Random forest regression on joint role of meteorological variables, demographic factors, and policy response measures in COVID-19 daily cases: global analysis in different climate zones.

Different sources of factors in environment can affect the spread of COVID-19 by influencing the diffusion of the virus transmission, but the collective influence of which has hardly been considered. This study aimed to utilize a machine learning algorithm to assess the joint effects of meteorological variables, demographic factors, and government response measures on COVID-19 daily cases globally at city level. Random forest regression models showed that population density was the most crucial determinant for COVID-19 transmission, followed by meteorological variables and response measures. Ultraviolet radiation and temperature dominated meteorological factors, but the associations with daily cases varied across different climate zones. Policy response measures have lag effect in containing the epidemic development, and the pandemic was more effectively contained with stricter response measures implemented, but the generalized measures might not be applicable to all climate conditions. This study explored the roles of demographic factors, meteorological variables, and policy response measures in the transmission of COVID-19, and provided evidence for policymakers that the design of appropriate policies for prevention and preparedness of future pandemics should be based on local climate conditions, population characteristics, and social activity characteristics. Future work should focus on discerning the interactions between numerous factors affecting COVID-19 transmission.

Joint Effect of Diabetes Mellitus and Hypertension on COVID-19 in-Hospital Mortality Stratified by Age Group and Other Comorbidities: A Cohort Retrospective Study Using Hospital-Based Data in Sleman, Yogyakarta.

The joint effect of diabetes mellitus and hypertension on COVID-19 has rarely been evaluated but had potential as a major risk factor. This study aims to investigate the joint effect between diabetes mellitus and hypertension on in-hospital mortality among COVID-19 patients in Yogyakarta stratified by age groups and other comorbidities status. Methods: This cohort retrospective study collected data from two major hospitals in the Sleman district and a total of 2779 hospitalized COVID-19 patients were included in this study. The study outcome was COVID-19 in-hospital mortality (deceased or discharged alive) and the main risk factors were diabetes mellitus (DM) and hypertension (HT). The multiple logistic regression model was utilized to estimate adjusted odds ratio (AOR) and calculate the joint effect. Results: COVID-19 patients who have both DM and hypertension were three times (AOR: 3.21; 95% CI: 2.45-4.19) more likely to have in-hospital mortality than those without both comorbidities. The highest risk of in-hospital mortality was found in COVID-19 patients without other comorbidities (other than DM and HT) and younger age (age 0-40 years), with AOR equal to 22.40 (95% CI: 6.61-75.99). Conclusions: This study identified a joint effect between diabetes mellitus and hypertension which increases the risk of in-hospital mortality among COVID-19 patients. Targeted public health, clinical, and health education intervention should be carried out on individuals with diabetes mellitus and/or hypertension.

How to investigate human health effects related to exposure to mixtures of per- and polyfluoroalkyl substances: A systematic review of statistical methods.

BACKGROUND: Humans are exposed to several per- and polyfluoroalkyl substances (PFAS) daily; however, most previous studies have focused on individual PFAS. Although attention to effects of exposure to mixtures of PFAS has grown in recent years, there is no consensus on the appropriate statistical methods that can be used to assess their combined effect on human health. OBJECTIVES: We aim to perform a comprehensive review of the statistical methods used in the existing studies which evaluate the association between exposure to mixtures of PFAS and any adverse human health effect. METHODS: The online databases PubMed, Embase and Scopus were searched for eligible studies, published during the last ten years (last search performed on April 08, 2021). Covidence software was used by two different reviewers to perform a title/abstract screening, followed by a full text revision of the selected papers. RESULTS: A total of 3640 papers were identified, and after the screening process, 53 papers were included in the current review. Most of the studies were published between 2019 and 2021 and were conducted mainly in North America and Europe; more than half of the studies (28 out of 53) were conducted on mother and child pairs. WQS (Weighted Quantile Sum) Regression and BKMR (Bayesian Kernel Machine Regression) were used in 36 out of 53 papers to model mixtures' effects. Health outcomes included in the studies are immunotoxicity (n = 8), fetal development (n = 7), neurodevelopment (n = 9), reproductive hormones (n = 6), thyroid hormones (n = 7), outcomes related to metabolic pathways (n = 16). CONCLUSION: Studies on human exposure to PFAS as complex mixtures and health consequences have substantially increased in the last few years. Based on our findings, we propose that addressing risk from PFAS mixtures will likely require combinations of approaches and implementation of constantly evolving statistical methods. Specific guidelines and tools for quality assessment and publication of mixture observational studies are warranted.