Racial, Ethnic, and Geographic Disparities in Novel Coronavirus (Severe Acute Respiratory Syndrome Coronavirus 2) Test Positivity in North Carolina

Abstract

Background: Emerging evidence suggests that black and Hispanic communities in the United States are disproportionately affected by coronavirus disease 2019 (COVID-19). A complex interplay of socioeconomic and healthcare disparities likely contribute to disproportionate COVID-19 risk.

Methods: We conducted a geospatial analysis to determine whether individual- and neighborhood-level attributes predict local odds of testing positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We analyzed 29 138 SARS-CoV-2 tests within the 6-county catchment area for Duke University Health System from March to June 2020. We used generalized additive models to analyze the spatial distribution of SARS-CoV-2 positivity. Adjusted models included individual-level age, gender, and race, as well as neighborhood-level Area Deprivation Index, population density, demographic composition, and household size.

Results: Our dataset included 27 099 negative and 2039 positive unique SARS-CoV-2 tests. The odds of a positive SARS-CoV-2 test were higher for males (odds ratio [OR], 1.43; 95% credible interval [CI], 1.30-1.58), blacks (OR, 1.47; 95% CI, 1.27-1.70), and Hispanics (OR, 4.25; 955 CI, 3.55-5.12). Among neighborhood-level predictors, percentage of black population (OR, 1.14; 95% CI, 1.05-1.25), and percentage Hispanic population (OR, 1.23; 95% CI, 1.07-1.41) also influenced the odds of a positive SARS-CoV-2 test. Population density, average household size, and Area Deprivation Index were not associated with SARS-CoV-2 test results after adjusting for race.

Conclusions: The odds of testing positive for SARS-CoV-2 were higher for both black and Hispanic individuals, as well as within neighborhoods with a higher proportion of black or Hispanic residents-confirming that black and Hispanic communities are disproportionately affected by SARS-CoV-2.

Keywords: Bayesian statistics; COVID-19; SARS-CoV-2; disparities; geographic information systems.

Figure 1.

Cohort selection. COVID-19, coronavirus disease 2019.

Figure 2.

Temporal trends in coronavirus disease 2019 (COVID-19) positivity by race/ethnicity. Proportion of positive COVID-19 tests over time, stratified by race/ethnicity. For ease of visualization, data are shown only for black, white, and Hispanic groups. Fitted lines represent a locally weighted scatter-plot smoother (LOESS) regression.

Figure 3.

Spatial distribution of coronavirus disease 2019 testing results. The study area depicted is a 6-county area around Durham, NC. The elliptical shape that intersects the study area was a 2-standard deviational ellipse, the smallest possible ellipse containing 95% of the subject locations. The odds of a positive test were modeled using the home address coordinate locations of individual subjects as a smoothed, 2-dimensional independent variable. These models were then predicted on a dense grid of coordinate pairs covering the study area. The local odds ratio (OR), depicted in the color background, was computed by dividing the odds at each coordinate pair in the prediction grid by the average odds. Areas circumscribed by high (red) or low (blue) contours are those in which the local OR has at least a 95% probability of differing from the average. Areas with the highest OR in our unadjusted model included the cities of Durham and Raleigh. Adjusting for individual and neighborhood variables eliminated much of the geographic heterogeneity in OR.