Product-related injury morbidity among Americans aged 0-19 years, 2001-2020.

INTRODUCTION: This study examined changes in product-related injury morbidity among under-20 Americans between 2001 and 2020. METHOD: Product-related injury morbidity data came from the National Electronic Injury Surveillance System (NEISS). Using age-standardized morbidity rates, the authors performed Joinpoint regression models to identify time periods with significant changes between 2001 and 2020 and quantified the annual magnitude of morbidity changes with annual percent changes (APCs) in rates and 95 % confidence intervals (CIs). RESULTS: Age-standardized product-related injury morbidity declined consistently among under-20 Americans from 2001 to 2020 (from 7449.3 to 4023.5 per 100,000 persons; APC = -1.5 %, 95 % CI: -2.3 %, -0.7 %), with the most striking morbidity drop in 2019-2020 (-1576.8 per 100,000 persons). Sports and recreation equipment and home were the most common product and location, respectively, for nonfatal pediatric product-related injuries. Large morbidity differences and varying spectrum by product and by occurring location existed across sex and age groups. CONCLUSIONS: Product-related injury morbidity declined significantly among under-20 Americans between 2001 and 2020, but large variations remained across sex and age groups. PRACTICAL APPLICATIONS: Further research is recommended to understand causal factors contributing to the observed decrease in product-related injury morbidity over the past 20 years and to understand product-related injury morbidity disparities across sex and age groups. Understanding of causal factors could lead to implementation of additional interventions to reduce product-related injury among children and adolescents.

ARTCDP: An automated data platform for monitoring emerging patterns concerning road traffic crashes in China.

Online media reports provide valuable information for road traffic injury prevention, but technical challenges concerning data acquisition and processing limit analysis and interpretation of such data. Integrating injury epidemiology theory and big data technology, we developed a data platform consisting of four layers (data acquisition, data processing, application and data storage) to automatically collect reports from online Chinese media concerning road traffic crashes every 24 h. We built a text classification model using 20,000 manually annotated news stories based on the Bidirectional Encoder Representations from Transformers (BERT) and then used natural language processing algorithms to extract data concerning 27 structured variables from the news sources. The accuracy of the BERT-based text classification model was 0.9271, with information extraction accuracy exceeding 80% for 22 variables. As of November 30, 2021, the data platform collected 244,650 eligible media reports covering all 333 prefecture-level divisions in China. These reports were from 37,073 websites or social media accounts, which were geographically located in all 31 provinces and over 98% of prefecture-level divisions. Data availability varied greatly from 0.9% to 100% across the 27 structured variables. Additionally, the platform identified 645,787 potentially relevant keywords when applying natural language processing techniques to the textual media reports. Platform data were highly correlated with road police data in province-based road traffic crash statistics (crashes, rs = 0.799; non-fatal injuries, rs = 0.802; deaths, rs = 0.775). In particular, the platform offers valuable data (like crashes involving electric vehicles) that are not included in official road traffic crash statistics. The new automated data platform shows great potential for timely detection of emerging characteristics of road traffic crashes. Further research is needed to improve the platform and apply it to real-time monitoring and analysis of road traffic injuries.

Coding quality of deaths and its impact on elderly unintentional fall mortality data from 1990 to 2019: a retrospective analysis of the WHO Mortality Database.

BACKGROUND: Several studies have assessed the reporting quality of all-cause mortality data from the WHO Mortality Database, but little is known about coding quality and its impact on elderly unintentional fall mortality data worldwide. We aimed to assess the coding quality of deaths and its impact on elderly unintentional fall mortality. METHODS: Using data from the WHO Mortality Database, 1990-2019, we calculated the number of countries/territories that had mortality data in the database, and the proportion of deaths with five types of problematic codes based on the 10th International Classification of Disease (unspecified deaths, injury deaths with undetermined intent, unspecified unintentional injury, unintentional falls with unspecified mechanism, unintentional falls with unknown occurrence place). We estimated age-adjusted unintentional fall mortality before and after correcting problematic codes. RESULTS: Only 64% (124/194) of WHO member states had at least 1 year of mortality data in the database during 1990-2019, and data unavailability was more common for underdeveloped countries/territories than for developed countries/territories. Coding quality was poor for many countries/territories. Among the study years when countries/territories possessed mortality data, 80, 53, 51, and 63% had a proportion of unintentional fall deaths with unspecified mechanism over 50% in low-income, lower middle-income, upper middle-income, and high-income countries/territories, respectively; comparable proportions for unintentional fall deaths with unknown occurrence place were 100, 42, 71, and 62%. Among the 94 countries/territories having mortality data, problematic codes caused a relative mortality difference ≥ 50% in 59 countries/territories (63%). After correcting problematic codes, 5 of 55 countries/territories with data witnessed a reverse in mortality changes between 2005 and 2015. Among the 82 countries/territories with mortality data for 5 or more years, 18 countries/territories (22%) experienced a directional reverse in linear regression coefficient. CONCLUSIONS: The availability and coding quality of global data related to elderly unintentional fall mortality was poor between 1990 and 2019. When data are available, varying coding quality across countries/territories and over time have a substantial impact on mortality estimates and mortality comparisons. Global agencies plus each individual government should be aware of the importance of collecting and sharing high-quality mortality data, and take action to improve data quality for inclusion in the WHO Mortality Database.

Changes in Six-Month Prevalence of Circulatory System Diseases among People Aged 20 Years and Older between 2013 and 2018 in Hunan, China.

Recent changes in population-based prevalence for circulatory system diseases (CSDs) remain unreported either nationally or locally for China. Data were from the two-round health service household interview survey of Hunan Province, China, in 2013 and 2018. A Rao-Scott chi-square test was performed to examine prevalence differences across socio-demographic variables. The overall age-standardized prevalence of CSDs increased substantially between 2013 and 2018 for inhabitants aged 20 years and older (14.25% vs. 21.25%; adjusted odds ratio (OR) = 1.59, 95% CI: 1.24-2.04). Hypertensive disease was the most prevalent type of CSD, accounting for 87.24% and 83.83% of all CSDs in 2013 and in 2018, respectively. After controlling for other socio-demographic factors, the prevalence of CSDs was significantly higher in 2018 (adjusted OR = 1.40), urban residents (adjusted OR = 1.43), females (adjusted OR = 1.12) and older age groups (adjusted OR = 5.36 for 50-59 years, 9.51 for 60-69 years, 15.19 for 70-79 years, and 12.90 for 80 years and older) than in 2013, rural residents, males and the youngest age group (20-49 years). The recent increase in the overall age-standardized CSD prevalence and the large prevalence disparities across urban/rural residents, sex and age groups merit the attention of policymakers and researchers. Further prevention efforts are needed to curb the increasing tendency and to reduce the prevalence of disparities across socio-demographic groups.

Media Reports about Violence against Medical Care Providers in China.

Improper, unprofessional, or misleading media reports about violence against medical care providers (typically doctors and nurses) may provoke copycat incidents. To examine whether media reports about violence against medical care providers in China follow professional journalism recommendations, we identified 10 influential incidents of violence against medical care providers in China through a systematic strategy and used standardized internet-based search techniques to retrieve media reports about these events from 2007-2017. Reports were evaluated independently by trained coders to assess adherence to professional journalism recommendations using a 14-item checklist. In total, 788 eligible media reports were considered. Of those, 50.5% and 47.3%, respectively, failed to mention the real and complete names of the writer and editor. Reports improperly mentioned specific details about the time, place, methods, and perpetrators of violence in 42.1%, 36.4%, 45.4%, and 54.6% of cases, respectively. Over 80% of reports excluded a suggestion to seek help from professional agencies or mediation by a third party and only 3.8% of reports mentioned the perspectives of all three key informants about an event: medical care providers, patients, and hospital administrators. Of those that mentioned medical care providers, patient, and/or hospital administrator perspectives, less than 20% indicated they had obtained the interviewee's consent to include their perspective. We concluded that most reports about violence against medical care providers in the Chinese media failed to strictly follow reporting recommendations from authoritative media bodies. Efforts are recommended to improve adherence to professional guidelines in media reports about violence against medical care providers in China, as adherence to those guidelines is likely to reduce future violent events against medical care providers like doctors and nurses.

Road traffic injury mortality and morbidity by country development status, 2011-2017.

PURPOSE: This research examined road traffic injury mortality and morbidity disparities across of country development status, and discussed the possibility of reducing country disparities by various actions to accelerate the pace of achieving Sustainable Development Goals target 3.6 - to halve the number of global deaths and injuries from road traffic accidents by 2020. METHODS: Data for road traffic mortality, morbidity, and socio-demographic index (SDI) were extracted by country from the estimates of the Global Burden of Disease study, and the implementation of the three types of national actions (legislation, prioritized vehicle safety standards, and trauma-related post-crash care service) were extracted from the Global Status Report on Road Safety by World Health Organization. We fitted joinpoint regression analysis to identify and quantify the significant rate changes from 2011 to 2017. RESULTS: Age-adjusted road traffic mortality decreased substantially for all the five SDI categories from 2011 to 2017 (by 7.52%-16.08%). Age-adjusted road traffic mortality decreased significantly as SDI increased in the study time period, while age-adjusted morbidity generally increased as SDI increased. Subgroup analysis by road user yielded similar results, but with two major differences during the study period of 2011 to 2017: (1) pedestrians in the high SDI countries experienced the lowest mortality (1.68-1.90 per 100,000 population) and morbidity (110.45-112.72 per 100,000 population for incidence and 487.48-491.24 per 100,000 population for prevalence), and (2) motor vehicle occupants in the high SDI countries had the lowest mortality (4.07-4.50 per 100,000 population) but the highest morbidity (428.74-467.78 per 100,000 population for incidence and 1025.70-1116.60 per 100,000 population for prevalence). Implementation of the three types of national actions remained nearly unchanged in all five SDI categories from 2011 to 2017 and was consistently stronger in the higher SDI countries than in the lower SDI countries. Lower income nations comprise the heaviest burden of global road traffic injuries and deaths. CONCLUSION: Global road traffic deaths would decrease substantially if the large mortality disparities across country development status were reduced through full implementation of proven national actions including legislation and law enforcement, prioritized vehicle safety standards and trauma-related post-crash care services.

DeepHE: Accurately predicting human essential genes based on deep learning.

Accurately predicting essential genes using computational methods can greatly reduce the effort in finding them via wet experiments at both time and resource scales, and further accelerate the process of drug discovery. Several computational methods have been proposed for predicting essential genes in model organisms by integrating multiple biological data sources either via centrality measures or machine learning based methods. However, the methods aiming to predict human essential genes are still limited and the performance still need improve. In addition, most of the machine learning based essential gene prediction methods are lack of skills to handle the imbalanced learning issue inherent in the essential gene prediction problem, which might be one factor affecting their performance. We propose a deep learning based method, DeepHE, to predict human essential genes by integrating features derived from sequence data and protein-protein interaction (PPI) network. A deep learning based network embedding method is utilized to automatically learn features from PPI network. In addition, 89 sequence features were derived from DNA sequence and protein sequence for each gene. These two types of features are integrated to train a multilayer neural network. A cost-sensitive technique is used to address the imbalanced learning problem when training the deep neural network. The experimental results for predicting human essential genes show that our proposed method, DeepHE, can accurately predict human gene essentiality with an average performance of AUC higher than 94%, the area under precision-recall curve (AP) higher than 90%, and the accuracy higher than 90%. We also compare DeepHE with several widely used traditional machine learning models (SVM, Naïve Bayes, Random Forest, and Adaboost) using the same features and utilizing the same cost-sensitive technique to against the imbalanced learning issue. The experimental results show that DeepHE significantly outperforms the compared machine learning models. We have demonstrated that human essential genes can be accurately predicted by designing effective machine learning algorithm and integrating representative features captured from available biological data. The proposed deep learning framework is effective for such task.

Unsafe Bicycling Behavior in Changsha, China: A Video-Based Observational Study.

The recent emergence of shared bikes has inspired renewed use of bicycles in urban China. However, incidence rates of unsafe cycling behaviors have not been reported using objective methods. We designed a video-based observational study in Changsha, China to estimate the incidence of five unsafe bicycling behaviors among both shared and personal bike riders and examine incidence differences across types of riders and cycling areas. A total of 112 h of video recorded 13,407 cyclists riding shared bikes and 2061 riding personal bikes. The incidences of not wearing a helmet, violating traffic lights, riding in the opposite direction of traffic, not holding the handlebar with both hands, and riding in a non-bicycle lane were 99.28%, 19.57%, 13.73%, 2.57%, and 64.06%, respectively. The incidence rate of all five kinds of behaviors differed significantly across four types of riding areas (shopping, university, office, and leisure) and the rates of the first three kinds of behaviors were statistically different between shared and personal bike riders. In situations where bicycle lanes were available, we observed the incidence of riding on the motorway and on the sidewalk to be 44.06% and 19.99%, respectively. We conclude that unsafe cycling behaviors occur with alarming frequency and differ somewhat between riders of shared versus personal bikes. Further research is recommended to interpret the occurrence of risky cycling and the incidence differences across types of riders and cycling areas.

Data on unsafe riding behaviors among 1960 shared bicycle riders in urban China.

This data article quantifies the extent of shared bicycle riding risks for shared-bicycle riders in urban China. The data were collected through a WeChat-based online survey, with a valid sample of 1960 respondents. It reports the basic descriptive statistics through eight tables concerning various unsafe shared bicycle riding behaviors, and complete frequency data from riders concerning eight unsafe riding behaviors. The data can be used for comparisons with other studies using the same outcome measures, which are valuable to generate specialized and targeted solutions to reduce unsafe riding behaviors. For further information, please refer to the full article entitled "Unsafe riding behaviors of shared-bicycle riders in urban China: A retrospective survey".(Wu et al., 2019).

Unsafe riding behaviors of shared-bicycle riders in urban China: A retrospective survey.

Shared-bicycle use has skyrocketed in urban China, but little is known about the safety of bicycle users. The Chinese popular media reports multiple risky riding behaviors among shared bicycle riders, but scientific research on the topic is lacking. Therefore, we conducted a retrospective WeChat-based online survey to examine how often shared bicycle riders report engaging in risky cycling behaviors in urban China. Eight unsafe shared bicycle riding behaviors were assessed: not wearing helmets, running red lights, cycling against the traffic flow, riding in lanes designed for motor vehicles, riding in lanes designed for pedestrians, carrying passengers on bicycles, using cell phones while riding, and eating while riding. In total, 1960 valid questionnaires were collected. The proportion of participants who reported always or often having unsafe riding behavior in the past month, ranged from 1.1% for carrying passengers on the bicycles to 97.6% for failing to wear a helmet. Demographic characteristics were associated with unsafe behaviors through multivariate logistic regression, with male riders and riders aged 25 years or younger more likely to ride while using cell phones than females (AOR = 2.94) and those 36 years or older (AOR = 3.57). Cyclists with undergraduate education were more likely to wear helmets than those with postgraduate education or higher (AOR = 0.21). Compared to riders from central municipalities governed directly by the central government, riders from provincial capitals, deputy provincial cities, and smaller cities were at higher risks of riding in lanes for pedestrians, respectively (AOR = 1.59, 2.82 and 1.61). Riders who rode over 5 h a week and who rode on weekends were more likely to carry passengers than those who rode less than 1 h a week (AOR = 4.72) and those who rode only on weekdays (AOR = 3.93). We conclude that shared-bicycle riders frequently engage in some unsafe riding behaviors in urban China. Younger age, lower level of education, and longer hours of riding each week are associated with greater risks of some unsafe riding behaviors. Shared bicycles offer substantial benefit to societal health and transportation, but evidence-based interventions should be considered to reduce risks from unsafe shared bicycle riding behaviors. A well-designed road infrastructure with dedicated on-road bicycle lanes and readily-accessible comfortable, low-cost, and safe helmets may also reduce unsafe riding behaviors and unwanted crashes and injuries for shared bicycle riders.

Needs Analysis for a Parenting App to Prevent Unintentional Injury in Newborn Babies and Toddlers: Focus Group and Survey Study Among Chinese Caregivers.

BACKGROUND: With the growing popularity of mobile health technology, app-based interventions delivered by smartphone have become an increasingly important strategy toward injury prevention. OBJECTIVE: This study aimed to develop a framework supporting the design of an app-based intervention to prevent unintentional injury, targeted for caregivers of Chinese children aged 0 to 6 years. METHODS: A theory-based mixed-method study, including focus groups and Web-based quantitative survey, was performed. Adult caregivers who care for children aged 0 to 6 years and own a smartphone were recruited into 2 sequential stages of research. First, focus groups were conducted among the caregivers at community health care centers and preschools from December 2015 to March 2016. Focus groups (8-10 participants per group) explored awareness, experiences, and opinions of caregivers toward using an app to prevent unintentional injury among children. Second, based on the focus groups findings, a Web-based quantitative survey was designed and distributed to caregivers in November 2016; it collected information on specific needs for the app-based intervention. Thematic analysis and quantitative descriptive analyses were performed. RESULTS: In total, 12 focus groups were completed, involving 108 caregivers. Most participants expressed a strong desire to learn knowledge and skills about unintentional child injury prevention and held positive attitudes toward app-based interventions. Participants expressed multiple preferences concerning the app-based intervention, including their contents, functions, interactive styles, installation and registration logistics, and privacy protection and information security. Following the focus groups, 1505 caregivers completed a WeChat-based quantitative survey, which generated roughly similar results to those of focus groups and added numerical metrics concerning participants' preferences on what to learn, when to learn it, and how to learn it. A detailed framework was established involving 5 components: (1) content design, (2) functional design, (3) interactive style, (4) installation and registration logistics, and (5) privacy protection and information security, and 15 specific requirements. CONCLUSIONS: We developed a framework that can be used as a guide to design app-based interventions for parents and caregivers, specifically for unintentional injury prevention of children aged 0 to 6 years.

Are Bus Company Regulations Associated with Crash Risk? Findings from a Retrospective Survey in Four Chinese Cities.

Bus crashes are common in urban China, and bus company regulations are hypothesized to be related to bus crash risk. We conducted a retrospective survey to examine the association in four large Chinese cities (Changsha, Shenzhen, Fuzhou, and Wuhan). Four types of bus crashes were considered: (a) passengers injured while riding the bus; (b) bus colliding with or scraping other motor vehicles; (c) bus colliding with non-motorized vehicles or pedestrians; and (d) bus damaging public facilities. Based on regulations governing the drivers' work, complete round trips per day, and their paid salary, three categories of companies were studied: type A: ≥14 h worked/day, ≥6 round trips/day, and >70% of salary based on performance; type B: 8-13 h/day, 4 or 5 round trips/day, and 36-70% of salary; and type C: <36% of salary and no other specified requirements. Of the 926 respondents, 20.7% reported one or more crashes or related risk events in the past month. Drivers from the three types of companies reported crash incidence rates of 31.9%, 8.8%, and 6.0%, respectively, in the past month. Type A crash rates were significantly higher than type C after controlling for relevant covariates (adjusted odds ratio (OR) = 7.1, 95% confidence interval (CI): 3.74-13.47). We conclude that more stringent bus company regulations, which mandate drivers to work long hours and obtain salary based on job performance in meeting demanding metrics, are associated with elevated bus-related crash risks. Local governments in China should regulate bus companies to ensure drivers work reasonable hours and are paid based on the quality of their work (e.g., safety).

Predicting essential proteins by integrating orthology, gene expressions, and PPI networks.

Identifying essential proteins is very important for understanding the minimal requirements of cellular life and finding human disease genes as well as potential drug targets. Experimental methods for identifying essential proteins are often costly, time-consuming, and laborious. Many computational methods for such task have been proposed based on the topological properties of protein-protein interaction networks (PINs). However, most of these methods have limited prediction accuracy due to the noisy and incomplete natures of PINs and the fact that protein essentiality may relate to multiple biological factors. In this work, we proposed a new centrality measure, OGN, by integrating orthologous information, gene expressions, and PINs together. OGN determines a protein's essentiality by capturing its co-clustering and co-expression properties, as well as its conservation in the evolution process. The performance of OGN was tested on the species of Saccharomyces cerevisiae. Compared with several published centrality measures, OGN achieves higher prediction accuracy in both working alone and ensemble.

Evaluating the Effectiveness of Implementing a More Severe Drunk-Driving Law in China: Findings from Two Open Access Data Sources.

In 2011, China implemented a more severe drunk-driving law. This study evaluated the effectiveness of the law on road traffic morbidity and mortality attributed to alcohol use. Data were from two open-access data sources, the Global Burden of Disease (GBD) 2015 update and police data. Poisson regression examined the significance of changes in morbidity and mortality. Large gaps in crude death estimates from road traffic crashes attributed to alcohol use emerged between the two data sources. For the GBD 2015 update, crude and age-standardized mortality displayed consistent trends between 1990 and 2015; age-standardized mortality per 100,000 persons increased from 5.71 in 1990 to 7.48 in 2005 and then continuously decreased down to 5.94 in 2015. Police data showed a decrease for crude mortality per 100,000 persons from 0.29 in 2006 to 0.15 in 2010 and then an increase to 0.19 in 2015. We conclude available data are inadequate to determine the effectiveness of the implementation of the more severe drunk-driving law in China since the two data sources present highly inconsistent results. Further effort is needed to tackle data inconsistencies and obtain reliable and accurate data on road traffic injury attributable to alcohol use in China.

An ensemble framework for identifying essential proteins.

BACKGROUND: Many centrality measures have been proposed to mine and characterize the correlations between network topological properties and protein essentiality. However, most of them show limited prediction accuracy, and the number of common predicted essential proteins by different methods is very small. RESULTS: In this paper, an ensemble framework is proposed which integrates gene expression data and protein-protein interaction networks (PINs). It aims to improve the prediction accuracy of basic centrality measures. The idea behind this ensemble framework is that different protein-protein interactions (PPIs) may show different contributions to protein essentiality. Five standard centrality measures (degree centrality, betweenness centrality, closeness centrality, eigenvector centrality, and subgraph centrality) are integrated into the ensemble framework respectively. We evaluated the performance of the proposed ensemble framework using yeast PINs and gene expression data. The results show that it can considerably improve the prediction accuracy of the five centrality measures individually. It can also remarkably increase the number of common predicted essential proteins among those predicted by each centrality measure individually and enable each centrality measure to find more low-degree essential proteins. CONCLUSIONS: This paper demonstrates that it is valuable to differentiate the contributions of different PPIs for identifying essential proteins based on network topological characteristics. The proposed ensemble framework is a successful paradigm to this end.

A new method for the discovery of essential proteins.

BACKGROUND: Experimental methods for the identification of essential proteins are always costly, time-consuming, and laborious. It is a challenging task to find protein essentiality only through experiments. With the development of high throughput technologies, a vast amount of protein-protein interactions are available, which enable the identification of essential proteins from the network level. Many computational methods for such task have been proposed based on the topological properties of protein-protein interaction (PPI) networks. However, the currently available PPI networks for each species are not complete, i.e. false negatives, and very noisy, i.e. high false positives, network topology-based centrality measures are often very sensitive to such noise. Therefore, exploring robust methods for identifying essential proteins would be of great value. METHOD: In this paper, a new essential protein discovery method, named CoEWC (Co-Expression Weighted by Clustering coefficient), has been proposed. CoEWC is based on the integration of the topological properties of PPI network and the co-expression of interacting proteins. The aim of CoEWC is to capture the common features of essential proteins in both date hubs and party hubs. The performance of CoEWC is validated based on the PPI network of Saccharomyces cerevisiae. Experimental results show that CoEWC significantly outperforms the classical centrality measures, and that it also outperforms PeC, a newly proposed essential protein discovery method which outperforms 15 other centrality measures on the PPI network of Saccharomyces cerevisiae. Especially, when predicting no more than 500 proteins, even more than 50% improvements are obtained by CoEWC over degree centrality (DC), a better centrality measure for identifying protein essentiality. CONCLUSIONS: We demonstrate that more robust essential protein discovery method can be developed by integrating the topological properties of PPI network and the co-expression of interacting proteins. The proposed centrality measure, CoEWC, is effective for the discovery of essential proteins.