An ecological study of socioeconomic predictors in detection of COVID-19 cases across neighborhoods in New York City

Abstract

Background: New York City was the first major urban center of the COVID-19 pandemic in the USA. Cases are clustered in the city, with certain neighborhoods experiencing more cases than others. We investigate whether potential socioeconomic factors can explain between-neighborhood variation in the COVID-19 test positivity rate. Methods: Data were collected from 177 Zip Code Tabulation Areas (ZCTA) in New York City (99.9% of the population). We fit multiple Bayesian Besag-York-Mollié (BYM) mixed models using positive COVID-19 tests as the outcome, a set of 11 representative demographic, economic, and health-care associated ZCTA-level parameters as potential predictors, and the total number of COVID-19 tests as the exposure. The BYM model includes both spatial and nonspatial random effects to account for clustering and overdispersion. Results: Multiple regression approaches indicated a consistent, statistically significant association between detected COVID-19 cases and dependent children (under 18 years old), population density, median household income, and race. In the final model, we found that an increase of only 5% in young population is associated with a 2.3% increase in COVID-19 positivity rate (95% confidence interval (CI) 0.4 to 4.2%, p = 0.021). An increase of 10,000 people per km 2 is associated with a 2.4% (95% CI 0.6 to 4.2%, p = 0.011) increase in positivity rate. A decrease of $10,000 median household income is associated with a 1.6% (95% CI 0.7 to 2.4%, p < 0.001) increase in COVID-19 positivity rate. With respect to race, a decrease of 10% in White population is associated with a 1.8% (95% CI 0.8 to 2.8%, p < 0.001) increase in positivity rate, while an increase of 10% in Black population is associated with a 1.1% (95% CI 0.3 to 1.8%, p < 0.001) increase in positivity rate. The percentage of Hispanic (p = 0.718), Asian (p = 0.966), or Other (p = 0.588) populations were not statistically significant factors. Conclusions: Our findings indicate associations between neighborhoods with a large dependent youth population, densely populated, low-income, and predominantly black neighborhoods and COVID-19 test positivity rate. The study highlights the importance of public health management during and after the current COVID-19 pandemic. Further work is warranted to fully understand the mechanisms by which these factors may have affected the positivity rate, either in terms of the true number of cases or access to testing.

Higher SARS-CoV-2 Seroprevalence in Workers with Lower Socioeconomic Status in Cape Town, South Africa.

“…Rather, our data highlights the fact that some people are less able to comply with non-pharmacological interventions to prevent transmission than others because of their living circumstances. Similar inequalities have been documented in highincome countries: in New York City the number of COVID-19 cases detected by RT-PCR was signi cantly associated with multiple socioeconomic indicators, including population density, median household income, and dependent children (18). Another study compared the number of proven cases and deaths due to COVID-19 in each county of the USA to a poverty index.…”

Racial and Ethnic Disparities in COVID-19–Related Infections, Hospitalizations, and Deaths

“…Results of the community-based study, in which more than 52% of individuals with a positive PCR result were asymptomatic at the time of testing, may signal that disparities in infection rates are more pronounced than is being detected through testing in health care settings ( 26 ). Eight ecological studies detected a disparity in infection rates among Hispanic populations compared with non-Hispanic White populations or generally among minority populations compared with non-Hispanic White populations ( 55 , 56 , 59–61 , 65 , 67 , 68 ). The studies that did not detect a disparity in SARS-CoV-2 infections included the large seroprevalence study of health care personnel discussed above that also did not identify a disparity for African American/Black populations ( 42 ).…”

Higher risk of COVID-19 hospitalization for unemployed: an analysis of 1,298,416 health insured individuals in Germany

“…These findings are in line with a currently growing number of studies from the US and England that report socioeconomic difference in the COVID-19 pandemic (for an overview see (11) or (12)). A recent study from New York (13), for example, compared the number of SARS-CoV-2 infections between neighborhoods and found that numbers were generally higher in more deprived neighborhoods than in rich areas (same for COVID-19-related hospitalization and mortality) (14). The same pattern became obvious across England in an analysis by the Office for National Statistics (ONS) that compared number of COVID-19 deaths between more than 32.000 areas and their level of deprivation (measured by the national index for multiple deprivation).…”