COVID-19 severity scale for claims data research.

OBJECTIVE: To create and validate a methodology to assign a severity level to an episode of COVID-19 for retrospective analysis in claims data. DATA SOURCE: Secondary data obtained by license agreement from Optum provided claims records nationally for 19,761,754 persons, of which, 692,094 persons had COVID-19 in 2020. STUDY DESIGN: The World Health Organization (WHO) COVID-19 Progression Scale was used as a model to identify endpoints as measures of episode severity within claims data. Endpoints used included symptoms, respiratory status, progression to levels of treatment and mortality. DATA COLLECTION/EXTRACTION METHODS: The strategy for identification of cases relied upon the February 2020 guidance from the Centers for Disease Control and Prevention (CDC). PRINCIPAL FINDINGS: A total of 709,846 persons (3.6%) met the criteria for one of the nine severity levels based on diagnosis codes with 692,094 having confirmatory diagnoses. The rates for each level varied considerably by age groups, with the older age groups reaching higher severity levels at a higher rate. Mean and median costs increased as severity level increased. Statistical validation of the severity scales revealed that the rates for each level varied considerably by age group, with the older ages reaching higher severity levels (p < 0.001). Other demographic factors such as race and ethnicity, geographic region, and comorbidity count had statistically significant associations with severity level of COVID-19. CONCLUSION: A standardized severity scale for use with claims data will allow researchers to evaluate episodes so that analyses can be conducted on the processes of intervention, effectiveness, efficiencies, costs and outcomes related to COVID-19.

Factors Associated With COVID-19 Death in the United States: Cohort Study.

BACKGROUND: Since the initial COVID-19 cases were identified in the United States in February 2020, the United States has experienced a high incidence of the disease. Understanding the risk factors for severe outcomes identifies the most vulnerable populations and helps in decision-making. OBJECTIVE: This study aims to assess the factors associated with COVID-19-related deaths from a large, national, individual-level data set. METHODS: A cohort study was conducted using data from the Optum de-identified COVID-19 electronic health record (EHR) data set; 1,271,033 adult participants were observed from February 1, 2020, to August 31, 2020, until their deaths due to COVID-19, deaths due to other reasons, or the end of the study. Cox proportional hazards models were constructed to evaluate the risks for each patient characteristic. RESULTS: A total of 1,271,033 participants (age: mean 52.6, SD 17.9 years; male: 507,574/1,271,033, 39.93%) were included in the study, and 3315 (0.26%) deaths were attributed to COVID-19. Factors associated with COVID-19-related death included older age (80 vs 50-59 years old: hazard ratio [HR] 13.28, 95% CI 11.46-15.39), male sex (HR 1.68, 95% CI 1.57-1.80), obesity (BMI 40 vs <30 kg/m2: HR 1.71, 95% CI 1.50-1.96), race (Hispanic White, African American, Asian vs non-Hispanic White: HR 2.46, 95% CI 2.01-3.02; HR 2.27, 95% CI 2.06-2.50; HR 2.06, 95% CI 1.65-2.57), region (South, Northeast, Midwest vs West: HR 1.62, 95% CI 1.33-1.98; HR 2.50, 95% CI 2.06-3.03; HR 1.35, 95% CI 1.11-1.64), chronic respiratory disease (HR 1.21, 95% CI 1.12-1.32), cardiac disease (HR 1.10, 95% CI 1.01-1.19), diabetes (HR 1.92, 95% CI 1.75-2.10), recent diagnosis of lung cancer (HR 1.70, 95% CI 1.14-2.55), severely reduced kidney function (HR 1.92, 95% CI 1.69-2.19), stroke or dementia (HR 1.25, 95% CI 1.15-1.36), other neurological diseases (HR 1.77, 95% CI 1.59-1.98), organ transplant (HR 1.35, 95% CI 1.09-1.67), and other immunosuppressive conditions (HR 1.21, 95% CI 1.01-1.46). CONCLUSIONS: This is one of the largest national cohort studies in the United States; we identified several patient characteristics associated with COVID-19-related deaths, and the results can serve as the basis for policy making. The study also offered directions for future studies, including the effect of other socioeconomic factors on the increased risk for minority groups.

Analysis of COVID-19 Infection and Mortality Among Patients With Psychiatric Disorders, 2020.

Importance: People with major psychiatric disorders are more likely to have comorbidities associated with worse outcomes of COVID-19. This fact alone could determine greater vulnerability of people with major psychiatric disorders to COVID-19. Objective: To assess the odds of testing positive for and mortality from COVID-19 among and between patients with schizophrenia, mood disorders, anxiety disorders and a reference group in a large national database. Design, Setting, and Participants: This cross-sectional study used an electronic health record data set aggregated from many national sources in the United States and licensed from Optum with current and historical data on patients tested for COVID-19 in 2020. Three psychiatric cohorts (patients with schizophrenia, mood disorders, or anxiety disorders) were compared with a reference group with no major psychiatric conditions. Statistical analysis was performed from March to April 2021. Exposure: The exposures observed include lab-confirmed positivity for COVID-19 and mortality. Main Outcomes and Measures: The odds of testing positive for COVID-19 in 2020 and the odds of death from COVID-19 were measured. Results: The population studied included 2 535 098 unique persons, 3350 with schizophrenia, 26 610 with mood disorders, and 18 550 with anxiety disorders. The mean (SD) age was 44 (23) years; 233 519 were non-Hispanic African American, 1 583 440 were non-Hispanic Caucasian; and 1 580 703 (62%) were female. The schizophrenia cohort (positivity rate: 9.86%; adjusted OR, 0.90 [95% CI, 0.84-0.97]) and the mood disorder cohort (positivity rate: 9.86%; adjusted OR, 0.93 [95% CI, 0.87-0.99]) had a significantly lower rate of positivity than the anxiety disorder cohort (positivity rate: 11.17%; adjusted OR, 1.05 [95% CI, 0.98-1.12) which was closer to the reference group (11.91%). After fully adjusting for demographic factors and comorbid conditions, patients with schizophrenia were nearly 4 times more likely to die from the disease than the reference group (OR, 3.74; 95% CI, 2.66-5.24). The mood disorders COVID-19 cohort had a 2.76 times greater odds of mortality than the reference group (OR, 2.76; 95% CI, 2.00-3.81), and the anxiety disorders cohort had a 2.39 times greater odds of mortality than the reference group (OR, 2.39; 95% CI, 1.68-3.27). Conclusions and Relevance: By examining a large database while controlling for multiple confounding factors such as age, race and ethnicity, and comorbid medical conditions, the present study found that patients with schizophrenia had much increased odds of mortality by COVID-19.

The association of social determinants of health with health outcomes.

OBJECTIVES: This study explored the contributions of social determinants of health (SDOH) to measures of population health-specifically cost, hospitalization rates, rate of emergency department utilization, and health status-in Texas. STUDY DESIGN: The study associated common SDOH metrics from public data sources (county specific) with health plan enrollment data (including demographics, counties, and zip codes) and medical and pharmaceutical annual claims data. METHODS: Following correlation analyses to reduce variables, the contribution of each SDOH individually and by category to the health outcomes was evaluated. Separate matrices for age populations (under age 19, general population [all ages], and ≥ 65 years) were created with assigned weights of influence for categories and the factors within each category. RESULTS: The contributions of the categories varied by population, confirming that different SDOH influence populations to varying degrees. This was reflected in each model. The largest contributor to cost for the general population and for the group 65 years and older was factors grouped as health outcomes (such as perceived health), at 43.5% contribution and 37.7% contribution, respectively. Yet for the population younger than 19 years, the largest contributor to cost was socioeconomic factors (such as unemployment rate), at 40.2%. The other performance measures also varied by population and the mix and weight of determinants. CONCLUSIONS: This study and the developed population-based matrices can provide a valuable framework for reporting the impact of SDOH on health care quality. The variation suggests the need for further research on how age groups react to the social environment.