ArticleBMC medicine2020
An ecological study of socioeconomic predictors in detection of COVID-19 cases across neighborhoods in New York City.
Article in BMC medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 90 papers, 2 of them syntheses that pooled it.
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Who cites it
90 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Methods Used in the Spatial and Spatiotemporal Analysis of COVID-19 Epidemiology: A Systematic Review.International journal of environmental research and public health · 2022Pooled it
- Racial and Ethnic Disparities in COVID-19-Related Infections, Hospitalizations, and Deaths : A Systematic Review.Annals of internal medicine · 2021Pooled it
- Socioeconomic determinants of COVID-19 incidence in a German rural area.BMC public health · 2026Article
- Assessing and adjusting for bias in ecological analysis using multiple sample datasets.BMC medical research methodology · 2025Article
- Adaption and Validation of Scales to Measure COVID-19 Stigma Attitudes and Perceptions.Stigma and health · 2024Article
- Building Environmental and Sociological Predictive Intelligence to Understand the Seasonal Threat of SARS-CoV-2 in Human Populations.The American journal of tropical medicine and hygiene · 2024Article
- Bayesian spatio-temporal analysis of the COVID-19 pandemic in Catalonia.Scientific reports · 2024Article
- The Influence of Socioeconomic and Educational Factors on the Level of Anxiety and Fear of COVID-19.Healthcare (Basel, Switzerland) · 2024Article
- Spatial distribution of SARS-CoV-2 incidence, social inequality, housing conditions, and density in South-Eastern France: keys for future epidemics.Frontiers in public health · 2024Article
- High-income ZIP codes in New York City demonstrate higher case rates during off-peak COVID-19 waves.Frontiers in public health · 2024Article
- Mapping the pandemic: a review of Geographical Information Systems-based spatial modeling of Covid-19.Journal of public health in Africa · 2023Article
- Bayesian spatial modelling of localised SARS-CoV-2 transmission through mobility networks across England.PLoS computational biology · 2023Article
- Impact of social and demographic factors on the spread of the SARS-CoV-2 epidemic in the town of Nice.BMC public health · 2023Article
- The Relationship Between a History of High-risk and Destructive Behaviors and COVID-19 Infection: Preliminary Study.JMIR formative research · 2023Article
- The Role of Functional Urban Areas in the Spread of COVID-19 Omicron (Northern Spain).Journal of urban health : bulletin of the New York Academy of Medicine · 2023Article
- Place, Race, and Case: Examining Racialized Economic Segregation and COVID-19 in Louisiana.Journal of racial and ethnic health disparities · 2023Article
- The Geographical Correlation Between Historical Preterm Birth Disparities and COVID-19 Burden.Reproductive sciences (Thousand Oaks, Calif.) · 2023Article
- Exposure, Susceptibility, and Recovery: A Framework for Examining the Intersection of the Social and Physical Environments and Infectious Disease Risk.American journal of epidemiology · 2023Article
- A neighborhood-level analysis of association between social vulnerability and COVID-19 in ahvaz, Iran.International journal of disaster risk reduction : IJDRR · 2023Article
- Geographic disparities in COVID-19 testing and outcomes in Florida.BMC public health · 2023Article
30 more citing papers are in PubMed but not listed here.
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundNew 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.
methodsData 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.
resultsMultiple 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
conclusionsOur 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.
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