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Neighbourhood inequity: Exploring the factors underlying racial and ethnic disparities in COVID‐19 testing and infection rates using ZIP code data in Chicago and New York

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  • Kevin Credit

Abstract

This paper compares ZIP code‐level data on observed COVID‐19 testing and case rates for the City of Chicago and New York City to better understand both: (i) the extent to which racial and ethnic disparities in COVID‐19 testing and case rates exist at the neighbourhood level; and (ii) the most important neighbourhood‐level drivers of these observed disparities. Through exploratory spatial mapping and econometric approaches, the paper finds that, across both cities, Hispanic‐majority neighbourhoods have significantly lower testing rates than other racial/ethnic neighbourhood types, even when controlling for observed infection rates—which are also significantly higher for Hispanic‐majority neighbourhoods. At the same time, White‐majority neighbourhoods have significantly higher testing rates and lower observed infection rates. Given this observed disparity, the paper also examines a range of underlying factors that are potentially driving observed neighbourhood‐level COVID‐19 case rates. The findings suggest that higher socio‐economic status and the provision of healthy, active built environments are significantly negatively associated with COVID‐19 infection rates, while several aspects of social vulnerability are significant positive predictors of COVID‐19 infection rates. These findings suggest that the health benefits from higher density, walkable built environments may play a larger “protective” role from observed COVID‐19 case rates at the neighbourhood‐level than previously assumed, while at the same time indicating that the increased prevalence of COVID‐19 in Hispanic‐ and Black‐majority neighbourhoods may be in part due to their greater risk of occupational exposure and multi‐generational household structure (particularly for Hispanic‐majority neighbourhoods). Este artículo compara datos a nivel de código postal de pruebas y tasas de casos de COVID‐19 observados entre la ciudad de Chicago y la ciudad de Nueva York con el fin de comprender mejor tanto: i) el alcance de la existencia de disparidades raciales y étnicas en las pruebas y tasas de casos de COVID‐19 a nivel de vecindario; y ii) los factores más importantes de las causas de estas disparidades observadas a nivel de vecindario. A través de un mapeo espacial exploratorio y enfoques econométricos, el artículo encuentra que, en ambas ciudades, los barrios de mayoría hispana tienen tasas de pruebas significativamente más bajas que otros barrios raciales/étnicos, incluso después de haber controlado las tasas de infección observadas, que también son significativamente más altas para los barrios de mayoría hispana. Al mismo tiempo, los barrios de mayoría blanca tienen tasas de pruebas significativamente más altas y tasas observadas de infección más bajas. Dada esta disparidad observada, el artículo examina también una serie de factores subyacentes que potencialmente impulsan las tasas observadas de casos de COVID‐19 a nivel de vecindario. Los hallazgos sugieren que el estatus socioeconómico y la provisión de entornos saludables diseñados así activamente están asociados significativamente de forma negativa con las tasas de infección de COVID‐19, mientras que varios aspectos de la vulnerabilidad social son predictores positivos significativos de las tasas de infección de COVID‐19. Estos hallazgos sugieren que los beneficios para la salud de los entornos construidos con una mayor densidad y transitables a pie pueden desempeñar un mayor papel de “protección” en las tasas de casos de COVID‐19 observados a nivel de vecindario de lo que se suponía anteriormente y, al mismo tiempo, pueden indicar que la mayor prevalencia de COVID19 en barrios de mayoría hispana o afroamericana puede deberse en parte a su mayor riesgo de exposición ocupacional y la estructura multigeneracional del hogar (en particular para los barrios de mayoría hispana). 本稿では、1)COVID‐19の検査率と発症率の人種および民族的な差異はneighbourhood(近隣住区)レベルでどの程度存在するか、と2)1)で認められた差異のneighbourhoodレベルでの最も重要な要因は何かの以上についてよく理解するために、観察下でのCOVID‐19検査に関するZIPコードレベルのデータと、シカゴ市とニューヨーク市のCOVID‐19発症率との比較を行う。探索的空間マッピングと計量経済学的分析から、いずれの都市でも、ヒスパニック系が多数派を占める地区では、他の人種/民族が多数派の地区よりも検査率が有意に低かった。これは、ヒスパニック系多数派地区で有意に高いことが確認された感染率を調整した場合でも変わらなかった。一方で、白人が多数派を占める地区では、検査率が有意に高く、感染率が低かった。このような差異が認められたため、本稿では、neighbourhoodレベルのCOVID‐19発症率を押し上げている可能性のある根底的な要因も検討した。その知見から、社会経済的に高い地位と健康的で活動的な建築環境にあることとCOVID‐19の感染率には有意なマイナスの関連性があることが示唆される一方、社会的脆弱性のいくつかの側面がCOVID‐19感染率の有意なプラスの予測因子であることが示唆される。これらの知見は、高密度で歩行可能な建築環境から得られる健康上の利益の、neighbourhoodレベルでのCOVID‐19発症率に対する保護的役割は、以前に考えられていたよりも大きい可能性があること、同時にヒスパニックと黒人が多数派を占める地域におけるCOVID‐19の高い有病率は、部分的には職業曝露と多世代世帯構造 (特にヒスパニック系住民の多い地区)による大きなリスクが原因である可能性があることを示している。

Suggested Citation

  • Kevin Credit, 2020. "Neighbourhood inequity: Exploring the factors underlying racial and ethnic disparities in COVID‐19 testing and infection rates using ZIP code data in Chicago and New York," Regional Science Policy & Practice, Wiley Blackwell, vol. 12(6), pages 1249-1271, December.
  • Handle: RePEc:bla:rgscpp:v:12:y:2020:i:6:p:1249-1271
    DOI: 10.1111/rsp3.12321
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    References listed on IDEAS

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    1. Saelens, B.E. & Sallis, J.F. & Black, J.B. & Chen, D., 2003. "Neighborhood-Based Differences in Physical Activity: An Environment Scale Evaluation," American Journal of Public Health, American Public Health Association, vol. 93(9), pages 1552-1558.
    2. Braveman, P.A. & Cubbin, C. & Egerter, S. & Williams, D.R. & Pamuk, E., 2010. "Socioeconomic disparities in health in the united States: What the patterns tell us," American Journal of Public Health, American Public Health Association, vol. 100(S1), pages 186-196.
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    1. Patrick Ballantyne & Alex Singleton & Les Dolega & Kevin Credit, 2022. "A framework for delineating the scale, extent and characteristics of American retail centre agglomerations," Environment and Planning B, , vol. 49(3), pages 1112-1128, March.
    2. Mitchell D. Ramuta & Christina M. Newman & Savannah F. Brakefield & Miranda R. Stauss & Roger W. Wiseman & Amanda Kita-Yarbro & Eli J. O’Connor & Neeti Dahal & Ailam Lim & Keith P. Poulsen & Nasia Saf, 2022. "SARS-CoV-2 and other respiratory pathogens are detected in continuous air samples from congregate settings," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    3. Lee, Haena & Andrasfay, Theresa & Riley, Alicia & Wu, Qiao & Crimmins, Eileen, 2022. "Do social determinants of health explain racial/ethnic disparities in COVID-19 infection?," Social Science & Medicine, Elsevier, vol. 306(C).
    4. Jingjing Wang & Xueying Wu & Ruoyu Wang & Dongsheng He & Dongying Li & Linchuan Yang & Yiyang Yang & Yi Lu, 2021. "Review of Associations between Built Environment Characteristics and Severe Acute Respiratory Syndrome Coronavirus 2 Infection Risk," IJERPH, MDPI, vol. 18(14), pages 1-16, July.
    5. Richard Florida & Charlotta Mellander, 2022. "The geography of COVID-19 in Sweden," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 68(1), pages 125-150, February.
    6. Carmen García-Peña & Julián Molina & José Damián Ruiz Sinoga, 2023. "Learning About the Incidence and Lethality of COVID-19 in Vulnerable Neighborhoods: The Case of Malaga (Spain)," International Regional Science Review, , vol. 46(5-6), pages 701-725, September.
    7. Daniel R. Y. Gan & John R. Best, 2021. "Prior Social Contact and Mental Health Trajectories during COVID-19: Neighborhood Friendship Protects Vulnerable Older Adults," IJERPH, MDPI, vol. 18(19), pages 1-9, September.
    8. Alireza Dehghani & Mehdi Alidadi & Ayyoob Sharifi, 2022. "Compact Development Policy and Urban Resilience: A Critical Review," Sustainability, MDPI, vol. 14(19), pages 1-19, September.

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