Dataset Information

COVID-19 testing, case, and death rates and spatial socio-demographics in New York City: An ecological analysis as of June 2020.

ABSTRACT: We assessed the geographic variation in socio-demographics, mobility, and built environmental factors in relation to COVID-19 testing, case, and death rates in New York City (NYC). COVID-19 rates (as of June 10, 2020), relevant socio-demographic information, and built environment characteristics were aggregated by ZIP Code Tabulation Area (ZCTA). Spatially adjusted multivariable regression models were fitted to account for spatial autocorrelation. The results show that different sets of neighborhood characteristics were independently associated with COVID-19 testing, case, and death rates. For example, the proportions of Blacks and Hispanics in a ZCTA were positively associated with COVID-19 case rate. Contrary to the conventional hypothesis, neighborhoods with low-density housing experienced higher COVID-19 case rates. In addition, demographic changes (e.g. out-migration) during the pandemic may bias the estimates of COVID-19 rates. Future research should further investigate these neighborhood-level factors and their interactions over time to better understand the mechanisms by which they affect COVID-19.

SUBMITTER: Kim B

PROVIDER: S-EPMC7895516 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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COVID-19 testing, case, and death rates and spatial socio-demographics in New York City: An ecological analysis as of June 2020.

Kim Byoungjun B Rundle Andrew G AG Goodwin Alicia T Singham ATS Morrison Christopher N CN Branas Charles C CC El-Sadr Wafaa W Duncan Dustin T DT

Health & place 20210219

We assessed the geographic variation in socio-demographics, mobility, and built environmental factors in relation to COVID-19 testing, case, and death rates in New York City (NYC). COVID-19 rates (as of June 10, 2020), relevant socio-demographic information, and built environment characteristics were aggregated by ZIP Code Tabulation Area (ZCTA). Spatially adjusted multivariable regression models were fitted to account for spatial autocorrelation. The results show that different sets of neighbor ...[more]

PMID: 33639446

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Project description:Income inequality presents a key challenge to urban sustainability across the developed economies. Traditionally, accurate high granularity income data are generally obtained from field surveys. However, due to privacy considerations, field subjects are hesitant to provide accurate personal income data. A Socio-economic & Spatial-Information-GP (SSIG) model is thereby developed to estimate district-based high granularity income for New York City (NYC). As compared to the state-of-the-art Gaussian Processes (GP) income estimation model based entirely on spatial information, SSIG incorporates socio-economic domain-specific knowledge into a GP model. For SSIG to be explainable, SHapley Additive exPlanations (SHAP) analysis is undertaken to evaluate the relative contribution of various key individual socio-economic variables to district-based per-capita and median household income in NYC. Differentiating from traditional income inequality studies based predominantly on linear or log-linear regression model, SSIG presents a novel income-based model architecture, capable of modelling complex non-linear relationships. In parallel, SHAP analysis serves an effective analytical tool for identifying the key attributes to income inequality. Results have shown that SSIG surpasses other state-of-the-art baselines in estimation accuracy, as far as per-capita and median household income estimation at the Tract-level and the ZIP-level in NYC are concerned. SHAP results have indicated that having a bachelor or a postgraduate degree can accurately predict income in NYC, despite that between-district income inequality due to Sex/Race remains prevalent. SHAP has further confirmed that between-district income gap is more associated with Race than Sex. Furthermore, ablation study shows that socio-economic information is more predictive of income at the ZIP-level, relative to the spatial information. This study carries significant implications for policy-making in a developed context. To promote urban economic sustainability in NYC, policymakers can attend to the growing income disparity (income inequality) contributed by Sex and Race, while giving more higher education opportunities to residents in the lower-income districts, as the estimated per-capita income is more sensitive to the proportion of adults ≥25 holding a bachelor's degree. Finally, interpretative SHAP analysis is useful for investigating the relative contribution of socio-economic inputs to any predicted outputs in future machine-learning-driven socio-economic analyses.

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Project description:ImportanceThe US has high rates of adverse birth outcomes, with substantial racial disparities augmented by stress and neighborhood disadvantage. Black people are more likely to live in neighborhoods with high rates of incarceration, which is a source of both stress and neighborhood disadvantage and, thus, may contribute to adverse birth outcomes.ObjectiveTo determine whether neighborhoods with high incarceration rates also have higher rates of adverse birth outcomes compared with neighborhoods with lower rates.Design, setting, and participantsThis cross-sectional study used publicly available data from the New York City Department of Health (2010-2014). Censored Poisson regression, with the US Census tract as the unit of analysis, was used to examine the association of neighborhood incarceration rate and birth outcomes. Multivariable models included percentage of births aggregated to the Census tract by maternal factors (age, parity, singleton vs multiple birth, insurance, and race) and neighborhood factors (poverty, education, and violent crime). Analyses were performed between May 2021 and October 2022.ExposureNeighborhood incarceration rate, categorized into quintiles.Main outcomes and measuresThe primary outcome was the incidence rate ratio (IRR) of preterm birth and low birth weight. Secondary outcomes were IRRs of very preterm birth, extremely preterm birth, and very low birth weight. Hypotheses were formulated before data collection.ResultsAmong 2061 Census tracts with 562 339 births, incarceration rates varied from 0 to 4545 people incarcerated per 100 000, and high-incarceration neighborhoods had more residents of Black race (54.00% vs 1.90%), living in poverty (32.30% vs 10.00%), and without a general educational development equivalent (28.00% vs 12.00%) compared with low-incarceration neighborhoods. In fully adjusted models, high-incarceration neighborhoods had a 13% higher IRR of preterm birth (IRR, 1.13; 95% CI, 1.08-1.18), 45% higher IRR of very preterm birth (IRR, 1.45; 95% CI, 1.24-1.71), 125% higher IRR of extremely preterm birth (IRR, 2.25; 95% CI, 1.59-3.18), 10% higher IRR of low birth weight (IRR, 1.10; 95% CI, 1.05-1.16), and 52% higher IRR of very low birth weight compared with low-incarceration neighborhoods (IRR, 1.52; 95% CI, 1.28-1.81).Conclusions and relevanceNeighborhood incarceration rate was positively associated with adverse birth outcomes, particularly those associated with infant mortality. Black people were significantly more likely to live in high-incarceration neighborhoods, suggesting that mass incarceration may contribute to racial disparities in birth outcomes.

Dataset Information

COVID-19 testing, case, and death rates and spatial socio-demographics in New York City: An ecological analysis as of June 2020.

Publications

COVID-19 testing, case, and death rates and spatial socio-demographics in New York City: An ecological analysis as of June 2020.

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