVID-19 mortality may be driven by a variety of factors, including complex interaction between social and structural determinates of health, barriers to accessing care, higher prevalence of underlying co-morbidities associated with more severe COVID-19 disease and adverse outcomes, and differential exposure to virus due to working and living conditions [ 24 , [29] , [30] , [31] , [32] , [33] , [34] ]—which maybe different and interact differently among the different groups (race/ethnicity and ethnicity/nativity) and subgroups (age-race/ethnicity and age-ethnicity/nativity groups) examined here.…”

“…Several studies have reported differential COVID-19 impacts across racial/ethnic groups including for incidence [ 1 , 35 ], severity [ [4] , [5] , [6] , [8] , [9] , [10] ], death [ 12 , 13 , 27 ], however few studies have examined outcomes by nativity [36] . Ecologic studies have reported COVID-19 incidence rates to be positively associated with higher population percentage Black [ 14 , 26 , 37 ], Hispanic/Latino [ 15 , 26 , 34 , 37 ], or foreign-born [ 26 , 37 ]. In a Massachusetts study the association for Latino population percentage was attenuated after accounting for percent foreign-born noncitizens living in a community, mean household size, and share of food service workers (all which were also positively associated with incidence) [37] .…”

Los Angeles County SARS-CoV-2 Epidemic: Critical Role of Multi-generational Intra-household Transmission

“…To be sure, we confirmed the quantitative importance of multi-generational household prevalence in a cross-sectional regression analysis that included other measures of poverty ( INC22 , SNAP ) as well as the proportion of households with high-risk workers ( OCCUP ) (Table 2 ). Still, one might posit that the critical underlying variable is the proportion of Spanish-speaking households with uninsured members (Vijayan et al 2020 ; Weng et al 2020 ). One might similarly contend that our variable MULTI , which relied on the presence of at least one younger adult (aged 18–34) and another older adult (aged 45 or more) in the household, was no better an indicator of multi-generational transmission risk than, say, the number of persons per bathroom in the household.…”

“…Hospital admission rates have been studied as an alternative to confirmed case incidence (Harris, 2020b , 2020g ), but such an endpoint would also depend on case severity. The test positivity rate—the proportion of positive tests among all persons tested—has been employed as an endpoint in cross-sectional studies (Cotti et al 2020 ; Vijayan et al 2020 ). Adaptation of this endpoint to a dynamic epidemic model is problematic, however, as the number of individuals tested is endogenous and must be modeled as well (Bhaduri et al 2020 ).…”

“…Here, by contrast, we rely upon detailed data on the dynamics of SARS-CoV-2 transmission among approximately 300 communities within Los Angeles County from February 24, 2020 through January 10, 2021. Focusing sharply on Los Angeles County—far and away the largest by population in the United States—we follow in the line of other recent studies attempting to relate transmission patterns to the fine microdetails of individual communities (Horn et al 2020 ; Vijayan et al 2020 ).…”