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Working Paper Series Harvard University
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Affiliations, 1 Department of Social and Behavioral Sciences Harvard T H Chan School of Public Health. 2 Department of Social and Behavioral Sciences Harvard T H Chan School of Public Health. Corresponding Author, Jarvis T Chen ScD Research Scientist Department of Social and Behavioral Sciences Harvard. T H Chan School of Public Health, Email jarvis hsph harvard edu. No national state or local public health monitoring data in the US currently exist regarding the. unequal economic and social burden of COVID 19 To address this gap we draw on methods of. the Public Health Disparities Geocoding Project whereby we merge county level cumulative. death counts with population counts and area based socioeconomic measures ABSMs. below poverty crowding and population of color and the Index of Concentration at. the Extremes and compute rates rate differences and rate ratios by category of county. level ABSMs To illustrate the performance of the method at finer levels of geographic. aggregation we analyze data on a confirmed cases in Illinois ZIP codes and b positive test. results in New York City ZIP codes with ZIP code level ABSMs We detect stark gradients. though complex gradients in COVID 19 deaths by county level ABSMs with dramatically. increased risk of death observed among residents of the most disadvantaged counties Monotonic. socioeconomic gradients in Illinois confirmed cases and New York City positive tests by ZIP. code level ABSMs were also observed We recommend that public health departments use these. straightforward cost effective methods to report on social inequities in COVID 19 outcomes to. provide an evidence base for policy and resource allocation. Chen JT Krieger N Revealing the unequal burden of COVID 19 by income race ethnicity and household crowding US county. vs ZIP code analyses Harvard Center for Population and Development Studies Working Paper Series Volume 19 Number 1. April 21 2020 https tinyurl com y7v72446, Title Revealing the unequal burden of COVID 19 by income race ethnicity and. household crowding US county vs ZIP code analyses, Authors Jarvis T Chen ScD.
Nancy Krieger PhD, Corresponding, author Jarvis T Chen ScD. Research Scientist, Department of Social and Behavioral Sciences. Harvard T H Chan School of Public Health, 677 Huntington Avenue. Boston MA 02115, Email jarvis hsph harvard edu, No national state or local public health monitoring data in the US currently exist regarding the. unequal economic and social burden of COVID 19 To address this gap we draw on methods of. the Public Health Disparities Geocoding Project whereby we merge county level cumulative. death counts with population counts and area based socioeconomic measures ABSMs below. poverty crowding and population of color and the Index of Concentration at the. Extremes and compute rates rate differences and rate ratios by category of county level. ABSMs To illustrate the performance of the method at finer levels of geographic aggregation. we analyze data on a confirmed cases in Illinois ZIP codes and b positive test results in New. York City ZIP codes with ZIP code level ABSMs We detect stark gradients though complex. gradients in COVID 19 deaths by county level ABSMs with dramatically increased risk of. death observed among residents of the most disadvantaged counties Monotonic socioeconomic. gradients in Illinois confirmed cases and New York City positive tests by ZIP code level ABSMs. were also observed We recommend that public health departments use these straightforward. cost effective methods to report on social inequities in COVID 19 outcomes to provide an. evidence base for policy and resource allocation, Suggested citation Chen JT Krieger N Revealing the unequal burden of COVID 19 by income.
race ethnicity and household crowding US county vs ZIP code analyses Harvard Center for. Population and Development Studies Working Paper Series Volume 19 Number 1 April 21. 2020 https tinyurl com y7v72446, Chen JT Krieger N Revealing the unequal burden of COVID 19 by income race ethnicity and household crowding US county. vs ZIP code analyses Harvard Center for Population and Development Studies Working Paper Series Volume 19 Number 1. April 21 2020 https tinyurl com y7v72446, INTRODUCTION. As communities in the United States US grapple with the COVID 19 pandemic there is an. urgent need for real time data to better understand how particular populations are affected. including who is most at risk of infection developing serious illness and dying 1 2 Informed. by an awareness of the critical importance of racial ethnic economic and gender inequalities in. shaping individuals exposure to and ability to protect themselves from SARS CoV 2 as well as. their ability to practice physical distancing maintain economic wellbeing and access appropriate. healthcare when sick there have been increasing calls for improved data to provide an evidence. base for action 1 4 Descriptive epidemiology which is vital to informing efforts to distribute. resources develop treatments and coordinate public policy is hampered by the paucity of. disaggregated data by important social variables like race ethnicity and socioeconomic position. in the data reported by public health departments For example data from the COVID 19. tracking project 5 suggests that only 21 states currently report COVID 19 cases or deaths. disaggregated by race ethnicity and among those that do substantial proportions typically. 50 of cases and deaths are of unknown or missing race ethnicity Data tables on the US. Centers for Disease Control and Prevention s own webpage reporting COVID 19 cases by. race ethnicity show upwards of 65 of reported cases with missing race ethnicity information. 6 Furthermore to our knowledge no states are reporting COVID 19 cases or deaths by. measures of individual socioeconomic position though US death certificates routinely collect. information on decedent s education 1 2 7, The Public Health Disparities Geocoding Project was established to address the absence of. socioeconomic data in most routinely collected public health surveillance data 8 12 By. geocoding health records and linking them to US Census derived data on neighborhood. Chen JT Krieger N Revealing the unequal burden of COVID 19 by income race ethnicity and household crowding US county. vs ZIP code analyses Harvard Center for Population and Development Studies Working Paper Series Volume 19 Number 1. April 21 2020 https tinyurl com y7v72446, socioeconomic variables we have shown that these methods can be used to compute valid. estimates of socioeconomic gradients in health and moreover that area based socioeconomic. measures ABSMs can be used to characterize the influence of neighborhood socioeconomic. context on health above and beyond their association with individual socioeconomic position. We have applied these techniques to a wide range of health outcomes from birth to death and. including cancer and infectious diseases and have shown that the resulting estimates of. socioeconomic gradients are valid and robust The series of papers 8 12 stemming from this. project have been cited over 3500 times and have had a demonstrable impact on US public health. surveillance systems and health research more generally. To respond to the urgent need in the United States for documentation of stark social. inequities in who is affected by the COVID 19 pandemic in this paper we quantify disparities in. COVID 19 death rate in the US by county level sociodemographic attributes using currently. available surveillance and US Census data To illustrate the performance of these methods at. finer levels of geographic aggregation we additionally analyze data on a cumulative incidence. of confirmed cases in Illinois ZIP codes and b cumulative incidence of positive test results in. New York City ZIP codes with ZIP code level ABSMs Our intention is to illustrate how state. and local health departments can easily implement these types of analyses using freely available. US Census data and provide tabular and graphic summaries of these social inequities to. contribute to discussions on policies and interventions In the discussion we also discuss. interpretation of these social inequities given limitations of the data and make recommendations. for how public health departments can readily incorporate area based socioeconomic measures. into surveillance and monitoring, Chen JT Krieger N Revealing the unequal burden of COVID 19 by income race ethnicity and household crowding US county.
vs ZIP code analyses Harvard Center for Population and Development Studies Working Paper Series Volume 19 Number 1. April 21 2020 https tinyurl com y7v72446, COVID 19 Data Sources. US county death data We obtained publicly available data on COVID 19 deaths at the county. level from the Johns Hopkins University Center for Systems Science and Engineering JHU. CSSE 13 and USA Facts 14 Both sources report time series of cumulative confirmed cases. and deaths but notably JHU CSSE reports a single entry for all of New York City aggregating. over the five counties corresponding to the city boroughs Because this aggregation obscures. substantial differences by boroughs for example death rates by borough were 128 3 per 100 000. in the Bronx 108 1 per 100 000 in Brooklyn 119 8 per 100 000 in Queens 65 5 per 100 000 in. Manhattan and 87 1 per 100 000 in Staten Island we used the USA Facts county dataset which. maintains separate reporting for New York counties Differences were observed between JHU. CSSE and USA Facts death counts on April 16 2020 for 241 out of 2 717 matched counties. with discrepancies exceeding 10 deaths for only 21 counties Unmatched entries in the USA. Facts datasets consisted of 421 counties with 0 deaths that did not appear in the JHU CSSE. dataset with the exception of a single death in Nantucket County MA Conversely 56. unmatched entries in the JHU CSSE dataset consisted of 50 entries 298 deaths in 50 states with. county unassigned plus 2 entries for 152 deaths on cruise ships and four entries for US. territories Guam Northern Mariana Islands Puerto Rico and US Virgin Islands 64 deaths. Our analytic sample consisted of 30 318 COVID 19 deaths reported in 3 144 US counties. excluding territories as of April 16 2020 We additionally present analyses of US COVID 19. cases as of April 16 2020 by county characteristics in the Supplemental Appendix. Chen JT Krieger N Revealing the unequal burden of COVID 19 by income race ethnicity and household crowding US county. vs ZIP code analyses Harvard Center for Population and Development Studies Working Paper Series Volume 19 Number 1. April 21 2020 https tinyurl com y7v72446, Illinois data on confirmed cases at the zip code level We obtained ZIP code tabulation area. ZCTA level data on confirmed cases in Illinois from the lookup tool developed by the Illinois. Department of Public Health and the Chicago Reporter 15 ZCTAs are US Census defined. geographic units that correspond to areas roughly covered by US Postal Service USPS ZIP. codes 16 While there is not always a one to one correspondence between ZCTAs and USPS. ZIP codes the US Census ZCTAs provide a basis for linking sociodemographic and economic. variables from the US Census American Community Survey to health records geocoded at the. ZIP code level As noted by the Illinois data source infections among incarcerated populations. are not fully represented in these data including Cook County Jail 60608 and Stateville. Correctional Center 60403 and possibly other ZIP codes Illinois also reported data. suppression for ZIP codes with 6 confirmed cases Our analytic sample thus consisted of. 24 675 confirmed cases reported in 372 Illinois ZCTAs as of April 16 2020. New York City data on positive tests at the zip code level We obtained ZCTA level data on. positive tests in New York City from the New York City Department of Health and Mental. Hygiene s COVID 19 GitHub repository 17 Our analytic sample consisted of 125 422 positive. tests reported in New York City 177 ZCTAs as of April 16 2020. Population denominator and area attributes data, We extracted county and ZCTA level population counts and sociodemographic attributes from. the American Community Survey ACS 2014 2018 five year estimates 18 using the. tidycensus package in R 19 ABSMs included of persons below poverty household. crowding and population of color defined as the proportion of population who are not White. Chen JT Krieger N Revealing the unequal burden of COVID 19 by income race ethnicity and household crowding US county. vs ZIP code analyses Harvard Center for Population and Development Studies Working Paper Series Volume 19 Number 1. April 21 2020 https tinyurl com y7v72446, Non Hispanic and a measure of racialized economic segregation using the Index of. Concentration at the Extremes 20 This measure captures the extent to which the population in. a given area is concentrated at either extreme of a social metric and ranges from 1 everyone in. the worst category to 1 everyone in the best category For our analyses we set the extremes. for this ICE as a high income White population versus b low income Black population 20. For analysis purposes we defined categories of ABSMs using a priori cutpoints for below. poverty 0 4 9 5 9 9 10 14 9 15 19 9 and 20 100 and quintile cutpoints based on. the distribution of county level attributes in the US county level death analysis or the. distribution of ZCTA attributes within Illinois and New York City ZCTA level analyses of. Email jarvis hsph harvard edu ABSTRACT No national state or local public health monitoring data in the US currently exist regarding the unequal economic and social burden of COVID 19 To address this gap we draw on methods of the Public Health Disparities Geocoding Project whereby we merge county level cumulative death counts with population counts and area based socioeconomic measures

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