Background:Road traffic injury is a significant global health challenge, and timely available data are significant to monitor this trend. We aimed to develop a cost-effective approach in resource-limited settings to estimate the number of road traffic crashes at the national level by utilizing media-reported data. Methods:Media-reported data about road traffic crashes were extracted from the Automated Road Traffic Crash Data Platform (ARTCDP) and augmented based on the available police reports with limited free-access. Besides, crash data were approximated according to the national disease surveillance point (DSP). We then fitted four common machine learning models (linear regression, artificial neural network, support vector machine, classification and regression tree) with six predictors to determine the best predictive model for road traffic crashes in China and correct underestimation of police-reported data. Results:Of the 50,850 media outlets indexed by the ARTCDP, 379 media outlets reporting road traffic crash news quarterly were determined as the most reliable media-reported data sources. Of the four machine learning methods, artificial neural network performed best, yielding an R 2 of 0.93 for training data, 0.92 for validation data, and 0.88 for testing. The number of road traffic crashes estimated by the approach closely matched actual trends in national number of crashes from official statistics. Conclusions:Our approach based on ARTCDP-collected media outlets demonstrated excellent predictive performance and has potential to be used for estimating national road traffic crash statistics in resource-limited locations where official statistics are absent, not freely accessible, not reliable, or not yet released.

PURPOSE:To examine associations of minimum legal drinking age (MLDA) laws with later-life alcohol use and alcohol-attributable mortality. METHODS:An ecological study was performed using the free-access data from the United States. Five outcome measures were considered: (1) drinking rate, (2) alcohol consumption per capita, and alcohol-attributable mortality for (3) all diseases and injuries, (4) non-injury diseases, and (5) injuries. Univariate statistical tests compared differences in 5 outcome measures during 1990-2021 across 3 types of states, classified based on different MLDA beer laws in 1970-1988. Multivariable regression examined MLDA laws' associations with 5 outcome variables, adjusting for covariates. Sensitivity analyses used MLDA classifications for wine and spirits. RESULTS:Based on MLDA beer laws of 1970-1988, the 50 states and the District of Columbia were classified as Type 1 (increasing MLDA), Type 2 (fluctuating MLDA), and Type 3 (steady MLDA of 21). For all years combined, Type 1 and Type 2 states had lower and higher drinking rates (51.05% and 55.20% vs. 53.23%) and alcohol consumption per capita (463.25 and 511.57 vs. 483.92 standard drinks). Compared to Type 2 and Type 3 states, Type 1 states had the highest alcohol-attributable injury mortality for Americans aged 30 years and older (4.30 vs. 3.93 and 3.87 per 100,000). After adjusting for the included covariates, 3 types of states demonstrated differing trends in drinking rate and alcohol-attributable injury mortality but highly similar trends in the other 3 outcome measures. Sensitivity analyses generated similar findings. CONCLUSIONS:MLDA was associated with later-life alcohol use and alcohol-attributable mortality.

Youth drinking is known to be a leading and avoidable risk factor of mortality. However, recent evidence on mortality attributable to youth drinking on a global scale is limited. Using risk-attributable mortality data from the Global Burden of Disease Study 2021, we conducted a longitudinal analysis to examine trends in deaths and mortality attributable to youth drinking among individuals under 20 years across 194 WHO member countries/territories from 1990 to 2021. Mortality differences were assessed by sex, socio-demographic index (SDI) levels, and countries/territories. Average annual percentage changes (AAPCs) with 95% confidence intervals (CIs) estimated by Joinpoint regression were used to quantify significant mortality changes. A total of 417,198 deaths attributable to youth drinking were reported from 1990 to 2021, primarily from transport injuries (36.1%) and interpersonal violence (17.7%). The number of total deaths attributable to youth drinking declined from 13,346 in 1990 to 10,563 in 2021 (AAPC = -0.73%, 95% CI: -0.77% to -0.68%). Mortality among males was 5.8-7.5 times that of females. Large mortality decreases occurred over time in countries/territories with high (AAPC=-2.71%, 95% CI: -2.78% to -2.67%) and high-middle SDI (AAPC=-2.06%, 95% CI: -2.26%% to -1.87%%), while those with lower SDI levels showed relatively stable or slightly increasing mortality trends. The percent change in mortality varied greatly across the 194 WHO countries/territories from 1990 to 2021, ranging from - 96.8% (Seychelles) to 772.8% (Libya). Increases occurred in 30.4% of countries (59/194), all in low or middle SDI levels. Despite a substantial decline in mortality, therefore, youth drinking remains a global health challenge that disproportionally affect males and young adults in countries with low or middle SDI levels. Implementation of actions outlined in the WHO Global Alcohol Action Plan 2022-2030 should be accelerated and enhanced, especially in those countries with high attributable mortality rates as well as those experiencing large increases in mortality during between 1990 and 2021.

INTRODUCTION:Many tools have been developed to assess fall risk and support fall prevention for older adults, but the authors are unaware of any study that compares the performance of predictive algorithms based on major composite assessment tools and commonly used single assessment tools. This study compares the predictive performance of the algorithms for two composite fall risk assessment tools and four single fall risk assessment tools among community-dwelling Chinese older adults. METHODS:A 12-month prospective study was conducted between April 2023 and June 2024 in Changsha, China. Two major composite assessment tools (Stopping Elderly Accidents, Deaths & Injuries [STEADI] and World Falls Guidelines [WFG] algorithms) and four single assessment tools (Stay Independent Brochure Questionnaire [SIB], Falls Efficacy Scale International [FES-I], Home Falls and Accidents Screening Tool [HOME FAST], and Timed Up and Go Test [TUGT]) were included. Primary performance measures included area under the receiver operating characteristic curves (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). RESULTS:Among the 1,428 enrolled older adults, 1,237 participants completed the study. All ten algorithms from the two composite assessment tools showed notably low AUC (0.527-0.575), sensitivity (11.3-36.5%) and PPV (12.7-31.7%), but acceptably high specificity (78.9-96.2%) and NPV (87.2-92.9%) to predict both falls and fall-related injuries throughout the 12-month follow-up period. Among the ten algorithms, WFG algorithm 3 exhibited the best performance in predicting both falls (AUC = 0.573, 95% CI: 0.544, 0.600) and fall-related injuries (AUC = 0.575, 95% CI: 0.546, 0.602). Compared to five algorithms based on four single assessment tools, WFG algorithm 3 did not demonstrate significantly superior performance (p>0.05). CONCLUSIONS:The performances of the optimal algorithm in predicting 12-month falls and fall-related injuries among older adults from the two composite risk assessment tools were not better than those from four common single risk assessment tools.

Objective: Behavioral and environmental risk exposure data are critical for child injury prevention, yet routine surveillance rarely collects such data. Using baseline data from a large cluster RCT conducted in China, we report prevalence rates of behavioral and environmental risks for child injury. Methods: Children aged 0–5 years were recruited between August 2024 and February 2025 from 12 streets/towns in Changsha, China, through local primary health care institutions in both urban and rural areas. Caregivers completed a WeChat-based questionnaire on socio-demographics and 46 behavioral and environmental child injury risk factors. We calculated composite scores for behavioral risk and environmental risk separately and dichotomized at the 66.7th percentile. Adjusted prevalence ratios (aPRs) were estimated using Poisson regression models with robust standard errors. Results: Among 6,701 children aged 0–5 (53.0% boys; 64.1% aged 3–5 years; 59.5% urban), composite behavioral and environmental risk scores were right-skewed, with exposure of specific risk factors ranging from 0.4% to 85.2%. Common risks included infant bed-sharing (85.2% among infants), non-use of child restraint systems (CRS) (34.7%), and climbing or jumping on beds or furniture (29.8%). Compared with infants, children aged 1–2 and 3–5 years had lower behavioral risk (aPR = 0.87, 95%CI: 0.78–0.98 and aPR = 0.90, 95%CI: 0.82–1.00) and lower environmental risk (aPR = 0.75, 95%CI: 0.66–0.86 and aPR = 0.78, 95%CI: 0.70–0.87). Boys (aPR = 1.15, 95%CI: 1.08–1.24) had higher behavioral risk than girls. Rural children had lower behavioral risk (aPR = 0.84, 95%CI: 0.77–0.91) but higher environmental risk (aPR = 1.42, 95%CI: 1.29–1.56). Conclusions: Several risky behaviors and hazardous environmental exposures are common among young Chinese children. Targeted injury prevention interventions are recommended. Practical applications: This study provides a comprehensive overview of modifiable injury risks for children aged 0–5 in China. The findings highlight the most prevalent hazards and can inform evidence-based interventions and resource allocation.

Background:Low-level automated vehicles (LAVs) have emerged as a new public health challenge, but the epidemiological characteristics of LAV-related crashes remain unknown. Methods:Based on media reports collected by the Automated Road Traffic Crash Data Platform (ARTCDP), we analysed the characteristics of LAV-related crashes in China between 1 January 2015 and 31 August 2025. Results:The ARTCDP captured 4,669 crashes involving LAVs and 324,869 involving other motor vehicles. Compared to other motor vehicles, LAVs were more frequently reported to crash during nighttime (65.3% vs. 29.1%; P < 0.001), on expressways (31.9% vs. 20.2%; P < 0.001), on straight roads without junctions (50.7% vs. 29.4%; P < 0.001), and on rainy days (57.7% vs. 53.6%; P < 0.001). They were primarily reported to crash in economically developed regions, with those in Zhejiang, Guangdong, and Shanghai accounting for 31.6% of all crashes involving LAVs. Furthermore, 20.3% of the LAVs crashes and 16.3% of other motor vehicles crashes were associated with two or more factors. Brake-related issues (48.0% vs. 26.1%; P < 0.01), hazardous road surface condition (55.6% vs. 47.9%; P < 0.01), and distracted driving behaviour (28.7% vs. 9.8%; P < 0.01) more frequently occurred in LAV-involved crashes. Conclusions:Internet-based media reports detected distinct characteristics of road traffic crashes involving LAVs, meriting the attention of policymakers and law enforcement.

What’s already known about this topic?:Unintentional injuries are a leading cause of morbidity and mortality in children. However, recent large population-based surveys in China remain limited. What is added by this report?:This population-based survey reported a 12-month unintentional injury incidence of 9.12% [95% confidence interval (CI): 8.47, 9.81] among children aged 0-5 years. The incidence rates were higher in boys, preschoolers, and urban children than in girls, younger children, and rural children. Falls accounted for 69.2% of reported injuries, most of which occurred at home during play. What are the implications for public health practice?:Home-based prevention efforts should prioritize boys and preschoolers.

Fall-prevention services for community-dwelling older adults are included in China’s National Essential Public Health Service Program (NEPHSP), but implementation status remains understudied. We conducted 79 semi-structured interviews and seven focus groups with 129 key stakeholders in Beijing and Changsha. Thematic analysis showed that services were largely limited to non-professional verbal health education, with a few exceptions supported by externally funded programs. Barriers included absence of standardized and actionable prevention protocols, constrained funding and material resources, inadequate workforce, limited fall-prevention expertise, and mismatch between community needs and practice. Facilitators consisted of explicit policy support, well-established provider-patient rapport, consensus on the importance of fall prevention, and frontline providers’ motivation for fall-prevention integration. Participants expected age-friendly, feasible, and integrated approaches involving risk screening, individualized feedback, routine education, high-risk management, digital support, training, and incentives. These findings informed development of a preliminary framework to design fall-prevention interventions within NEPHSP workflows.

Introduction: The World Falls Guidelines (WFG) Task Force published a falls risk stratification algorithm in 2022, but its predictive performance was reported only in Ireland, the United States, the Netherlands, Australia, and Malaysia. Methods: Using a nationally representative dataset, the China Health and Retirement Longitudinal Study (CHARLS), we analyzed data from six follow-up cohort visits (2, 3, 4, 5, 7, 9 years). The Cochran-Armitage trend test examined trends in fall and fall-related injury rate across the WFG algorithm. Multivariable logistic regression models examined associations between the WFG algorithm and fall and fall-related injury incidence rates. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and 95% confidence intervals (95% CIs) were calculated to assess predictive performance of the WFG algorithm. Sensitivity analyses were performed to assess the impact of missing values on principal findings. Results: We included 9,735, 5,377, 4,092, 9,426, 7,776, and 3,355 eligible older adults across the six follow-up time periods, with sample sizes varying due to the study’s dynamic recruitment strategy. Fall risk categorized by WFG algorithm was significantly associated with falls and fall-related injuries at all six follow-up cohorts (p < 0.05). However, its predictive performance for both falls and fall-related injuries was unacceptable, with sensitivity ranging from 20.2% to 32.5% for both outcomes across the six follow-up visits. Sensitivity analyses displayed highly similar results. Conclusion: The WFG algorithm is valuable for predicting future falls and fall-related injuries among older Chinese community-dwellers, but its predictive performance is unacceptable for practical use without considering other contributing factors. Practical Applications: Further methodological modifications of the WFG algorithm are recommended to improve its predictive performance.

Introduction: To compare predictive performances of existing cutoffs developed outside China for four commonlyused fall risk assessment tools and new cutoffs based on a Chinese sample. Method: A 12-month prospective study was conducted between April 2023 and June 2024 in Changsha, China. The Stay Independent Brochure Questionnaire (SIB), Fall Efficacy Scale International (FES-I), Home Falls and Accidents Screening Tool (HOME FAST), and Time Up and Go Test (TUGT) were included. New cutoffs were developed by plotting receiver operating characteristic (ROC) curves and calculating the largest Youden index. Chi-square test was used to compare predictive performances of new and old cutoffs for the same assessment tools. Results: The study was completed by 1,237 older adults. The new cutoffs for SIB, FES-I, HOME FAST, and TUGT in predicting falls were 3 points, 18 points, 5 points, and 10.6s, respectively, with AUC values ranging from 0.513 (95% CI: 0.485-0.542) for HOME FAST to 0.631 (95% CI: 0.604-0.658) for SIB. For predicting fall-related injuries, the new cutoffs were 3 points for SIB, 26 points for FES-I, 5 points for HOME FAST, and 11.7s for TUGT, with AUC values ranging from 0.530 (95% CI: 0.502-0.558) for HOME FAST to 0.628 (95% CI: 0.601-0.655) for SIB. Compared with the existing cutoffs, the new cutoff values for all tools generally showed significantly improved sensitivities but reduced specificities. Conclusions: The newly-determined cutoffs showed somewhat improved predictive performance over the existing cutoffs in sensitivity, but neither set of cutoffs achieved good predictive performance among older Chinese community-dwellers when the fall risk assessment results were used solely, without supplemental information. Practical Applications: Considering the poor predictive performances of the four commonly-used fall risk assessment tools with either set of cutoffs, we recommend use of the tools along with supplemental information to predict future fall risk among older adults in China.

INTRODUCTION:Previous interactive education interventions for child injury prevention are suboptimal for implementation in rural areas. This study evaluated the effectiveness of a standardised school-based interactive education intervention to prevent unintentional injury for rural preschoolers. METHODS:A 9-month single-blinded cluster randomised controlled trial was conducted in rural China, involving 12 preschools with 2518 preschoolers aged 3-6 years old. The control group received routine education, and the intervention group added the standardised Safety Experience Room intervention. The primary outcome was unintentional injury incidence rate among preschoolers during the 9-month period. The secondary outcome was the proportion of preschoolers correctly answering questions about the prevention knowledge of road traffic injury, falls and drowning. Principal data analyses were conducted using an intention-to-treat approach. Adjusted ORs (aORs) with 95% CIs were calculated based on generalised linear mixed models and bootstrapped SEs with 1000 replications. Sensitivity analyses were employed based on the full analysis set to assess the impact of missing values on principal findings. RESULTS:Compared with the control group, the intervention group had a significantly lower unintentional injury incidence rate (aOR=0.78, 95% CI: 0.64 to 0.94) and higher proportions of correct answers to the 18 injury prevention questions (with aORs ranging from 1.78 to 22.10) after adjusting for socio-demographic factors and baseline values of the outcome variables. Sensitivity analyses supported the principal findings. CONCLUSION:The 9-month interactive intervention decreased unintentional injury incidence and substantially increased preschooler's safety knowledge in rural China, offering an effective option for implementing or integrating in multifactorial interventions in resource-limited areas. TRIAL REGISTRATION NUMBER:Chinese Clinical Trial Registry, CHiCTR2000038025.

Background:Freely accessible data concerning modifiable risk factors for road traffic injury are critical for research and for evidence-based policymaking. This study investigated free-access availability and the major characteristics of nationally representative data on eight major risk factors for road traffic injury across 194 World Health Organization member countries/territories from 2000 to 2019. Methods:We systematically searched and reviewed data sources from governmental departments, multi-country road safety research projects, and international organisations. Two researchers independently searched, screened, and extracted data. We assessed free-access availability of data for eight risk factors based on the presence of data from 2000 to 2019. Major data characteristics were evaluated for all included data sources, consisting of operational definitions, method of data collection, and sampling method. Results:We identified 79 sources providing free-access available data on at least one of the eight risk factors. During 2000-2019, the number of countries/territories with freely-access data generally rose over time. However, only 134 of 194 countries/territories (69%) had at least one year of free-access data involving one or more risk factors, and 70% of those 134 countries/territories were high-income or upper middle-income countries. Large data heterogeneity existed across the data sources in terms of operational definitions used, method of data collection, years of data coverage, and sampling method. Operational definitions varied widely across the eight risk factors, ranging from 3 definitions used for fatigue driving to 17 definitions for seatbelts; and the proportion of data sources that adopted the recommended Global Road Safety Partnership (GRSP) definitions ranged from 25.5% for distracted driving to 77.8% for child restraint systems. Roadside observations were predominantly used to collect exposure data for six risk factors. Many free-access data sets were completely or partially based on non-probability sampling, and the sampling information was unknown for some additional data sources. Conclusions:Availability of free-access data on road traffic injury risks generally improved globally, but was still absent for 60 countries/territories. The substantial heterogeneity of free-access data across the existing data sources warrants further research efforts and international coordination.

Introduction: With the rapid expansion of food delivery services, crashes involving professional delivery e-bike riders have emerged as a critical traffic safety issue in China. However, limited research has systematically examined the epidemiological characteristics of such crashes. Methods: We analyzed media-reported crashes from 2009-2023 collected in the Automated Road Traffic Crash Data Platform (ARTCDP), which uses natural language processing (NLP) and BERT algorithms to extract data from publicly-available media reports. Differences in crash characteristics, geographic distribution, and contributing factors were compared between professional delivery and other e-bike riders. Results: A total of 4,417 professional delivery e-bike rider crashes and 17,025 other e-bike rider crashes were included. Pedestrians were the most common collision object (37%). Delivery riders were less likely to collide with motor vehicles (29% vs. 42%) and more likely to hit non-motorized vehicles (22% vs. 12%) and infrastructure (12% vs. 9%) than non-delivery riders. Intersections were the top crash site, but crashes involving delivery riders occurred more often at pedestrian crossings (21%) and bus stops (11%). The crashes demonstrated inconsistent geographic distributions. About 40% of all crashes had two or more contributing factors. Compared with crashes among other e-bike riders, those involving professional delivery e-bike riders had a higher proportion of distracted riding (27% vs. 17%), speeding (18% vs. 12%), riding in the opposite lane (15% vs. 6%), and illegal parking (12% vs. 4%). Malfunctioning tires, a lack of a barrier between opposite lanes, and impeded view by obstacles also occurred more frequently in crashes involving professional delivery e-bike riders than those involving other e-bike riders (40% vs. 20%, 35% vs. 22%, and 35% vs. 18%, respectively). Conclusions: Crashes involving professional delivery e-bike riders in China show distinct patterns linked to occupational exposure, underscoring the need for targeted, multifaceted road safety interventions. Practical Applications: Findings support tailored interventions for delivery riders, including vehicle checks, rider training, and infrastructure improvements near pedestrian crossings and bus stops.

BACKGROUND:China implemented the 'One Helmet, One Belt' road safety campaign in April 2020 to improve helmet use among cyclists and motorcyclists. We evaluated its long-term effect. METHODS:We analysed crash data from 379 media sources that reported at least one road traffic crash per quarter between January 2019 and September 2024, as indexed by the Automated Road Traffic Crash Data Platform. The proportion of media-reported crashes involving helmeted motorcyclists and cyclists was compared before and after implementation of the national campaign. We calculated adjusted (aORs) using logistic regression, controlling for season and the COVID-19 pandemic period and fitted Joinpoint regression models to identify periods with significant changes. RESULTS:Among 20 546 media-reported crashes involving motorcyclists and cyclists, the overall proportion of riders wearing helmets increased significantly following the campaign's implementation (aOR: 2.37; 95% CI: 2.08 to 2.72). However, this increase emerged only in the first 8 months of the campaign (April-December 2020), after which proportions plateaued. Subgroup analyses detected distinct temporal patterns for motorcyclists (an initial increase followed by decline), professional delivery riders (no significant trends) and publicly shared vehicle riders (consistently increasing trends). CONCLUSIONS:Our study documents an initial increase followed by stabilisation in proportion of media-reported crashes involving helmeted motorcyclists and cyclists following the national injury prevention campaign. The intervention's effectiveness varied across rider subgroups. These findings underscore the importance of sustaining and enhancing the national campaign while developing targeted interventions for distinct user groups in China, also suggesting similar initiatives might be effective in other international contexts.

INTRODUCTION:To examine trends in product-related traumatic brain injury morbidity by sex and age group in the Unites States between 2004 and 2023. METHOD:Product-related injury morbidity data were extracted from the National Electronic Injury Surveillance System (NEISS). Eight types of products, five injury locations, two sexes, and five age groups were categorized. Joinpoint regression models were performed to detect time periods showing significant product-related traumatic brain injury morbidity changes between 2004 and 2023. Average annual percent changes (AAPCs) in morbidity rates and 95% confidence intervals (95% CIs) quantified significant morbidity changes. RESULTS:The age-standardized product-related traumatic brain injury morbidity rate more than doubled in the United States between 2004 and 2023, rising from 236.6 to 574.8 per 100,000 persons (AAPC = 5.1%, 95% CI: 3.7%, 6.5%), with notable fluctuation during the COVID pandemic in 2019-2021. Under-15 children and old adults ≥ 65 years had the highest product-related traumatic brain injury morbidity rates between 2004 and 2023. Floors/flooring materials and stairs/steps were the most common products causing traumatic brain injuries, accounting for 31.7% of injuries, and homes were the most frequent occurring location, accounting for 59.3% of total product-related injury morbidity. Morbidity rates and spectrums by type of product and by occurring location varied greatly across sex and age groups. CONCLUSIONS:Product-related traumatic brain injury morbidity rates increased in the United States between 2004 and 2023, with some morbidity fluctuations in 2019-2021, likely reflecting the effect of the COVID-19 pandemic. PRACTICAL APPLICATIONS:Further research and prevention efforts are recommended to interpret the observed morbidity changes, curb recent product-related traumatic brain injury morbidity increases and reduce morbidity disparities across sex and age groups.

The effectiveness of mHealth interventions to reduce child unintentional injuries in resource-limited areas like rural areas remains poorly understood. This study aims to assess the effectiveness of a theory-driven and culturally-adapted app intervention to reduce unintentional injury incidence among rural preschoolers. A 12-month single-blinded cluster randomized controlled trial (Chinese Clinical Trial Registry, ChiCTR2000037606, registered on August 29, 2020) was conducted among 3836 preschoolers and their caregivers from 24 preschools in rural China. Participating preschools were randomly allocated using a 1:1 ratio to the intervention or control group to receive app-based education including or excluding child injury prevention. Primary outcome was the 12-month preschooler unintentional injury incidence. Secondary outcomes included caregiver’s safety-related attitudes, supervision behaviors, and home environment scores. Of the 3836 enrolled participants, 3338 (87.0%) completed the study. Results demonstrated the intervention significantly reduced preschooler unintentional injury incidence (aRR = 0.93, 95% CI: 0.86, 0.98) and improved caregivers’ safety-related attitudes (b = 0.51, 95% CI: 0.32, 0.73), supervision behaviors (b = 3.83, 95% CI: 3.15, 4.46), and home environment (b = 3.38, 95% CI: 2.51, 4.16). This trial suggests the app-based intervention effectively enhances child safety among rural preschoolers, and could be disseminated broadly in China and tailored to other resource-limited settings.

Background: The COVID-19 pandemic represents one of the most challenging public health emergencies in recent world history, causing about 7.07 million deaths globally by September 24, 2024. Accurate, timely, and consistent data are critical for early response to situations like the COVID-19 pandemic. Objective: This study aimed to evaluate consistency of daily reported COVID-19 cases in 191 countries from the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE) and the World Health Organization (WHO) dashboards during 2020-2022. Methods: We retrieved data concerning new daily COVID-19 cases in 191 countries covered by both data sources from January 22, 2020, to December 31, 2022. The ratios of numbers of daily reported cases from the 2 sources were calculated to measure data consistency. We performed simple linear regression to examine significant changes in the ratio of numbers of daily reported cases during the study period. Results: Of 191 WHO member countries, only 60 displayed excellent data consistency in the number of daily reported COVID-19 cases between the WHO and JHU CSSE dashboards (mean ratio 0.9-1.1). Data consistency changed greatly across the 191 countries from 2020 to 2022 and differed across 4 types of countries, categorized by income. Data inconsistency between the 2 data sources generally decreased slightly over time, both for the 191 countries combined and within the 4 types of income-defined countries. The absolute relative difference between the 2 data sources increased in 84 countries, particularly for Malta (R2=0.25), Montenegro (R2=0.30), and the United States (R2=0.29), but it decreased significantly in 40 countries. Conclusions: The inconsistency between the 2 data sources warrants further research. Construction of public health surveillance and data collection systems for public health emergencies like the COVID-19 pandemic should be strengthened in the future.

Unintentional injury is a leading cause of childhood morbidity and mortality worldwide. In China, real-world implementation of child injury prevention efforts remains inadequate due to constrained workforce capacity and a lack of operational frameworks. This study aims to assess the effectiveness of a mobile health (mHealth) intervention, the Intelligent Child Unintentional-Injury Reduction & Education (iCURE) project, embedded within China's National Basic Public Health Service Program. The intervention relies on a WeChat (Tencent) service account for caregivers and a web-based platform for health care providers to deliver standardized unintentional injury prevention strategies for young children. Key features of the program include interactive questions and answers, injury risk assessment with instant feedback, a tailored injury prevention knowledge disseminator, and regular reminders to caregivers. A double-blind, 12-month follow-up, cluster randomized controlled trial will be implemented in Changsha, Hunan Province, China. Caregivers of children aged ≤5 years will be recruited. Randomization will be conducted at the street or town level. The control group will receive routine safety education, while the intervention group will receive both routine safety education and the iCURE mHealth intervention focused on unintentional injury prevention and delivered via WeChat. Data will be collected at baseline and every 3 months during the study period. The primary outcome is 12-month incidence of unintentional injuries among children, including minor injuries and as reported by caregivers. Secondary outcomes include children's injury risk level and caregiver supervision behaviors assessed using a standard questionnaire. Data analysis will be conducted using generalized linear mixed models with a Poisson link function and generalized estimating equations to assess the effectiveness of the iCURE intervention, following intention-to-treat principles. Sensitivity analyses will be conducted with per-protocol principles and excluding participants with missing primary outcomes. As of May 2025, a total of 6701 participants have been successfully enrolled and baseline data were collected for all participants. Of those enrolled, 87.2% (5842/6,701) completed the first follow-up assessment. This trial will examine the effectiveness of an intelligent mHealth intervention for child unintentional injury prevention building on China's National Basic Public Health Service Program. If successful, the iCURE intervention may provide a cost-effective strategy for child injury prevention in low- and middle-income countries.

OBJECTIVES:Public health and social measures are essential tools for countries to control epidemic transmission but may also disrupt normal socioeconomic activities. This study aims to analyze differences in public health and social measures policy stringency, temporal trends, and their association with corona virus disease 2019 (COVID-19) control outcomes in 118 countries from January 2020 to September 2022, providing evidence to support the formulation of scientifically grounded public health and social measures policies. METHODS:Boxplots were used to describe the distribution of overall and individual public health and social measures policy stringency scores across 118 countries during the pandemic. Linear and nonlinear models were fitted to examine temporal trends in public health and social measures policy stringency. A two-way fixed-effects model was applied to analyze the association between public health and social measures policy stringency and the effective reproduction number of COVID-19. RESULTS:The overall public health and social measures stringency scores across the 188 countries ranged from 7.78 to 69.75. Among these countries, temporal trend models for public health and social measures stringency were statistically significant in 108 countries (all third-order polynomial models), with coefficients of determination exceeding 0.25. Overall public health and social measures stringency increased over time in 59 countries but decreased in 49 countries. After adjusting for covariates, country-level fixed effects, and time fixed effects, the overall public health and social measures policy stringency score was negatively associated with effective reproduction number (β'=-0.076, P<0.05). Five individual public health and social measures components, school closures, workplace closures, gathering restrictions, domestic movement restrictions, and mask-wearing policies, were each negatively associated with the effective reproduction number (β'=-0.020, β'=-0.046, β'=-0.032, β'=-0.011, and β'=-0.030, respectively; all P<0.05). In contrast, international travel restrictions were positively associated with the effective reproduction number (β'=0.053, P<0.05). CONCLUSIONS:During the COVID-19 pandemic, the average intensity of public health and social measures implementation varied widely across 118 countries. School closures, workplace closures, gathering restrictions, domestic movement restrictions, and mask-wearing measures effectively curbed COVID-19 transmission, whereas the effectiveness of international travel restrictions diminished over time.