No abstract

The purpose of this study was to elucidate the association between weight cycling and clinical outcomes such as type 2 diabetes and hypertension with differential effects of baseline age and obesity. Nationwide data from 6,132,569 healthy adults who underwent five or more health screenings between 2002 and 2011 were analyzed and followed until December 2019 for type 2 diabetes and hypertension. Weight cycling was defined as a change in body weight followed by another change in the opposite direction. Through the Cox proportional hazards model, the number and degree of weight cycles were positively associated with increased risk of type 2 diabetes and hypertension. The hazard ratios (HRs) of weight cycling for type 2 diabetes and hypertension were as high as 1.263 (1.213–1.315) and 1.175 (1.144–1.207) at two or more weight cycles of 10% of body weight (BW), respectively. The association was stronger for females, individuals with normal body weight/BMI, and older individuals. Weight cycling was significantly associated with an increased risk of adverse health outcomes and was stronger in individuals with normal BMI and females, indicating that these people should be informed about the potential risk of weight cycling.

No abstract

Background: Ambient air pollutants are considered significant factors that influence allergic diseases. This study aimed to evaluate the effect of ambient air pollution on the medical costs of allergic rhinitis (AR) through a time-series analysis.Methods: We used data from the Korean National Health Insurance Database from January 2016 to December 2019. The total cost for medical management and outpatient prescriptions for AR, estimated by the National Health Insurance Service for 25 districts, was summed as the total medical costs. The monthly concentrations of PM 10 , PM 2.5 , NO 2 , CO, O 3 , and SO 2 were calculated, and time-series analysis was performed to evaluate the effect of ambient air pollutants on the medical cost of AR using the seasonal autoregressive integrated moving average (SARIMA) model.Results: Time-series analysis revealed that the concentrations of PM 10 , NO 2 , and CO were significantly associated with an increased medical cost of AR when adjusting for monthly differences and seasonality (p = 0.048, p = 0.001, and p = 0.001 respectively). The increase in medical costs of AR was 6.22% (95% confidence interval [CI]: 0.0%-12.37%), 11.27% (95% CI: 6.03%-16.50%), and 11.05% (95% CI: 7.09%-15.01%) per one standard deviation increase in PM1 0 , NO2, and CO respectively.Conclusions: The monthly concentrations of PM1 0 , NO 2 , and CO were significantly associated with the increased medical costs of AR when adjusting for monthly differences and seasonality. This analysis provides a better understanding of the health and economic effects of ambient air pollutants on AR.

No abstract