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Injury Mortality (injury + mortality)
Selected AbstractsUrban-Rural Disparities in Injury Mortality in China, 2006THE JOURNAL OF RURAL HEALTH, Issue 1 2010Guoqing Hu PhD Abstract Context: Urban-rural disparity is an important issue for injury control in China. Details of the urban-rural disparities in fatal injuries have not been analyzed. Purpose: To target key injury causes that most contribute to the urban-rural disparity, we decomposed total urban-rural differences in 2006 injury mortality by gender, age, and cause. Methods: Mortality data came from the Chinese Vital Registration data, covering a sample of about 10% of the total population. The chi-square test was used to test the significance of urban-rural disparities. Findings: For all ages combined, the injury death rate for males was 60.1/100,000 in rural areas compared with 40.9 in urban areas; for females, the respective rates were 31.5 and 23.6/100,000. The greatest disparity was at age <1 year for both sexes, where the rate from unintentional suffocation in rural areas was more than twice the urban rate. The higher mortality from drowning among males of all ages and among females ages 1-24 and 35+ contributed substantially to the age-specific urban-rural disparities. For both sexes, transportation incidents and suicide were the most important contributors to higher rates among rural residents ages 15+. Conclusions: Unintentional suffocation, drowning, transportation incidents, and suicide not only are the major causes of injury death, but also play a key role in explaining the urban-rural disparities in fatal injuries. Further research is needed to identify factors leading to higher rural death rates and to explore economical and feasible interventions for reducing injuries and narrowing the urban-rural gap in injury mortality. [source] The Impact of Injury Coding Schemes on Predicting Hospital Mortality After Pediatric InjuryACADEMIC EMERGENCY MEDICINE, Issue 7 2009Randall S. Burd MD Abstract Objectives:, Accurate adjustment for injury severity is needed to evaluate the effectiveness of trauma management. While the choice of injury coding scheme used for modeling affects performance, the impact of combining coding schemes on performance has not been evaluated. The purpose of this study was to use Bayesian logistic regression to develop models predicting hospital mortality in injured children and to compare the performance of models developed using different injury coding schemes. Methods:, Records of children (age < 15 years) admitted after injury were obtained from the National Trauma Data Bank (NTDB) and the National Pediatric Trauma Registry (NPTR) and used to train Bayesian logistic regression models predicting mortality using three injury coding schemes (International Classification of Disease-9th revision [ICD-9] injury codes, the Abbreviated Injury Scale [AIS] severity scores, and the Barell matrix) and their combinations. Model performance was evaluated using independent data from the NTDB and the Kids' Inpatient Database 2003 (KID). Results:, Discrimination was optimal when modeling both ICD-9 and AIS severity codes (area under the receiver operating curve [AUC] = 0.921 [NTDB] and 0.967 [KID], Hosmer-Lemeshow [HL] h-statistic = 115 [NTDB] and 147 [KID]), while calibration was optimal when modeling coding based on the Barell matrix (AUC = 0.882 [NTDB] and 0.936 [KID], HL h-statistic = 19 [NTDB] and 69 [KID]). When compared to models based on ICD-9 codes alone, models that also included AIS severity scores and coding from the Barell matrix showed improved discrimination and calibration. Conclusions:, Mortality models that incorporate additional injury coding schemes perform better than those based on ICD-9 codes alone in the setting of pediatric trauma. Combining injury coding schemes may be an effective approach for improving the predictive performance of empirically derived estimates of injury mortality. [source] Baseline indicators for measuring progress in preventing falls injury in older peopleAUSTRALIAN AND NEW ZEALAND JOURNAL OF PUBLIC HEALTH, Issue 5 2009Annaliese M. Dowling Abstract Objective: Over recent years, there has been increasing attention given to preventing falls and falls injury in older people through policy and other initiatives. This paper presents a baseline set of fall injury outcome indicators against which these preventive efforts can be assessed in terms of monitoring the rate of fall-related deaths and hospitalisations. Methods: ICD-10-AM coded hospital separations, Australian Bureau of Statistics (ABS) mortality and ABS population data were used to determine the rate of fall-related injury mortality and hospitalisations occurring in people aged 65+ years in New South Wales (NSW), Australia, over the six-year period from 1998/99 to 2003/04, inclusive. Results: Baseline trends for one fatality and five separations-based metrics are presented. Overall, fall mortality rates increased over the six years, with higher rates in males. Falls hospitalisation rates also increased slightly, with higher rates in females. The rates of hip fracture and pelvic fracture hospital separations generally declined over the six years and were highest in females. The level of unspecified and missing information about the place where falls occur increased by 1.5%. Conclusion: Baseline trends in fall injury outcome metrics highlight the severity and frequency of fall injuries before wide scale implementation of the Management Policy to Reduce Fall Injury Among Older People in NSW. Implications: Future use of these metrics will help to evaluate and monitor the progress of falls prevention in older people in NSW. They could also be adopted in other jurisdictions. [source] Bayesian Logistic Injury Severity Score: A Method for Predicting Mortality Using International Classification of Disease-9 CodesACADEMIC EMERGENCY MEDICINE, Issue 5 2008Randall S. Burd MD Abstract Objectives:, Owing to the large number of injury International Classification of Disease-9 revision (ICD-9) codes, it is not feasible to use standard regression methods to estimate the independent risk of death for each injury code. Bayesian logistic regression is a method that can select among a large numbers of predictors without loss of model performance. The purpose of this study was to develop a model for predicting in-hospital trauma deaths based on this method and to compare its performance with the ICD-9,based Injury Severity Score (ICISS). Methods:, The authors used Bayesian logistic regression to train and test models for predicting mortality based on injury ICD-9 codes (2,210 codes) and injury codes with two-way interactions (243,037 codes and interactions) using data from the National Trauma Data Bank (NTDB). They evaluated discrimination using area under the receiver operating curve (AUC) and calibration with the Hosmer-Lemeshow (HL) h-statistic. The authors compared performance of these models with one developed using ICISS. Results:, The discrimination of a model developed using individual ICD-9 codes was similar to that of a model developed using individual codes and their interactions (AUC = 0.888 vs. 0.892). Inclusion of injury interactions, however, improved model calibration (HL h-statistic = 2,737 vs. 1,347). A model based on ICISS had similar discrimination (AUC = .855) but showed worse calibration (HL h-statistic = 45,237) than those based on regression. Conclusions:, A model that incorporates injury interactions had better predictive performance than one based only on individual injuries. A regression approach to predicting injury mortality based on injury ICD-9 codes yields models with better predictive performance than ICISS. [source] | ||||||||||