Wastewater-based epidemiology has emerged as a transformative surveillance tool for estimating substance consumption and monitoring disease prevalence, particularly during the COVID-19 pandemic. It enables the population-level monitoring of illicit drug use, pathogen prevalence, and environmental pollutant exposure. In this perspective, we summarize the key challenges specific to the Chinese context: (1) Sampling inconsistencies, necessitating standardized 24-hour composite protocols with high-frequency autosamplers (≤ 15 min/event) to improve the representativeness of samples; (2) Biomarker validation, requiring rigorous assessment of excretion profiles and in-sewer stability; (3) Analytical method disparities, demanding inter-laboratory proficiency testing and the development of automated pretreatment instruments; (4) Catchment population dynamics, reducing estimation uncertainties through mobile phone data, flow-based models, or hydrochemical parameters; and (5) Ethical and data management concerns, including privacy risks for small communities, mitigated through data de-identification and tiered reporting platforms. To address these challenges, we propose an integrated framework that features adaptive sampling networks, multi-scale wastewater sample banks, biomarker databases with multidimensional metadata, and intelligent data dashboards. In summary, wastewater-based epidemiology offers unparalleled scalability for equitable health surveillance and can improve the health of the entire population by providing timely and objective information to guide the development of targeted policies.
China/epidemiology* ; Humans ; Wastewater/analysis* ; COVID-19/epidemiology* ; Public Health ; Wastewater-Based Epidemiological Monitoring ; SARS-CoV-2

Objective:To explore the risk factors associated with a three-month prognosis in patients with aortic acute cerebral infarction.Methods:A prospective study was conducted on 191 patients with aorthropathic acute cerebral infarction included in the Department of Neurology from June 2018 to December 2019, and the patients were divided into good prognosis group (153 cases) and poor prognosis group (38 cases) according to the MRS score of the patient's 3-month prognosis, and the general data, past medical history and blood pressure variability evaluation index (BPV) between the two groups were correlated analysis. The t-test was used to compare the measurement data with normal distribution, the χ 2 test was used to compare the counting data, and the Logistic regression analysis was used to analyze the risk factors. Results:The proportion of patients with diabetes history in the poor prognosis group (20.3% (31/153)), admission NIHSS score ((3.03±2.01) points), standard deviation (SD) ((12.06±4.46) mmHg) and coefficient of variation (CV) ((8.61±3.08)%) of systolic blood pressure at 24 h were lower than those in the good prognosis group (47.4% (18/38), (5.61±3.84) points, (14.75±3.46) mmHg, (10.41±2.18)%), the differences were statistically significant (the statistical values were χ 2=11.73, t=4.01, t=3.46, t=3.38; P values were 0.001, <0.001, 0.001, and 0.001, respectively). Because 24 h systolic blood pressure SD and 24 h systolic blood pressure CV had obvious collinearity, they were respectively included in the Logistic regression model. Taking diabetes history, NIHSS score and 24 h systolic blood pressure SD into the variables, the multivariate Logistic regression results of adverse prognostic risk factors in patients with acute cerebral infarction showed that the history of diabetes mellitus ( OR=3.649, 95% CI: 1.545-8.648, P=0.003), NIHSS score ( OR=1.472, 95% CI: 1.247-1.725, P<0.001) and 24 h systolic blood pressure SD ( OR=1.201, 95% CI: 1.085-1.336, P<0.001). Taking diabetes history, NIHSS score and 24 h systolic blood pressure CV into consideration, multivariate Logistic regression results of adverse prognostic risk factors in patients with acute cerebral infarction showed that the history of diabetes mellitus ( OR=4.695, 95% CI: 1.873-11.766, P=0.001), admission NIHSS score ( OR=1.922, 95% CI: 1.513-2.441, P<0.001) and 24 h systolic blood pressure CV ( OR=1.220, 95% CI: 1.045-1.425, P=0.012). All are independent risk factors influencing the prognosis of patients. Conclusion:The effect of 24 h systolic blood pressure SD and 24 h systolic blood pressure CV on patient prognosis was more valuable in clinical prediction, and the prognosis value of controlling blood glucose levels in patients with diabetes was higher in patients with cerebral infarction.