RESUMO
Scarlet fever (SF) is an acute respiratory transmitted disease that primarily affects children. The influence of meteorological factors and air pollutants on SF in children has been proved, but the relevant evidence in Northwest China is still lacking. Based on the weekly reported cases of SF in children in Lanzhou, northwest China, from 2014 to 2018, we used geographical detectors, distributed lag nonlinear models (DLNM), and bivariate response models to explore the influence of meteorological factors and air pollutants with SF. It was found that ozone (O3), carbon monoxide (CO), sulfur dioxide (SO2), temperature, pressure, water vapor pressure and wind speed were significantly correlated with SF based on geographical detectors. With the median as reference, the influence of high temperature, low pressure and high pressure on SF has a risk effect (relative risk (RR) > 1), and under extreme conditions, the dangerous effect was still significant. High O3 had the strongest effect at a 6-week delay, with an RR of 5.43 (95%CI: 1.74,16.96). The risk effect of high SO2 was strongest in the week of exposure, and the maximum risk effect was 1.37 (95%CI: 1.08,1.73). The interactions showed synergistic effects between high temperatures and O3, high pressure and high SO2, high nitrogen dioxide (NO2) and high particulate matter with diameter of less than 10 µm (PM10), respectively. In conclusion, high temperature, pressure, high O3 and SO2 were the most important factors affecting the occurrence of SF in children, which will provide theoretical support for follow-up research and disease prevention policy formulation.
RESUMO
The particulate matter with diameter of less than 2.5 µm (PM2.5) is an important risk factor for respiratory infectious diseases, such as scarlet fever, tuberculosis, and similar diseases. However, it is not clear which component of PM2.5 is more important for respiratory infectious diseases. Based on data from 31 provinces in mainland China obtained between 2013 and 2019, this study investigated the effects of different PM2.5 components, i.e., sulfate (SO42-), nitrate (NO3-), ammonium (NH4+), and organic matter (OM), and black carbon (BC), on respiratory infectious diseases incidence [pulmonary tuberculosis (PTB), scarlet fever (SF), influenza, hand, foot, and mouth disease (HFMD), and mumps]. Geographical probes and the Bayesian kernel machine regression (BKMR) model were used to investigate correlations, single-component effects, joint effects, and interactions between components, and subgroup analysis was used to assess regional and temporal heterogeneity. The results of geographical probes showed that the chemical components of PM2.5 were associated with the incidence of respiratory infectious diseases. BKMR results showed that the five components of PM2.5 were the main factors affecting the incidence of respiratory infectious diseases (PIP>0.5). The joint effect of influenza and mumps by co-exposure to the components showed a significant positive correlation, and the exposure-response curve for a single component was approximately linear. And single-component modelling revealed that OM and BC may be the most important factors influencing the incidence of respiratory infections. Moreover, respiratory infectious diseases in southern and southwestern China may be less affected by the PM2.5 component. This study is the first to explore the relationship between different components of PM2.5 and the incidence of five common respiratory infectious diseases in 31 provinces of mainland China, which provides a certain theoretical basis for future research.
Assuntos
Material Particulado , China/epidemiologia , Material Particulado/análise , Material Particulado/efeitos adversos , Humanos , Incidência , Infecções Respiratórias/epidemiologia , Poluentes Atmosféricos/análise , Poluentes Atmosféricos/efeitos adversos , Fatores de Risco , Teorema de Bayes , Influenza Humana/epidemiologia , Doenças Transmissíveis/epidemiologiaRESUMO
Disturbances in the enterohepatic circulation are important biological mechanisms for causing gallstones and also have important effects on the metabolism of Per- and polyfluoroalkyl substances (PFAS). Moreover, PFAS is associated with sex hormone disorder which is another important cause of gallstones. However, it remains unclear whether PFAS is associated with gallstones. In this study, we used logistic regression, restricted cubic spline (RCS), quantile g-computation (qg-comp), Bayesian kernel machine regression (BKMR), and subgroup analysis to assess the individual and joint associations of PFAS with gallstones and effect modifiers. We observed that the individual associations of perfluorodecanoic acid (PFDeA) (OR: 0.600, 95% CI: 0.444 to 0.811), perfluoroundecanoic acid (PFUA) (OR: 0.630, 95% CI: 0.453 to 0.877), n-perfluorooctane sulfonic acid (n-PFOS) (OR: 0.719, 95% CI: 0.571 to 0.906), and perfluoromethylheptane sulfonic acid isomers (Sm-PFOS) (OR: 0.768, 95% CI: 0.602 to 0.981) with gallstones were linearly negative. Qg-comp showed that the PFAS mixture (OR: 0.777, 95% CI: 0.514 to 1.175) was negatively associated with gallstones, but the difference was not statistically significant, and PFDeA had the highest negative association. Moreover, smoking modified the association of perfluorononanoic acid (PFNA) with gallstones. BKMR showed that PFDeA, PFNA, and PFUA had the highest groupPIP (groupPIP = 0.93); PFDeA (condPIP = 0.82), n-perfluorooctanoic acid (n-PFOA) (condPIP = 0.68), and n-PFOS (condPIP = 0.56) also had high condPIPs. Compared with the median level, the joint association of the PFAS mixture with gallstones showed a negative trend; when the PFAS mixture level was at the 70th percentile or higher, they were negatively associated with gallstones. Meanwhile, when other PFAS were fixed at the 25th, 50th, and 75th percentiles, PFDeA had negative associations with gallstones. Our evidence emphasizes that PFAS is negatively associated with gallstones, and more studies are needed in the future to definite the associations of PFAS with gallstones and explore the underlying biological mechanisms.