key: cord-0051635-conm20ai authors: Anderson, Jaclyn B.; Laughter, Melissa R.; Nguyen, Alexander; Erlandson, Kristine M. title: Comorbidities and health care systems differences among states as it relates to COVID-19 date: 2020-08-24 journal: nan DOI: 10.1017/cts.2020.528 sha: 74bfd59a4ee9e0907ca4683c6c2420a2087f9a3b doc_id: 51635 cord_uid: conm20ai nan Comorbidities, such as cardiovascular disease (CVD), congestive obstructive pulmonary disease (COPD), diabetes mellitus (DM), hypertension (HTN), and obesity, have been associated with poorer COVID-19-related prognoses [1] . However, little is known about how these comorbidities and socioeconomic factors (e.g., minority percentage, uninsured status, nursing homes/1000, number of hospital beds/1000) collectively preclude worse COVID-19 outcomes. We compared characteristics of each state with their corresponding COVID-19 case fatality rate to develop a deeper understanding of state-by-state COVID-19 risk and inform allocation of resources, prevention strategies, and policy. Data on demographics, comorbidities, hospital systems, and COVID-19 case fatality rates across 50 states and the District of Columbia (DC) were analyzed and stratified by the United States average. Comorbidities data were obtained from the Centers for Disease Control and Behavioral Risk Factor Surveillance System [2,3]. Data regarding demographics and hospital systems in State Health Facts were used with permission from the Kaiser Family Foundation [4] [5] [6] [7] [8] [9] . We compared case fatality rates for all regions (51 values per predictor) above and below the national average for that predictor using independent-samples t-test, assuming unequal variances, with significance at a p-value of <0.05. A multiple linear regression analysis was performed on 12 independent variable predictors to determine the possible case fatality rates at the state level. Maine, West Virginia, and Vermont had the highest rates of asthma (123, 123, and 120 cases/ 1000 persons), while Texas, South Dakota, and Iowa had the lowest rates (74, 79, and 79 cases/ 1000 persons). Maine, Vermont, and Florida had the oldest mean age of the total population (37, 36, and 35 years), while Utah, DC, and Texas had the youngest (21, 22, and 24 years). Alaska, North Dakota, and Wyoming had the highest male to female (M:F) gender ratio (1.04), while DC, Virginia, and Tennessee had the lowest ratio (0.89, 0.92, and 0.92). Case fatality rates were significantly greater in states above vs. below the national averages for asthma (p-value 0.013), age (p-value 0.040), and M:F (p-value 0.0014). All other variables investigated were not significantly different between states (all p-value ≤ 0.15) ( Table 1) . A multiple linear regression analysis indicated coefficients and p-values for each independent variable as follows: Age >55 Understanding the risk and potential impact of the COVID-19 pandemic at the state level is vital for outbreak preparedness and community management. This review is consistent with current literature indicating increased rates of preexisting disease are associated with worse health outcomes [10, 11] . Asthma and increased age were significantly greater among states with higher case fatality. In contrast to reportedly higher COVID-19-related deaths among men, lower case fatalities were observed in states with higher M:F ratios. This study expands upon individual hospital-level data to identify state-wide risk factors for COVID-19. Social factors such as accessibility to healthcare, uninsured rates, urban vs. rural populations, and unemployment status affect the care patients receive. Structural inequalities such as poverty rates, healthcare racial bias, and increased preexisting conditions impact minority groups differently. To address some of the limitations in an ecological study design, we further evaluated associations in multiple linear regression. While no single variable was significantly associated with mortality, in combination, our multiple linear regression suggested~27% of the case fatality rate can be predicted by the comorbidity and hospital system variables discussed in this study. There may be other factors not addressed in this study that may impact the case fatality rate thus stressing the need for additional research. Data regarding deaths can be confounded when a patient has multiple comorbidities. Additionally, the available data are limited and partially self-reported, leading to less accurate measurement of predictors (i.e., comorbidities). Deaths can be difficult to compare when each state records deaths at different frequencies and older patients were disproportionately affected early during the pandemic, diverting states' initial infectious trajectory (e.g., New York). Other limitations are the exclusion of Hawaiian and Pacific Islander populations and Native Americans along with the inability to separate risk factors between minority groups. Despite the initial decline in cases, with reopening, some states may be at higher risk for COVID-19 outbreaks, due in part to older populations and high asthma rates. Preparing at the state level is imperative to combating COVID-19 outbreaks, limiting spread, and guiding resource allocation. A state-specific COVID-19-Readiness Score may help identify the highest risk states for COVID-19 outbreaks, ensure adequate prevention mechanisms, and help direct further resources. Disclosures. The authors have no conflicts of interest to declare. Presenting characteristics, comorbidities, and outcomes among 5700 patients hospitalized with COVID-19 in the New York city area Center for Chronic Disease Prevention and Health Promotion, Division of Population Health. BRFSS Prevalence & Trends Data United States COVID-19 Cases and Deaths by State The Kaiser Family Foundation State Health Facts, Data Source: Census Bureau's American Community Survey The Kaiser Family Foundation State Health Facts. Data Source: Census Bureau's American Community Survey The Kaiser Family Foundation State Health Facts, Census Bureau's American Community Survey The Kaiser Family Foundation State Health Facts. Data Source: Census Bureau's American Community Survey The Kaiser Family Foundation State Health Facts. DataSource: Certification and Survey Provider Enhanced Reports (CASPER) data Hospital Beds per 1,000 Population by Ownership Type, The Kaiser Family Foundation State Health Facts Comorbidity and its impact on 1590 patients with COVID-19 in China: a nationwide analysis Case-fatality rate and characteristics of patients dying in relation to COVID-19 in Italy