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Higher hospitalization and mortality rates among SARS‐CoV‐2‐infected persons in rural America

Abstract Purpose Rural communities are among the most underserved and resource‐scarce populations in the United States. However, there are limited data on COVID‐19 outcomes in rural America. This study aims to compare hospitalization rates and inpatient mortality among SARS‐CoV‐2‐infected persons stratified by residential rurality. Methods This retrospective cohort study from the National COVID Cohort Collaborative (N3C) assesses 1,033,229 patients from 44 US hospital systems diagnosed with SARS‐CoV‐2 infection between January 2020 and June 2021. Primary outcomes were hospitalization and all‐cause inpatient mortality. Secondary outcomes were utilization of supplemental oxygen, invasive mechanical ventilation, vasopressor support, extracorporeal membrane oxygenation, and incidence of major adverse cardiovascular events or hospital readmission. The analytic approach estimates 90‐day survival in hospitalized patients and associations between rurality, hospitalization, and inpatient adverse events while controlling for major risk factors using Kaplan‐Meier survival estimates and mixed‐effects logistic regression. Findings Of 1,033,229 diagnosed COVID‐19 patients included, 186,882 required hospitalization. After adjusting for demographic differences and comorbidities, urban‐adjacent and nonurban‐adjacent rural dwellers with COVID‐19 were more likely to be hospitalized (adjusted odds ratio [aOR] 1.18, 95% confidence interval [CI], 1.16‐1.21 and aOR 1.29, CI 1.24‐1.1.34) and to die or be transferred to hospice (aOR 1.36, CI 1.29‐1.43 and 1.37, CI 1.26‐1.50), respectively. All secondary outcomes were more likely among rural patients. Conclusions Hospitalization, inpatient mortality, and other adverse outcomes are higher among rural persons with COVID‐19, even after adjusting for demographic differences and comorbidities. Further research is needed to understand the factors that drive health disparities in rural populations.


INTRODUCTION
The novel coronavirus (SARS-CoV-2) was the third leading cause of death in the United States in 2020 1 and is responsible for more than 1 million US deaths to date. 2 During the initial 4 months of the US SARS-CoV-2 epidemic, cases were most concentrated in urban areas.
However, by late 2020, rural communities experienced a surge in SARS-CoV-2 infections, with some of the highest case rates in the nation. 3 Nonmetropolitan areas constitute 97% of the US land area, with approximately 20% of the population residing in rural areas. 4 Rural compared with urban inhabitants are older, less likely to engage in behaviors to prevent SARS-CoV-2 infection, 5 and have a higher prevalence of comorbidities (eg, obesity) associated with more severe COVID-19 (C19) and death. 6 They have also experienced a greater disparity in life expectancy over the last 50 years, 7 which is likely multifactorial, resulting from, but not limited to, decreased access to care, increased disability, and socioeconomic factors. 8 Population-level analyses conducted early in the SARS-CoV-2 pandemic demonstrated higher mortality rates in metropolitan areas than rural or micropolitan counties in the United States. 9 Subsequent studies have shown differences in infection clustering and higher C19 mortality rates in rural areas of the United States, driven by social determinants, 10 social vulnerability, and the differences in mitigation policies 11 between rural and urban communities.
The relative paucity of rigorous research and surveillance data concerning rural dwellers relative to those dwelling in urban settings and communities is well documented in the social sciences literature. 12 This research and information gap extends to the biomedical sciences.
Community hospital and public health data from rural communities are often sparse and preclude meaningful comparison across regions. Rural disparities are complex and diverse, with varying challenges across different regions and communities. A potent societal stressor, the C19 pandemic, both creates acute problems and spotlights chronic weaknesses of rural health and health care systems. The unique challenges in optimally responding to C19 among rural dwellers include fewer intensive care beds per capita; more limited access to relevant infectious disease and other specialty care providers; greater baseline comorbidities, including age, obesity, and diabetes; often relatively adverse social determinants of health, such as income and education; and physical travel distances resulting in a delay in treatment. 13 To better understand potential drivers of C19 outcomes in rural America, we assessed hospitalization and mortality using the National COVID Cohort Collaborative (N3C), a National Institutes of Healthsupported data enclave containing electronic health record (EHR) information on nearly 9 million persons tested for SARS-CoV-2 across 65 US sites and more than 2.9 million patients with a definitive diagnosis or lab result of SARS-CoV-2 infection.
While the relationship between rural and urban hospitalization and mortality has been studied for chronic conditions, limited research has evaluated SARS-CoV-2 infected rural-urban discrepancies. To our knowledge, this is the largest cohort of C19 cases in North America using data at the patient level, which provides detail unavailable in population-based studies. Previous large-scale studies have been restricted to single states 14 or utilized public health reporting systems. 15 The purpose of this study was to (1) estimate differences in hospitalization and mortality among rural and urban individuals with SARS-CoV-2 infection using real-world data adjusted for underlying demographic differences and comorbid burden and (2) quantify differences in rural outcomes based on region and degree of rurality. We hypothesized that rural dwellers would have outcomes similar to their urban counterparts after adjustment for demographic differences and comorbid conditions. is performed as part of the N3C ingestion process based on site, age, gender, race, and ethnicity. 18 Source system C19 testing protocols are mapped to standard terminologies for labs (LOINC) and conditions (ICD-10 CM and SNOMED CT) by the N3C Data Ingestion and Harmonization Workstream, which maintains a computable phenotype for defining the presence of C19. 19 To capture patients during the early stages of the pandemic (before 5/1/2020), patients with 2 weak diagnostic codes (such as ICD10 J80* Acute Respiratory Distress Syndrome and R43.0 Anosmia) probabilistic of C19 are also included (see Supplementary Methods for a summary of the ingestion and harmonization process, sampling approaches, concept definitions, and computable phenotypes). 20

Cohort identification
Rural and urban categories were identified by 5-digit ZIP Codes that were then mapped to the 2010 Rural-Urban Continuum Codes (RUCA), distinguished by population density, degree of urbanization, and adjacency to metropolitan areas. 21 For purposes of this study, 3 categories are defined: urban, urban-adjacent rural (UAR), and nonurban-adjacent rural (NAR). 22,23 This classification has been commonly used to attribute rurality based on census tract or ZIP Code. 24,25 To validate the representativeness of the cohort population with the overall US population, we compared the population percentages for each category in N3C with the US population using public datasets (Table S1). 2,26,27 N3C data partners are contributing institutions encompassing multiple providers and potentially numerous care sites. We developed and utilized a data robustness screening matrix to determine minimum fact reporting per patient across key domains for each data partner.
This follows a similar approach used by the 4 source data models that all rely on data quality dashboards to enhance site reporting for inclusion in network studies: Observational Medical Outcomes Partnership (OMOP), 28 Accrual to Clinical Trials (ACT), 29 TriNetX, 30 and Patient-Centered Clinical Research Network (PCORnet). 31 Where possible, we categorically excluded data partners rather than individual participants based on minimum data reporting requirements and robustness measures. We excluded data partners with limited data robustness (less than 1 standard deviation below mean reporting) in 2 key domains: death reporting and measurement reporting, which was used to calculate body mass index (BMI). We excluded patients with missing age, gender, and 5-digit ZIP Codes across all data partners ( Figure 1). Covariates N3C provides patient ZIP Codes for most patients (∼66% of all subjects). The majority of missing ZIP Code information is from specific data providers, who elect not to provide 5-digit ZIP Codes in their data transmissions--these sites were excluded from this study. Based on data availability, we relied on a RUCA code crosswalk to match ZIP Codes and RUCA classifications. 34

Statistical analyses
Summary statistics using Pearson chi-squared tests for nominal data and Kruskal-Wallis tests for numerical data were calculated on all C19 patients and hospitalized C19 patients stratified by rural-urban categories. 36 Covariates examined include gender, age, race, ethnicity, BMI, Charlson Comorbidity Index (CCI) 37 composite score (a higher score indicating worse health), comorbidity categories, tobacco usage, hospitalization, and Census subregion. Mixed-effects logistic regression models were calculated for hospitalization and adverse event in the hospitalized cohort. All models included fixed effects for gender, race, ethnicity, BMI, age at visit start, CCI score, Census subregion, and rural category and random effects for data partner.
We assessed model specification using a methodology that checked for specification change influence on the estimated rural versus urban effect. Among the possible specification changes assessed were (1) transformations of main effects included in the model, (2) all 2-way interactions of main effects included in the model, (3) potential addition of available comorbidities not included in the model, and (4) inclusion of data-providing organization as a fixed or random effect. In addition, a stepwise assessment approach was used to determine if combinations of any such potential changes resulted in a difference in the estimated rural versus urban effect. The only specification change identified was the need to include the data provider organization as a fixed or random effect. The choice of effect type (fixed vs random) was found to be irrelevant. As a result, the initial model was modified to include data-providing organization as a random effect. This helps mitigate the possibility that our final model's estimated rural effects are artifacts of mortality differences across those organizations' patient populations as well as differences in reporting practices.
The risk adjustment process employed in our modeling used information about pre-COVID comorbidities. As patient data in N3C differ in the availability of pre-COVID clinical data, ranging from none to 2 years of pre-COVID clinical data, we examined the possibility that estimated rural effects stemmed from rural and urban patients differing in the extent of pre-COVID comorbidity information. To evaluate baseline differences in outcomes, we ran the same analyses on the SARS-CoV-2-uninfected patient population, evaluating differences in inpatient death or transfer to hospice.
The variables associated with more severe outcomes in the logistic models--rurality, CCI, age, and period of SARS-CoV-2 diagnosis--were secondarily evaluated using Kaplan-Meier estimates of the overall time to death or transfer to hospice, starting from hospital admission, censored at 90 days or upon nonhospice discharge.

Demographics
Our final cohort included data from 44 data partners ( Figure 1

Underlying health disparities and observable vulnerabilities
Among all patients, rural populations had higher rates of comorbidities across 14 of the 15 comorbidity categories and had notably higher rates of obesity (Table 1). Among our C19 cohorts, between 65% and 80% of patients in each category had prior visit history and between 63% and 74% had prior conditions reported in the pre-COVID period, suggesting that our data robustness matrix ( Figure 1) sufficiently captured patients with high-fidelity data. The median number of pre-C19 visits was 10 (IQR 3, 27) across all patients included in this study, with similar distribution across rural categories.
Assessing the date of SARS-CoV-2 infection and hospitalization, as a proxy for changes in clinical practice and treatment, we found that rural C19 patients were more likely to be diagnosed (Table 1) and subsequently hospitalized later in the pandemic ( Table 2) when treatment practices were leading to better outcomes. Urban dwellers had higher caseloads in the first 3 quarters of 2020 (January-September, 2020: 32% urban, 23% UAR, and 19% NAR; P<.001) and rural dwellers had higher caseloads in all subsequent periods (October, 2020-June, 2021: 68% urban, 77% UAR, and 81% NAR; P<.001). Hospitalization rates were consistent with caseloads by rural categories across time periods with urban dwellers seeing greater hospitalization loads in the first 3 quarters of 2020 (January-September, 2020: 38% urban, 25% UAR, and 25% NAR; P<.001), while rural dwellers saw higher hospitalization loads in all subsequent quarters (October, 2020-June, 2021: 62% urban, 75% UAR, and 75% NAR; P<.001).
Kaplan-Meier survival estimates demonstrate significantly higher mortality 90 days after hospitalization among rural C19 patients compared to their urban counterparts. As shown in Figure 4, hospitalized C19 patients with a higher CCI (indicating higher comorbid burden) and diagnosis earlier in the pandemic demonstrated significantly higher mortality (P<.0001).

Secondary outcomes
We included several secondary outcomes reflective of C19 complications, including oxygen support, invasive mechanical ventilation, MACE, ECMO, and hospital readmission after initial hospitalization

SARS-CoV-2-uninfected comparison group sensitivity analysis
To compare the baseline differences between SARS-CoV-2-infected and -uninfected patients, we ran a sensitivity analysis using the same inclusion/exclusion criteria. We relied on earliest available negative  Figure 2 shows the geospatial distribution of the N3C COVID-19-positive population. N3C contains data from 65 data contributors from across the United States, 52 of whom include sufficient location information to spatially map by ZIP Code centroid.
Of those sites, we selected 44 whose data met our minimum robustness qualifications for inclusion in our study. This bubble map is to scale with larger bubbles representing more patients. Numbers represent population distribution, in thousands 15% higher for rural C19 hospitalized patients, UAR (aOR 1.15, 95% CI, 1.12-1.18) and NAR (aOR 1.16, 95% CI, 1.12-1.21) (Table S3).

DISCUSSION
This retrospective cohort study from a large representative data enclave of C19 patients found significantly higher mortality rates among hospitalized rural C19 patients. Mortality was approximately 36% higher among rural C19 patients after adjustment for age, gender, race, ethnicity, BMI, CCI composite score, date of diagnosis, Census subregion, and differences derived from the data contributor.
The SARS-CoV-2 pandemic provides an important lens to examine all-cause mortality by area of residence. SARS-CoV-2 is a new virus, rapidly transmitted in an immunologically naïve human population and initially without known effective treatment. The rapidity with which first urban then rural communities in the United States experienced surges of SARS-CoV-2 led us to hypothesize that an urban-rural mortality differential would not be observed. That, however, is not the case.
Although rural populations are older and have higher rates of comorbid conditions, 38 such as diabetes mellitus and obesity, 39 which have been associated with increased disease severity and death in SARS-CoV-2 infection, adjustment for these factors did not change the finding of higher rural mortality. 40,41 The gradient of risk for chronic diseases and mortality between urban and rural inhabitants is a relatively recent development in the United States. Prior to 1980, mortality rates in rural and urban areas of the United States were comparable. Since then, mortality rates in rural America have exceeded urban rates, and the gap has accelerated since 1999, even when adjusted for age. 39,40,42 This mortality disparity has been referred to as "the nonmetropolitan penalty," 43 Figure 3 shows the crude (A) and adjusted (B) odds ratios for being hospitalized, dying or being transferred to hospice after hospitalization, requiring any inpatient oxygen support, having a major adverse cardiovascular event, requiring invasive mechanical ventilation, requiring extracorporeal membrane oxygenation, or having a hospital readmission after initial hospitalization in the SARS-CoV-2-infected population in N3C by rural category. Risk is similar between adjusted and unadjusted models, suggesting a real impact of rurality on adverse events. Adjusted models include adjustments for gender, race, ethnicity, BMI category, age, Charlson Comorbidity Index (CCI) composite score, rurality, quarter of diagnosis, and Census subregion. Data provider is included as a random effect in the adjusted models to account for differences across source data systems The etiology of this "penalty" is likely multifactorial. Poverty, unemployment, and lower levels of educational attainment are more prevalent in rural areas, and these factors may partially explain the urban-rural mortality disparity. Diminished access to care has also been described as contributing to rural-urban mortality differences.
One study concluded that having 1 or more specialist visits during the previous year was associated with 16.6% lower mortality for those with chronic conditions. 45 Although limited research has been done on socioeconomic risk factors and case-fatality rates over the first year of the SARS-Cov-2 pandemic, a recent study from Japan observed higher C19-related mortality in prefectures with the lowest household incomes. 48 A recent study conducted using spatial models in the United States found rurality to be one of several ecologic determinants of C19 mortality. 15 Our results again raise the question of why rural populations experience higher mortality rates after adjustment for multiple factors,

Limitations
N3C is an observational registry compiling data from multiple diverse participating sites. Therefore, some information may be entirely or partially and nonrandomly missing from the database in rural versus urban residents. In our C19-positive cohort, we report the incidence of comorbidities in more than two-thirds of our study population, which is similar to those reported in a COVID-19 study across OCHIN, a network of 396 community health centers across 14 states. 52 Nonetheless, we examined the possibility that estimated rural effects stemmed from rural and urban patients differing in the extent of pre-COVID comorbidity information and found this not to be the case.
To some degree, such potential bias can be partly assessed by future analyses, which would include data based on all diagnosed C19 populations in geographic areas rather than data limited to C19 patients who received care at N3C collaborating provider systems.
We believe the sample to be a good representation of the United States in terms of raw distribution of both region and rurality, but further research using community health centers would provide additional insight into differences in outcomes across the severity spectrum and would provide less uncertainty about the severity of disease at admission. Additionally, health care organizations contributing data to N3C may have cared for more severely ill patients, providing a potential source of bias. In any case, understanding differences in those persons requiring treatment at tertiary care centers provides value; in addition, the relative distribution of deaths is similar to that for the overall population reported in public health systems, which suggests a nondifferential risk of misclassification.
Other limitations of the N3C data source include data aggregated from different health systems with different local practices, regulations, and data models, resulting in potential reporting differences, despite our application of data robustness checks. Additionally, the comparison group is limited only to those prematched at the site level from patients who have had a confirmed negative SARS-CoV-2 test.
Although 54% of American Indian and Alaska Native populations in the United States live in rural areas and have been disproportionately affected by C19, 53,54 their racial demographic is unavailable for explicit study in accordance with tribal sovereignty policies. Finally, these analyses are limited to residents in the United States and may not be generalizable to other countries.

CONCLUSIONS
Hospitalization, death, and other adverse events were significantly higher among rural C19 patients than their urban counterparts after adjusting for multiple factors, including age, sex, race, Census subregion, and comorbidities. These data provide evidence-based documentation of rural health disparities. Further research is needed to understand this disparity for both acute and chronic health conditions. We gratefully acknowledge contributions from the following N3C core teams: