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Association between neighborhood socioeconomic status, built environment and SARS‐CoV‐2 infection among cancer patients treated at a Tertiary Cancer Center in New York City

Association between neighborhood socioeconomic status, built environment and SARS‐CoV‐2 infection... INTRODUCTIONRacial and ethnic minority groups experience a disproportionate burden of SARS‐CoV‐2 illness.1,2 In New York City, the incidence of COVID‐19 varies substantially based on area of residence and is higher in neighborhoods traditionally characterized by lower socioeconomic status.3,4 Additionally, there were two significant outbreaks (March–May and December) of COVID‐19 in 2020 in New York City.5 Previous studies suggest that cancer patients are at a particular risk for severe SARS‐CoV‐2 infection.6 Our objective was to determine the interplay of race and neighborhood socioeconomic factors on SARS‐CoV‐2 infection among oncology patients during a period of wide community spread of the virus.METHODSWe performed a cross‐sectional study of residents of New York City receiving treatment for cancer at a tertiary cancer center from March 1, 2020 to December 31, 2020. Patients were identified by ICD‐10 cancer diagnosis codes in combination with billing codes for chemotherapy, targeted therapy or radiotherapy. SARS‐CoV‐2 status was determined by review of SARS‐CoV‐2 test results or documentation of infection from the medical record. Any SARS‐CoV‐2 test used to determine infection status was included in the study. Other factors extracted from the electronic medical record include age, sex, marital status, race/ethnicity, insurance status, cancer type, cancer treatment, and period when the patient tested positive for COVID (February–June, July–September, and October–December 2020). Subjects were linked by their primary address to the US Census Bureau's American Community Survey data, a nationwide survey including demographic, housing and socioeconomic data, and to real estate tax data from New York's Department of City Planning.7,8 We abstracted each patient's building‐level characteristics, including assessed value (mean), residential units per building, and neighborhood level variables, including unemployment rate, racial and ethnic composition, median household income, percentage of families below the poverty rate, number of occupants per room (household crowding), average household size and membership, highest educational attainment, primary language spoken within the home, and population density of the neighborhood. New York City neighborhood tabulation areas were used to define a neighborhood.8We fit unadjusted logistic regression models to estimate odds ratios (ORs) between each neighborhood socioeconomic and environment variable and SARS‐CoV‐2 infection, accounting for neighborhood clustering as described previously.9 We fit similar models further adjusting for patient characteristics selected on the basis of their a priori possibility of confounding the association. Analyses were conducted using SAS 9.4 (SAS Institute Inc., Cary, North Carolina). This study was approved by the Columbia University Institutional Review Board. The authors have no conflict of interest in relation to this work.RESULTSWe identified 2489 New York City residents with cancer receiving treatment including 2350 (94.4%) that were linked to neighborhoods and buildings in the city. Overall, 214 (9.1%) were infected with SARS‐CoV‐2 (Table 1).1TABLEClinical and demographic characteristics of the overall cohort and COVID positive cohortOverallCOVID PositiveN%N%2350–2149.1Age (Years)18–391918.12310.840–492028.6177.950–5930613.03616.860–6958624.95324.870–7963727.15324.8≥8042818.23215.0SexFemale129455.110950.9Male105644.910549.1Marital statusSingle131956.112156.5Married97541.58841.1Other/unknown562.452.3RaceWhite71530.43918.2Black32013.63114.5Hispanic77432.910850.5Asian, American Indian, Alaska, Pacific Islander944.041.9Other1395.9125.6Unknown30813.1209.4Insurance statusNone90.410.5Commercial66928.54320.1Medicare117349.910649.5Medicaid48420.66028.0Other/unknown150.641.9Cancer typeHead and neck261.131.4Gastrointestinal31713.54119.2Thoracic2058.7115.1Bone and soft tissue130.610.5Skin562.452.3Peripheral nerves and soft tissues893.8146.5Breast42418.03114.5Gynecologic1677.1146.5Genitourinary34414.62813Brain and nervous system1024.362.8Endocrine271.210.5Secondary cancers or unknown primary site632.752.3Hematologic51722.05425.2Treatment typeChemotherapy219593.420696.3Radiation48320.65023.4Period when testing COVID positive (2020)February–June––14567.8July–September––2511.7October–December––4420.6The likelihood of SARS‐CoV‐2 varied across measures of neighborhood socioeconomic status and built environment (Figure 1). The odds of SARS‐CoV‐2 infection were lowest in patients living in neighborhoods with higher median property values (OR per $100 000 increase = 0.88; 95% CI, 0.82–0.95), higher rates of bachelor degrees (OR per 1% increase = 0.98; 95% CI, 0.97–0.99), higher rates of English speaking only households (OR per 1% increase = 0.98; 95% CI, 0.97–0.99), higher rates of females compared to the total population (OR per 1% increase = 0.92; 95% CI, 0.86–0.99) and higher household income (OR = 0.98 per $1000 increase; 95% CI, 0.98–0.99). The odds of infection were higher in neighborhoods with higher Hispanic/Latino populations (OR per 1% increase = 1.02; 95% CI, 1.01–1.02), higher unemployment rates (OR per 1% increase = 1.13; 95% CI, 1.09–1.18), higher rates of poverty (OR per 1% increase = 1.03; 95% CI, 1.02–1.04), in neighborhoods with more households with >1 person per room (OR per 1% increase = 1.06; 95% CI, 1.03–1.09), increased average household size (OR = 2.13; 95% CI, 1.53–2.97) and higher population density (OR = 2.25; 95% CI, 1.60–3.17).1FIGUREBuilt environment and neighborhood socioeconomic factors associated with SARS‐CoV‐2 infection among cancer patients getting treatment. The crude probability of detecting SARS‐CoV‐2 infection by neighborhood characteristics: (A) Percent of Hispanic/Latino population; (B) unemployment rate; (C) median household income; (D) percent of families below poverty line; (E) percent of greater than 1 occupant per room; (F) average household size; (G) percent of population with a bachelor degree or higher; (H) percent of population where English only spoken at home; (I) population density. Blue line indicated the predicted probability of SARS‐CoV‐2 infection from unadjusted logistic regression model. Shade indicated 95% confidence intervals. Bubbles indicated the mean of neighborhood characteristic by the observed SARS‐CoV‐2 infection rate of each decile, with bubble size proportionate to the number of patients in each decileIn adjusted models, the percentage of Hispanic/Latino population (aOR = 1.01; 95% CI, 1.005–1.02), unemployment rate (aOR = 1.10; 95% CI, 1.05–1.16), poverty rates (aOR = 1.02; 95% CI, 1.0002–1.03), rate of >1 person per room (aOR = 1.04; 95% CI, 1.01–1.07), average household size (aOR = 1.79; 95% CI, 1.23–2.59) and population density (aOR = 1.86; 95% CI, 1.27–2.72) were associated with SARS‐CoV‐2 infection (Supplemental Table).DISCUSSIONAmong cancer patients in New York City receiving anti‐cancer therapy, SARS‐CoV‐2 infection was associated with building‐ and neighborhood‐level markers of household crowding, larger household membership, and low socioeconomic status. With ongoing surges of SARS‐CoV‐2 infections, these data may help in the design and targeting of interventions to reduce the morbidity and mortality associated with SARS‐CoV‐2 among cancer patients.Our study was limited to cancer patients in New York City and the findings may not be generalizable to other populations, there may be under capture of SARS‐CoV‐2 infection including asymptomatic infections, we lack data on the severity of infection, and there was significant correlation between neighborhood‐level variables which hinder multivariable analysis. However, this study suggests that differences in urban environment may be an important social determinant of SARS‐CoV‐2 transmission among cancer patients.AUTHOR CONTRIBUTIONSShayan Dioun: Conceptualization (equal); formal analysis (equal); investigation (equal); writing – original draft (equal); writing – review and editing (equal). Ling Chen: Data curation (equal); formal analysis (equal); methodology (equal); writing – original draft (equal); writing – review and editing (equal). Grace Clarke Hillyer: Conceptualization (equal); formal analysis (equal); investigation (equal); writing – review and editing (equal). Nicholas Tatonetti: Data curation (equal); formal analysis (equal); methodology (equal); writing – review and editing (equal). Benjamin May: Data curation (equal); formal analysis (equal); methodology (equal); writing – review and editing (equal). Alexander Melamed: Conceptualization (equal); formal analysis (equal); investigation (equal); writing – review and editing (equal). Jason Wright: Conceptualization (equal); formal analysis (equal); investigation (equal); supervision (equal); writing – original draft (equal); writing – review and editing (equal).ACKNOWLEDGMENTSDr. Wright has served as a consultant for Clovis Oncology, received royalties from UpToDate, and received research support from Merck.CONFLICT OF INTERESTThe authors have stated explicitly that there are no conflicts of interest in connection with this article.DATA AVAILABILITY STATEMENTThe data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.ETHICS STATEMENTAuthors confirm that all procedures followed, were in accordance with the ethical standards with the Helsinki declaration of 1975, as revised in 2000.REFERENCESWebb Hooper M, Nápoles AM, Pérez‐Stable EJ. COVID‐19 and racial/ethnic disparities. JAMA J Am Med Assoc. 2020;323(24):2466‐2467.Thompson CN, Baumgartner J, Pichardo C, et al. COVID‐19 Outbreak—New York City, February 29–June 1, 2020. MMWR Morbidity and Mortality Weekly Report [Internet] 2020;69(46):1725–9. https://www.cdc.gov/mmwr/volumes/69/wr/mm6946a2.htmWadhera RK, Wadhera P, Gaba P, et al. Variation in COVID‐19 hospitalizations and deaths across new York City boroughs [internet]. JAMA J Am Med Assoc. 2020;323(21):2192‐2195. https://pubmed.ncbi.nlm.nih.gov/32347898/Bialek S, Bowen V, Chow N, et al. Geographic differences in COVID‐19 cases, deaths, and incidence—United States, February 12–April 7, 2020. MMWR Morb Mortal Wkly Rep. 2020;69(15):465‐471.Previous U.S. COVID‐19 case data. CDC [internet]. Accessed April 8, 2022. https://www.cdc.gov/coronavirus/2019-ncov/covid-data/previouscases.htmlKuderer NM, Choueiri TK, Shah DP, et al. Clinical impact of COVID‐19 on patients with cancer (CCC19): a cohort study. Lancet [Internet]. 2020;395(10241):1907‐1918. https://pubmed.ncbi.nlm.nih.gov/32473681/PLUTO database New York City Department of City Planning [Internet]. Accessed May 31, 2021. https://www1.nyc.gov/site/planning/data-maps/open-data.pageAmerican Community Survey by neighborhood tabulation area. New York City Department of City Planning [Internet]. Accessed May 31, 2021. www1.nyc.gov/site/planning/data‐maps/open‐data/dwn‐acs‐nta.pageEmeruwa UN, Ona S, Shaman JL, et al. Associations between built environment, neighborhood socioeconomic status, and SARS‐CoV‐2 infection among pregnant women in new York City. JAMA J Am Med Assoc. 2020;324(4):390‐392. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Cancer Reports Wiley

Association between neighborhood socioeconomic status, built environment and SARS‐CoV‐2 infection among cancer patients treated at a Tertiary Cancer Center in New York City

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Publisher
Wiley
Copyright
© 2023 Wiley Periodicals LLC.
eISSN
2573-8348
DOI
10.1002/cnr2.1714
Publisher site
See Article on Publisher Site

Abstract

INTRODUCTIONRacial and ethnic minority groups experience a disproportionate burden of SARS‐CoV‐2 illness.1,2 In New York City, the incidence of COVID‐19 varies substantially based on area of residence and is higher in neighborhoods traditionally characterized by lower socioeconomic status.3,4 Additionally, there were two significant outbreaks (March–May and December) of COVID‐19 in 2020 in New York City.5 Previous studies suggest that cancer patients are at a particular risk for severe SARS‐CoV‐2 infection.6 Our objective was to determine the interplay of race and neighborhood socioeconomic factors on SARS‐CoV‐2 infection among oncology patients during a period of wide community spread of the virus.METHODSWe performed a cross‐sectional study of residents of New York City receiving treatment for cancer at a tertiary cancer center from March 1, 2020 to December 31, 2020. Patients were identified by ICD‐10 cancer diagnosis codes in combination with billing codes for chemotherapy, targeted therapy or radiotherapy. SARS‐CoV‐2 status was determined by review of SARS‐CoV‐2 test results or documentation of infection from the medical record. Any SARS‐CoV‐2 test used to determine infection status was included in the study. Other factors extracted from the electronic medical record include age, sex, marital status, race/ethnicity, insurance status, cancer type, cancer treatment, and period when the patient tested positive for COVID (February–June, July–September, and October–December 2020). Subjects were linked by their primary address to the US Census Bureau's American Community Survey data, a nationwide survey including demographic, housing and socioeconomic data, and to real estate tax data from New York's Department of City Planning.7,8 We abstracted each patient's building‐level characteristics, including assessed value (mean), residential units per building, and neighborhood level variables, including unemployment rate, racial and ethnic composition, median household income, percentage of families below the poverty rate, number of occupants per room (household crowding), average household size and membership, highest educational attainment, primary language spoken within the home, and population density of the neighborhood. New York City neighborhood tabulation areas were used to define a neighborhood.8We fit unadjusted logistic regression models to estimate odds ratios (ORs) between each neighborhood socioeconomic and environment variable and SARS‐CoV‐2 infection, accounting for neighborhood clustering as described previously.9 We fit similar models further adjusting for patient characteristics selected on the basis of their a priori possibility of confounding the association. Analyses were conducted using SAS 9.4 (SAS Institute Inc., Cary, North Carolina). This study was approved by the Columbia University Institutional Review Board. The authors have no conflict of interest in relation to this work.RESULTSWe identified 2489 New York City residents with cancer receiving treatment including 2350 (94.4%) that were linked to neighborhoods and buildings in the city. Overall, 214 (9.1%) were infected with SARS‐CoV‐2 (Table 1).1TABLEClinical and demographic characteristics of the overall cohort and COVID positive cohortOverallCOVID PositiveN%N%2350–2149.1Age (Years)18–391918.12310.840–492028.6177.950–5930613.03616.860–6958624.95324.870–7963727.15324.8≥8042818.23215.0SexFemale129455.110950.9Male105644.910549.1Marital statusSingle131956.112156.5Married97541.58841.1Other/unknown562.452.3RaceWhite71530.43918.2Black32013.63114.5Hispanic77432.910850.5Asian, American Indian, Alaska, Pacific Islander944.041.9Other1395.9125.6Unknown30813.1209.4Insurance statusNone90.410.5Commercial66928.54320.1Medicare117349.910649.5Medicaid48420.66028.0Other/unknown150.641.9Cancer typeHead and neck261.131.4Gastrointestinal31713.54119.2Thoracic2058.7115.1Bone and soft tissue130.610.5Skin562.452.3Peripheral nerves and soft tissues893.8146.5Breast42418.03114.5Gynecologic1677.1146.5Genitourinary34414.62813Brain and nervous system1024.362.8Endocrine271.210.5Secondary cancers or unknown primary site632.752.3Hematologic51722.05425.2Treatment typeChemotherapy219593.420696.3Radiation48320.65023.4Period when testing COVID positive (2020)February–June––14567.8July–September––2511.7October–December––4420.6The likelihood of SARS‐CoV‐2 varied across measures of neighborhood socioeconomic status and built environment (Figure 1). The odds of SARS‐CoV‐2 infection were lowest in patients living in neighborhoods with higher median property values (OR per $100 000 increase = 0.88; 95% CI, 0.82–0.95), higher rates of bachelor degrees (OR per 1% increase = 0.98; 95% CI, 0.97–0.99), higher rates of English speaking only households (OR per 1% increase = 0.98; 95% CI, 0.97–0.99), higher rates of females compared to the total population (OR per 1% increase = 0.92; 95% CI, 0.86–0.99) and higher household income (OR = 0.98 per $1000 increase; 95% CI, 0.98–0.99). The odds of infection were higher in neighborhoods with higher Hispanic/Latino populations (OR per 1% increase = 1.02; 95% CI, 1.01–1.02), higher unemployment rates (OR per 1% increase = 1.13; 95% CI, 1.09–1.18), higher rates of poverty (OR per 1% increase = 1.03; 95% CI, 1.02–1.04), in neighborhoods with more households with >1 person per room (OR per 1% increase = 1.06; 95% CI, 1.03–1.09), increased average household size (OR = 2.13; 95% CI, 1.53–2.97) and higher population density (OR = 2.25; 95% CI, 1.60–3.17).1FIGUREBuilt environment and neighborhood socioeconomic factors associated with SARS‐CoV‐2 infection among cancer patients getting treatment. The crude probability of detecting SARS‐CoV‐2 infection by neighborhood characteristics: (A) Percent of Hispanic/Latino population; (B) unemployment rate; (C) median household income; (D) percent of families below poverty line; (E) percent of greater than 1 occupant per room; (F) average household size; (G) percent of population with a bachelor degree or higher; (H) percent of population where English only spoken at home; (I) population density. Blue line indicated the predicted probability of SARS‐CoV‐2 infection from unadjusted logistic regression model. Shade indicated 95% confidence intervals. Bubbles indicated the mean of neighborhood characteristic by the observed SARS‐CoV‐2 infection rate of each decile, with bubble size proportionate to the number of patients in each decileIn adjusted models, the percentage of Hispanic/Latino population (aOR = 1.01; 95% CI, 1.005–1.02), unemployment rate (aOR = 1.10; 95% CI, 1.05–1.16), poverty rates (aOR = 1.02; 95% CI, 1.0002–1.03), rate of >1 person per room (aOR = 1.04; 95% CI, 1.01–1.07), average household size (aOR = 1.79; 95% CI, 1.23–2.59) and population density (aOR = 1.86; 95% CI, 1.27–2.72) were associated with SARS‐CoV‐2 infection (Supplemental Table).DISCUSSIONAmong cancer patients in New York City receiving anti‐cancer therapy, SARS‐CoV‐2 infection was associated with building‐ and neighborhood‐level markers of household crowding, larger household membership, and low socioeconomic status. With ongoing surges of SARS‐CoV‐2 infections, these data may help in the design and targeting of interventions to reduce the morbidity and mortality associated with SARS‐CoV‐2 among cancer patients.Our study was limited to cancer patients in New York City and the findings may not be generalizable to other populations, there may be under capture of SARS‐CoV‐2 infection including asymptomatic infections, we lack data on the severity of infection, and there was significant correlation between neighborhood‐level variables which hinder multivariable analysis. However, this study suggests that differences in urban environment may be an important social determinant of SARS‐CoV‐2 transmission among cancer patients.AUTHOR CONTRIBUTIONSShayan Dioun: Conceptualization (equal); formal analysis (equal); investigation (equal); writing – original draft (equal); writing – review and editing (equal). Ling Chen: Data curation (equal); formal analysis (equal); methodology (equal); writing – original draft (equal); writing – review and editing (equal). Grace Clarke Hillyer: Conceptualization (equal); formal analysis (equal); investigation (equal); writing – review and editing (equal). Nicholas Tatonetti: Data curation (equal); formal analysis (equal); methodology (equal); writing – review and editing (equal). Benjamin May: Data curation (equal); formal analysis (equal); methodology (equal); writing – review and editing (equal). Alexander Melamed: Conceptualization (equal); formal analysis (equal); investigation (equal); writing – review and editing (equal). Jason Wright: Conceptualization (equal); formal analysis (equal); investigation (equal); supervision (equal); writing – original draft (equal); writing – review and editing (equal).ACKNOWLEDGMENTSDr. Wright has served as a consultant for Clovis Oncology, received royalties from UpToDate, and received research support from Merck.CONFLICT OF INTERESTThe authors have stated explicitly that there are no conflicts of interest in connection with this article.DATA AVAILABILITY STATEMENTThe data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.ETHICS STATEMENTAuthors confirm that all procedures followed, were in accordance with the ethical standards with the Helsinki declaration of 1975, as revised in 2000.REFERENCESWebb Hooper M, Nápoles AM, Pérez‐Stable EJ. COVID‐19 and racial/ethnic disparities. JAMA J Am Med Assoc. 2020;323(24):2466‐2467.Thompson CN, Baumgartner J, Pichardo C, et al. COVID‐19 Outbreak—New York City, February 29–June 1, 2020. MMWR Morbidity and Mortality Weekly Report [Internet] 2020;69(46):1725–9. https://www.cdc.gov/mmwr/volumes/69/wr/mm6946a2.htmWadhera RK, Wadhera P, Gaba P, et al. Variation in COVID‐19 hospitalizations and deaths across new York City boroughs [internet]. JAMA J Am Med Assoc. 2020;323(21):2192‐2195. https://pubmed.ncbi.nlm.nih.gov/32347898/Bialek S, Bowen V, Chow N, et al. Geographic differences in COVID‐19 cases, deaths, and incidence—United States, February 12–April 7, 2020. MMWR Morb Mortal Wkly Rep. 2020;69(15):465‐471.Previous U.S. COVID‐19 case data. CDC [internet]. Accessed April 8, 2022. https://www.cdc.gov/coronavirus/2019-ncov/covid-data/previouscases.htmlKuderer NM, Choueiri TK, Shah DP, et al. Clinical impact of COVID‐19 on patients with cancer (CCC19): a cohort study. Lancet [Internet]. 2020;395(10241):1907‐1918. https://pubmed.ncbi.nlm.nih.gov/32473681/PLUTO database New York City Department of City Planning [Internet]. Accessed May 31, 2021. https://www1.nyc.gov/site/planning/data-maps/open-data.pageAmerican Community Survey by neighborhood tabulation area. New York City Department of City Planning [Internet]. Accessed May 31, 2021. www1.nyc.gov/site/planning/data‐maps/open‐data/dwn‐acs‐nta.pageEmeruwa UN, Ona S, Shaman JL, et al. Associations between built environment, neighborhood socioeconomic status, and SARS‐CoV‐2 infection among pregnant women in new York City. JAMA J Am Med Assoc. 2020;324(4):390‐392.

Journal

Cancer Reports – Wiley

Published: Feb 1, 2023

Keywords: built environment; cancer; neighborhood socioeconomic status; SARS‐COV‐2

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