Binbin Su

Background: Beyond the direct effect of COVID-19 infection on young people, the wider impact of the pandemic on other infectious diseases remains unknown. Objective: This study aims to assess changes in the incidence and mortality of 42 notifiable infectious diseases during the pandemic among children and adolescents in China, compared with prepandemic levels. Methods: The Notifiable Infectious Disease Surveillance System of China was used to detect new cases and fatalities among individuals aged 5-22 years across 42 notifiable infectious diseases spanning from 2018 to 2021. These infectious diseases were categorized into 5 groups: respiratory, gastrointestinal and enterovirus, sexually transmitted and blood-borne, zoonotic, and vector-borne diseases. Each year (2018-2021) was segmented into 4 phases: phase 1 (January 1-22), phase 2 (January 23-April 7), phase 3 (April 8-August 31), and phase 4 (September 1-December 31) according to the varying intensities of pandemic restrictive measures in 2020. Generalized linear models were applied to assess the change in the incidence and mortality within each disease category, using 2018 and 2019 as the reference. Results: A total of 4,898,260 incident cases and 3701 deaths were included. The overall incidence of notifiable infectious diseases decreased sharply during the first year of the COVID-19 pandemic (2020) compared with prepandemic levels (2018 and 2019), and then rebounded in 2021, particularly in South China. Across the past 4 years, the number of deaths steadily decreased. The incidence of diseases rebounded differentially by the pandemic phase. For instance, although seasonal influenza dominated respiratory diseases in 2019, it showed a substantial decline during the pandemic (percent change in phase 2 2020: 0.21, 95% CI 0.09-0.50), which persisted until 2021 (percent change in phase 4 2021: 1.02, 95% CI 0.74-1.41). The incidence of gastrointestinal and enterovirus diseases decreased by 33.6% during 2020 but rebounded by 56.9% in 2021, mainly driven by hand, foot, and mouth disease (percent change in phase 3 2021: 1.28, 95% CI 1.17-1.41) and infectious diarrhea (percent change in phase 3 2020: 1.22, 95% CI 1.17-1.28). Sexually transmitted and blood-borne diseases were restrained during the first year of 2021 but rebounded quickly in 2021, mainly driven by syphilis (percent change in phase 3 2020: 1.31, 95% CI 1.23-1.40) and gonorrhea (percent change in phase 3 2020: 1.10, 95% CI 1.05-1.16). Zoonotic diseases were not dampened by the pandemic but continued to increase across the study period, mainly due to brucellosis (percent change in phase 2 2020: 0.94, 95% CI 0.75-1.16). Vector-borne diseases showed a continuous decline during 2020, dominated by hemorrhagic fever (percent change in phase 2 2020: 0.68, 95% CI 0.53-0.87), but rebounded in 2021. Conclusions: The COVID-19 pandemic was associated with a marked decline in notifiable infectious diseases in Chinese children and adolescents. These effects were not sustained, with evidence of a rebound to prepandemic levels by late 2021. To effectively address the postpandemic resurgence of infectious diseases in children and adolescents, it will be essential to maintain disease surveillance and strengthen the implementation of various initiatives. These include extending immunization programs, prioritizing the management of sexually transmitted infections, continuing feasible nonpharmaceutical intervention projects, and effectively managing imported infections.

Early-life tobacco exposure serves as a non-negligible risk factor for aging-related diseases. To understand the underlying mechanisms, we explored the associations of early-life tobacco exposure with accelerated biological aging and further assessed the joint effects of tobacco exposure and genetic susceptibility. Compared with those without in utero exposure, participants with in utero tobacco exposure had an increase in Klemera-Doubal biological age (KDM-BA) and PhenoAge acceleration of 0.26 and 0.49 years, respectively, but a decrease in telomere length of 5.34% among 276,259 participants. We also found significant dose-response associations between the age of smoking initiation and accelerated biological aging. Furthermore, the joint effects revealed that high–polygenic risk score participants with in utero exposure and smoking initiation in childhood had the highest accelerated biological aging. There were interactions between early-life tobacco exposure and age, sex, deprivation, and diet on KDM-BA and PhenoAge acceleration. These findings highlight the importance of reducing early-life tobacco exposure to improve healthy aging.

Background: An accelerated epidemiological transition, spurred by economic development and urbanization, has led to a rapid transformation of the disease spectrum. However, this transition has resulted in a divergent change in the burden of infectious diseases between urban and rural areas. The objective of our study was to evaluate the long-term urban–rural disparities in infectious diseases among children, adolescents, and youths in China, while also examining the specific diseases driving these disparities. Methods and findings: This observational study examined data on 43 notifiable infectious diseases from 8,442,956 cases from individuals aged 4 to 24 years, with 4,487,043 cases in urban areas and 3,955,913 in rural areas. The data from 2013 to 2021 were obtained from China’s Notifiable Infectious Disease Surveillance System. The 43 infectious diseases were categorized into 7 categories: vaccine-preventable, bacterial, gastrointestinal and enterovirus, sexually transmitted and bloodborne, vectorborne, zoonotic, and quarantinable diseases. The calculation of infectious disease incidence was stratified by urban and rural areas. We used the index of incidence rate ratio (IRR), calculated by dividing the urban incidence rate by the rural incidence rate for each disease category, to assess the urban–rural disparity. During the nine-year study period, most notifiable infectious diseases in both urban and rural areas exhibited either a decreased or stable pattern. However, a significant and progressively widening urban–rural disparity in notifiable infectious diseases was observed. Children, adolescents, and youths in urban areas experienced a higher average yearly incidence compared to their rural counterparts, with rates of 439 per 100,000 compared to 211 per 100,000, respectively (IRR: 2.078, 95% CI [2.075, 2.081]; p < 0.001). From 2013 to 2021, this disparity was primarily driven by higher incidences of pertussis (IRR: 1.782, 95% CI [1.705, 1.862]; p < 0.001) and seasonal influenza (IRR: 3.213, 95% CI [3.205, 3.220]; p < 0.001) among vaccine-preventable diseases, tuberculosis (IRR: 1.011, 95% CI [1.006, 1.015]; p < 0.001), and scarlet fever (IRR: 2.942, 95% CI [2.918, 2.966]; p < 0.001) among bacterial diseases, infectious diarrhea (IRR: 1.932, 95% CI [1.924, 1.939]; p < 0.001), and hand, foot, and mouth disease (IRR: 2.501, 95% CI [2.491, 2.510]; p < 0.001) among gastrointestinal and enterovirus diseases, dengue (IRR: 11.952, 95% CI [11.313, 12.628]; p < 0.001) among vectorborne diseases, and 4 sexually transmitted and bloodborne diseases (syphilis: IRR 1.743, 95% CI [1.731, 1.755], p < 0.001; gonorrhea: IRR 2.658, 95% CI [2.635, 2.682], p < 0.001; HIV/AIDS: IRR 2.269, 95% CI [2.239, 2.299], p < 0.001; hepatitis C: IRR 1.540, 95% CI [1.506, 1.575], p < 0.001), but was partially offset by lower incidences of most zoonotic and quarantinable diseases in urban areas (for example, brucellosis among zoonotic: IRR 0.516, 95% CI [0.498, 0.534], p < 0.001; hemorrhagic fever among quarantinable: IRR 0.930, 95% CI [0.881, 0.981], p = 0.008). Additionally, the overall urban–rural disparity was particularly pronounced in the middle (IRR: 1.704, 95% CI [1.699, 1.708]; p < 0.001) and northeastern regions (IRR: 1.713, 95% CI [1.700, 1.726]; p < 0.001) of China. A primary limitation of our study is that the incidence was calculated based on annual average population data without accounting for population mobility. Conclusions: A significant urban–rural disparity in notifiable infectious diseases among children, adolescents, and youths was evident from our study. The burden in urban areas exceeded that in rural areas by more than 2-fold, and this gap appears to be widening, particularly influenced by tuberculosis, scarlet fever, infectious diarrhea, and typhus. These findings underscore the urgent need for interventions to mitigate infectious diseases and address the growing urban–rural disparity.

Despite evidence indicating increased risk of psychiatric issues among COVID-19 survivors, questions persist about long-term mental health outcomes and the protective effect of vaccination. Using UK Biobank data, three cohorts were constructed: SARS-CoV-2 infection (n = 26,101), contemporary control with no evidence of infection (n = 380,337) and historical control predating the pandemic (n = 390,621). Compared with contemporary controls, infected participants had higher subsequent risks of incident mental health at 1 year (hazard ratio (HR): 1.54, 95% CI 1.42–1.67; P = 1.70 × 10⁻²⁴; difference in incidence rate: 27.36, 95% CI 21.16–34.10 per 1,000 person-years), including psychotic, mood, anxiety, alcohol use and sleep disorders, and prescriptions for antipsychotics, antidepressants, benzodiazepines, mood stabilizers and opioids. Risks were higher for hospitalized individuals (2.17, 1.70–2.78; P = 5.80 × 10⁻¹⁰) than those not hospitalized (1.41, 1.30–1.53; P = 1.46 × 10⁻¹⁶), and were reduced in fully vaccinated people (0.97, 0.80–1.19; P = 0.799) compared with non-vaccinated or partially vaccinated individuals (1.64, 1.49–1.79; P = 4.95 × 10⁻²⁶). Breakthrough infections showed similar risk of psychiatric diagnosis (0.91, 0.78–1.07; P = 0.278) but increased prescription risk (1.42, 1.00–2.02; P = 0.053) compared with uninfected controls. Early identification and treatment of psychiatric disorders in COVID-19 survivors, especially those severely affected or unvaccinated, should be a priority in the management of long COVID. With the accumulation of breakthrough infections in the post-pandemic era, the findings highlight the need for continued optimization of strategies to foster resilience and prevent escalation of subclinical mental health symptoms to severe disorders.

What is already known about this topic? Over the past several decades, China has experienced substantial improvements in life expectancy (LE), signifying major advancements in public health outcomes. What is added by this report? This study offers an in-depth analysis of the contributions made by various diseases and age demographics to the growth of LE in China over the past 35 years, highlighting crucial factors that influence population health. What are the implications for public health practice? The results highlight the need for interventions tailored to various disease types and age groups in order to enhance LE and improve public health outcomes. Public health strategies should prioritize disease prevention, control initiatives, and enhancements in healthcare services that are specifically designed to meet the needs of distinct population demographics.

Background: Accurate prognostication of oncological outcomes is crucial for the optimal management of patients with renal cell carcinoma (RCC) after surgery. Previous prediction models were developed mainly based on retrospective data in the Western populations, and their predicting accuracy remains limited in contemporary, prospective validation. We aimed to develop contemporary RCC prognostic models for recurrence and overall survival (OS) using prospective population-based patient cohorts and compare their performance with existing, mostly utilized ones. Methods: In this prospective analysis and external validation study, the development set included 11 128 consecutive patients with non-metastatic RCC treated at a tertiary urology center in China between 2006 and 2022, and the validation set included 853 patients treated at 13 medical centers in the USA between 1996 and 2013. The primary outcome was progression-free survival (PFS), and the secondary outcome was OS. Multivariable Cox regression was used for variable selection and model development. Model performance was assessed by discrimination [Harrell’s C-index and time-dependent areas under the curve (AUC)] and calibration (calibration plots). Models were validated internally by bootstrapping and externally by examining their performance in the validation set. The predictive accuracy of the models was compared with validated models commonly used in clinical trial designs and with recently developed models without extensive validation. Results: Of the 11 128 patients included in the development set, 633 PFS and 588 OS events occurred over a median follow-up of 4.3 years [interquartile range (IQR) 1.7–7.8]. Six common clinicopathologic variables (tumor necrosis, size, grade, thrombus, nodal involvement, and perinephric or renal sinus fat invasion) were included in each model. The models demonstrated similar C-indices in the development set (0.790 [95% CI 0.773–0.806] for PFS and 0.793 [95% CI 0.773–0.811] for OS) and in the external validation set (0.773 [0.731–0.816] and 0.723 [0.731–0.816]). A relatively stable predictive ability of the models was observed in the development set (PFS: time-dependent AUC 0.832 at 1 year to 0.760 at 9 years; OS: 0.828 at 1 year to 0.794 at 9 years). The models were well calibrated and their predictions correlated with the observed outcome at 3, 5, and 7 years in both development and validation sets. In comparison to existing prognostic models, the present models showed superior performance, as indicated by C-indices ranging from 0.722 to 0.755 (all PP<0.0001) for OS. The predictive accuracy of the current models was robust in patients with clear-cell and non-clear-cell RCC. Conclusions: Based on a prospective population-based patient cohort, the newly developed prognostic models were externally validated and outperformed the currently available models for predicting recurrence and survival in patients with non-metastatic RCC after surgery. The current models have the potential to aid in clinical trial design and facilitate clinical decision-making for both clear-cell and non-clear-cell RCC patients at varying risk of recurrence and survival.

Introduction Disparities in life expectancy between rural and urban populations are well established but how it varies with epidemics and pandemics remains poorly understood. We aimed to quantify the rural–urban differences in the mortality burden of COVID-19 and to contribute to understanding the disparity trends in life expectancy between 1987 and 2021 in China. Methods We used monthly death counts from death registration systems. Rural–urban gap estimation and decomposition were carried out using period life tables to calculate life expectancy, the Arriaga decomposition technique to break down into age-specific and cause-specific mortality, and the Lee-Carter forecasts to estimate the expected gap. Results The rural–urban gap increased to 22.7 months (95% credible interval (CI) 19.6 to 25.8) in 2020 and further to 23.7 months (95% CI 19.6 to 26.7) in 2021, and was larger than expected under the continuation of the prepandemic trends. Compared with that in the recent 2003 SARS-CoV-2 epidemic and the 2009 influenza epidemic, excess rural–urban gaps in the COVID-19 pandemic changed from urban disadvantage to rural disadvantage, and the contributions shifted toward old age groups and circulatory diseases. Variations in the rural–urban gap since 1987 were positively correlated with the rural–urban disparity in public health expenditures, especially among ages Conclusions Our findings identified a widening rural–urban gap in life expectancy since COVID-19, and a shifting trend towards old ages and circulatory diseases, disrupting the diminishing trend of the gap over 35 years. The findings highlight the unequal impact of the pandemic on different communities in terms of mortality burdens.

Background Telomere length (TL) is a biomarker of genomic aging. The evidence on the association between TL and air pollution was inconsistent. Besides, the modification effect of genetic susceptibility on the air pollution-TL association remains unknown. Objective We aimed to evaluate the association of ambient air pollution with TL and further assess the modification effect of genetic susceptibility. Methods 433,535 participants with complete data of TL and air pollutants in UK Biobank were included. Annual average exposure of NO₂, NO_x, PM₁₀ and PM_2.5 was estimated by applying land use regression models. Genetic risk score (GRS) was constructed using reported telomere-related SNPs. Leukocyte TL was measured by quantitative polymerase chain reaction (qPCR). Multivariable linear regression models were employed to conduct associational analyses. Results Categorical exposure models and RCS models both indicated U-shaped (for NO₂ and NO_x) and L-shaped (for PM₁₀ and PM_2.5) correlations between air pollution and TL. In comparison to the lowest quartile, the 2nd and 3rd quartile of NO₂ (q2: −1.3% [−2.1%, −0.4%]; q3: −1.2% [−2.0%, −0.3%], NO_x (q2: −1.3% [−2.1%, −0.5%]; q3: −1.4% [−2.2%, −0.5%]), PM_2.5 (q2: −0.8% [−1.7%, 0.0%]; q3: −1.3% [−2.2%, −0.5%]), and the third quartile of PM₁₀ (q3: −1.1% [−1.9%, −0.2%]) were inversely associated with TL. The highest quartile of NO₂ was positively correlated with TL (q4: 1.0% [0.0%, 2.0%]), whereas the negative correlation between the highest quartile of other pollutants and TL was also attenuated and no longer significant. In the genetic analyses, synergistic interactions were observed between the 4th quartile of three air pollutants (NO₂, NO_x, and PM_2.5) and genetic risk. Impact statement Our study for the first time revealed a non-linear trend for the association between air pollution and telomere length. The genetic analyses suggested synergistic interactions between air pollution and genetic risk on the air pollution-TL association. These findings may shed new light on air pollution’s health effects, offer suggestions for identifying at-risk individuals, and provide hints regarding further investigation into gene-environment interactions.