Lymphocyte count is a universal predictor to the health status and outcomes of patients with coronavirus disease 2019 (COVID-19): A systematic review and meta-regression analysis

Background This study aimed to evaluate the prediction capabilities of clinical laboratory biomarkers to the prognosis of COVID-19 patients. Methods Observational studies reporting at least 30 cases of COVID-19 describing disease severity or mortality were included. Meta-data of demographics, clinical symptoms, vital signs, comorbidities, and 14 clinical laboratory biomarkers on initial hospital presentation were extracted. Taking the outcome group as the analysis unit, meta-regression analysis with the generalized estimating equations (GEE) method for clustered data was performed sequentially. The unadjusted effect of each potential predictor of the three binary outcome variables (i.e., severe vs. non-severe, critically severe vs. non-critically severe, and dead vs. alive) was examined one by one by fitting three series of simple GEE logistic regression models due to missing data. The worst one was dropped one at a time. Then, a final multiple GEE logistic regression model for each of the three outcome variables was obtained. Findings Meta-data was extracted from 76 articles, reporting a total of 26,627 cases of COVID-19. Patients were recruited across 16 countries. The number of studies (patients) included in the final models of the analysis for severity, critical severity, and mortality was 38 studies (9,764 patients), 21 studies (4,792 patients), and 24 studies (14,825 patients), respectively. After adjusting for the effect of age, lymphocyte count mean or median ≤ 1.03 (estimated hazard ratio [HR] = 46.2594, p < 0.0001), smaller lymphocyte count mean or median (HR < 0.0001, p = 0.0028), and lymphocyte count mean or median ≤ 0.8714 (HR = 17.3756, p = 0.0079) were the strongest predictor of severity, critical severity, and mortality, respectively. Interpretation Lymphocyte count should be closely watched for COVID-19 patients in clinical practice. Keywords Laboratory data, lymphocyte, logistic regression analysis, clustered data, GEE.

A Generalized Estimating Equations Approach for Modeling Spatially Clustered Data

Clustering in spatial data is very common phenomena in various fields such as disease mapping, ecology, environmental science and so on. Analysis of spatially clustered data should be different from conventional analysis of spatial data because of the nature of clusters in the data. Because it is expected that the observations of same cluster are more similar than the observations from different clusters. In this study, a method has been proposed for the analysis of spatially clustered areal data based on generalized estimating equations which were originally developed for analyzing longitudinal data. The performance of the model for known clusters is tested in terms of how well it estimates the regression parameters and how well it captures the true spatial process. These results are presented and compared with the conditional auto-regressive model which is the most frequently used spatial model. In the simulation study, the proposed generalized estimating equations approach yields better results than the popular conditional auto-regressive model from the both perspectives of parameter estimation and spatial process capturing. A real life data on the vitamin A supplement coverage among postpartum women in Bangladesh is then analyzed for demonstration of the method. The existing divisional clustering behavior of vitamin A supplement coverage in Bangladesh is identified more accurately by the proposed approach than that by the conditional auto-regressive model.