中华流行病学杂志最新文献

Real-world studies have been widely used in clinical and public health research to evaluate effectiveness, assess risks, and inform health policy decision-making. However, real-world data typically lack randomization and controlled interventions, are often heterogeneous, and exhibit substantial population heterogeneity, posing major challenges for uncertainty assessment and multi-source evidence integration in statistical inference. Bayesian methods, by combining prior information with observed data, provide a flexible and coherent framework for statistical inference in complex data environments. The paper systematically reviews the major applications of Bayesian methods in real-world studies, with a particular focus on the theoretical foundations, modeling strategies, and applicability of state-of-the-art information borrowing approaches. In addition, we summarize recent advances in Bayesian causal inference, hierarchical modeling, and related methods in real-world research. Finally, this paper discusses the methodological issues that need to be addressed when applying Bayesian methods to real-world research, providing a reference for related research.

HIV/AIDS, viral hepatitis, and other infectious diseases are concurrently prevalent among some key populations and in certain service settings. Their routes of transmission and risk behaviors also overlap to some extent, exposing affected populations to multiple infection risks. Disease-specific prevention and control strategies have played an important role in controlling major infectious diseases. However, in the context of coexisting infections and highly overlapping target populations, limitations remain in service integration and coordinated implementation. After years of development, China has established a relatively comprehensive HIV/AIDS prevention and control system. Stable service networks for key populations have been formed in settings such as voluntary counseling and testing clinics, sexually transmitted infection clinics, methadone maintenance treatment clinics, HIV antiretroviral therapy clinics, and community-based organizations. These networks provide a foundation for the coordinated prevention and control of related infectious diseases. Against this background, it has become an important direction for optimizing the prevention and control system to explore integrated approaches to the prevention, testing, and management of HIV/AIDS, viral hepatitis, and other infectious diseases, based on shared transmission routes, key populations, and major service settings. This article reviews the theoretical basis and practical progress of integrated multi-disease prevention and control for HIV/AIDS, viral hepatitis, and other infectious diseases; summarizes relevant strategies and experiences in China and internationally; analyzes major challenges in institutional design, implementation pathways, and evidence generation; and discusses future directions for research and practice, with the aim of providing reference for improving China's integrated prevention and control system for related infectious diseases.

Objective: To investigate the drug resistance profiles and molecular epidemiological characteristics of Aeromonas species isolated from human clinical samples. Methods: A total of 72 Aeromonas isolates were collected and identified to the species level using biochemical tests, mass spectrometry, and average nucleotide identity based on whole-genome sequencing. Antimicrobial susceptibility testing was performed by the broth microdilution method. The phylogenetic relationships among isolates were reconstructed based on core single-nucleotide polymorphisms. Whole-genome sequencing data were used to determine multilocus sequence types and identify the virulence genes. The correlation between identified resistance genes and the corresponding resistant phenotypes was assessed. Results: Conventional biochemical and mass spectrometry methods yielded inconsistent species identification results for some Aeromonas strains compared with whole-genome sequencing. The predominant strains of human Aeromonas infections were Aeromonas veronii (48/72, 66.66%). The prevalent sequence types were ST425 (5/72, 6.94%). Antibiotic susceptibility testing revealed that human-derived Aeromonas species were sensitive to most antimicrobial agents, with slightly higher resistance rates to tetracycline (18.06%) and compound sulfamethoxazole (13.89%). A high concordance rate was observed between the antimicrobial resistance genotypes and phenotypes of Aeromonas. The number of toxin gene-positive strains is large, and the number of toxin gene-positive strains of different species of Aeromonas is different. Conclusions: Aeromonas veronii was the predominant species among human-derived Aeromonas isolates, and the sequence types showed high diversity. The overall antimicrobial resistance rate was low, with most resistance phenotypes mediated by resistance genes, and the number of positive strains of Aeromonas toxin genes is different among different species.

The prior distribution refers to a probabilistic description of the uncertainty of unknown parameters by researchers before observing sample data. It is a prerequisite and key step in Bayesian statistical methods, as well as a major challenge encountered in practical applications. Based on four dimensions-information content, probabilistic characteristics, relationship with the likelihood function, and structural hierarchy-this paper comprehensively sorts out the types of prior distributions, illustrates the impact of prior distributions on results through a combination of theory and case studies, and discusses in depth the key issues that need to be prioritized when selecting prior distributions. In addition, this paper elaborates on the importance and implementation methods of sensitivity analysis for prior distributions and introduces key points for reporting Bayesian statistical inference results. Only by rationally selecting prior distributions and standardizing Bayesian statistical inference can the scientific value of Bayesian statistics be fully realized.

Objective: To analyze the epidemic trends and spatiotemporal clustering characteristics of influenza in Shijiazhuang during the 2023-2026 surveillance years, and to provide evidence-based support for the precision prevention and control of influenza. Methods: Data on influenza cases from March 1, 2023 to March 31, 2026 were obtained from the Infectious Disease Reporting Information Management System of Chinese Information System for Disease Control and Prevention. Descriptive epidemiological methods were used to analyze the epidemic characteristics of influenza. Local indicators of spatial association and the SaTScan Poisson-based spatial scan statistic were used for spatial autocorrelation analysis. Results: During the 2023-2026 surveillance years, the annualized incidence rate of influenza in Shijiazhuang was 644.7 per 100 000 person- years, with rates of 370.0 per 100 000 person-years, 616.1 per 100 000 person-years, and 971.9 per 100 000 person-years in 2023, 2024, and 2025 surveillance years, respectively. Students accounted for the largest proportion of cases (30.9%). Three epidemic peaks were identified, occurring in the winters of 2023, 2024, and 2025, and the number of reported cases across these three winter peaks increased year by year. The 2025 influenza season began approximately 2-3 weeks earlier than in the previous two years. Spatial autocorrelation analysis showed a global Moran's I of 0.375 for the surveillance period, with clusters concentrated in the five main urban districts. The global Moran's I values of the second and third epidemic peaks were similar (0.360 and 0.359). Clustering began to stabilize from the second epidemic peak, showing a transmission trend of outward diffusion from the five main urban districts. Conclusions: After the gradual withdrawal of non-pharmaceutical interventions during the 2023-2026 surveillance years, influenza in Shijiazhuang showed a continuously expanding epidemic scale, with the three epidemic peaks increasing in size year by year; students were the most affected population. Spatially, influenza transmission showed a pattern of spreading from the five main urban districts to the near suburbs and spilling over to the outer suburbs, for which the transportation hub effect and regular cross-regional commuting may be the main driving factors. It is recommended to strengthen the promotion of influenza vaccination among school-age children before the start of the autumn semester and to enhance school-based influenza surveillance, and to strengthen influenza surveillance in key venues such as high-speed rail stations and airports and among key populations such as regular commuters.

Objective: To evaluate the relationship between serum micronutrient levels, including calcium, phosphorus, iron, and 25-hydroxyvitamin D, and the incidence of osteoporotic fracture. Methods: A retrospective analysis was performed in 5 984 patients hospitalized in the Department of Osteoporosis, West China Fourth Hospital, Sichuan University, from December 2017 to October 2024. The patients with osteoporotic fracture were divided into case group and those without osteoporotic fracture were divided into control group. Missing data were addressed using multiple imputation. Logistic regression model was used to analyze the association between different micronutrient levels and fracture risk, and mediating effect of bone mineral density was further evaluated. Model discrimination was evaluated using receiver operating characteristic curve and area under the curve (AUC), and differences in AUC were compared by DeLong test. Results: The calcium, phosphorus, and iron levels were significantly lower in patients with osteoporotic fracture than in controls, the difference was significant (all P<0.05). After adjustment for age, sex, BMI, and comorbidity, including diabetes, coronary heart disease, and hypertension, the logistic regression analysis showed that calcium level was significantly inversely associated with osteoporotic fractures, and phosphorus, iron, and 25-hydroxyvitamin D were not significantly associated with osteoporotic fractures. After additional mutual adjustment for phosphorus, iron and 25-hydroxyvitamin D, the association between calcium and fracture remained stable (OR=0.88, 95%CI: 0.82-0.94, P<0.001). This relationship was consistent in different subgroups, with no significant interactions. Bone mineral density had no effect to mediate the association between micronutrients and fracture. Using calcium level in the baseline model significantly increased the AUC, enhancing its discriminative ability (P=0.002). Conclusions: In patients with osteoporosis, the serum calcium levels showed an inverse association with fracture risk, and maintaining adequate calcium may help reduce this risk. The etiological roles of other serum micronutrients, including phosphorus, iron, and 25-hydroxyvitamin D, remain to be further analyzed.

Environmental pharmacoepidemiology (EPE) is an emerging interdisciplinary field developed to address the health challenges arising from the complex interactions between environmental factors and drug effects. This paper systematically introduces the definitions, core research domains, methodological framework, and public health significance of EPE. The field's primary objective is to integrate data on environmental exposures and drug utilization to evaluate how the environment influences the efficacy and safety of pharmaceuticals, as well as the potential impacts of drugs on the environment. By leveraging exposomics, phenome-wide data, and real-world big data, while applying advanced statistical methods like propensity scores and multilevel models, EPE facilitates a deeper investigation into the intricate "environment-drug-health" nexus. While facing challenges such as dynamic exposure assessment, poorly understood interaction mechanisms, and statistical modeling limitations, future advancements in EPE will provide crucial scientific support for achieving precision medicine and advancing the synergistic governance of environmental health.

Bayesian statistics dates back to the 18th century. Its development was once hindered by computational complexity. However, as computing methods and technologies have advanced, Bayesian statistics has demonstrated its advantages across many research scenarios, including small-population studies, information borrowing, complex and innovative designs, and dynamic decision-making. Recently, regulatory authorities in China, the United States, and Europe have issued guidelines, acknowledging the role of Bayesian statistics in new drug research and development. This paper introduced the basic concepts, theoretical foundations, and inference frameworks of Bayesian statistics. Bayesian statistics uses Bayes' theorem to combine prior information with sample data to derive the posterior distribution, which serves as the basis for Bayesian inference and decision-making. Unlike the traditional frequentist approach, which focuses on "analyzing only the data from the current study", the Bayesian approach aims to "integrate and use previous information together with current data," thereby forming a statistical decision-making approach that continuously accumulates and makes dynamic decision-making. The paper also discussed the strengths and weaknesses of the frequentist and the Bayesian, as well as their respective strengths and weaknesses. The authors note that the two inference frameworks will be used in a complementary manner in the future, and Bayesian methods play an invaluable role in advancing innovative medical research.

Objective: Analyze the contribution of common respiratory pathogens to influenza-like illness (ILI) in Beijing during the 2024-2025 surveillance years, and identify the dominant local pathogens and their differences across age groups. Methods: Collect ILI consultation data from secondary-level and above medical institutions in Beijing during the 2024-2025 surveillance year, as well as the positive rates for 22 respiratory pathogens from 19 sentinel hospitals. We used the elastic network model to screen for pathogens, and construct generalized additive models (GAM) for the entire population and each age group separately. Based on the correction of confounding factors such as time and holidays, calculate the attributable contribution of each pathogen to the ILI through the attributable fraction (AF). Results: A total of 1 389 954 cases of ILI were monitored, averaging 26 730 cases per week. A total of 14 pathogens had a positive rate of ≥1.00% among the 22 pathogens. The top 5 pathogens with the highest positive rates were haemophilus influenzae (10.78%,2 188/20 296), streptococcus pneumoniae (10.62%,2 155/20 296), and 2019-nCoV (9.96%,2 021/20 296), influenza virus (9.34%, 1 895/20 296) and rhinovirus (5.79%, 1 175/20 296). In the pathogen attribution analysis, the elastic network model screened out a total of 9 pathogens, with a combined AF of 52.88%, and the number of attributed ILI was 735 008 cases. The top three pathogens by AF (and number of attributed cases) were influenza virus at 20.46% (284 385 cases), rhinovirus at 10.47% (145 528 cases), and human metapneumovirus at 8.09% (112 447 cases). Among the age groups of 0-4 years, 15-59 years and over 60 years, AF of influenza virus was the highest, being 11.71% (95%CI: 0.00%-17.77%), 26.38% (95%CI: 13.60%- 36.94%), and 29.89% (95%CI: 3.30%-37.66%) respectively; among the 5-14 years age group, AF of rhinovirus was the highest, being 9.82% (95%CI: 0.00%-18.37%). Conclusions: During the 2024-2025 surveillance years, the number of patients visiting the outpatient and emergency departments with ILI was caused by multiple pathogens. Influenza virus, rhinovirus, and human metapneumovirus were the main causative pathogens, and their contributions varied across age groups. GAM can effectively measure the disease burden of different pathogens and provide reliable data support for the precise monitoring and stratified prevention and control of respiratory infectious diseases.

Public participation in the surveillance and early warning of infectious diseases has demonstrated its unique value in implementing the "Four Early" principle of prevention and control of infectious diseases, as well as in the research and evaluation of epidemic situations and the formulation of prevention and control strategies. It is an important supplementary measure to enhance surveillance and early warning capacity for infectious diseases and to address the shortcomings of the professional surveillance system. China's current public participation in infectious disease surveillance and early warning is still in its infancy, and there are problems such as insufficient systematization and limited intelligence. This paper analyzes and summarizes the experience and successful cases by systematically sorting out the legal basis, practical experience, and application effect of public participation in infectious disease surveillance and early warning at home and abroad, to provide theoretical support for the construction of China's public participation in the working mechanism and management system of infectious disease surveillance and early warning.