Using data linkage to assess the impact of motorized recreational vehicle-related injuries in Ohio

Problem

Motorized recreational vehicle (MRV)-related injuries can result in severe medical and financial consequences. The objective of this study was to describe the epidemiology, and clinical and financial impact of MRV-related injuries in Ohio.

Method

Probabilistically linked statewide Emergency Medical Services (EMS) and hospital (inpatient and emergency department) data for 2003 and 2004 were examined. Record pairs with a MRV-related E-code (E821-E823, E825) were included in this study.

Results

There were 2,893 patients with MRV-related injuries, who had linked EMS and hospital records, resulting in more than $15 million in hospital charges and 1,921 inpatient days of hospitalization. The male-to-female ratio was nearly 4:1, and 19% were younger than 16. Almost 82% of cases were not wearing a helmet; there was a trend of decreasing helmet use with increasing age. Mean (SE) inpatient hospital charges and length of stay (LOS) were $22,218 ($1,290) and 3.8 (0.2) days, respectively. The mean (SE) Injury Severity Score (ISS) for inpatients was 9.2 (0.4). Individuals injured on a street/highway were 3.20 times more likely to sustain an ISS ≥ 16 (95% CI: 1.03, 9.88; p = 0.044) and 3.05 times more likely to sustain a traumatic brain injury (TBI) (95% CI: 1.17, 7.94; p = 0.024) than those who were injured at a place designated for sport or recreation. Children aged 12 to 15 and young adults aged 16 to 25 were 2.47 and 2.14 times more likely, respectively, to sustain a TBI than adults aged 36 or older (aged 12 to 15: 95% CI: 1.13, 5.38; p = 0.024; aged 16 to 25: 95% CI: 1.26, 3.64; p = 0.005). Higher ISS was associated with both higher total charges (p < 0.001) and longer LOS (p < 0.001).

Discussion

This study demonstrates that MRV-related injuries are an important public health problem in Ohio, with a substantial clinical and financial impact.

Impact on Industry

Enactment and enforcement of statewide MRV safety legislation and training of MRV users offer valuable opportunities to prevent these costly injuries.     

THE PALMER DROUGHT SEVERITY INDEX AS A MEASURE OF HYDROLOGIC DROUGHT1

ABSTRACT: The Palmer Drought Severity Index (PDSI) is perhaps the most widely used regional drought index. However, there is considerable ambiguity about its value as a measure of hydrologic drought. In this paper the PDSI for climatic divisions in New Jersey is compared to the occurrence within each climatic division of streamflows in their lower quartile for the month (streamflow index), and ground-water levels in their lower quartile for the month (ground-water index). These indices are found to have distinct properties. It is not uncommon for PDSI values to indicate “severe” or “extreme” drought at times when the streamflow or groundwater index is above its lower quartile at many stations within the climatic division. The PDSI values and groundwater index indicate more persistent subnormal conditions than the streamflow index for truncation levels yielding the same total duration of drought over a period. The ground-water index tends to indicate a later beginning to droughts and of the three indices is the most conservative indicator of a drought's end. Drought timing and duration properties for the ground-water index are found to be highly influenced by the average depth to water in the well. Overall, the three indices of drought can provide three very different characterizations of drought. In particular, the results indicate that considerable caution should be exercised in drawing conclusions about hydrologic drought from the PDSI.     

Occupational risks and the value and modelling of a measurement of severity

In studies of occupational risks, severity, which is a component of the estimation of every risk, appears as a multifaceted entity assessable according to numerous criteria. A method of measuring the degree of severity of the consequences of potentially dangerous events would be of undeniable value to organisations seeking to improve their understanding of the complexity of such events. The need to control severity is highlighted by scientifically acquired improvements in the understanding of occupational risks, by certain new regulatory obligations in Europe, and by some requirements in the financial management of organisations. We put forward a statistical way of integrating several constituent elements of severity and hence of determining a relevant, synthetic, one-dimensional index. This is achieved by means of principal component analysis (PCA), which is used here to calculate a resultant severity, as in some physical measurements. We also investigate how severity may be statistically modelled, with the aim of contributing to the quantitative assessment of occupational risks. The choice of parametric models is detailed and illustrated by the search for a suitable model for workplace accidents in an organisational setting. The practical value of modelling severity is two-fold. First, one is able to study the distribution of the numerical values of severity over a continuum (a theoretically infinite numerical set) rather than through a limited number of arbitrarily defined categories. Second, with a generally applicable parametric model, one can estimate the law of probability of a measurement of severity in a particular situation, notably recent or new. Lastly, the statistical concept of risk curve is defined and discussed. The goal is to incorporate the severity component into the risk assessment in the form of a probability law, thus circumventing the difficulties associated with an analysis of scenarios.     

Prioritizing prevention opportunities in the Washington State construction industry, 2003-2007

Objective

This study compares construction industry groups in Washington State by injury severity and cost, and ranks industry groups according to potential for prevention.

Methods

All Washington State workers' compensation compensable claims with date of injury between 2003 and 2007 were classified into North American Industrial Classification System (NAICS) industry groups. Claims were then aggregated by injury type and industry groups were ranked according to a prevention index (PI). The PI is the average of the rank orders of the claim count and the claim incidence rate. A lower PI indicates a higher need for prevention activities. The severity rate was calculated as the number of days of time loss per 10,000 full-time equivalents (FTEs).

Results

For all injury types, construction industry groups occupy 7 of the top 15 PI ranks in Washington State. The severity rate among construction industry groups was twice that for non-construction groups for all injury types. Foundation, structure, and building exterior contractors (NAICS 2381) ranked highest in prevention potential and severity among construction industry groups for most common injury types including falls from elevation, fall on same level, struck by/against, and musculo-skeletal disorders of the neck, back, and upper extremity (WMSDs). Median claim costs by injury type were generally higher among construction industry groups.

Conclusions

The construction industry in Washington State has a high severity rate and potential for prevention. The methods used for characterizing these industry groups can be adapted for comparison within and between other industries and states.

Impact on Industry

These data can be used by industry groups and employers to identify higher cost and higher severity injury types. Knowledge about the relative frequencies and costs associated with different injury types will help employers and construction industry associations make better informed decisions about where prevention efforts are most needed and may have the greatest impact. The results of this study can also be used by industry stakeholders to cooperatively focus on high cost and high severity injuries and explore best practices, interventions, and solutions as demonstrated by efforts to prevent musculoskeletal disorders in masonry (Entzel, Albers, & Welch, 2007). Initiating construction industry groups to focus on high cost and high severity injuries may also help prevent other types of injuries.     

Thermal research on the uncontrolled behavior of styrene bulk polymerization

Thermal runaway can occur during the styrene bulk polymerization process because of easily formed local hotspots resulting from the high viscosity of reactants and the difficulty of heat dissipation. To obtain the thermal hazard characteristics, the polymerization behavior of styrene was investigated using differential scanning calorimetry (DSC) at a scanning rate of β = 2 °C/min. Experimental results showed that the exothermic peaks obtained for heat initiation were different from those obtained when initiator was added. The exothermic peak changed from one to two after the initiator was added. The exothermic onset temperature (T₀) was also reduced. Phi-tech II was utilized to study the bulk polymerization of styrene in an adiabatic environment. The adiabatic temperature rise (ΔT_ad), starting temperature of uncontrolled polymerization (T_star), maximum temperature (T_end), and heat of polymerization (ΔH) under different conditions were acquired. When the dose of the additive was increased, the starting temperature of uncontrolled polymerization (T_star) decreased and the adiabatic temperature rise (ΔT_ad) increased gradually. Severity grading was performed based on the severity evaluation criteria of runaway reaction. The results can help designers decide whether it is necessary to take certain measures to reduce risk.     

Dynamic response and effect of apertures on explosion parameters of flameproof apparatus

Explosion parameters for closed flameproof apparatus are changed when apertures like gap (e.g. push button) and porous structures (breathing element) are introduced on the cover or wall of the flameproof enclosures. Similarly, an interconnecting tube between two enclosures, results in significant change in explosion parameters. It is observed that the maximum explosion pressure, maximum rate of pressure rise and severity index are higher for enclosures with apertures on cover or body than that of enclosures without apertures. In case of two interconnected identical enclosures, the explosion parameters are increased in the secondary enclosure and higher than that of primary enclosure and also of isolated enclosure.     

Severity of emergency natural gas distribution pipeline incidents: Application of an integrated spatio-temporal approach fused with text mining

The transportation of natural gas often relies on pipelines which require constant monitoring and regular maintenance to prevent spills or leaks. Pipeline incidents could pose a huge adverse impact on people, the environment, and society. Numerous efforts have been invested to identify contributing factors to pipeline incidents so that countermeasures could be developed to proactively prevent some incidents and reduce incident severities or impacts. However, the countermeasures may need to vary for different incidents due to the potential heterogeneity between incidents, and such heterogeneity is likely related to the geology, weather, and built environment which vary across space and time domain. The objective of this study is to revisit the correlates of pipeline incidents, focusing on the spatial and temporal patterns of the correlations between natural gas pipeline incident severity and contributing factors. This study leveraged an integrated spatio-temporal modeling approach, namely the Geographically and Temporally Weighted Ordered Logistic Regression (GTWOLR) to model the natural gas pipeline incident report data (2010–2019) from the U.S. Pipeline and Hazardous Material Safety Administration. Text mining was performed to extract additional information from the narratives in reports. Results show several factors have significant spatiotemporally varying correlations with the pipeline incident severity, and these factors include excavation damage, gas explosion, iron pipes, longer incident response time, and longer pipe lifetime. Findings from this study are valuable for pipeline operators, end-users, responders to jointly develop localized strategies to maintain the natural gas distribution system. More implications are discussed in the paper.     

Estimating the contributions of speeding and impaired driving to insurance claim cost

INTRODUCTION: Logistic regression modelling of crash counts likely associated with speeding and impaired driving was earlier found [Zheng, Y., Cooper, P. J., & Dean, C. B. (2007). Modeling the contribution of speeding and impaired driving to insurance claim counts and costs when contributing factors are unknown. Journal of Safety Research, 38(1)] to successfully predict known outcomes in a series of simulations, but the same types of models did not accurately predict average crash costs. The aim of the study reported here was to develop a means to adjust classification model results that would improve their cost-predicting efficiency. METHOD: A classification modelling process was adjusted at the back-end using non-linear optimization to rationalize the classified proportions with the true proportions when the model was applied to representative subsets of the training data. Corrections were developed to account for cost (severity) differences arising from the classification process that were not due to true variations. The process was then applied to insurance claim test data where crash contributors were unknown. RESULTS: The optimization and severity correction procedure resulted in substantial improvement in average crash cost prediction for both impaired and unsafe speed collision involvements. The error measured against true values in 20 simulations was about half for the adjusted classification model of what it was for either unadjusted classification or logistic regression models. CONCLUSIONS: Non-linear optimization of classification matrices appears to be a workable tool for improving the predictive efficiency of models where desired outcomes represent average characteristics of records as compared to simple counts or proportions. IMPACT ON INDUSTRY: Using the methodology on a full-year of insurance claim data indicated that reliance on police-reported records alone would have underestimated the total cost of unsafe speed and impaired crashes by about 40%. Since most jurisdictions use police data to base policy decisions and set program spending around such safety issues, this finding has important implications.     

Analysis of the severity of vehicle-bicycle crashes with data mining techniques

Introduction: Although cycling is increasingly being promoted for transportation, the safety concern of bicyclists is one of the major impediments to their adoption. A thorough investigation on the contributing factors to fatalities and injuries involving bicyclist. Method: This paper designs an integrated data mining framework to determine the significant factors that contribute to the severity of vehicle-bicycle crashes based on the crash dataset of Victorian, Australia (2013–2018). The framework integrates imbalanced data resampling, learning-based feature extraction with gradient boosting algorithm and marginal effect analysis. The top 10 significant predictors of the severity of vehicle-bicycle crashes are extracted, which gives an area under ROC curve (AUC) value of 0.8236 and computing time as 37.8 s. Results: The findings provide insights for understanding and developing countermeasures or policy initiatives to reduce severe vehicle-bicycle crashes.