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Public Health Rep ; 136(3): 295-300, 2021 05.
Article in English | MEDLINE | ID: covidwho-1085222

ABSTRACT

Coronavirus disease 2019 (COVID-19) has disproportionately and negatively affected communities of color in the United States, especially Black, Latinx, and Indigenous populations. We report a cluster of COVID-19 cases among the Maya in Alameda County, California, most of whom were misclassified in public health data as nonindigenous Spanish-speaking people. We conducted a retrospective cohort study of all COVID-19 tests performed from April 1 through May 31, 2020, at Alameda Health System. A total of 1561 tests from 1533 patients were performed, with an overall test positivity rate of 17.0% (N = 265). We used the language field from the electronic health record to identify 29 patients as speaking an indigenous Mayan language; by medical record review, we identified 52 additional Maya patients. Maya patients had a test positivity rate of 72.8% as compared with 27.1% (P < .001) for nonindigenous Latinx patients and 8.2% (P < .001) for all other patients. In our sample, 39.6% of patients who had a positive test result for COVID-19 were hospitalized, 11.3% required admission to the intensive care unit (ICU), and 4.9% died of COVID-19. Maya patients had lower rates of hospitalization, ICU admission, and 30-day in-hospital mortality than non-Maya patients. We shared our data with the county health department to inform responses for education, testing, and isolation for Maya patients in Alameda County. Ongoing COVID-19 public health efforts should assess the community prevalence of COVID-19 in the Maya community and other indigenous communities and implement interventions that are linguistically and culturally appropriate.


Subject(s)
COVID-19/diagnosis , COVID-19/ethnology , COVID-19/epidemiology , Indigenous Peoples , Adult , COVID-19 Testing , California/epidemiology , California/ethnology , Cohort Studies , Comorbidity , Critical Care/statistics & numerical data , Female , Hospital Mortality , Hospitalization/statistics & numerical data , Humans , Male , Middle Aged , Prevalence , Retrospective Studies , Risk Factors , SARS-CoV-2 , Safety-net Providers
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