A systems model for community injury prevention

The purpose of this study was to develop, explicate, and validate a comprehensive model in order to more effectively assess community injury prevention needs, plan and target efforts, identify potential interventions, and provide a framework for an outcome-based evaluation of the effectiveness of interventions. A systems model approach was developed to conceptualize the major components of inputs, efforts, outcomes and feedback within a community setting. Profiling of multiple data sources demonstrated a community feedback mechanism that increased awareness of priority issues and elicited support from traditional as well as non-traditional injury prevention partners. Injury countermeasures including education, enforcement, engineering, and economic incentives were presented for their potential synergistic effect impacting on knowledge, attitudes, or behaviors of a targeted population. Levels of outcome data were classified into ultimate, intermediate and immediate indicators to assist with determining the effectiveness of intervention efforts. A collaboration between business and health care was successful in achieving data access and use of an emergency department level of injury data for monitoring of the impact of community interventions. Evaluation of injury events and preventive efforts within the context of a dynamic community systems environment was applied to a study community with examples detailing actual profiling and trending of injuries. The resulting model of community injury prevention was validated using a community focus group, community injury prevention coordinators, and injury prevention national experts. ^

How accurate is the identification of serious traffic injuries by Police? The concordance between Police and hospital reported traffic injuries

Police reported crash data are the primary source of crash information in most jurisdictions. However, the definition of serious injury within police-reported data is not consistent across jurisdictions and may not be accurate. With the Australian National Road Safety Strategy targeting the reduction of serious injuries, there is a greater need to assess the accuracy of the methods used to identify these injuries. A possible source of more accurate information relating to injury severity is hospital data. While other studies have compared police and hospital data to highlight the under-reporting in police-reported data, little attention has been given to the accuracy of the methods used by police to identify serious injuries. The current study aimed to assess how accurate the identification of serious injuries is in police-reported crash data, by comparing the profiles of transport-related injuries in the Queensland Road Crash Database with an aligned sample of data from the Queensland Hospital Admitted Patients Data Collection. Results showed that, while a similar number of traffic injuries were recorded in both data sets, the profile of these injuries was different based on gender, age, location, and road user. The results suggest that the ‘hospitalisation’ severity category used by police may not reflect true hospitalisations in all cases. Further, it highlights the wide variety of severity levels within hospitalised cases that are not captured by the current police-reported definitions. While a data linkage study is required to confirm these results, they highlight that a reliance on police-reported serious traffic injury data alone could result in inaccurate estimates of the impact and cost of crashes and lead to a misallocation of valuable resources.

Sequential monitoring of hospital adverse events when control charts fail : the example of fall injuries in hospitals

Objective To evaluate methods for monitoring monthly aggregated hospital adverse event data that display clustering, non-linear trends and possible autocorrelation. Design Retrospective audit. Setting The Northern Hospital, Melbourne, Australia. Participants 171,059 patients admitted between January 2001 and December 2006. Measurements The analysis is illustrated with 72 months of patient fall injury data using a modified Shewhart U control chart, and charts derived from a quasi-Poisson generalised linear model (GLM) and a generalised additive mixed model (GAMM) that included an approximate upper control limit. Results The data were overdispersed and displayed a downward trend and possible autocorrelation. The downward trend was followed by a predictable period after December 2003. The GLM-estimated incidence rate ratio was 0.98 (95% CI 0.98 to 0.99) per month. The GAMM-fitted count fell from 12.67 (95% CI 10.05 to 15.97) in January 2001 to 5.23 (95% CI 3.82 to 7.15) in December 2006 (p<0.001). The corresponding values for the GLM were 11.9 and 3.94. Residual plots suggested that the GLM underestimated the rate at the beginning and end of the series and overestimated it in the middle. The data suggested a more rapid rate fall before 2004 and a steady state thereafter, a pattern reflected in the GAMM chart. The approximate upper two-sigma equivalent control limit in the GLM and GAMM charts identified 2 months that showed possible special-cause variation. Conclusion Charts based on GAMM analysis are a suitable alternative to Shewhart U control charts with these data.

Inovação tecnológica: análise comparativa entre dois métodos de coleta de dados sobre riscos e danos em enfermagem

Estudo de comparação entre dois métodos de coleta de dados, através da aplicação de um software, para avaliação dos fatores de risco e danos no trabalho de enfermagem em hospital. Objetiva analisar o uso do software (eletrônico) em comparação com o uso do instrumento impresso. Trata-se de um estudo estatístico, descritivo com abordagem quantitativa, desenvolvido nas enfermarias dos Serviços de Internações Clínicas e Serviços de Internações Cirúrgicas de um Hospital Universitário, no estado do Rio de Janeiro. A população do estudo foram os trabalhadores de enfermagem das unidades. A amostra foi definida por meio de amostragem não-probabilística e alocação da amostra ocorreu de forma aleatória em dois grupos, denominados grupo impresso e grupo eletrônico, com 52 participantes cada. Previamente a coleta de dados foram implementadas estratégias de pesquisa denominada teaser, através da comunicação digital aos trabalhadores. Posteriormente, foi ofertado aos participantes do formato impresso o questionário impresso, e os participantes do formato eletrônico receberam um link de acesso a home page. Os dados foram analisados através da estatística descritiva simples. Após a aplicação do questionário nos dois formatos, obteve-se resposta de 47 trabalhadores do grupo impresso (90,3%), e 17 trabalhadores do grupo eletrônico (32,7%). A aplicação do questionário impresso revelou algumas vantagens como o número de pessoas atingidas pela pesquisa, maior interação pesquisador e participante, taxa de retorno mais alta, e quanto às desvantagens a demanda maior de tempo, erros de transcrição, formulação de banco de dados, possibilidades de resposta em branco e erros de preenchimento. No formato eletrônico as vantagens incluem a facilidade de tabulação e análise dos dados, impossibilidade de não resposta, metodologia limpa e rápida, e como desvantagens, o acesso à internet no período de coleta de dados, saber usar o computador e menor taxa de resposta. Ambos os grupos observaram que o questionário possui boas instruções e fácil compreensão, além de curto tempo para resposta. Os trabalhadores perceberam a existência dos riscos ocupacionais, principalmente os ergonômicos, biológicos e de acidentes. Os principais danos à saúde provocados ou agravos pelo trabalho percebidos pelos trabalhadores foram os problemas osteomusculares, estresse, transtornos do sono, mudanças de humor e alterações de comportamento e varizes. Pode-se afirmar que não ocorreram diferenças acentuadas de percentual ao comparar a percepção dos trabalhadores do grupo impresso e do grupo eletrônico frente aos riscos e danos à saúde. Conclui-se que os dois processos de coleta de dados tiveram boa aceitação, no entanto, deve ser indicada a aplicação do questionário eletrônico junto com a ferramenta de acesso, no caso o computador, tablet.

Acclimation to short-term low temperatures in two Eucalyptus globulus clones with contrasting drought resistance

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Características das lesões desportivas e fatores associados com lesão em iniciantes de ginástica artística do sexo feminino

The artistic gymnastics is a modality that associates arts with biomechanical gestures, and it has been prominent among children and adolescents. Its practice can lead to sports injuries; therefore, it is important to know the factors inherent to trauma for the formulation of preventive models. Thus, the objective of this study was to characterize sports injuries and to verify factors associated with injury in people practicing artistic gymnastics with different levels of competitiveness. Forty-six gymnasts were interviewed with mean age of 10.1±2.0 years for female participants, who were classified in two competitive levels, i.e, initiation and training. We used the morbidity questionnaire adapted to sports characteristics to collect personal, training, and injury data. It was observed that injury risk was 0.3 injuries per athlete and 1.4 injuries per injured athlete, in which the gymnasts of the training category showed a higher frequency of the injury (83.3%; n=10) compared with the ones in the initiation category (10.5%; n=4). For both levels of competitiveness, training moment and light severity were the most reported variables. In the mechanism, contactless was more prevalent in the training category (90%; n=9) and the direct contact was more common at initiation category (75%; n=3). Anthropometric and training variables were considered as factors associated with injury to the gymnasts. It is concluded that gymnasts of the training category have higher injury frequency. Anthropometric and training variables were factors associated with injury. Characteristics of the injuries depend on the competitiveness level of the ­gymnasts.

Epidemiology of injuries in First Division Spanish football

Abstract The aim was to examine the injuries sustained by Spanish football players in the First Division and to compare injury-related variables in the context of both competition and training. The injury data were prospectively collected from 16 teams (427 players) using a specific web-based survey during the 2008/2009 season. A total of 1293 injuries were identified (145 were recurring injuries). The overall injury incidence was 5.65 injuries per 1000 h of exposure. Injuries were much more common during competition than during training (43.53 vs. 3.55 injuries per 1000 h of exposure, P menor que 0.05). Most of the injuries (89.6%) involved the lower extremities, and overuse (65.7%) was the main cause. Muscle and tendon injuries were the most common types of injury (53.8%) among the players. The incidence of training injuries was greater during the pre-season and tended to decrease throughout the season, while the incidence of competition injuries increased throughout the season (all P menor que 0.05). In conclusion, the results of this study suggest the need for injury prevention protocols in the First Division of the Spanish Football League to reduce the number of overuse injuries in the muscles and tendons in the lower extremities. In addition, special attention should be paid during the pre-season and the competitive phase II (the last four months of the season) in order to prevent training and competition injuries, respectively.

Perceptions of French and Creole Among First-Generation Adult Haitian English Language Learners

Context: Due to a unique combination of factors, outdoor athletes in the Southeastern United States are at high risk of lightning deaths and injuries. Lightning detection methods are available to minimize lightning strike victims. Objective: Becoming aware of the risk factors that predispose athletes to lightning strikes and determining the most reliable detection method against hazardous weather will enable Certified Athletic Trainers to develop protocols that protect athletes from injury. Data Sources: A comprehensive literature review of Medline and Pubmed using key words: lightning, lightning risk factors, lightning safety, lightning detection, and athletic trainers and lightning was completed. Data Synthesis: Factors predisposing athletes to lighting death or injury include: time of year, time of day, the athlete’s age, geographical location, physical location, sex, perspiration level, and lack of education and preparedness by athletes and staff. Although handheld lightning detectors have become widely accessible to detect lightning strikes, their performance has not been independently or objectively confirmed. There is evidence that these detectors inaccurately detect strike locations by recording false strikes and not recording actual strikes. Conclusions: Lightning education and preparation are two factors that can be controlled. Measures need to be taken by Certified Athletic Trainers to ensure the safety of athletes during outdoor athletics. It is critical for athletic trainers and supervising staff members to become fully aware of the risks of lightning strikes in order to most effectively protect everyone under their supervision. Even though lightning detectors have been manufactured in an attempt to minimize death and injuries due to lightning strikes, none of the detectors have been proven to be 100% effective. Educating coaches, athletes, and parents on the risks of lightning and the detection methods available, while implementing an emergency action plan for lightning safety, is crucial to ensure the well being of the student-athlete population.

Sources of coding discrepancies in injury morbidity data: Implications for injury surveillance

Objective: To examine the sources of coding discrepancy for injury morbidity data and explore the implications of these sources for injury surveillance.-------- Method: An on-site medical record review and recoding study was conducted for 4373 injury-related hospital admissions across Australia. Codes from the original dataset were compared to the recoded data to explore the reliability of coded data aand sources of discrepancy.---------- Results: The most common reason for differences in coding overall was assigning the case to a different external cause category with 8.5% assigned to a different category. Differences in the specificity of codes assigned within a category accounted for 7.8% of coder difference. Differences in intent assignment accounted for 3.7% of the differences in code assignment.---------- Conclusions: In the situation where 8 percent of cases are misclassified by major category, the setting of injury targets on the basis of extent of burden is a somewhat blunt instrument Monitoring the effect of prevention programs aimed at reducing risk factors is not possible in datasets with this level of misclassification error in injury cause subcategories. Future research is needed to build the evidence base around the quality and utility of the ICD classification system and application of use of this for injury surveillance in the hospital environment.

Quality of child abuse information in emergency department injury surveillance data : linking hospital and child welfare data

Emergency departments (EDs) are often the first point of contact with an abused child. Despite legal mandate, the reporting of definite or suspected abusive injury to child safety authorities by ED clinicians varies due to a number of factors including training, access to child safety professionals, departmental culture and a fear of ‘getting it wrong’. This study examined the quality of documentation and coding of child abuse captured by ED based injury surveillance data and ED medical records in the state of Queensland and the concordance of these data with child welfare records. A retrospective medical record review was used to examine the clinical documentation of almost 1000 injured children included in the Queensland Injury Surveillance Unit database (QISU) from 10 hospitals in urban and rural centres. Independent experts re-coded the records based on their review of the notes. A data linkage methodology was then used to link these records with records in the state government’s child welfare database. Cases were sampled from three sub-groups according to the surveillance intent codes: Maltreatment by parent, Undetermined and Unintentional injury. Only 0.1% of cases coded as unintentional injury were recoded to maltreatment by parent, while 1.2% of cases coded as maltreatment by parent were reclassified as unintentional and 5% of cases where the intent was undetermined by the triage nurse were recoded as maltreatment by parent. Quality of documentation varied across type of hospital (tertiary referral centre, children’s, urban, regional and remote). Concordance of health data with child welfare data varied across patient subgroups. Outcomes from this research will guide initiatives to improve the quality of intentional child injury surveillance systems.

Feasibility of using health data sources to inform product safety surveillance in Queensland : a report for the Queensland Injury Prevention Council

This report provides an evaluation of the current available evidence-base for identification and surveillance of product-related injuries in children in Queensland. While the focal population was children in Queensland, the identification of information needs and data sources for product safety surveillance has applicability nationally for all age groups. The report firstly summarises the data needs of product safety regulators regarding product-related injury in children, describing the current sources of information informing product safety policy and practice, and documenting the priority product surveillance areas affecting children which have been a focus over recent years in Queensland. Health data sources in Queensland which have the potential to inform product safety surveillance initiatives were evaluated in terms of their ability to address the information needs of product safety regulators. Patterns in product-related injuries in children were analysed using routinely available health data to identify areas for future intervention, and the patterns in product-related injuries in children identified in health data were compared to those identified by product safety regulators. Recommendations were made for information system improvements and improved access to and utilisation of health data for more proactive approaches to product safety surveillance in the future.

A needle in a haystack : the use of routinely collected emergency department injury surveillance data to help identify physical child abuse

A retrospective, descriptive analysis of a sample of children under 18 years presenting to a hospital emergency department (ED) for treatment of an injury was conducted. The aim was to explore characteristics and identify differences between children assigned abuse codes and children assigned unintentional injury codes using an injury surveillance database. Only 0.1% of children had been assigned the abuse code and 3.9% a code indicating possible abuse. Children between 2-5 years formed the largest proportion of those coded to abuse. Superficial injury and bruising were the most common types of injury seen in children in the abuse group and the possible abuse group (26.9% and 18.8% respectively), whereas those with unintentional injury were most likely to present with open wounds (18.4%). This study demonstrates that routinely collected injury surveillance data can be a useful source of information for describing injury characteristics in children assigned abuse codes compared to those assigned no abuse codes.

Machine learning approaches to analysing textual injury surveillance data: A systematic review

Objective To synthesise recent research on the use of machine learning approaches to mining textual injury surveillance data. Design Systematic review. Data sources The electronic databases which were searched included PubMed, Cinahl, Medline, Google Scholar, and Proquest. The bibliography of all relevant articles was examined and associated articles were identified using a snowballing technique. Selection criteria For inclusion, articles were required to meet the following criteria: (a) used a health-related database, (b) focused on injury-related cases, AND used machine learning approaches to analyse textual data. Methods The papers identified through the search were screened resulting in 16 papers selected for review. Articles were reviewed to describe the databases and methodology used, the strength and limitations of different techniques, and quality assurance approaches used. Due to heterogeneity between studies meta-analysis was not performed. Results Occupational injuries were the focus of half of the machine learning studies and the most common methods described were Bayesian probability or Bayesian network based methods to either predict injury categories or extract common injury scenarios. Models were evaluated through either comparison with gold standard data or content expert evaluation or statistical measures of quality. Machine learning was found to provide high precision and accuracy when predicting a small number of categories, was valuable for visualisation of injury patterns and prediction of future outcomes. However, difficulties related to generalizability, source data quality, complexity of models and integration of content and technical knowledge were discussed. Conclusions The use of narrative text for injury surveillance has grown in popularity, complexity and quality over recent years. With advances in data mining techniques, increased capacity for analysis of large databases, and involvement of computer scientists in the injury prevention field, along with more comprehensive use and description of quality assurance methods in text mining approaches, it is likely that we will see a continued growth and advancement in knowledge of text mining in the injury field.

Infant product-related injuries: comparing specialised injury surveillance and routine emergency department data

- Objective To explore the potential for using a basic text search of routine emergency department data to identify product-related injury in infants and to compare the patterns from routine ED data and specialised injury surveillance data. - Methods Data was sourced from the Emergency Department Information System (EDIS) and the Queensland Injury Surveillance Unit (QISU) for all injured infants between 2009 and 2011. A basic text search was developed to identify the top five infant products in QISU. Sensitivity, specificity, and positive predictive value were calculated and a refined search was used with EDIS. Results were manually reviewed to assess validity. Descriptive analysis was conducted to examine patterns between datasets. - Results The basic text search for all products showed high sensitivity and specificity, and most searches showed high positive predictive value. EDIS patterns were similar to QISU patterns with strikingly similar month-of-age injury peaks, admission proportions and types of injuries. - Conclusions This study demonstrated a capacity to identify a sample of valid cases of product-related injuries for specified products using simple text searching of routine ED data. - Implications As the capacity for large datasets grows and the capability to reliably mine text improves, opportunities for expanded sources of injury surveillance data increase. This will ultimately assist stakeholders such as consumer product safety regulators and child safety advocates to appropriately target prevention initiatives.

Harnessing information from injury narrative in the 'big data' era: Understanding and applying machine learning for injury surveillance

Objective Vast amounts of injury narratives are collected daily and are available electronically in real time and have great potential for use in injury surveillance and evaluation. Machine learning algorithms have been developed to assist in identifying cases and classifying mechanisms leading to injury in a much timelier manner than is possible when relying on manual coding of narratives. The aim of this paper is to describe the background, growth, value, challenges and future directions of machine learning as applied to injury surveillance. Methods This paper reviews key aspects of machine learning using injury narratives, providing a case study to demonstrate an application to an established human-machine learning approach. Results The range of applications and utility of narrative text has increased greatly with advancements in computing techniques over time. Practical and feasible methods exist for semi-automatic classification of injury narratives which are accurate, efficient and meaningful. The human-machine learning approach described in the case study achieved high sensitivity and positive predictive value and reduced the need for human coding to less than one-third of cases in one large occupational injury database. Conclusion The last 20 years have seen a dramatic change in the potential for technological advancements in injury surveillance. Machine learning of ‘big injury narrative data’ opens up many possibilities for expanded sources of data which can provide more comprehensive, ongoing and timely surveillance to inform future injury prevention policy and practice.