Skip to main content
Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Census tract socioeconomic indicators and COVID-19-associated hospitalization rates—COVID-NET surveillance areas in 14 states, March 1–April 30, 2020

  • Jonathan M. Wortham ,

    Roles Conceptualization, Data curation, Investigation, Methodology, Project administration, Supervision, Writing – original draft

    vij5@cdc.gov

    Affiliations CDC COVID-NET Team, Atlanta, GA, United States of America, US Public Health Service, United States of America

  • Seth A. Meador,

    Roles Conceptualization, Formal analysis, Software, Validation, Visualization, Writing – original draft, Writing – review & editing

    Affiliation CDC COVID-NET Team, Atlanta, GA, United States of America

  • James L. Hadler,

    Roles Conceptualization, Data curation, Methodology, Writing – review & editing

    Affiliation Connecticut Emerging Infections Program, Yale School of Public Health, New Haven, CT, United States of America

  • Kimberly Yousey-Hindes,

    Roles Conceptualization, Investigation, Validation, Writing – review & editing

    Affiliation Connecticut Emerging Infections Program, Yale School of Public Health, New Haven, CT, United States of America

  • Isaac See,

    Roles Conceptualization, Writing – review & editing

    Affiliations CDC COVID-NET Team, Atlanta, GA, United States of America, US Public Health Service, United States of America

  • Michael Whitaker,

    Roles Formal analysis, Methodology, Software, Writing – review & editing

    Affiliation CDC COVID-NET Team, Atlanta, GA, United States of America

  • Alissa O’Halloran,

    Roles Data curation, Software, Validation, Writing – review & editing

    Affiliation CDC COVID-NET Team, Atlanta, GA, United States of America

  • Jennifer Milucky,

    Roles Project administration, Validation, Writing – review & editing

    Affiliation CDC COVID-NET Team, Atlanta, GA, United States of America

  • Shua J. Chai,

    Roles Data curation, Supervision, Writing – review & editing

    Affiliations California Emerging Infections Program, Oakland, CA, United States of America, CDC Career Epidemiology Field Officer, Oakland, CA, United States of America

  • Arthur Reingold,

    Roles Data curation, Funding acquisition, Investigation, Supervision, Writing – review & editing

    Affiliation California Emerging Infections Program, Oakland, CA, United States of America

  • Nisha B. Alden,

    Roles Data curation, Funding acquisition, Investigation, Supervision, Writing – review & editing

    Affiliation Colorado Department of Public Health and Environment, Denver, CO, United States of America

  • Breanna Kawasaki,

    Roles Data curation, Funding acquisition, Investigation, Supervision, Writing – review & editing

    Affiliation Colorado Department of Public Health and Environment, Denver, CO, United States of America

  • Evan J. Anderson,

    Roles Data curation, Funding acquisition, Investigation, Supervision, Writing – review & editing

    Affiliations Emerging Infections Program, Georgia Department of Public Health, Atlanta, GA, United States of America, Veterans Affairs Medical Center, Atlanta, GA, United States of America, Division of Infectious Diseases, School of Medicine, Emory University, Atlanta, GA, United States of America

  • Kyle P. Openo,

    Roles Data curation, Funding acquisition, Investigation, Supervision, Writing – review & editing

    Affiliations Emerging Infections Program, Georgia Department of Public Health, Atlanta, GA, United States of America, Veterans Affairs Medical Center, Atlanta, GA, United States of America, Division of Infectious Diseases, School of Medicine, Emory University, Atlanta, GA, United States of America

  • Andrew Weigel,

    Roles Data curation, Funding acquisition, Investigation, Supervision, Writing – review & editing

    Affiliation Iowa Department of Public Health, Des Moines, IA, United States of America

  • Maya L. Monroe,

    Roles Data curation, Funding acquisition, Investigation, Supervision, Writing – review & editing

    Affiliation Maryland Department of Health, Baltimore, MD, United States of America

  • Patricia A. Ryan,

    Roles Data curation, Funding acquisition, Investigation, Supervision, Writing – review & editing

    Affiliation Maryland Department of Health, Baltimore, MD, United States of America

  • Sue Kim,

    Roles Data curation, Funding acquisition, Investigation, Supervision, Writing – review & editing

    Affiliation Michigan Department of Health and Human Services, Lansing, MI, United States of America

  • Libby Reeg,

    Roles Data curation, Funding acquisition, Investigation, Supervision, Writing – review & editing

    Affiliation Michigan Department of Health and Human Services, Lansing, MI, United States of America

  • Ruth Lynfield,

    Roles Data curation, Funding acquisition, Investigation, Supervision, Writing – review & editing

    Affiliation Minnesota Department of Health, St. Paul, MN, United States of America

  • Melissa McMahon,

    Roles Data curation, Funding acquisition, Investigation, Supervision, Writing – review & editing

    Affiliation Minnesota Department of Health, St. Paul, MN, United States of America

  • Daniel M. Sosin,

    Roles Data curation, Funding acquisition, Investigation, Supervision, Writing – review & editing

    Affiliation New Mexico Department of Health, Santa Fe, NM, United States of America

  • Nancy Eisenberg,

    Roles Data curation, Funding acquisition, Investigation, Supervision, Writing – review & editing

    Affiliation University of New Mexico Emerging Infections Program, Albuquerque, NM, United States of America

  • Adam Rowe,

    Roles Data curation, Funding acquisition, Investigation, Supervision, Writing – review & editing

    Affiliation New York State Department of Health, Albany, NY, United States of America

  • Grant Barney,

    Roles Data curation, Funding acquisition, Investigation, Supervision, Writing – review & editing

    Affiliation New York State Department of Health, Albany, NY, United States of America

  • Nancy M. Bennett,

    Roles Data curation, Funding acquisition, Investigation, Supervision, Writing – review & editing

    Affiliation University of Rochester School of Medicine and Dentistry, Rochester, NY, United States of America

  • Sophrena Bushey,

    Roles Data curation, Funding acquisition, Investigation, Supervision, Writing – review & editing

    Affiliation University of Rochester School of Medicine and Dentistry, Rochester, NY, United States of America

  • Laurie M. Billing,

    Roles Data curation, Funding acquisition, Investigation, Supervision, Writing – review & editing

    Affiliation Ohio Department of Health, Columbus, OH, United States of America

  • Jess Shiltz,

    Roles Data curation, Funding acquisition, Investigation, Supervision, Writing – review & editing

    Affiliation Ohio Department of Health, Columbus, OH, United States of America

  • Melissa Sutton,

    Roles Data curation, Funding acquisition, Investigation, Supervision, Writing – review & editing

    Affiliation Public Health Division, Oregon Health Authority, Portland, OR, United States of America

  • Nicole West,

    Roles Data curation, Funding acquisition, Investigation, Supervision, Writing – review & editing

    Affiliation Public Health Division, Oregon Health Authority, Portland, OR, United States of America

  • H. Keipp Talbot,

    Roles Data curation, Funding acquisition, Investigation, Supervision, Writing – review & editing

    Affiliation Vanderbilt University Medical Center, Nashville, TN, United States of America

  • William Schaffner,

    Roles Data curation, Funding acquisition, Investigation, Supervision, Writing – review & editing

    Affiliation Vanderbilt University Medical Center, Nashville, TN, United States of America

  • Keegan McCaffrey,

    Roles Data curation, Funding acquisition, Investigation, Supervision, Writing – review & editing

    Affiliation Utah Department of Health, Salt Lake City, UT, United States of America

  • Melanie Spencer,

    Roles Data curation, Funding acquisition, Investigation, Supervision, Writing – review & editing

    Affiliation Salt Lake County Health Department, Salt Lake City, UT, United States of America

  • Anita K. Kambhampati,

    Roles Project administration, Writing – review & editing

    Affiliation CDC COVID-NET Team, Atlanta, GA, United States of America

  • Onika Anglin,

    Roles Project administration, Writing – review & editing

    Affiliation CDC COVID-NET Team, Atlanta, GA, United States of America

  • Alexandra M. Piasecki,

    Roles Project administration, Writing – review & editing

    Affiliation CDC COVID-NET Team, Atlanta, GA, United States of America

  • Rachel Holstein,

    Roles Project administration, Supervision, Writing – review & editing

    Affiliation CDC COVID-NET Team, Atlanta, GA, United States of America

  • Aron J. Hall,

    Roles Supervision, Writing – review & editing

    Affiliation CDC COVID-NET Team, Atlanta, GA, United States of America

  • Alicia M. Fry,

    Roles Supervision, Writing – review & editing

    Affiliations CDC COVID-NET Team, Atlanta, GA, United States of America, US Public Health Service, United States of America

  • Shikha Garg,

    Roles Conceptualization, Formal analysis, Methodology, Supervision, Writing – original draft, Writing – review & editing

    Affiliations CDC COVID-NET Team, Atlanta, GA, United States of America, US Public Health Service, United States of America

  •  [ ... ],
  • Lindsay Kim

    Roles Conceptualization, Formal analysis, Methodology, Supervision, Writing – original draft, Writing – review & editing

    Affiliations CDC COVID-NET Team, Atlanta, GA, United States of America, US Public Health Service, United States of America

  • [ view all ]
  • [ view less ]

Abstract

Objectives

Some studies suggested more COVID-19-associated hospitalizations among racial and ethnic minorities. To inform public health practice, the COVID-19-associated Hospitalization Surveillance Network (COVID-NET) quantified associations between race/ethnicity, census tract socioeconomic indicators, and COVID-19-associated hospitalization rates.

Methods

Using data from COVID-NET population-based surveillance reported during March 1–April 30, 2020 along with socioeconomic and denominator data from the US Census Bureau, we calculated COVID-19-associated hospitalization rates by racial/ethnic and census tract-level socioeconomic strata.

Results

Among 16,000 COVID-19-associated hospitalizations, 34.8% occurred among non-Hispanic White (White) persons, 36.3% among non-Hispanic Black (Black) persons, and 18.2% among Hispanic or Latino (Hispanic) persons. Age-adjusted COVID-19-associated hospitalization rate were 151.6 (95% Confidence Interval (CI): 147.1–156.1) in census tracts with >15.2%–83.2% of persons living below the federal poverty level (high-poverty census tracts) and 75.5 (95% CI: 72.9–78.1) in census tracts with 0%–4.9% of persons living below the federal poverty level (low-poverty census tracts). Among White, Black, and Hispanic persons living in high-poverty census tracts, age-adjusted hospitalization rates were 120.3 (95% CI: 112.3–128.2), 252.2 (95% CI: 241.4–263.0), and 341.1 (95% CI: 317.3–365.0), respectively, compared with 58.2 (95% CI: 55.4–61.1), 304.0 (95%: 282.4–325.6), and 540.3 (95% CI: 477.0–603.6), respectively, in low-poverty census tracts.

Conclusions

Overall, COVID-19-associated hospitalization rates were highest in high-poverty census tracts, but rates among Black and Hispanic persons were high regardless of poverty level. Public health practitioners must ensure mitigation measures and vaccination campaigns address needs of racial/ethnic minority groups and people living in high-poverty census tracts.

Background

Since SARS-CoV-2, the novel coronavirus that causes coronavirus disease 2019 (COVID-19), was first detected in December 2019 [1], more than 100 million cases have been reported worldwide, including more than 25 million in the United States [2, 3]. While most persons infected with SARS-CoV-2 are asymptomatic or develop only mild symptoms, approximately 14% require hospitalization [4]. Older age, especially age ≥65 years, and underlying medical conditions are risk factors for COVID-19-associated hospitalizations [5, 6] and mortality [79]. In addition, some studies have documented increased morbidity in certain racial/ethnic groups [6, 10, 11]. Some have proposed that these observations are the result of underlying socioeconomic characteristics [12], which have been implicated in morbidity associated with many infectious conditions [1317].

Understanding relationships between socioeconomic characteristics and COVID-19-associated hospitalizations could inform the ongoing public health response to COVID-19 in at least two ways. First, quantifying relationships between COVID-19-associated hospitalizations and socioeconomic indicators could demonstrate the utility and facilitate the use of these known characteristics in local and national public health practice. Second, further characterization of the relationship between socioeconomic indicators and hospitalization rates might provide better insight into the degree to which racial/ethnic disparities in COVID-19-associated hospitalizations result from underlying differences in socioeconomic status that often overlap with race and ethnicity in the United States [18]. This knowledge might guide efforts targeted towards underlying causes of racial/ethnic disparities in COVID-19-associated hospitalization rates. Therefore, we sought to examine associations between COVID-19-associated hospitalization rates, census tract-level socioeconomic status indicators, and race/ethnicity.

Methods

To obtain counts of COVID-19-associated hospitalizations, we used data from the COVID-19-Associated Hospitalization Surveillance Network (COVID-NET), which conducts population-based surveillance in 99 counties in 14 states (California, Colorado, Connecticut, Georgia, Iowa, Maryland, Michigan, Minnesota, New Mexico, New York, Ohio, Oregon, Tennessee, and Utah). Approximately 32 million people, or 10% of the U.S. population, live within the COVID-NET surveillance area. COVID-NET defines a case as a resident of the surveillance catchment area with a positive test for SARS-CoV-2 within 14 days of hospitalization. Trained public health surveillance staff proactively query healthcare facilities and public health reporting systems to ensure complete case ascertainment; using a standardized case report form, these staff collect detailed case demographic and clinical data through medical chart review. To verify that cases reside within the surveillance area, COVID-NET obtains residential addresses. Using available software, surveillance personnel obtained geographic coordinates of residential addresses and matched them with census tracts (i.e., geocode) [19]. In this analysis, we included cases among persons ≥18 years, including those residing in long-term care facilities and correctional facilities. We excluded cases with missing addresses, addresses that could not be geocoded, and those that represented post office boxes. We also excluded cases among persons experiencing homelessness.

To examine socioeconomic indicators, we used data from the 2018 American Community Survey (ACS), which is conducted by the U.S. Census Bureau and captures census tract-level socioeconomic indicators, such as poverty, employment information, economic information, and educational attainment [20, 21]. The year 2018 was the most recent survey year for which the tract-level data necessary for our analysis were available. The ACS is sent to a sample of addresses (about 3.5 million) in the 50 states, District of Columbia, and Puerto Rico. We considered all census tracts within the surveillance catchment area and divided these census tracts into quartiles based on each of four indicators: the percentage of persons living below the federal poverty level, the percentage of employed persons aged ≥16 years working in service industry occupations, the percentage of persons commuting to work using public transportation, and the percentage of persons aged ≥25 years without bachelors’ degrees. Denominators were total populations residing in each socioeconomic indicator quartile and appropriate racial/ethnic/age group, using data from the 2010 decennial U.S. Census [22]. Numerators were the total numbers of COVID-NET cases residing in census tracts in the socioeconomic indicator quartiles; rate calculations were performed using these numerators and the corresponding indicator-specific denominator [22]. We used Poisson regression to calculate 95% confidence intervals for hospitalization rates. Dividing indicators into quartiles facilitated assessment of relationships between socioeconomic several indicator levels and COVID-19-associated hospitalization rates. Additionally, dividing indicators into quartiles facilitated comparisons across socioeconomic indicators, especially with ones that are not as commonly considered (e.g., public transportation use) in health studies. To assess effect modification by race/ethnicity (i.e., differences in the association between census tract poverty and COVID-19-associated hospitalization rates), we stratified the analysis by 3 racial/ethnic groups, non-Hispanic Black (Black), non-Hispanic White (White), and Hispanic/Latino (Hispanic); persons not reported as Hispanic were considered non-Hispanic. Because age is a risk factor for hospitalization and severe COVID-19, we also stratified analyses by age (i.e., compared rates among persons aged 18–64 with those among persons aged ≥65 years) and calculated age-adjusted rates to account for differences in the age distribution among different racial/ethnic and socioeconomic groups in the COVID-NET catchment area using population denominators from the 2010 decennial U.S. Census and age strata 18–49, 50–64, and ≥65 years[21]. All analyses were performed using SAS 9.4.

This activity was reviewed by the United States Centers for Disease Control and Prevention (CDC) and was conducted consistent with applicable federal law and CDC policy (See e.g., 45 C.F.R. part 46, 21 C.F.R. part 56; 42 U.S.C. §241(d); 5 U.S.C. §552a; 44 U.S.C. §3501 et seq). Participating sites obtained approval for COVID-NET surveillance from their respective state and local IRBs as required.

Results

During March 1–April 30, 2020, COVID-NET captured 18,540 cases among patients aged ≥18 years. Of these, 2,540 (14%) cases were excluded from the analysis because there was no residential address (n = 812), the address could not be assigned to a census tract (n = 1,351), represented a post office box (n = 285), or occurred in a patient experiencing homelessness (n = 92). Therefore, geocoded residential addresses were available for 16,000 (86%) cases; of these, 1,863 (11%) resided in long-term care facilities and 14 (<1%) resided in correctional facilities. Homeless status was undetermined for 7,455 (47%) cases.

Of the 16,000 cases with geocoded residential addresses, 7,488 (46.8%) occurred in persons ≥65 years old (Table 1). Black persons comprised 36.3%, White persons 34.8%, Hispanic persons 18.2%, non-Hispanic, Asian or Pacific Islander (Asian) persons 4.6%, and non-Hispanic American Indian/Alaska Native persons 0.6%. Race/ethnicity was unknown or unavailable pending further record review for 1,442 (9.0%) cases.

thumbnail
Table 1. Characteristics of adult patients (≥18 years old) hospitalized with COVID-19 and available geocoding information—COVID-NET catchment areas in 14 states, March 1–April 30, 2020.

https://doi.org/10.1371/journal.pone.0257622.t001

Of the 16,000 COVID-19-associated hospitalizations with geocoded addresses, 4,473 (28.0%) occurred among persons living in the quartile of census tracts with the highest percentages of persons living below the federal poverty level (high-poverty census tracts), 5,296 (33.1%) occurred among persons living in the quartile of census tracts with the highest percentages of service industry workers, 4,932 (30.8%) cases occurred among persons living in the quartile of census tracts with the highest percentages of commuters using public transportation, and 4,801 (30.0%) cases occurred among persons living in the quartile census tracts with the highest percentages of persons aged ≥25 years without bachelor’s degrees (Table 1).

Overall, unadjusted COVID-19-associated hospitalization rates were highest in high-poverty census tracts. Unadjusted COVID-19-associated hospitalization rates were 133.5 per 100,000 persons (95% confidence interval (CI): 129.6–137.4) in high-poverty census tracts and 80.1 (95% CI: 77.4–82.9) in low-poverty census tracts (i.e., those with 0%–4.9%, the lowest quartile, of persons living below the federal poverty level) (Table 2). Age-adjusted COVID-19-associated hospitalization rates by poverty level followed similar trends (Fig 1).

thumbnail
Fig 1. Age-adjusted COVID-19-associated hospitalization rates by census tract-level poverty strata (quartile)—COVID-NET catchment areas in 14 states, March–April 2020.

https://doi.org/10.1371/journal.pone.0257622.g001

thumbnail
Table 2. Unadjusted COVID-19-associated hospitalization rates, by socioeconomic strata within census tracts—COVID-NET catchment areas in 14 states, March–April 2020.

https://doi.org/10.1371/journal.pone.0257622.t002

While overall age-adjusted hospitalization rates were associated with high-poverty census tracts, the relationships between COVID-19-associated hospitalizations and census tract-level poverty differed by race/ethnicity. Among White persons, age-adjusted COVID-19-associated hospitalization rates were lowest in low-poverty census tracts and increased with increasing census tract poverty level (Fig 2). While the absolute rates in all racial/ethnic groups were highest among those aged ≥65 years regardless of socioeconomic status, COVID-19-associated hospitalization rates increased with census tract poverty level among White persons, whether aged 18–64 or ≥65 years (S1 Fig).

thumbnail
Fig 2. Age adjusted COVID-19-associated hospitalization rates by census tract-level poverty strata (quartiles) and race/ethnicity—COVID-NET catchment areas in 14 states, March–April 2020.

https://doi.org/10.1371/journal.pone.0257622.g002

Among Black and Hispanic persons, trends were different. Age-adjusted COVID-19-associated hospitalization rates per 100,000 persons were 304.0 (95% CI: 282.4–325.6) and 540.3 (95% CI: 477.0–603.6), respectively, for Black and Hispanic persons residing in low-poverty census tracts and 252.2 (95% CI: 241.4–263.0) and 341.1 (95% CI: 317.3–365.0), respectively, for Black and Hispanic persons residing in high-poverty census tracts. Age-adjusted rates in both racial/ethnic groups decreased with increasing census tract poverty level (Fig 2). Nonetheless, there were no statistically significant differences in hospitalization rates between census tracts representing the 3 lowest poverty quartiles for either racial/ethnic group (Fig 2).

The magnitude of the differences in COVID-19-associated hospitalization rates between racial/ethnic groups varied by census tract poverty quartile (Fig 3). Differences between age-adjusted, race/ethnicity-specific COVID-19-associated hospitalization rates were largest in low-poverty census tracts; rates among Hispanic persons and Black persons were 9.2 and 5.3 times, respectively, greater than rates among White persons in low-poverty census tracts. Differences in age-adjusted, race/ethnicity-specific COVID-19-associated hospitalization rates decreased as census tract poverty increased. While differences in age-adjusted, race/ethnicity specific rates persisted despite poverty level, these differences were smallest in high-poverty census tracts; rates among Hispanic persons and Black persons were 2.9 and 2.2 times, respectively, greater than rates among White persons in high-poverty census tracts. Age-stratified rates followed similar trends (S1 Table).

thumbnail
Fig 3. Age-adjusted COVID-19-associated hospitalization rates by census tract-level poverty strata (quartiles) and race/ethnicity—COVID-NET catchment areas in 14 states, March–April 2020.

https://doi.org/10.1371/journal.pone.0257622.g003

COVID-19-associated hospitalization rates were also associated with the percentage of service-industry workers residing within census tracts. Of the 5,296 hospitalized cases living in census tracts with the highest percentages of service industry workers, 2,995 (56.6%) also lived in high-poverty census tracts. In census tracts with the highest quartile of service industry workers, the COVID-19-associated hospitalization rate was 155.5 (95% CI: 151.4–159.7) per 100,000 persons compared with a rate of 78.7 (95% CI: 75.9–81.5) in census tracts with the lowest quartile of service industry workers (Table 2).

Similarly, COVID-19-associated hospitalization rates were associated with the percentage of persons within census tracts who used public transportation. Of the 4,932 hospitalized cases living in census tracts with the highest percentages of people who used public transportation, 2,087 (42.3%) also lived in high poverty census tracts. In census tracts with the highest quartile of commuters using public transportation, the COVID-19-associated hospitalization rate was 142.9 (95% CI: 139.0–146.9) per 100,000 persons compared with 77.4 (95% CI: 74.8–80.1) in census tracts with the lowest quartile of commuters using public transportation (Table 2).

COVID-19-associated hospitalization rates were also associated with the percentages of persons aged ≥25 years without bachelor’s degrees. In census tracts with percentages of persons aged ≥25 years without bachelor’s degrees within the highest quartile, the COVID-19-associated hospitalization rate was 145.5 (95% CI: 141.4–149.6) per 100,000 persons compared with 71.2 (95% CI: 68.6–73.8) in census tracts in the lowest quartile (Table 2).

Discussion

COVID-19-associated hospitalization rates were associated with four census tract-level socioeconomic indicators. Hospitalization rates were highest in high-poverty census tracts and those with the highest percentages of service industry workers, commuters using public transportation, and persons without bachelor’s degrees. Nonetheless, associations between hospitalization rates and census tract poverty differed between racial/ethnic groups; among White persons, lower census tract poverty was associated with higher hospitalization rates. Because hospitalization rates among Black and Hispanic persons were high regardless of census tract poverty, racial/ethnic disparities in hospitalization rates were largest in low-poverty census tracts.

Race/ethnicity and area-based socioeconomic indicators might identify groups of persons who are at increased risk of becoming infected with SARS-CoV-2. The trends in COVID-19-associated hospitalization rates might result from racial/ethnic and socioeconomic differences in SARS-CoV-2 infection secondary to differences in the ability to adhere to mitigation recommendations, such as stay-at-home orders, while fulfilling essential responsibilities, such as employment [23]. During March–April 2020, mitigation measures decreased risk of becoming infected with SARS-CoV-2 by reducing the number of persons who share airspace. During this time, 42 states instituted stay-at-home orders; implementation of these orders varied by location. Among states with COVID-NET surveillance catchment areas, California implemented the first stay-at-home orders on March 19 [24]. Twelve of the 13 other states implemented stay-at-home orders during March 19–April 2; Iowa did not implement stay-at-home orders during March–April 2020 [24]. Therefore, the effects of stay-at-home orders on hospitalization rates might have differed across COVID-NET sites. Nonetheless, because all of these orders allowed essential work to continue, disproportionately high COVID-19-associated hospitalization rates in census tracts with lower socioeconomic indicators and among racial/ethnic minorities might represent cases in essential workers, particularly if these persons did not have access to properly fitted personal protective equipment [25, 26]. Indeed, COVID-19-associated hospitalization rates were associated with the proportion of workers in service industry occupations; many service industry occupations, such as restaurant work, require working onsite and sharing airspace with others. Similarly, essential workers being exposed during commutes could explain the association between hospitalization rates and commuters using public transportation. Other data also suggest that the demand for public transportation during pandemic-related lockdowns occurred in communities serving essential workers and racial/ethnic minorities, the same two groups with increased hospitalization rates in this analysis [27]. Another possibility is that these two socioeconomic indicators, the percentage of service industry workers and the percentage of commuters using public transportation, are associated with poverty (or race/ethnicity) and their associated hospitalization risks. It is also possible that these findings are influenced by the demographics of the COVID-NET sites most affected by the pandemic during this period. Other structural factors, such as differences in employment opportunity opportunities or neighborhood-specific high-risk exposures, might also account for the socioeconomic and racial/ethnic disparities in COVID-19-associated hospitalization rates.

Alternatively, the findings from this analysis might represent some racial/ethnic and socioeconomic differences in hospitalization risk after infection. Racial/ethnic differences in prevalence of underlying medical conditions that confer increased risk of hospitalization after infection, such as diabetes, might contribute to the findings of this analysis [9, 28, 29]. Similarly, higher prevalence of underlying medical conditions that confer increased risk of hospitalization with COVID-19 has been associated with lower census tract-level socioeconomic indicators and geographic region of the country [29, 30].

COVID-19-associated hospitalization rates were associated with census tract poverty among White persons, but not other racial/ethnic groups. In Black and Hispanic persons, associations between census tract poverty and COVID-19-associated hospitalization rates were more challenging to explain; rates were highest in census tracts in low-poverty census tracts. At least three factors might have contributed to this observation. First, social networks among Black and Hispanic persons might span socioeconomic strata more often than networks among White persons [31]. If true within COVID-NET catchment areas, SARS-CoV-2 transmission chains involving Black and Hispanic persons might involve persons living in census tracts within different poverty quartiles more often and could explain some of the different relationships between census tract poverty and hospitalization rates among Black and Hispanic persons. A second potential explanation is that prevalence of underlying medical conditions that confer increased risk of hospitalization after SARS-CoV-2 infection is highest among racial/ethnic minorities in the highest socioeconomic strata, resulting in higher hospitalization rates among Black and Hispanic persons in this low-poverty quartile compared with rates among White residents of the same census tracts [32]. Another possibility is that laboratory testing for SARS-CoV-2 infection, which was required to meet the COVID-NET case definition, was performed in a differential manner with respect to race/ethnicity or socioeconomic status due to variations in medical practice or scarcity of test kits [33, 34]. Systematic studies of these non-socioeconomic reasons for these racial/ethnic differences might further inform the interpretation of these data. Finally, racial/ethnic or socioeconomic differences in access to healthcare, care seeking, or admissions decisions could also contribute to these findings [35].

This analysis is subject to limitations. This analysis examined relationships between census tract-level socioeconomic indicators, not personal indicators, and hospitalization rates. Because there is likely heterogeneity within census tracts with regards to income, education, occupation, and public transportation use, some individuals might have been mischaracterized with respect to their personal status with respect to these indicators. Therefore, this analysis cannot measure associations between personal socioeconomic characteristics and hospitalization rates. However, associations between area-level socioeconomic indicators and hospitalization rates might be more useful for local and national health systems planning purposes. For example, the association between area-level indicators could be used to target interventions, such as vaccination campaigns, towards neighborhoods likely to have higher COVID-19-associated hospitalization rates. Second, these data were obtained from a discrete surveillance area during the early part of the pandemic in the United States; therefore, the generalizability of these findings across geography or time are unknown because the number of COVID-19 cases has increased since April 2020, and the epidemiology could be different with respect to case characteristics, geography, or time. Nonetheless, data from this population-based surveillance system from this period, during which a substantial number of states and localities issued stay-at-home orders, are still important, because the findings might be helpful to consider when predicting the impact of future stay-at-home orders for COVID-19 or another disease with similar transmission dynamics. Third, during the analysis period, March–April 2020, testing for SARS-CoV-2, which was required to meet the COVID-NET case definition, was relatively limited and often performed at the discretion of treating medical teams [34]. Therefore, because some persons with COVID-19 might not have been tested, we might have underestimated COVID-19-associated hospitalizations. Additionally, if testing was associated with socioeconomic indicators, we might have mischaracterized associations between these indicators and hospitalization rates. Fourth, we had to exclude persons experiencing homelessness and those with only post office boxes from the analysis; because it is likely that these persons were unevenly distributed with respect to census tract socioeconomic indicators, we might have underestimated COVID-19-associated hospitalization rates in census tracts with disproportionate numbers of persons who fit these descriptions (e.g., rural census tracts where more people have post office boxes and high-poverty census tracts with more people experiencing homelessness). Finally, because we included persons in long-term care facilities and correctional facilities, outbreaks in these congregate settings might have influenced these findings. Nonetheless, one strength of population-based surveillance is the ability to report rates of disease; because these persons are represented in census denominators, including them among cases facilitates the examination of the relationship between census tract socioeconomic indicators and hospitalization rates.

Despite these limitations, collecting geocoded data can provide critically important information for public health practice. Not only might these data be helpful in forecasting demands on health systems during the current COVID-19 pandemic and other infectious disease outbreaks with similar transmission dynamics, but they might also guide efforts to tailor public health interventions, such as campaigns for mask wearing and vaccine distribution, towards groups with high hospitalization rates. Consistent with other work, this analysis highlights a statistically significant association between census tract-level socioeconomic indicators and corresponding COVID-19-associated hospitalization rates [36]. Despite adjustment for socioeconomic status, racial/ethnic disparities in COVID-19-associated hospitalization rates persisted; even in low-poverty census tracts, age-adjusted hospitalization rates among Hispanic and Black persons were 9 and 5 times, respectively, rates among White persons. Public health practitioners must ensure the mitigation measures and vaccination campaigns address the social, behavioral, and medical needs of racial/ethnic minority groups and people living in census tracts with lower socioeconomic indicators.

Supporting information

S1 Fig. COVID-19-associated hospitalization rates by census tract-level poverty strata (quartiles), race/ethnicity, and age groups—COVID-NET catchment areas in 14 states, March–April 2020.

https://doi.org/10.1371/journal.pone.0257622.s001

(TIF)

S1 Table. COVID-19-associated hospitalization rates by census tract-level poverty strata (quartiles), race/ethnicity, and age groups—COVID-NET catchment areas in 14 states, March–April 2020.

https://doi.org/10.1371/journal.pone.0257622.s002

(TIF)

Acknowledgments

Acknowledgments: COVID-NET Surveillance Team: Jeremy Roland, Pam Daily Kirley, California Emerging Infections Program; Paula Clogher, Maria Correa, Alexandra Edmudson, Hazal Kayalioglu, Amber Masla, Adam Misiorski, Danyel Olson, Christina Parisi, James Meek, Connecticut Emerging Infections Program; Sarah McLafferty, Millen Tsegaye, Colorado Department of Public Health and Environment; David Y. Ankey, Alvin Shultz, Sonja Mali Nti-Berko, Rainy Henry, Centers for Disease Control and Prevention; Mimi Huynh, Council of State and Territorial Epidemiologists; Kathy M. Angeles, Emily B. Hancock, Yadira Salazar-Sanchez, Meaghan Novi, Sarah A. Khanlian, Melissa Christian, Mayvilynne Poblete, Caroline Habrun, New Mexico Emerging Infections Program; Karen Leib, Katie Dyer, Terri McMinn, Danielle Ndi, Anise Elie, Kathy Billings, Manideepthi Pemmaraju, John Ujwok, Tiffanie Markus, Vanderbilt University Medical Center; Emily Fawcett; Siyeh Gretzinger; Katelyn Lengacher; Jeremiah Williams, Stephanie Lehman, Allison Roebling, Chandler Surell, Taylor Eisenstein, Emerging Infections Program Georgia Department of Health Veterans Affairs Medical Center Foundation for Atlanta Veterans Education and Research; David Blythe; Alicia Brooks, Patricia Ryan, Elisabeth Vaeth, Cindy Zerrlaut, Maryland Department of Health; Rachel Park, Michelle Wilson, Maryland Emerging Infections Program–The Johns Hopkins Bloomberg School of Public Health; Alison Muse, Nancy Spina, Suzanne McGuire, Kerrianne Engesser, New York State Department of Health; Emily Holodick, Kayla Bilski, Emma Contestabile, Claire Henrichsen, Julie Hottinger, Lisa Nguyen, Kristen Ehresmann, Minnesota Department of Health; Jim Collins, Justin Henderson, Shannon Johnson, Alex Kohrman, Sam Hawkins, Val Tellez Nunez, Michigan Department of Health and Human Services; Ama Owusu-Dommey, Public Health Division, Oregon Health Authority; Virginia Cafferky, Christina Felsen, Maria Gaitan, RaeAnne Kurtz, Christine Long, Kevin Popham, Savanah Russ, Marissa Tracey, University of Rochester School of Medicine and Dentistry; Melanie Spencer, Andrea George, Andrea Price, Ashley Swain, Laine McCullough, Jake Ortega, Ryan Chatelain, Ilene Risk, Ian Buchta, Tyler Riedesel, Andrew Haraghey, Caitlin Shaw, Mary Hill, Amanda Carter, Salt Lake County Health Department; Tanya Libby, Department of Epidemiology, University of Washington.

References

  1. 1. Guan WJ, Ni ZY, Hu Y, Liang W, Ou C, He J, et al. China Medical Treatment Expert Group for Covid-19. Clinical characteristics of coronavirus disease 2019 in China. N Engl J Med 2020 Apr 30;382(18):1708–1720. pmid:32109013
  2. 2. Johns Hopkins University & Medicine. COVID-19 Dashboard [Cited 10 November 2020]. In: COVID-19 Dashboard [Internet]. Baltimore, MD: Johns Hopkins University, 2020 -. Available from: https://coronavirus.jhu.edu/map.html.
  3. 3. CDC. COVID Data Tracker: Trends in Number of COVID-19 Cases and Deaths in the US Reported to CDC by State/Territory [cited 12 August 2021]. In: COVID Data Tracker [Internet]. Atlanta, Georgia: US Department of Health and Human Services, CDC 2020 -. Available from: https://covid.cdc.gov/covid-data-tracker/#trends_dailytrendscases.
  4. 4. Reese H, Iuliano AD, Patel NN, Garg S, Kim L, Silk BJ, et al. Estimated incidence of COVID-19 illness and hospitalization—United States, February–September 2020. Clin Infect Dis 2021 Jun 15;72(12);e1010–e1017. pmid:33237993
  5. 5. Richardson S, Hirsch JS, Narasimhan M, Crawford JM, McGinn T, Davidson KW, et al. Presenting Characteristics, Comorbidities, and Outcomes Among 5700 Patients Hospitalized With COVID-19 in the New York City Area. JAMA. 2020 May 26;323(20):2052–2059. pmid:32320003
  6. 6. Garg S, Kim L, Whitaker M, O’Halloran A, Cummings C, Holstein A, et al. Hospitalization Rates and Characteristics of Patients Hospitalized with Laboratory-Confirmed Coronavirus Disease 2019—COVID-NET, 14 States, March 1–30, 2020. MMWR Morb Mortal Wkly Rep 2020 Apr 17;69(15):458–464. pmid:32298251
  7. 7. Wortham JM, Lee JT, Althomsons S, Latash J, Davidson A, Guerra K, et al. Characteristics of Persons Who Died with COVID-19—United States, February 12-May 18, 2020. MMWR Morb Mortal Wkly Rep. 2020 Jul 17;69(28):923–929. pmid:32673298
  8. 8. Kim L, Garg S, O’Halloran A, Whitaker M, Pham H, Anderson EJ, et al. Risk Factors for Intensive Care Unit Admission and In-hospital Mortality among Hospitalized Adults Identified through the U.S. Coronavirus Disease 2019 (COVID-19)-Associated Hospitalization Surveillance Network (COVID-NET). Clin Infect Dis. 2021 May 4;72(9): e206–e214. pmid:32674114
  9. 9. Ko JY, Danielson ML, Town M, Derado G, Greenlund KJ, Daily Kirley P, et al. Risk Factors for COVID-19-associated hospitalization: COVID-19-Associated Hospitalization Surveillance Network and Behavioral Risk Factor Surveillance System. Clin Infect Dis. 2021 Jun 1;72(11):e695–e703. pmid:32945846
  10. 10. Stokes EK, Zambrano LD, Anderson KN, Marder EP, Raz KM, Felix S, et al. Coronavirus Disease 2019 Case Surveillance—United States, January 22–May 30, 2020. MMWR Morb Mortal Wkly Rep 2020 Jun 19;69(24):759–765. pmid:32555134
  11. 11. Killerby ME, Link-Gelles R, Haight SC, Schrodt CA, England L, Gomes DJ, et al. Characteristics Associated with Hospitalization Among Patients with COVID-19—Metropolitan Atlanta, Georgia, March–April 2020. MMWR Morb Mortal Wkly Rep. 2020 Jun 26;69(25):790–794. pmid:32584797
  12. 12. Wadhera RK, Wadhera P, Gaba P, Figueroa JF, Maddox KEJ, Yeh RW, et al. Variation in COVID-19 hospitalizations and deaths across New York City boroughs. JAMA. 2020 Jun 2;323(21):2192–2195. pmid:32347898
  13. 13. See I, Wesson P, Gualandi N, Dumyati G, Harrison LH, Lesher L, et al. Socioeconomic Factors Explain Racial Disparities in Invasive Community-Associated Methicillin-Resistant Staphylococcus aureus. Clin Infect Dis. 2017 Mar 1;64(5):597–604. pmid:28362911
  14. 14. Younsey-Hindes K, Hadler J. Neighborhood Socioeconomic Status and Influenza Hospitalizations Among Children: New Haven County, Connecticut, 2003–2010. Am J Public Health. 2011 Sep;101(9):1785–9. pmid:21778498
  15. 15. Hadler JL, Younsey-Hindes K, Perez A, Anderson EJ, Bargsten M, Bohm SR, et al. Influenza-Related Hospitalizations and Poverty Levels—United States, 2010–2012. MMWR Morb Mortal Wkly Rep. 2016 Feb 12;65(5):101–5. pmid:26866729
  16. 16. Chandrasekhar R, Sloan C, Mitchel E, Ndi D, Alden N, Thomas A, et al. Social determinants of influenza hospitalization in the United States. Influenza Other Respir Viruses. 2017 Nov;11(6):479–488. pmid:28872776
  17. 17. Tam K, Yousey-Hindes K, Hadler JL. Influenza-related hospitalization of adults associated with low census tract socioeconomic status and female sex in New Haven County, Connecticut, 2007–2011. Influenza Other Respir Viruses. 2014 May;8(3):274–81. pmid:24382111
  18. 18. Noel RA. Race, Economics, And Social Status [cited 15 October 2020]. In: Spotlight on Statistics [Internet]. Washington: US Bureau of Labor Statistics 2018 -. Available from: https://www.bls.gov/spotlight/2018/race-economics-and-social-status/pdf/race-economics-and-social-status.pdf.
  19. 19. Krieger N, Waterman PD, Chen JT, Rehkopf DH, Subramanian SV. The Public Health Disparities Geocoding Monograph [cited 15 October 2020] In: The Public Health Disparities Geocoding Project [Internet]. Boston: Harvard School of Public Health. Available from: https://www.hsph.harvard.edu/thegeocodingproject/public-health-disparities-geocoding-project-monograph-june-2004/.
  20. 20. U.S. Census Bureau. American Community Survey, 2018 American Community Survey 5-Year Estimates [cited 15 October 2020]. Washington: U.S. Census Bureau. Available from: http://ftp2.census.gov/.
  21. 21. U.S. Census Bureau. Recent Changes in the Census Industry and Occupation Classification Systems [cited 30 November 2020]. Washington: U.S. Census Bureau. Available from: https://www.census.gov/library/publications/2020/demo/acs-tp78.html.
  22. 22. U.S. Census Bureau. 2010 US Census [cited November 1, 2020]. Washington: U.S. Census Bureau. Available from: http://ftp2.census.gov/.
  23. 23. Hawkins D. Differential occupational risk for COVID-19 and other infection exposure according to race and ethnicity. Am J Ind Med. 2020 Sep;63(9):817–820. pmid:32539166
  24. 24. Mervosh S, Lu D, Swales V. See Which States and Cities Have Told Residents to Stay at Home. The New York Times. 2020 Apr 20 [cited 3 February 2021]. Available from https://www.nytimes.com/interactive/2020/us/coronavirus-stay-at-home-order.html.
  25. 25. Ranney ML, Griffeth V, and Jha A. Critical supply shortages—the need for ventilators and personal protective equipment during the COVID-19 pandemic. N Engl J Med. 2020 Apr 30;382(18):e41. pmid:32212516
  26. 26. US Department of Health and Human Services. Hospital experiences responding to the COVID-19 pandemic: results of a national pulse survey March 23–27, 2020 [cited 12 February 2021]. Washington: US Department of Health and Human Services. Available from: https://oig.hhs.gov/oei/reports/oei-06-20-00300.pdf.
  27. 27. Liu L, Miller HJ, Scheff J. The impacts of COVID-19 pandemic on public transit demand in the United States. PLoS One. 2020 Nov 18;15(11):e0242476. pmid:33206721
  28. 28. Yang J, Zheng Y, Gou X, Pu K, Chen Z, Guo Q, et al. Prevalence of comorbidities and its effects in patients infected with SARS-CoV-2: a systematic review and meta-analysis. Int J Infect Dis. 2020 May;94:91–95. pmid:32173574
  29. 29. Gaskin DJ, Thorpe RJ Jr, McGinty EE, Bower K, Rohde C, Young JH, et al. Disparities in diabetes: the nexus of race, poverty, and place. Am J Public Health. 2014 Nov;104(11):2147–55. pmid:24228660
  30. 30. Ahmad K, Chen EW, Nazir U, Cotts W, Andrade A, Trivedi AN, et al. Regional Variation in the Association of Poverty and Heart Failure Mortality in the 3135 Counties of the United States. J Am Heart Assoc. 2019 Sep 17;8(18):e012422. pmid:31480884
  31. 31. Finney N, Kapadia D, and Peters S. How are poverty, ethnicity, and social networks related? 2015 Mar 1 [Cited 10 November 2020]. York, United Kingdom: Joseph Rowntree Foundation 2015 -. Available from: https://www.jrf.org.uk/report/how-are-poverty-ethnicity-and-social-networks-related.
  32. 32. Cunningham TJ, Croft JB, Liu Y, Lu H, Eke PI, Giles WH. Vital Signs: Racial Disparities in Age-Specific Mortality Among Blacks or African Americans—United States, 1999–2015. MMWR Morb Mortal Wkly Rep 2017 May 5;66(17):444–456. pmid:28472021
  33. 33. Rubin-Miller L, Alban C, Artiga S, Sullivan S. COVID-19 Racial Disparities in Testing, Infection, Hospitalization, and Death: Analysis of Epic Patient Data 2020 Sept 16 [Cited 10 November 2020]. San Francisco: Kaiser Family Foundation. Available from: https://www.kff.org/report-section/covid-19-racial-disparities-in-testing-infection-hospitalization-and-death-analysis-of-epic-patient-data-issue-brief/.
  34. 34. Weaver C, Ballhaus R. Coronavirus Testing Hampered by Disarray, Shortages, Backlogs. The Wall Street Journal. 2020 Apr 19 [Cited 1 November 2020]. Available from: https://www.wsj.com/articles/coronavirus-testing-hampered-by-disarray-shortages-backlogs-11587328441.
  35. 35. Edward R. Berchick, Emily Hood, and Jessica C. Barnett. Health Insurance Coverage in the United States: 2017 2018 Sept 12 [cited 1 February 2020]. Washington: U.S. Census Bureau, 2018. Available from: https://www.census.gov/library/publications/2018/demo/p60-264.html.
  36. 36. Cromer SJ, Lakhani CM, Wexler DJ, Burnett-Bowie SM, Udler M, Patel CJ. Geospatial Analysis of Individual and Community-Level Socioeconomic Factors Impacting SARS-CoV-2 Prevalence and Outcomes. Preprint. medRxiv. 2020 Sep 30;2020.09.30.20201830. pmid:33024982