To analyze the trends in the incidence and mortality of infectious diarrhea in China from 1990 to 2021 and predict the incidence and mortality rates for 2022 to 2026, providing evidence for surveillance and prevention strategies.

Data on the incidence and mortality of infectious diarrhea in China from 1990 to 2021 was collected from the 2021 Global Burden of Disease (GBD 2021) database. The Joinpoint regression model was used to analyze temporal trends in incidence and mortality. The age-period-cohort model was applied to explore the age, period, and cohort effects on the risk of incidence and mortality. An autoregressive integrated moving average (ARIMA) model was used to predict the incidence and mortality rates of infectious diarrhea in China from 2022 to 2026.

Joinpoint regression analysis revealed that from 1990 to 2021, the total, male, and female incidence rates of infectious diarrhea in China showed a declining trend (all P < 0.05), with a slower decrease before 2000 and a more rapid decline thereafter. Total, male, and female mortality rates also declined (all P < 0.05), with a faster decrease prior to 2014 and a slower decline after 2014. Age-period-cohort model analysis revealed that the age effects on incidence and mortality risks for the total, male, and female first decreased and then increased. The RRs of incidence decreased from high values of 2.432, 3.364, and 2.002 in the 0- < 5 age group to the lowest values of 0.478, 0.535, and 0.451 in the 25- < 30 age group. After that, it rose to peak values of 3.204, 2.523, and 3.774 in the ≥85 age group, with all P < 0.001. The RRs of mortality decreased from high values of 3.403, 3.084, and 4.312 in the 0- < 5 age group to the lowest values of 0.061, 0.063, and 0.071 in the 15- < 20 age group, and subsequently increased to peak values of 131.617, 134.317, and 133.610 in the ≥85 age group, with all P < 0.001. The period effects showed a decline trend, with both incidence and mortality RRs reaching their lowest values in 2017-2021, at 0.521, 0.582, 0.498 and 0.273, 0.283, 0.265, respectively, with all P < 0.001. The cohort effects initially increased and then decreased. The RRs of incidence rose from the lowest values of 0.254, 0.312, and 0.214 in 1907-1911 (all P < 0.001) to peak values of 1.868, 1.493, and 2.142 in 1997-2001 (P < 0.001 for the total population and females, P=0.009 for males), and then decreased to low values of 1.212, 0.873, and 1.352 in 2017-2021 (all P>0.05). The RRs of mortality increased from low values of 0.144, 0.165, and 0.143 in 1907-1911 (all P < 0.001) to peak values of 3.613, 3.265, and 3.584 in 2002-2006 (all P < 0.001), and then decreased to low values of 2.315, 2.054, and 2.313 in 2017-2021 (P=0.011 for the total population, P=0.027 for males, P=0.016 for females). Predictions from the ARIMA model suggested that from 2022 to 2026, the incidence of infectious diarrhea in China is projected to show an upward trend, while mortality is expected to continue declining.

The incidence and mortality of infectious diarrhea in China are influenced by age, period, and cohort effects. While the incidence shows an upward trend, mortality continues to decline. Children and the elderly remain high-risk populations for both onset and death. Targeted prevention strategies should be implemented to reduce the disease burden of infectious diarrhea.

To analyze the epidemiological trends of influenza in Macao Special Administrative Region of China from 2009 to 2024, and to provide scientific evidence for influenza prevention and policy-making.

Data were obtained from the mandatory reporting disease statistics on the Macao Health Bureau's website. The annual reported crude incidence rates and incidence rates by gender and age groups were calculated. Additionally, the Joinpoint regression model was used to analyze the trends of the crude incidence rate of influenza in Macao SAR, the annual percentage change (APC), the average annual percentage change (AAPC).

The median of reported influenza cases in Macao Special Administrative Region, China was 3 363 from 2009 to 2024; and the median crude incidence rate was 502.83 per 100 000; the male's (51.09%) median incidence rate was 513.62 per 100 000, and the female's (48.91%) was 492.82 per 100 000. Furthermore, the median incidence rate for the 0- < 15 age group was the highest at 2 553.50 per 100 000, which was statistically significantly different from that of other age groups (all P < 0.001). AAPC in the crude annual incidence rate of influenza for the entire population was 3.77% (95% CI: -14.31%-26.32%); the trend of APC was 12.57% (95% CI: 1.49%-24.85%, P=0.027) from 2009 to 2018, -58.41% (95% CI: -86.63%-29.36%) from 2018 to 2021, and 314.08% (95% CI: 134.79%-630.27%, P < 0.001) from 2021 to 2024.

The reported crude incidence rate of influenza in Macao has shown an increasing trend, with the most significant rise observed during 2021-2024, particularly among children and adtoolescents. Therefore, it is recommended to strengthen health education and promotion on influenza prevention and to enhance influenza vaccination coverage for children and adolescents.

To evaluate the variation of human mobility patterns in Kunming during holiday and non-holiday periods, and to assess their impact on the transmission of influenza.

We collected influenza surveillance data in Kunming from July 31, 2023, to August 4, 2024, and mobile phone signaling data from September 1, 2023, to February 29, 2024. We analyzed variations in the influenza composite index, and compared mobility indicators between holiday and non-holiday periods. We also developed an infectious disease transmission model that integrated human mobility using mobile phone signaling data. The differences in the total number of cumulative influenza cases under holiday and non-holiday scenarios to quantify the impact of holidays on influenza transmission.

During the holiday period, the influenza composite index showed a downward trend, followed by an upward trend after the public holidays. The average daily number of travelers and average travel frequency per person during the National Day and Mid-Autumn Festival (48.36/10 000, 0.62 per person), Spring Festival (33.24/10 000, 0.48 per person), and winter vacation(51.87/10 000, 0.69 per person) were lower than those on regular days (all P < 0.05), whereas there was no significant difference on New Year's Day (Z=-0.71, P=0.531). Model simulations indicated that different holidays had varying impacts on the transmission of influenza: the Spring Festival reduces the cumulative number of flu cases by 1 556, while the National Day, Mid-Autumn Festival, New Year's Day, and winter vacation increase the cases by 867, 11, and 1 548, respectively. Additionally, substantial spatial heterogeneity was observed across different administrative districts.

Holidays altered the human mobility patterns and affect the transmission of influenza in Kunming. The impact varied across different types of holidays. In the future, attention should be paid to the interaction between holidays, human mobility, and influenza transmission.

Using bayesian network analysis to explore the relationship between parental technoference, parental psychological distance, loneliness, and adolescents' short video addiction risk.

In November December 2024, a multi-stage stratified cluster sampling method was used to test 2 548 adolescents using parental technoference scale, parental psychological distance scale, loneliness scale and short video addiction scale, and bayesian networks were performed using JASP 0.95.1 software.

The key node of parental technoference was "When I eat with parents, they take out their phones or other mobile devices to check messages" (expected influence=0.831, strength=0.818, closeness=0.186, betweenness=0.280). The key node of parental psychological distance was "psychological distance from mother" (expected influence=0.224, strength=0.161, closeness=0.592, betweenness=0.658). The key node of loneliness was "I feel lack of others' company" (expected influence=0.891, strength=0.824, closeness=0.815, betweenness=1.215). The key node of short video addiction risk was "withdrawal" (expected influence=0.950, strength=1.299, closeness=1.304, betweenness=2.510).

Intervention can be carried out for key nodes to reduce the impact of parental technoference, parental psychological distance, loneliness and short video addiction risk on adolescents.

To explore the current perceptions, challenges, and recommendations of clinical physicians, hospital administrators, and centers for disease control and prevention (CDC) staff regarding the surveillance and reporting of pneumonia of unknown etiology (PUE).

In July 2024 and January 2025, one-on-one in-depth interviews were conducted with clinicians, administrators, and CDC staff from various medical institutions in Chongqing Municipality and Jingzhou City, Hubei Province. The interview outline was designed based on the Consolidated Framework for Implementation Research, with interview content was coded, categorized and analyzed using Nvivo 15.0.

Forty participants were included: 17 clinicians, 13 hospital administrators, and 10 CDC staff. Across PUE surveillance and reporting practices, facilitators and barriers were identified at the levels of characteristics of individuals, intervention characteristics, outer setting, inner setting and process. The top three facilitators were familiarity with monitoring protocols and systems, sufficient resource support, and a well-established implementation environment. The top three barriers were the need to improve the specificity of case definitions, complex reporting procedures, and a lack of information technology and incentive mechanisms.

This study indicate that PUE surveillance and reporting are influenced by factors including characteristics of individuals, intervention characteristics, outer setting, inner setting and implementation processes. These findings suggest that optimization of PUE surveillance and reporting requires establishing effective incentive mechanisms, strengthening informatization, and expanding surveillance pathways, with the aim of enhancing the efficiency and quality of surveillance and reporting.

To explore the relative contribution of multidimensional influential factors on depressive symptoms among college students and to compare the differences in key influencing factors between different years, to provide evidence for the preventive intervention of depression among current college students.

Clinical medicine students from a medical university were selected as research subjects in 2011 and 2024, respectively. The center for epidemiological studies depression scale (CES-D), life events scale, and social support rating scale were used to assess depressive symptoms, recent negative life events, and social support levels. Network analysis was employed to construct networks of life events, social support, and depressive symptoms among university students. The association strengths between different dimensions of life events, social support, and depressive symptoms were analyzed to identify the most central "symptoms" and activating factors within the networks. Network comparison tests (NCT) were used to explore differences between the networks of the two years.

The detection rates of depressive symptoms among college students in the two survey years were 28.3% and 37.1%, respectively. Statistically significant differences (P < 0.05) were observed in scores for objective support (t=101.68), subjective support (t=7.31), total social support (t=50.93), loss factor (t=2.54), punishment factor (t=-3.62), academic factor (t=5.19), and other factors (t=-8.57). Were statistically significant. Varying strengths of association were found between different dimensions of depressive symptoms and recent negative life events and social support among college students. The node with the highest expected influence (EI) in both years was D1 (depressive mood). The node with the highest bridge expected influence (BEI) was D3 (somatic symptoms) and D1 (depressive mood) in the respective years. Network comparison test results showed a statistically significant difference in the overall network strength between the two years (Global Strength₂₀₁₁=3.83, Global Strength₂₀₂₄=4.40, P < 0.001). The EI values of E2 (punishment factor), E5 (health adaptation factor), SS1 (objective support), and SS2 (subjective support) differed significantly between the year groups (P < 0.001). The BEI values of SS2 (subjective support) and D3 (somatic symptoms) also differed between the year groups (P=0.01).

The detection rate of depressive symptoms among college students in 2024 was higher than that in 2011, and the pattern of associations between social support, recent negative life events, and depressive symptoms has changed. The findings highlight the important roles of academic pressure, peer relationships, and social support for contemporary college students.

To examine the impact of frailty status on the risk of multimorbidity among middle-aged and older adults in China.

Utilizing the China Health and Retirement Longitudinal Study (CHARLS) database, data from 2011 were employed as the baseline for enrollment, and participants who completed follow-up assessments from 2013 to 2020 were matched by ID. The study included middle-aged and older adults aged 45 years and above who were free of multimorbidity at baseline. The questionnaire encompassed basic information, frailty assessments, and evaluations of chronic disease. The Cox regression model was applied to analyze the relationship between frailty and incident multimorbidity.

A total of 4 832 participants were included in the study. The mean age of participants at baseline was (61.09±9.52) years, with 50.12% (2 422 individuals) being men. During the follow-up period, 2 502 individuals (51.78%) developed multimorbidity. The results of the Cox regression model, after adjusting for covariates, indicated that compared with robust individuals at baseline, those in the pre-frailty (HR=1.127, 95% CI: 1.035-1.227) and frailty (HR=1.363, 95% CI: 1.126-1.650) categories were at a higher risk of developing multimorbidity.

Frailty is associated with an increased risk of incident multimorbidity among middle-aged and older adults in China. Both pre-frailty and frailty significantly elevate the risk of multimorbidity. Frailty should be considered a crucial pre-interventional stage in the prevention of chronic diseases to mitigate the risk of multimorbidity.

To examine the association between dietary diversity and all-cause, cardiovascular disease, and cancer mortality among Chinese, and to examine the possibility of exposure-response relationships.

We established a historical cohort by linking health examination records of 57 737 adults (2015-2022) with death registry data. The dietary diversity score (DDS, 0-9 scores) was assessed and divided into three groups based on the number of points (0-3 scores), moderate (4-6 scores) and sufficient (7-9 scores). We used restricted cubic splines (RCS) combined with Cox proportional hazards regression model to analyze the association between DDS and mortality, and Kaplan-Meier curves was applied to describe the survival status across DDS groups.

The association between DDS and cancer mortality was not statistically significant (P>0.05). In contrast, nonlinear associations were observed for all-cause and cardiovascular disease mortality (P < 0.01). When DDS was lower than 3, risks of all-cause and cardiovascular disease mortality increased progressively with decreasing DDS. Compared with a DDS of 9, participants with DDS scores of 4-8 had no significant increase in cardiovascular disease mortality risk. For all-cause mortality, the lowest risk was observed at a DDS of 6, with the protective effect attenuating at scores >6 (β=0.01, P < 0.001). Compared with the adequate group, individuals with moderate and inadequate dietary diversity had 26% (HR=1.26, 95% CI: 1.06-1.50) and 45% (HR=1.45, 95% CI: 1.03-2.03) higher risks of cardiovascular disease mortality, respectively.

Inadequate dietary diversity was significantly and nonlinearly associated with increased risks of all-cause and cardiovascular disease mortality. The lowest risk of death was observed at a moderate DDS level, with no additional benefit at higher scores. Individuals with a DDS < 3 were at substantially higher risk of both all-cause and cardiovascular disease mortality.