If you build it, will they come? Is test site availability a root cause of geographic disparities in COVID-19 testing?

OBJECTIVES: The purpose of this study was to examine the relationship between test site availability and testing rate within the context of social determinants of health. STUDY DESIGN: A retrospective ecological investigation was conducted using statewide COVID-19 testing data between March 2020 and December 2021. METHODS: Ordinary least squares and geographically weighted regression were used to estimate state and ZIP code level associations between testing rate and testing sites per capita, adjusting for neighbourhood-level confounders. RESULTS: The findings indicate that site availability is positively associated with the ZIP code level testing rate and that this association is amplified in communities of greater economic deprivation. In addition, economic deprivation is a key factor for consideration when examining ethnic differences in testing in medically underserved states. CONCLUSION: The study findings could be used to guide the delivery of testing facilities in resource-constrained states.

Excess Profits, Taxpayer-Subsidized Philanthropy, and the Coronavirus Crisis: Charitable Giving of the Tech Elite in Response to the Pandemic

This paper addresses how the tech elite has benefited financially from the Coronavirus crisis, as well as how they have sought to give back some of their gains in order to help the broader population. We have gathered data on the stock prices, corporate revenues, and profits of the Big Tech firms and on the incomes and wealth of the tech elite, and we compare these winnings with their philanthropic giving during the pandemic year of 2020. We note that tax policies undergird both the explosion of tech profits and the growth of philanthropic giving in response to the crisis. We find that the winners among the tech elite have benefited dramatically from the pandemic without necessarily donating large amounts of money relative to their wealth. We argue that tax reforms are necessary to ensure that more of the social product comes under the democratic control of the public treasury. Copyright © 2021 John Torpey, Hilke Brockmann, Braelyn Hendricks

Evaluation of Rural-Urban Differences in Hospitalization and Mortality Rates for US COVID-19 Patients in the United States

Background. Rural communities are among the most vulnerable and resourcescarce populations in the United States. Rural data is rarely centralized, precluding comparability across regions, and no significant studies have studied this population at scale. The purpose of this study is to present findings from the National COVID Cohort Collaborative (N3C) to provide insight into future research and highlight the urgent need to address health disparities in rural populations. N3C Patient Distribution This figure shows the geospatial distribution of the N3C COVID-19 positive population. N3C contains data from 55 data contributors from across the United States, 40 of whom include sufficient location information to map by ZIP Code centroid spatially. Of those sites, we selected 27 whose data met our minimum robustness qualifications for inclusion in our study. This bubble map is to scale with larger bubbles representing more patients. A. shows all N3C patients. B. shows only urban N3C distribution. C. shows the urban-adjacent rural patient distribution. D. shows the nonurban-adjacent rural patient distribution, representing the most isolated patients in N3C. Methods. This retrospective cohort of 573,018 patients from 27 hospital systems presenting with COVID-19 between January 2020 and March 2021, of whom 117,897 were admitted (see Data Analysis Plan diagram for inclusion/exclusion criteria), analyzes outcomes and 30-day survival for the hospitalized population by the degree of rurality. Multivariate Cox regression analysis and mixed-effects models were used to estimate the association between rurality, hospitalization, and all-cause mortality, controlling for major risk factors associated with rural-urban health discrepancies and differences in health system outcomes. The difference in distribution by rurality is described as well as supplemented by population-level statistics to confirm representativeness. Data Analysis Plan This data analysis plan includes an overview of study inclusion and exclusion criteria, the matrix for data robustness to determine potential sites to include, and our covariate selection, model building, and residual testing strategy. Results. This study demonstrates a significant difference between hospital admissions and outcomes in urban versus urban-adjacent rural (UAR) and nonurban-adjacent rural (NAR) lines. Hospital admissions for UAR (OR 1.41, p< 0.001, 95% CI: 1.37 - 1.45) and NAR (OR 1.42, p< 0.001, 95% CI: 1.35 - 1.50) were significantly higher than their urban counterparts. Similar distributions were present for all-cause mortality for UAR (OR 1.39, p< 0.001, 95% CI: 1.30 - 1.49) and NAR (OR 1.38, p< 0.001, 95% CI: 1.22 - 1.55) compared to urban populations. These associations persisted despite adjustments for significant differences in BMI, Charlson Comorbidity index Score, gender, age, and the quarter of diagnosis for COVID-19. Baseline Characteristics Hospitalized COVID-19 Positive Population by Rurality Category, January 2020 - March 2021 Survival Curves in Hospitalized Patients Over 30 Days from Day of Admission This figure shows a survival plot of COVID-19 positive hospitalized patients in N3C by rural category (A), Charlson Comorbidity Index (B), Quarter of Diagnosis (C), and Age Group (D) from hospital admission through day 30. Events were censored at day 30 based on the incidence of death or transfer to hospice care. These four factors had the highest predictive power of the covariates evaluated in this study. Unadjusted and Adjusted Odds Ratios for Hospitalization and All-Cause Mortality by Rural Category, January 2020 - March 2021 This figure shows the adjusted and unadjusted odds ratios for being hospitalized or dying after hospitalization for the COVID-19 positive population in N3C. Risk is similar between adjusted and unadjusted models, suggesting a real impact of rurality on all-cause mortality. A shows the unadjusted odds ratios for admission to the hospital after a positive COVID-19 diagnosis for all N3C patients. B shows the unadjusted odds ratios for all-cause mortalit at any point after hospitalization for COVID-19 positive patients. C shows the adjusted odds ratios for being admitted to the hospital after a positive COVID-19 diagnosis for all N3C patients. D shows the adjusted odds ratios for all-cause mortality for all-cause mortality at any point after hospitalization for COVID-19 positive patients. Adjusted models include adjustments for gender, race, ethnicity, BMI, age, Charlson Comorbidity Index (CCI) composite score, rurality, and quarter of diagnosis. The data provider is included as a random effect in all models. Conclusion. In N3C, we found that hospitalizations and all-cause mortality were greater among rural populations when compared to urban populations after adjustment for several factors, including age and co-morbidities. This study also identified key demographic and clinical disparities among rural patients that require further investigation.

An Ordinal Scale Assessing SARS-CoV-2 Infected Patient Outcomes Using Electronic Health Records

Background. A major challenge to identifying effective treatments for COVID-19 has been the conflicting results offered by small, often underpowered clinical trials. The World Health Organization (WHO) Ordinal Scale (OS) has been used to measure clinical improvement among clinical trial participants and has the benefit of measuring effect across the spectrum of clinical illness. We modified the WHO OS to enable assessment of COVID-19 patient outcomes using electronic health record (EHR) data. Methods. Employing the National COVID Cohort Collaborative (N3C) database of EHR data from 50 sites in the United States, we assessed patient outcomes, April 1,2020 to March 31, 2021, among those with a SARS-CoV-2 diagnosis, using the following modification of the WHO OS: 1=Outpatient, 3=Hospitalized, 5=Required Oxygen (any), 7=Mechanical Ventilation, 9=Organ Support (pressors;ECMO), 11=Death. OS is defined over 4 weeks beginning at first diagnosis and recalculated each week using the patient's maximum OS value in the corresponding 7-day period. Modified OS distributions were compared across time using a Pearson Chi-Squared test. Results. The study sample included 1,446,831 patients, 54.7% women, 14.7% Black, 14.6% Hispanic/Latinx. Pearson Chi-Sq P< 0.0001 was obtained comparing the distribution of 2nd Quarter 2020 OS with the distribution of later time points for Week 4. The study sample included 1,446,831 patients, 54.7% women, 14.7% Black, 14.6% Hispanic/Latinx. Pearson Chi-Sq P< 0.0001 was obtained comparing the distribution of 2nd Quarter 2020 OS with the distribution of later time points for Week 4. Conclusion. All Week 4 OS distributions significantly improved from the initial period (April-June 2020) compared with subsequent months, suggesting improved management. Further work is needed to determine which elements of care are driving the improved outcomes. Time series analyses must be included when assessing impact of therapeutic modalities across the COVID pandemic time frame.