2.1. Study design and setting

We conducted an observational, retrospective analysis of all cases of SARS-CoV-2 diagnosed within the University of Colorado Health (UCH) and Denver Health (DH) healthcare systems from May 1, 2020, through December 15, 2020 (before vaccines became extensively available). We excluded cases from March and April 2020 due to limited testing for mild illness during the earliest months of the pandemic, which inflated the hospitalization rate (60% of those who tested positive were hospitalized in March 2020, as shown in our data).

UCH includes a network of hospitals and facilities throughout Colorado including the University of Colorado Hospital, a tertiary care center associated with the University of Colorado School of Medicine. DH is an integrated safety net healthcare system that includes an acute care hospital, 9 federally qualified health centers, and 17 school-based clinics [42]. UCH serves patients with a variety of payer sources while the majority of patients who receive care through DH are covered by Medicaid or uninsured. Overall, around 6.5% of those with a positive test in the UCH and DH systems were uninsured at the time of the study.

The analysis was limited to individuals seeking care through UCH and DH within the seven traditional counties of the Denver metropolitan area (Adams, Arapahoe, Boulder, Broomfield, Denver, Douglas, and Jefferson), plus the urbanized parts of Weld County (see Fig 1) [43]. The Denver metropolitan area is a highly urbanized region along the Colorado “Front Range” and has experienced rapid population growth over the past decade [44]. During that time, it has undergone significant population shifts characterized by densification and gentrification of the urban core and increased concentrations of lower-income people of color in some suburban communities [45].

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Fig 1. Density of SARS-CoV-2 cases and hospitalizations in the study area (Denver metropolitan area) and the City of Denver.

https://doi.org/10.1371/journal.pone.0286119.g001

We queried electronic health records (EHRs) from the two institutions for evidence of a positive SARS-CoV-2 polymerase chain reaction test result for individuals aged 18–100 years during the study period. Demographic variables extracted from the EHR included date of birth, race, ethnicity, gender, and home address at the time of the positive test. We excluded records with missing gender, race or ethnicity, or address (including homeless, Post Office Box only, or the address listed was for a shelter, jail, or congregate care facility such as a nursing home or rehabilitation facility). We excluded these records because environmental variables either could not be coded, as in the case of homelessness or Post Office boxes, or did not reflect the person’s interaction with the built environment, as in the case of persons in jails or congregate care facilities. We extracted health variables from disease registries and international classification of diseases (ICD) codes associated with the individual’s medical record. These included diabetes, hypertension, chronic pulmonary diseases (asthma and chronic obstructive pulmonary disease), HIV, and chronic kidney diseases. We included chronic liver disease, cardiovascular disease, pregnancy, cancer, and immunocompromised states in the query but chart reviews revealed inconsistent reporting and coding of these conditions; therefore, they were not included in the final dataset. Additional variables extracted from the EHR included current tobacco use, height, weight, and body mass index. We chose demographic variables and conditions based on current understanding of factors associated with COVID-19 disease severity [46,47].

When possible, we individually reviewed and updated records containing unlikely BMI values with additional data from chart review. We calculated missing BMI values for those patients with an available weight using the median observed height value for their gender and reported race/ethnicity. Medians heights from our study cohort were compared with national data and determined to be within 2 centimeters for all subsets [48]. We did not impute missing values for weights given the larger variability in possible weights and greater impact on BMI results. Individuals without documented BMIs or weights were thus excluded from the analyses. We calculated BMI values from the most recent height and weight data and compared these to extracted BMI values with additional chart reviews conducted in cases of discrepancies. The final BMI value assigned to each record in the dataset was the value obtained from measurements taken closest to the time of the positive SARS-CoV-2 test.

We then queried the EHRs for evidence of hospital admission at DH or UCH within two weeks of the positive SARS-CoV-2 test for individuals in the cohort. The reason for hospitalization was not routinely available, and therefore individuals could have been hospitalized for COVID-19 or unrelated reasons. In the case of multiple positive SARS-CoV-2 tests, we included only the hospitalization status associated with the initial positive test in the analysis. We did so to avoid biasing the results by including the same individual multiple times. In some cases, a patient may have been hospitalized and had multiple tests sent from different anatomic sites (e.g., nasopharyngeal, saliva) or repeated multiple times during the hospitalization due to admission requirements of long term care facilities. In other cases, individuals were discharged and readmitted in the next few weeks for unrelated issues, yet because of universal screening for SARS-CoV-2 at that point in the pandemic, still had positive results when admitted. Finally, individuals who had multiple positive tests several months apart are likely to have other predisposing factors to illness and that could also bias the results if they were included more than once.

2.2. Neighborhood built environment

We created a four-pronged classification of neighborhood built environment features based on our critical reading of recent reviews and theoretical articles about COVID-19 and the built environment [49–53] and our evaluation of the current empirical research on this topic. The four domains we focused on were population density and crowding (e.g., living in an apartment), environmental hazards (e.g., PM_2.5, proximity to a highway), environmental amenities (e.g., parks access, park acreage), and mobility options (e.g., transit access, cycling infrastructure).

We recoded home addresses for individuals in the cohort to geographical coordinates using the Texas A&M University (TAMU) Geocoder (version 4.01, College Station, TX) and the Bing Maps Geocoder (version 2.0, Redmond, WA). Prior use of geocoders has been associated with the inadvertent sharing of protected health information (PHI) [54]. To ensure this did not occur, we uploaded addresses to the geocoders in small batches (less than 2,500 cases) on different days and used various internet protocol (IP) addresses to do so. Additionally, we only uploaded street addresses and unique identifiers, and we deleted addresses from the geocoders after each use. Further, the TAMU geocoder, which is the main service we used, deletes all its data every seven days [55]. If the addresses were accessed while they were temporarily stored in the geocoders, they would not be traceable to this study because the IP addresses from which the data was uploaded varied and are not clearly linked to the two hospital systems. Therefore, there would be no evidence that an individual with a given street address was associated with this study cohort.

We then used Python (version 3.6.1) to clean several variables describing the neighborhood built environment and linked them to the residential geographic coordinates using ArcGIS Pro (version 2.7, Redlands, CA). We list descriptions of these variables and their associated data sources in Table 1. We chose the specific variables for each neighborhood built environment domain based on data availability for the entire metro Denver region, alignment with variables used in previous studies on associations between the built environment and COVID-19 outcomes, and from preliminary tests to assess multicollinearity issues. We removed the population density variable due to strong multicollinearity with residential density (Pearson’s r = 0.95), and the Social Vulnerability Index (SVI) variable due to multicollinearity with percent housing burdened households (r = 0.66), percent essential workers (r = 0.67), and several other variables. In preliminary multivariate models, population density and SVI had Variance Inflation Factors above 4 [56]. For variables describing distances to specific locations (e.g., a park or a highway), we calculated the distance between home addresses and such locations. For other variables (e.g., residential density), we attributed the values of the census block groups within which the residential addresses were located (see Table 1 for more details).

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Table 1. Independent variables describing the neighborhood built environment and neighborhood-level control variables.

https://doi.org/10.1371/journal.pone.0286119.t001

We also considered potential demographic neighborhood-level confounding factors that have been or could be associated with increased risk for severe COVID-19 (Table 1). These included the percentage of housing burdened households, the percentage of “essential workers,” and the percentage of “essential workers” commuting via transit in a census block group [64–67].

2.3. Outcome

The outcome of interest in the study was whether individuals who tested positive for SARS-CoV-2 were hospitalized. We analyzed the association between factors in the built environment around one’s home address and hospitalization in the entire cohort and in sub-cohort of individuals who identified as Hispanic/Latinx (all races) and those who identified as non-Hispanic white.

2.4. Statistical analysis

We used basic descriptive statistics to characterize the cohorts of SARS-CoV-2 positive patients diagnosed through UCH and DH. We calculated all descriptive statistics, univariate (unadjusted), and multivariate (adjusted) models in R (version 4.0, Vienna, Austria). See the code here: https://github.com/ucd-brenn/CODEN.

For our regression analyses, we considered various options given our binary outcome variable (being hospitalized or not). In large cohort studies where the outcome variable is common, such as our study, logistic regression models tend to overestimate incident rate ratios [68]. Thus, we evaluated alternatives such as Poisson and negative binomial models, both options in studies with large cohorts and common binary outcome variables [68]. To do so, we first conducted a dispersion test for the outcome variable (hospitalization), showing that the mean (0.29) is slightly larger than the variance (0.20). Poisson distributions describe cases wherein the mean is equal to the variance, whereas, in negative binomial distributions, the variance is larger than the mean. Then, we used the odTest function in the pscl package to “to test the null hypothesis that the restriction implicit in the Poisson model is true” ([69], p. 45). This likelihood ratio test resulted in a Chi-Square Test Statistic = -0.286 and p-value = 0.5, lending support for the null hypothesis and suggesting that Poisson regression is preferable. Because our data are under-dispersed relative to the Poisson distribution, we ran Poisson regressions with robust standard errors [68].

We first ran univariate Poisson regressions with robust standard errors to test associations of individual variables with the incident rate ratio (IRR) of hospitalization. We selected variables for multivariate models from relevant literature and display these in Table 1, and we chose to retain independent variables not associated with the dependent variable to account for the possible presence of confounding and suppressor variables [70]. Before running the univariate and multivariate regressions, we standardized the continuous variables listed in Table 1.

In multivariate Poisson regressions with robust standard errors, we evaluated the neighborhood built environment factors associated with the IRR of hospitalization among those who tested positive for SARS-CoV-2, while controlling for individual and neighborhood demographics as well as individual comorbidities. We performed one main analysis for all individuals with complete data, and then two subgroup analyses for non-Hispanic white individuals, and Latinx individuals. We did not compute models for other racial/ethnic groups (e.g., non-Hispanic Black people) given the small size of their subsample in our cohort compared to Latinx and non-Hispanic white people. We were particularly interested in understanding how associations between neighborhood built environment and IRRs of hospitalization varied by race/ethnicity because people of color in the U.S. have higher rates of COVID-19 hospitalization [41] and live in places with disproportionately high exposures to environmental hazards, such as air pollution [71].

To test for potential spatial autocorrelation in the model’s residuals of the Poisson regression with robust standard errors, we computed global Moran’s Is for the model residuals. We used the moran.test function in R’s spdep package to calculate Moran’s I with a distance-based spatial matrix that considers the four closest neighbors to each home address of the patients in our sample with complete records. We created three different spatial matrices for the entire sample and the two subsamples. Moran’s I tests were significant for the entire sample, the Latinx subsample, and the non-Hispanic white subsample. Thus, we ran spatial filtering models using the ME function in R’s spdep package. Spatial filtering models calculate eigenvectors intended to remove spatial autocorrelations [72]. Through a stepwise process, some eigenvectors are included in the model, and their inclusion reduces the p-value of Moran’s I of the model’s residual below 0.05.

2.5. IRB approval

The Institutional Review Board at The University of Utah and the Colorado Multiple Institutional Review Board (COMIRB) approved the study. We utilized research use agreements for data sharing across the authors’ institutions. COMIRB, which oversees the two hospital systems in this study, waived informed consent for this study due to the retrospective nature of the research: Obtaining informed consent was not feasible and the risk for adverse events was considered extremely low. Names and medical record numbers were removed from the dataset prior to analysis and dates of birth were converted to years of age at the time of the positive test, but other PHI elements that were considered critical to the study were retained. These included the home address and year of testing and hospitalization.

3.1. Descriptive statistics

From May 1 to December 31, 2020, 23,471 adults living in the Denver metropolitan area were diagnosed with SARS-CoV-2 infection through UCH and DH. Of those, we removed 163 records due to incomplete/missing addresses. We imputed missing heights for 159 individuals to calculate BMI. Of the remaining 23,308 records, we removed 4,101 individuals due to missing weight data since BMI could not be calculated. We removed an additional 532 records due to missing race and ethnicity data. Further, we excluded 557 records due to lack of available transit, bike, or walk scores, and 76 records due to lack of available data from the U.S. Census Bureau (e.g., housing or employment data). The final dataset included 18,042 individuals. See S1 Fig in the S1 Appendix for a flowchart describing the selection of patients based on data availability.

Table 2 lists the characteristics of the final cohort. Fig 1 shows the geographic distribution of all cases (1a) and hospitalized cases (1b). Overall, 5,239 individuals (29.03%) were hospitalized within 2 weeks of a positive SARS-CoV-2 test result. Compared to those who were not hospitalized, hospitalized individuals had higher BMI, were older, were more often Latinx or non-Hispanic Black, and had a higher prevalence of the included medical conditions.

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Table 2. Demographic and clinical characteristics for the final cohort.

https://doi.org/10.1371/journal.pone.0286119.t002

Table 3 summarizes the descriptive statistics for the neighborhood built environments. For example, 24.6% of individuals in the sample live in a multi-family housing unit. Nearly half (47.4%) of the cohort lived within a half-mile of a park, and scores for walkability, bikeability, and transit access varied widely. See S1 and S2 Tables in the S1 Appendix for additional details.

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Table 3. Descriptive statistics for neighborhood built environment variables for the final cohort (n = 18,042).

https://doi.org/10.1371/journal.pone.0286119.t003

3.2. Associations between neighborhood built environment and COVID-19 hospitalizations

In univariate analyses, the four domains of neighborhood built environments were significantly associated with the incident rate ratio (IRR) of being hospitalized (Table 4). The IRR was higher for those living in neighborhoods with larger shares of overcrowded households and for those living in a multi-family building (density and crowding). Also, the IRR of hospitalization was higher for people living in neighborhoods with higher PM_2.5 levels (environmental hazards) and lower for people living in neighborhoods with higher overall greenness, measured through NDVI (environmental amenities). Furthermore, the IRR of hospitalization was higher for people living in neighborhoods with higher Transit Scores^®.

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Table 4. Poisson regressions with robust standard errors predicting the incidence rate ratios (IRR) of hospitalization among individuals with positive SARS-CoV-2 PCR tests.

https://doi.org/10.1371/journal.pone.0286119.t004

In multivariable analyses adjusting for demographics and comorbidities (age, gender, race/ethnicity, BMI, tobacco smoking, diabetes, hypertension, chronic kidney disease, chronic lung disease), certain elements of the neighborhood built environment remained significantly associated with the IRR of hospitalization (Table 4). The IRR was higher for individuals living in an apartment (p < 0.001), but not for the other density and crowding variables (e.g., percent overcrowding). Specifically, the IRR of hospitalization was 14.2% higher for people living in a multi-family building than for people in a single-family unit. Living in a neighborhood with higher PM_2.5 levels was associated with a higher IRR of hospitalization (p < 0.001). None of the environmental amenities (parks and NDVI) showed significant protective associations, but living within a half-mile of a park was linked with a higher IRR of hospitalization, although this result was significant only at the 0.05 level. Results for mobility were mixed. The IRR of hospitalization was higher for individuals whose neighborhood had a lower Walk Score ^® (p < 0.05), lower Bike Score ^® (p < 0.001), and higher Transit Score ^® (p < 0.001). Incidence rate ratios (IRRs) and significant levels for the control variables are in S3 Table in S1 Appendix.

Tukey-adjusted post-hoc pairwise comparisons showed that, when controlling for other variables, non-Hispanic white people with SARS-CoV-2 were less likely to be hospitalized than people of color (see S4 Table in the S1 Appendix). Also, to test whether adding the built environment variables to the individual-level comorbidities and demographics improved the model fit, we ran a Poisson regression with robust standard errors that only included the control variables. The model with the built environment variables had a lower Akaike Information Criterion (AIC = 21,934.92) and a lower Bayesian Information (BIC = 22,761.77) than the model without such variables (AIC = 21,952.56, BIC = 22857.41). This shows that adding the built environment variables to the individual-level comorbidities and demographics improved the model fit, albeit only slightly.

In subgroup models of non-Hispanic white and Latinx subsamples (Table 5 and S5 Table in the S1 Appendix), we observed some variations in the significant associations and effect sizes. Higher PM_2.5 levels had stronger associations with hospitalization in the Latinx subsample (IRR = 1.347, 95% CI = 1.289–1.407) than in the non-Hispanic white subsample (IRR = 1.087, 95% CI = 1.041–1.136), and the two confidence intervals did not overlap. Also, a lower Walk Score^® was associated with a higher incidence rate ratio (IRR) of hospitalization for Latinx individuals, but not for non-Hispanic white individuals. On the contrary, a lower Bike Score^®, higher Transit Score^®, higher percent of overcrowded units, and living within > ½ mile from a park (within 800 m) were all associated with a higher IRR of hospitalization in the non-Hispanic white sample, but not in the Latinx sample. Finally, living in a multi-family unit was associated with a higher IRR of hospitalization in both samples, but the effect size was larger in the non-Hispanic white cohort.

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Table 5. Poisson regressions with robust standard errors predicting the incidence rate ratios (IRR) of hospitalizations for individuals who tested positive for SARS-CoV-2, broken down between two samples: Non-Hispanic white and Latinx people.

https://doi.org/10.1371/journal.pone.0286119.t005

4.1. Summary of findings

Among a cohort of more than 18,000 individuals with SARS-CoV-2 infection, living in a multi-family building, living in a neighborhood with higher PM_2.5 levels, and living in a neighborhood with lower walkability and bikeability were associated with a greater incident rate ratio (IRR) of hospitalization, even when controlling for socioeconomic vulnerability and individual-level demographic and medical characteristics. NDVI was associated with a lower IRR of hospitalization in univariate models but the association did not remain significant in multivariable models. Also, in multivariate models, living within a half-mile of a park was associated with a higher IRR of hospitalizations. And although walkability and bikeability were observed to be protective against hospitalization, transit score–a marker of transit quality and access–was associated with a higher IRR of hospitalization.

When stratifying by race and ethnicity, we observed notable variations in the results for the Latinx and non-Hispanic white cohorts. For example, although higher PM_2.5 was significantly associated with a higher IRR of hospitalizations in both cohorts, the effect size was much larger for the Latinx than the non-Hispanic white cohort. This suggests that reducing harmful emissions might provide greater benefits to Latinx populations, a group that was hospitalized at 2.8 times the rate of non-Hispanic white populations in the U.S. during the first year of the COVID-19 pandemic [26]. In contrast to PM_2.5 levels, markers of density and overcrowding were more strongly associated with a greater IRR of hospitalization among non-Hispanic white than Latinx individuals with SARS-CoV-2 infection, when controlling for other factors as described above. Similarly, living within a half-mile of a park was associated with a higher IRR of hospitalization among non-Hispanic white people but not among Latinx people. The reasons for the differences in these associations between the two cohorts are unclear.

Of the environmental hazards we studied, higher PM_2.5 levels were most consistently associated with a greater IRR of hospitalization. This finding supports the results of previous work showing that long-term exposure to air pollution–especially PM_2.5 –is associated with more SARS-CoV-2 transmission, more severe COVID-19, and higher COVID-19 mortality [13,15,73]. Given the well-established link between pollution and cardiovascular disease [74], a known risk factor for severe COVID-19 [7], it is possible that undiagnosed cardiovascular disease or unrecognized cardiovascular damage associated with higher PM_2.5 levels drives poorer outcomes among those who become ill with COVID-19. Long-term PM_2.5 exposure can also lead to chronic respiratory stress, which in turn can render individuals susceptible to complications from COVID-19 [75]. A prior study found that individuals with chronic lung disease who were exposed to higher levels were PM_2.5 were significantly more likely to be hospitalized from COVID-19, suggesting an exacerbation of the underlying disease [76]. PM_2.5 and other forms of air pollution may also worsen disease severity by increasing epithelial permeability, increasing expression of ACE2 receptors in the airways, and causing oxidative stress, enhanced inflammatory responses, and immune dysregulation [77–79]. Most of these studies have relied on ecological data, which, although informative, have several limitations. Only a handful of studies in the US have relied on individual-level patient data [16,76]. One found that the association between PM_2.5 and COVID-19 hospitalizations was contingent on patients having pre-existing asthma or chronic pulmonary disorder [76]. Similar to our results, Bowe and colleagues found that the annual average PM_2.5 levels in 2018 were linked with an increased risk of hospitalization among a very large cohort of United States Veterans and that Black individuals were more susceptible to the effect of PM_2.5 on COVID-19 illness than white individuals [16].

Our study adds strong evidence of associations between air pollution and COVID-19 severity, particularly among Latinx populations. These findings are particularly important given that people of color and socioeconomically disadvantaged groups in the United States are systematically exposed to higher air pollution levels [71,80], and that there exist substantial disparities in the risk of COVID-19 infection by race/ethnicity and socioeconomic conditions [81]. The confluence of environmental injustices with infection rate disparities and disease severity among people of color illustrates the compound effects of these forces on health outcomes.

Our findings on mobility partially align with those of previous work [20,82,83] showing that living in more walkable and bikeable neighborhoods lowers the IRR of hospitalization among people with COVID-19. A recent study by Sallis and colleagues showed that being physically inactive was linked to higher risks of hospitalization, admission to an Intensive Care Unit, and death [84]. Thus, we hypothesize that living in places conducive to walking and/or biking leads to more physical activity, which may be protective against those outcomes. For unclear reasons, the effects of bikeability and walkability varied significantly between the two sub-cohorts, suggesting the need for further research into whether these scores may reflect different experiences for racial and ethnic subgroups in the Denver metropolitan area and elsewhere.

We noted a harmful association between better transit access and hospitalization risk. Wang and colleagues theorized that transit riders might have a higher risk of infection due to possible prolonged exposure to SARS-CoV-2 in small indoor settings [20]. Although this theory would not fully explain our findings on the risk for hospitalization among those who contracted SARS-CoV-2, it is possible that transit riding may have other unidentified health risks or that people who continued to use public transit during the first year of the COVID-19 pandemic may have had health conditions not captured in our study [85]. Like the bike and walk score results, associations with transit scores varied between the subgroups, limiting the generalizability of the finding at this time.

We also found that living within a half-mile of a park was associated with a higher IRR of hospitalization for people with SARS-CoV-2 infection in the entire cohort and the non-Hispanic white subsample, but not the Latinx subsample. These findings are unlike those of previous studies, which largely found that higher densities of green space were associated with

COVID-19-related mortality and hospitalization risks [16–21]. The reasons for these differences are unclear, but they might be due to our inclusion of many other built environment variables in our model, including markers of environmental hazards, mobility, and density and crowding, which other studies on the association between green space and COVID-19 outcomes rarely considered.

4.2. Strengths, limitations, and future research

Our study has several strengths. Importantly, we used individual-level demographic and clinical data for >18,000 individuals with SARS-CoV-2 infection and linked each case to secondary data sources, using the geocoded addresses to measure neighborhood built environment domains and additional control variables describing socioeconomic factors. To our knowledge, this is one of the first and largest studies to utilize individual-level data to determine environmental correlates of COVID-19 disease severity. Further, we considered how associations between neighborhood built environment and COVID-19 hospitalization differed between Latinx and non-Hispanic white people, and found notable differences in such associations, such as the larger impact of particulate matter in the Latinx cohort.

Our study also has several limitations. First, we used an observational design, which may be subject to residual confounding due to unmeasured covariates or missing data. We tried to mitigate this concern by including a wide range of individual-level covariates but did not have individual-level data describing socioeconomic status, and therefore we used such data at the census block group level. Certain comorbid conditions may not have been documented in the EHR leading to possible misclassification. In particular, chronic lung disease encompasses a range of conditions with potentially differing susceptibilities to complications resulting from environmental exposures such as particulate matter. Second, the large number of records censored from the cohort due to missing weight/BMI values or mobility score values (e.g., Transit Score ^®) may have skewed the cohort toward those with more healthcare utilization due to higher healthcare needs. Third, the reason for hospitalization was not available in the dataset, and therefore some patients might have incidentally been positive for SARS-CoV-2 upon screening but were not hospitalized because of COVID-19. The Infection Prevention teams at DH and UCH report that asymptomatic SARS-CoV-2 infections typically represented less than 5% of hospitalized cases in 2020, but the exact prevalence of this scenario in our cohort is unknown.

Fourth, we relied on PM_2.5 concentrations for the year 2016. It is possible that in response to lockdown policies during the pandemic, fluctuations and short-term PM_2.5 concentrations may have differed from 2016 levels. Fifth, and relatedly, some of the data sources we used do not completely align temporally; for example, demographic data are for 2015–2019, hospitalization data are for 2020, and data about walkability, bikeability, and transit access are for 2021. Sixth, we did not include measures of park quality because data needed to model quality were not available homogeneously across the various jurisdictions. Seventh, the results of the two subgroup analyses for non-Hispanic white and Latinx individuals might include inflated false positive rates [86]. Yet many statistically significant associations for key variables (e.g., PM _2.5) in the two subsamples were at the 0.01 levels or lower, which strengthens our confidence in the accuracy of the results. Finally, our dataset included all SARS-CoV-2-positive results from the two largest public hospitals in the Denver metropolitan area and thus accounted for many, but not all, of the region’s cases. It is possible that individuals who tested for SARS-CoV-2 outside of these healthcare systems may be healthier and differently impacted by the environmental variables assessed in this study.

Future research could build on our line of inquiry in several ways. Reproducing these results in other jurisdictions will be critical to understanding the generalizability and validity of our findings, particularly in jurisdictions with different racial and ethnic distributions. Further, environmental exposures describing only one’s neighborhood fail to represent other places where individuals spend significant time, an issue defined as the neighborhood effect averaging problem (NEAP) [87]. Therefore, subsequent studies are also needed to examine the effects of environmental exposures in the locations where one works or partakes in leisure. Finally, future studies could assess the effects of different components of air pollution, including different particulate matter sizes.

4.3. Implications for urban planning, urban policy, and public health

The findings of this study show that certain neighborhood built environment characteristics are associated with increased or decreased IRR of being hospitalized among individuals who tested positive for SARS-CoV-2. These associations are statistically significant even when controlling for several demographic characteristics and comorbidities. As such, these findings could be used to inform future public health and urban planning interventions that could help limit the severity of COVID-19 and other airborne infectious diseases.

Our results for density and crowding could help direct public health vaccination and testing efforts to areas with a higher prevalence of multifamily housing. These efforts could mitigate the risk of severe disease through the prevention of infection and augmented immunity from vaccines, and by encouraging earlier access to therapeutics for those who test positive for SARS-CoV-2 [88–90]. Living in a transit-rich neighborhood was also associated with a higher risk for COVID-19 hospitalization, so public health efforts targeting these neighborhoods could be helpful, as could additional public health measures like masking and educational messaging on transit itself.

Ambient PM_2.5 exposure is an increasing concern worldwide due to its association with numerous poor health outcomes [91–93] and its disproportionate effect on people of color [94–96]. Our findings underscore the urgency of lowering PM emissions to improve public health, especially respiratory-related diseases. Ways to reduce PM emissions include facilitating opportunities for public transport or active transportation, controlling industrial sources, limiting highway expansions, and increasing green space [97]. To have the greatest impact, principles of environmental justice should be at the core of future planning efforts.

Our findings for mobility suggest the need to make existing neighborhoods more walkable and bikeable, and to build new neighborhoods conducive to walking and biking, investing in pedestrian and cycling infrastructure and promoting mixed land uses and residential densities. Since walkability was particularly protective for Latinx individuals, planners should prioritize pedestrian investments in majority-Latinx neighborhoods. Walkability investments in communities of color should follow a holistic environmental justice approach with meaningful community engagement in planning [98]. In this context, investments need to ensure that increased walkability and bikeability do not result in the over-policing of people of color, as research has shown disproportionate cycling citations in Black and Latinx communities [99].