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An ecological study of socioeconomic predictors in detection of COVID-19 cases across neighborhoods in New York CityRichard S Whittle et al. BMC Med. 2020.
doi: 10.1186/s12916-020-01731-6. AffiliationsItem in Clipboard
AbstractBackground: 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 km2 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.
Keywords: Besag-York-Mollié model; COVID-19; Income; Population density; Positivity rate; Race; Socioeconomic factors; Youth dependency.
Conflict of interest statementThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
FiguresFig. 1
New York City detected COVID-19…
Fig. 1
New York City detected COVID-19 cases by Zip Code Tabulation Area (ZCTA). As…
Fig. 1New York City detected COVID-19 cases by Zip Code Tabulation Area (ZCTA). As at 5 April 2020. a Histogram of detected cases by ZCTA, grouped by borough. b Positivity rate, or detected cases as a percentage of total tests
Fig. 2
New York City demographic predictors…
Fig. 2
New York City demographic predictors by Zip Code Tabulation Area (ZCTA). Data based…
Fig. 2New York City demographic predictors by Zip Code Tabulation Area (ZCTA). Data based on American Community Survey (ACS) 2018 5-year estimates. aYoung, percentage of population aged under 18. bAged, percentage of population aged 65+. cMFR, males per 100 females. dRace, percentage of population that identify as white (alone or in combination with another race). eDensity, population density in ’000s persons per km2
Fig. 3
New York City economic predictors…
Fig. 3
New York City economic predictors by Zip Code Tabulation Area (ZCTA). Data based…
Fig. 3New York City economic predictors by Zip Code Tabulation Area (ZCTA). Data based on American Community Survey (ACS) 2018 5-year estimates. aGini, Gini index. bIncome, median household income. cUnemployment, percentage of working age population unemployed. dPoverty, percentage of total population living below the poverty threshold
Fig. 4
New York City health predictors…
Fig. 4
New York City health predictors by Zip Code Tabulation Area (ZCTA). Data based…
Fig. 4New York City health predictors by Zip Code Tabulation Area (ZCTA). Data based on American Community Survey (ACS) 2018 5-year estimates. aUninsured, percentage of total population uninsured. bBeds, total number of hospital beds per 1000 people within 5 km. c Total COVID-19 tests (exposure). d neighborhood connectivity
Fig. 5
Disease mapping model for COVID-19…
Fig. 5
Disease mapping model for COVID-19 cases in New York City by Zip Code…
Fig. 5Disease mapping model for COVID-19 cases in New York City by Zip Code Tabulation Area (ZCTA). As at April 5, 2020, using base Poisson BYM2 model with no predictors. The area specific relative risk is multiplied by the total population average COVID-19 positivity rate (56.47%) to give the area specific positivity rate. a Area-specific relative risk, ζi. b Posterior probability for relative risk, p(ζi>1|y)
Fig. 6
Panel plot showing bivariate relationships…
Fig. 6
Panel plot showing bivariate relationships between predictors. Diagonal : Distribution of all 11…
Fig. 6Panel plot showing bivariate relationships between predictors. Diagonal: Distribution of all 11 predictor variables. Lower: Bivariate scatter plots. Upper: Pearson correlations between pairs of predictors
Fig. 7
Ecological regression model for COVID-19…
Fig. 7
Ecological regression model for COVID-19 cases in New York City by Zip Code…
Fig. 7Ecological regression model for COVID-19 cases in New York City by Zip Code Tabulation Area (ZCTA). As at April 5, 2020, final Poisson BYM2 model including percentage of young population, percentage of population identifying as white (alone or in combination with another race), population density, and median household income as predictors. a Area-specific relative risk, ζi. b Posterior probability for relative risk, p(ζi>1|y)
Fig. 8
Positivity rate for total COVID-19…
Fig. 8
Positivity rate for total COVID-19 tests in New York City by Zip Code…
Fig. 8Positivity rate for total COVID-19 tests in New York City by Zip Code Tabulation Area (ZCTA) against predictors used in final model. As at 5 April 2020, using final Poisson BYM2 model. Red regression lines show model estimates and 95% confidence interval (CI) with other predictors held at their mean values. a Percentage of young population. b Percentage of population that identify as white (alone or in combination with another race). c Population density. d Median household income
Fig. 9
Positivity rate for total COVID-19…
Fig. 9
Positivity rate for total COVID-19 tests in New York City by Zip Code…
Fig. 9Positivity rate for total COVID-19 tests in New York City by Zip Code Tabulation Area (ZCTA) as a function of race. As at 5 April 2020, Poisson BYM2 models incorporating explicit racial groupings along with young population (Young), population density (Density), and median household income (Income) as predictors. Regression lines show model estimates and 95% confidence interval (CI) with other predictors held at their mean values. a Percentage of population identifying as white. b Percentage of population identifying as Black
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