Search

Background: Social and ecological differences in early SARS-CoV-2 pandemic screening and outcomes have been documented, but the means by which these differences have arisen are not well understood. Objective: To characterize socioeconomic and chronic disease-related mechanisms underlying these differences. Design: Observational cohort study. Setting: Outpatient and emergency care. Patients: 12900 Cleveland Clinic Health System patients referred for SARS-CoV-2 testing between March 17 and April 15, 2020. Interventions: Nasopharyngeal PCR test for SARS-CoV-2 infection. Measurements: Test location (emergency department, ED, vs. outpatient care), COVID-19 symptoms, test positivity and hospitalization among positive cases. Results: We identified six classes of symptoms, ranging in test positivity from 3.4% to 23%. Non-Hispanic Black race/ethnicity was disproportionately represented in the group with highest positivity rates. Non-Hispanic Black patients ranged from 1.81 [95% confidence interval: 0.91–3.59] times (at age 20) to 2.37 [1.54–3.65] times (at age 80) more likely to test positive for the SARS-CoV-2 virus than non-Hispanic White patients, while test positivity was not significantly different across the neighborhood income spectrum. Testing in the emergency department (OR: 5.4 [3.9, 7.5]) and cardiovascular disease (OR: 2.5 [1.7, 3.8]) were related to increased risk of hospitalization among the 1247 patients who tested positive. Limitations: Constraints on availability of test kits forced providers to selectively test for SARS-Cov-2. Conclusion: Non-Hispanic Black patients and patients from low-income neighborhoods tended toward more severe and prolonged symptom profiles and increased comorbidity burden. These factors were associated with higher rates of testing in the ED. Non-Hispanic Black patients also had higher test positivity rates. [ABSTRACT FROM AUTHOR]

The contemporary scientific community places a growing emphasis on the reproducibility of research. The projects R package is a free, open-source package created in the interest of facilitating reproducible research workflows. It adds to existing software tools for reproducible research and introduces several practical features that are helpful for scientists and their collaborative research teams. For each individual project, it supplies a framework for storing raw and cleaned study data sets, and it provides script templates for protocol creation, data cleaning, data analysis and manuscript development. Internal databases of project and author information are generated and displayed, and manuscript title pages containing author lists and their affiliations are automatically generated from the internal database. File management tools allow teams to organize multiple projects. When used on a shared file system, multiple researchers can harmoniously contribute to the same project in a less punctuated manner, reducing the frequency of misunderstandings and the need for status updates. [ABSTRACT FROM AUTHOR]

In epidemiology, gerontology, human development and the social sciences, age-period-cohort (APC) models are used to study the variability in trajectories of change over time. A well-known issue exists in simultaneously identifying age, period and birth cohort effects, namely that the three characteristics comprise a perfectly collinear system. That is, since age = period−cohort, only two of these effects are estimable at a time. In this paper, we introduce an alternative framework for considering effects relating to age, period and birth cohort. In particular, instead of directly modeling age in the presence of period and cohort effects, we propose a risk modeling approach to characterize age-related risk (i.e., a hybrid of multiple biological and sociological influences to evaluate phenomena associated with growing older). The properties of this approach, termed risk-period-cohort (RPC), are described in this paper and studied by simulations. We show that, except for pathological circumstances where risk is uniquely determined by age, using such risk indices obviates the problem of collinearity. We also show that the size of the chronological age effect in the risk prediction model associates with the correlation between a risk index and chronological age and that the RPC approach can satisfactorily recover cohort and period effects in most cases. We illustrate the advantages of RPC compared to traditional APC analysis on 27496 individuals from NHANES survey data (2005–2016) to study the longitudinal variability in depression screening over time. Our RPC method has broad implications for examining processes of change over time in longitudinal studies. [ABSTRACT FROM AUTHOR]