Background Platelet parameters are important indicators of cardiovascular risk, and environmental pollutants such as polychlorinated biphenyls (PCBs) may impair platelet function through oxidative stress. Objective To investigate the differential effects of single and mixed exposure to PCBs on platelet parameters among individuals with normal glucose tolerance (NGT), impaired fasting glucose (IFG), and type 2 diabetes mellitus (T2DM), and to evaluate the potential modifying role of a healthy lifestyle. Methods This study included 2249 participants (including 707 with NGT, 759 with IFG, and 783 with T2DM). Plasma PCB concentrations were measured using triple quadrupole gaschromatography-tandem mass spectrometry. Generalized linear regression was used to assess the associations between individual PCB congeners and platelet parameters. Quantile g-computation (QGC) and Bayesian kernel machine regression (BKMR) models were used to evaluate the overall effects of PCBs mixture exposure on platelet parameters across different glycemic states, as well as its interaction with healthy lifestyle score (HLS). Results Generalized linear regression analyses showed significant differences in the effects of PCBs on platelet parameters across different glycemic states (P<0.05). After adjusting for confounders, PCBs mixture exposure was significantly associated with lower platelet counts (PLT) in individuals with NGT (b=−10.60, 95%CI: −16.48, −4.71) and IFG (b=−12.91, 95%CI: −18.90, −6.92), whereas no significant association was observed in individuals with T2DM (P=0.051). Mean platelet volume (MPV) and platelet-large cell ratio (P-LCR) increased significantly with higher PCBs exposure levels across all three groups (P<0.05). BKMR analysis showed a positive association between PCBs mixture exposure and P-LCR, with the strongest association observed in the NGT group. Furthermore, a significant interaction was observed between HLS and PCBs mixture exposure, and a higher HLS attenuated the effects of PCBs on P-LCR. Conclusion Glycemic glycemic states may modify the effects of PCBs on platelets. Individuals with NGT appear more sensitive to PCBs exposure, whereas the T2DM state may attenuate this effect. Moreover, healthy lifestyles, including not smoking, moderate alcohol consumption, maintaining moderate-to-high physical activity, a healthy diet, and an appropriate body mass index (BMI), may mitigate the adverse effects of most PCBs on platelet parameters.

Background Platelet parameters are important indicators of cardiovascular risk, and environmental pollutants such as polychlorinated biphenyls (PCBs) may impair platelet function through oxidative stress. Objective To investigate the differential effects of single and mixed exposure to PCBs on platelet parameters among individuals with normal glucose tolerance (NGT), impaired fasting glucose (IFG), and type 2 diabetes mellitus (T2DM), and to evaluate the potential modifying role of a healthy lifestyle. Methods This study included 2249 participants (including 707 with NGT, 759 with IFG, and 783 with T2DM). Plasma PCB concentrations were measured using triple quadrupole gaschromatography-tandem mass spectrometry. Generalized linear regression was used to assess the associations between individual PCB congeners and platelet parameters. Quantile g-computation (QGC) and Bayesian kernel machine regression (BKMR) models were used to evaluate the overall effects of PCBs mixture exposure on platelet parameters across different glycemic states, as well as its interaction with healthy lifestyle score (HLS). Results Generalized linear regression analyses showed significant differences in the effects of PCBs on platelet parameters across different glycemic states (P<0.05). After adjusting for confounders, PCBs mixture exposure was significantly associated with lower platelet counts (PLT) in individuals with NGT (b=−10.60, 95%CI: −16.48, −4.71) and IFG (b=−12.91, 95%CI: −18.90, −6.92), whereas no significant association was observed in individuals with T2DM (P=0.051). Mean platelet volume (MPV) and platelet-large cell ratio (P-LCR) increased significantly with higher PCBs exposure levels across all three groups (P<0.05). BKMR analysis showed a positive association between PCBs mixture exposure and P-LCR, with the strongest association observed in the NGT group. Furthermore, a significant interaction was observed between HLS and PCBs mixture exposure, and a higher HLS attenuated the effects of PCBs on P-LCR. Conclusion Glycemic glycemic states may modify the effects of PCBs on platelets. Individuals with NGT appear more sensitive to PCBs exposure, whereas the T2DM state may attenuate this effect. Moreover, healthy lifestyles, including not smoking, moderate alcohol consumption, maintaining moderate-to-high physical activity, a healthy diet, and an appropriate body mass index (BMI), may mitigate the adverse effects of most PCBs on platelet parameters.

Objective The occurrence of chickenpox in rapidly developing areas poses substantial seasonal risk to children.However,certain factors influencing local chickenpox outbreaks have not been studied.Here,we examined the relationship between spatial clustering,heterogeneity of chickenpox outbreaks,and socioeconomic factors in Southern China. Methods We assessed chickenpox outbreak data from Southern China between 2006 and 2021,comprising both relatively fast-growing parts and slower sub-regions,and provides a representative sample of many developing regions.We analyzed the spatial clustering attributes associated with chickenpox outbreaks using Moran's I and local indicators of spatial association and quantified their socioeconomic determinants using Geodetector q statistics. Results There were significant spatial heterogeneity in the risk of chickenpox outbreaks,with strong correlations between chickenpox risk and various factors,particularly demographics and living environment.Furthermore,interactive effects among specific are factors,such as population density and per capita residential building area,percentage of households with toilets,percentage of rental housing,exhibited q statistics of 0.28,0.25,and 0.24,respectively. Conclusion This study provides valuable insights into the spatial dynamics of chickenpox outbreaks in rapidly developing regions,revealing the socioeconomic factors affecting disease transmission.These implications extend the formulation of effective public health strategies and interventions to prevent and control chickenpox outbreaks in similar global contexts.

To explore the epidemiological characteristics of public health emergency events (PHEE) of varicella in the Beijing-Tianjin-Hebei region and analyze its related influencing factors. Excel was used to organize the varicella data in the Beijing-Tianjin-Hebei region from 2006 to 2021, reported by the management information system of PHEE, to describe the epidemiological characteristics of varicella events. Spatial autocorrelation and spatial scanning methods were used to test and determine its spatial clusters. Geographic detectors were used to analyze the impact of socio-economic factors. From 2006 to 2021, there were 644 reported varicella events in the Beijing-Tianjin-Hebei region, with a total of 18 052 cases and an incidence rate of 2.78%. The number, duration and response time M ( Q1, Q3) of each reported event were 22 (15, 35) cases, 19 (7, 34) days and 7 (4, 17) days, respectively. Hebei Province had a shorter response time and duration of events compared to Beijing and Tianjin. The most reported varicella events were in 2006 and 2007, with 112 and 106 events, respectively. By 2014, the number of events had decreased yearly, and there was a small peak from 2017 to 2019 between 2014 and 2021. From 2006 to 2021, the PHEE of varicella showed a seasonal bimodal distribution from March to June and from October to January of the following year, with peaks in May and December. There was a total of 500 reported varicella events in primary schools, including 218 events in rural primary schools (34%), 142 events in county and town primary schools (22%) and 140 events in urban primary schools (22%). The distribution of varicella events showed a positive spatial autocorrelation and strong spatial clustering, with Moran′s I of 0.31. The Class 1 clustering area was centered in Kuancheng Manchu Autonomous County, Chengde City, with a radius of 207 km and included 58 districts ( LLR=3 550.23, RR=3.78). The most explanatory factor among socio-economic factors was the proportion of the population aged 0-24 years old ( q=0.22), and the interaction effect between each factor was stronger than the independent effect. Overall, there are differences in the level of handling varicella events across Beijing, Tianjin and Hebei. The main occurrence of varicella events is in primary schools, especially in rural areas. Varicella events exhibit spatial clustering. Population structure-related factors have a strong impact on the risk of the incidence of varicella events.

To explore the epidemiological characteristics of public health emergency events (PHEE) of varicella in the Beijing-Tianjin-Hebei region and analyze its related influencing factors. Excel was used to organize the varicella data in the Beijing-Tianjin-Hebei region from 2006 to 2021, reported by the management information system of PHEE, to describe the epidemiological characteristics of varicella events. Spatial autocorrelation and spatial scanning methods were used to test and determine its spatial clusters. Geographic detectors were used to analyze the impact of socio-economic factors. From 2006 to 2021, there were 644 reported varicella events in the Beijing-Tianjin-Hebei region, with a total of 18 052 cases and an incidence rate of 2.78%. The number, duration and response time M ( Q1, Q3) of each reported event were 22 (15, 35) cases, 19 (7, 34) days and 7 (4, 17) days, respectively. Hebei Province had a shorter response time and duration of events compared to Beijing and Tianjin. The most reported varicella events were in 2006 and 2007, with 112 and 106 events, respectively. By 2014, the number of events had decreased yearly, and there was a small peak from 2017 to 2019 between 2014 and 2021. From 2006 to 2021, the PHEE of varicella showed a seasonal bimodal distribution from March to June and from October to January of the following year, with peaks in May and December. There was a total of 500 reported varicella events in primary schools, including 218 events in rural primary schools (34%), 142 events in county and town primary schools (22%) and 140 events in urban primary schools (22%). The distribution of varicella events showed a positive spatial autocorrelation and strong spatial clustering, with Moran′s I of 0.31. The Class 1 clustering area was centered in Kuancheng Manchu Autonomous County, Chengde City, with a radius of 207 km and included 58 districts ( LLR=3 550.23, RR=3.78). The most explanatory factor among socio-economic factors was the proportion of the population aged 0-24 years old ( q=0.22), and the interaction effect between each factor was stronger than the independent effect. Overall, there are differences in the level of handling varicella events across Beijing, Tianjin and Hebei. The main occurrence of varicella events is in primary schools, especially in rural areas. Varicella events exhibit spatial clustering. Population structure-related factors have a strong impact on the risk of the incidence of varicella events.