The epidemiological features of Cryptosporidium infection among school-age children in China still remain unclear. Hereby, a cross-sectional study of 1637 children aged 3–9 years was designed to investigate the risk factors and spatial clusters of Cryptosporidium infection in a rural region of Eastern China. Stool specimens collected from participants were examined using the auramine-phenol and modified acid-fast staining. Univariable and multivariable analyses were performed to identify the risk factors of Cryptospordium infection. The spatial clusters were analyzed by a discrete Poisson model using SaTScan software. Our results showed that the overall prevalence of Cryptosporidium infection was 11‰ in the research region. At the age of 3–6 years (odds ratios (OR) = 3.072, 95% confidence intervals (CI): 1.001–9.427), not washing hands before eating and after defecation (OR = 3.003, 95% CI: 1.060–8.511) were recognized as risk factors. Furthermore, a high-risk spatial cluster (relative risk = 4.220, p = 0.025) was identified. These findings call for effective sustainable interventions including family and school-based hygienic education to reduce the prevalence of Cryptosporidium infection. Therefore, an early warning system based spatiotemporal models with risk factors is required to further improve the effectiveness and efficiency of cryptosporidiosis control in the future.

Although fine particulate matter (PM_2.5) pollution is widely linked with diabetes-related morbidity and mortality, research on the short-term effects of its components across diabetes subtypes and complications is limited. We conducted an individual-level time-stratified case-crossover study by investigating over 55 thousand diabetes-related deaths in Jiangsu province of eastern China, between 2016 and 2019. Daily concentrations of total PM_2.5 and its five primary components, including black carbon (BC), organic matter (OM), nitrate (NO₃^-), sulfate (SO₄^2-), and ammonium (NH₄⁺) were assigned to each death on case and control days at the residential address. Using conditional logistic regression and weighted quantile sum regression models, we assessed the associations between single and combined exposures to PM_2.5 components and mortality from overall diabetes, its subtypes, and complications. Population fractions of diabetes deaths attributable to PM_2.5 and its constituents were estimated using counterfactual analyses. Each interquartile range (IQR) increase in the 2-day (the current and prior day) moving average of PM_2.5 components was consistently associated with an elevated risk of overall diabetes mortality. The highest odds ratio (OR) per IQR increase was 1.033 (95 % confidence interval: 1.018, 1.049) for NO₃^- (IQR: 10.8 μg/m³), followed by 1.030 (1.015, 1.045) for NH₄⁺ (IQR: 6.4 μg/m³), 1.027 (1.014, 1.040) for OM (IQR: 7.5 μg/m³), 1.025 (1.012, 1.038) for BC (IQR: 1.3 μg/m³), and 1.020 (1.006, 1.034) for SO₄^2- (IQR: 6.3 μg/m³). Parallel associations with various PM_2.5 components were only observed for type 2 diabetes (T2D) instead of type 1 diabetes. Among all diabetic complications, only diabetic nephropathy showed a notable nexus with PM_2.5 mass and its constituents. Natural cubic smoothing analyses exhibited nearly linear, threshold-free concentration-response relationships between cause-specific diabetes-related mortality risk and PM_2.5 components. For an IQR-equivalent increase in joint exposure, the estimated mortality ORs were 1.061 (1.033, 1.091) for overall diabetes, 1.065 (1.034, 1.097) for T2D, and 1.076 (1.011, 1.146) for diabetic nephropathy, respectively. Among PM_2.5 constituents, NO₃^- and OM showed the highest estimated weights in the joint exposure model, accounting for over 70 % of the combined effect on diabetes-related mortality. We estimated that 1.55 % (0.45 %, 2.64 %) to 5.44 % (2.99 %, 7.90 %) of overall diabetes-related fatalities could be attributable to exposure to PM_2.5 constituents. Our findings highlight the heightened mortality risks of T2D and diabetic nephropathy associated with short-term exposure to PM_2.5 constituents. Our findings can aid environmental health policymakers in developing air quality standards for PM_2.5 constituents and mitigating PM_2.5-related loss of life among diabetic patients in China.