Impacts of Individual Patient Language and Neighborhood Ethnic Enclave on COVID-19 Test Positivity Among Hispanic/Latinx Patients in San Francisco

Abstract

Background: Given the known disparities in COVID-19 within the Hispanic/Latinx community, we sought to examine the interaction between individual-level and neighborhood-level social determinants of health using linked electronic health record data.

Methods: We examined electronic health record data linked to neighborhood data among Hispanic/Latinx patients tested for COVID-19 between March 1, 2020, and February 28, 2021, from 2 large health care systems in San Francisco. Hispanic/Latinx ethnic enclave is measured using an index of census-tract level indicators of ethnicity, nativity, and language. Multilevel logistic regression models examined associations between ethnic enclave and COVID-19 positivity (COVID-19+), adjusting for patient-level sociodemographic and clinical characteristics and health system. Cross-level interactions were used to test whether associations between ethnic enclave and COVID-19+ differed by patient language preference.

Results: Among 26,871 patients, mean age was 37 years, 56% had Spanish-language preference, and 21% were COVID-19+. In unadjusted models, patients living in the highest versus lowest Hispanic/Latinx enclave had 3.2 higher odds of COVID-19+ (95% CI, 2.45-4.24). Adjusted, the relationship between ethnic enclave and COVID-19+ was attenuated, but not eliminated (odds ratio: 1.4; 95% CI, 1.13-1.17). Our results demonstrated a significant cross-level interaction, such that the influence of ethnic enclave was modified by patient language preference. For individuals with Spanish-language preference, risk of COVID-19+ was high regardless of neighborhood context, whereas for those with English preference, neighborhood ethnic enclave more than doubled the odds of infection.

Conclusions: Findings suggest that a multilevel and intersectional approach to the study of COVID-19 inequities may illuminate dimensions of health inequity that affect marginalized communities and offer insights for targeted clinical and community-based interventions.