Motor Vehicle Injuries among Semi Truck Drivers and Sleeper Berth Passengers

Introduction: Injuries and fatalities due to large truck and other vehicle crashes have decreased over the last decade, but motor vehicle injuries remain a leading cause of death for both the working and general populations. The present study was undertaken to determine semi truck driver and sleeper berth passenger injury risk in a moving semi truck collision using a matched-pair cohort study. Method: Study data were obtained from the Kentucky Collision Report Analysis for Safer Highways (CRASH) electronic files for 2000 - 2010. A matched-pair cohort study was used to compare the odds of injury of both drivers and sleeper berth passengers within the same semi truck controlling for variables specific to the crash or the semi truck. The crude odds ratio of injury was estimated and a statistical model for a correlated outcome using generalized estimating equations was utilized. Results: In a moving semi truck collision, the odds for an injury were increased by 2.25 times for both semi truck drivers and sleeper berth passengers who did not use occupant safety restraints compared to semi truck drivers and sleeper berth passengers who used occupant safety restraints at the time of the collision. The driver seat or sleeper berth position in the vehicle was not a significant factor (p-value= 0.31) associated with a moving semi truck collision injury. Conclusion: Nonuse of occupant safety restraints by either drivers or sleeper berth passengers significantly increased the odds of an injury in a moving semi truck collision; semi truck seating position (driver’s seat or sleeper berth) did not increase the odds for an injury in moving collisions. Impact on Industry: Trucking companies should include the mandatory use of occupant safety restraints by both semi truck drivers and sleeper berth passengers in their company safety policies

Workers Compensation-Reported Injuries Among Security and Law Enforcement Personnel in the Private Versus Public Sectors

Background: Private and Public security and law enforcement (SLE) sectors perform multiple overlapping job duties. Methods: Workers’ compensation (WC) SLE first reports of injury (FROI) data (2005–2015) were analyzed to describe injuries, identify differences in awarded WC benefits, and compare the probability of a FROI resulting in awarded benefits between Public and Private SLE. A Pearson’s chi-square test was utilized and reverse selection logistic regression was performed to estimate the odds ratio that a FROI would result in an awarded benefit for Private vs. Public SLE, while adjusting for relevant covariates. Results: Private SLE had higher FROI percentages for younger and for older workers, fall injuries, and back injuries, compared to Public SLE. The adjusted odds that a FROI resulted in an awarded benefit was 1.4 times higher for Private SLE compared to Public SLE; (95% confidence interval [CI] = 1.09,1.69). Middle-aged SLE employee adjusted odds of awarded benefits was 3.3 times (95% CI [1.96, 5.39]) higher compared to younger employees. Adjusted odds of awarded benefits was 3.8 times (95% CI [1.34, 10.61]) higher for gunshots and 1.7 times (95% CI [1.22, 2.39]) higher for fractures/dislocations compared to other nature of injuries. Motor vehicle injury, fall/slip, and strain related FROIs had elevated adjusted odds of awarded benefits compared to other injury causes. Conclusions: Results highlight the importance of injury prevention education and worker safety training for Private and Public SLE sector workers on fall prevention (especially in Private SLE) and strain prevention (especially in Public SLE), as well as motor vehicle safety

Drug Overdose Deaths, Hospitalizations, and Emergency Department Visits in Kentucky, 2000‐2012

This report presents drug overdose morbidity and mortality data for Kentucky residents, using multiple data sources: Kentucky Death Certificate Files, Kentucky Office of Vital Statistics, 2000‐2012 (data captured as of October 21, 2013). The 2009‐2012 files are provisional and subject to change. Kentucky Inpatient Hospitalization (IH) Discharge Files, Cabinet for Health and Family Services, Office of Health Policy, 2000‐2012 (data for 2010‐2012 are provisional and subject to change). Kentucky Emergency Department (ED) Discharge Files, Cabinet for Health and Family Services, Office of Health Policy, 2008‐2012 (data for 2010‐2012 are provisional and subject to change)

Enhancing Timeliness of Drug Overdose Mortality Surveillance: A Machine Learning Approach

BACKGROUND: Timely data is key to effective public health responses to epidemics. Drug overdose deaths are identified in surveillance systems through ICD-10 codes present on death certificates. ICD-10 coding takes time, but free-text information is available on death certificates prior to ICD-10 coding. The objective of this study was to develop a machine learning method to classify free-text death certificates as drug overdoses to provide faster drug overdose mortality surveillance. METHODS: Using 2017–2018 Kentucky death certificate data, free-text fields were tokenized and features were created from these tokens using natural language processing (NLP). Word, bigram, and trigram features were created as well as features indicating the part-of-speech of each word. These features were then used to train machine learning classifiers on 2017 data. The resulting models were tested on 2018 Kentucky data and compared to a simple rule-based classification approach. Documented code for this method is available for reuse and extensions: https://github.com/pjward5656/dcnlp. RESULTS: The top scoring machine learning model achieved 0.96 positive predictive value (PPV) and 0.98 sensitivity for an F-score of 0.97 in identification of fatal drug overdoses on test data. This machine learning model achieved significantly higher performance for sensitivity (p \u3c 0.001) than the rule-based approach. Additional feature engineering may improve the model’s prediction. This model can be deployed on death certificates as soon as the free-text is available, eliminating the time needed to code the death certificates. CONCLUSION: Machine learning using natural language processing is a relatively new approach in the context of surveillance of health conditions. This method presents an accessible application of machine learning that improves the timeliness of drug overdose mortality surveillance. As such, it can be employed to inform public health responses to the drug overdose epidemic in near-real time as opposed to several weeks following events

Testing for Fictive Learning in Decision-Making Under Uncertainty

We conduct two experiments where subjects make a sequence of binary choices between risky and ambiguous binary lotteries. Risky lotteries are defined as lotteries where the relative frequencies of outcomes are known. Ambiguous lotteries are lotteries where the relative frequencies of outcomes are not known or may not exist. The trials in each experiment are divided into three phases: pre-treatment, treatment and post-treatment. The trials in the pre-treatment and post-treatment phases are the same. As such, the trials before and after the treatment phase are dependent, clustered matched-pairs, that we analyze with the alternating logistic regression (ALR) package in SAS. In both experiments, we reveal to each subject the outcomes of her actual and counterfactual choices in the treatment phase. The treatments differ in the complexity of the random process used to generate the relative frequencies of the payoffs of the ambiguous lotteries. In the first experiment, the probabilities can be inferred from the converging sample averages of the observed actual and counterfactual outcomes of the ambiguous lotteries. In the second experiment the sample averages do not converge. If we define fictive learning in an experiment as statistically significant changes in the responses of subjects before and after the treatment phase of an experiment, then we expect fictive learning in the first experiment, but no fictive learning in the second experiment. The surprising finding in this paper is the presence of fictive learning in the second experiment. We attribute this counterintuitive result to apophenia: “seeing meaningful patterns in meaningless or random data.” A refinement of this result is the inference from a subsequent Chi-squared test, that the effects of fictive learning in the first experiment are significantly different from the effects of fictive learning in the second experiment

Tailored education for older patients to facilitate engagement in falls prevention strategies after hospital discharge—A pilot randomized controlled trial

Background The aims of the study were to evaluate the effect of providing tailored falls prevention education in hospital on: i) engagement in targeted falls prevention behaviors in the month after discharge: ii) patients’ self-perceived risk and knowledge about falls and falls prevention strategies after receiving the education. Methods A pilot randomized controlled trial (n = 50): baseline and outcome assessments conducted by blinded researchers. Participants: hospital inpatients 60 years or older, discharged to the community. Participants were randomized into two groups. The intervention was a tailored education package consisting of multimedia falls prevention information with trained health professional follow-up, delivered in addition to usual care. Outcome measures were engagement in falls prevention behaviors in the month after discharge measured at one month after discharge with a structured survey, and participants’ knowledge, confidence and motivation levels before and after receiving the education. The feasibility of providing the intervention was examined and falls outcomes (falls, fall-related injuries) were also collected. Results Forty-eight patients (98%) provided follow-up data. The complete package was provided to 21 (84%) intervention group participants. Participants in the intervention group were significantly more likely to plan how to safely restart functional activities [Adjusted odds ratio 3.80, 95% CI (1.07, 13.52), p = 0.04] and more likely to complete other targeted behaviors such as completing their own home exercise program [Adjusted odds ratio 2.76, 95% CI (0.72, 10.50), p = 0.14] than the control group. The intervention group was significantly more knowledgeable, confident and motivated to engage in falls prevention strategies after receiving the education than the control group. There were 23 falls (n = 5 intervention; n = 18 control) and falls rates were 5.4/1000 patient days (intervention); 18.7/1000 patient days (control). Conclusion This tailored education was received positively by older people, resulted in increased engagement in falls prevention strategies after discharge and is feasible to deliver to older hospital patients. Trial registration The study was registered with the Australian New Zealand Clinical Trials Registry; ACTRN12611000963921 on 8th November 2011

Narrative Text Analysis of Kentucky Tractor Fatality Reports

Narrative information in fatality investigation reports contains data elements not routinely analyzed with coded occupational injury surveillance data. A narrative text analysis of 69 Kentucky Fatality Assessment and Control Evaluation (FACE) agricultural tractor fatality reports from 1994 to 2004 was performed. The FACE reports were developed using the National Institute for Occupational Safety and Health, Division of Safety Research-recommended FACE report format that incorporates Haddon\u27s matrix. Haddon\u27s matrix separates the fatal incident into three event phases and is used to develop points of intervention based on human, organizational, and environmental factors. A multivariate logistic regression analysis for association between identified exposure variables and the outcomes of interest was undertaken. The operation of a tractor with an attached bucket, muddy terrain, and being thrown from the tractor were independent risk factors for being declared dead at the scene . A tractor rollover and operation of a tractor on a slope were independent risk factors for being crushed by a tractor. Narrative text analysis of FACE fatality investigation reports is a valuable tool for the identification of additional factors contributing to tractor fatalities that can inform farm safety training, identify new areas for agricultural interventions, and support the development of new agricultural engineering strategies. (c) 2007 Elsevier Ltd. All rights reserved

Enhancing timeliness of drug overdose mortality surveillance: A machine learning approach.

BACKGROUND:Timely data is key to effective public health responses to epidemics. Drug overdose deaths are identified in surveillance systems through ICD-10 codes present on death certificates. ICD-10 coding takes time, but free-text information is available on death certificates prior to ICD-10 coding. The objective of this study was to develop a machine learning method to classify free-text death certificates as drug overdoses to provide faster drug overdose mortality surveillance. METHODS:Using 2017-2018 Kentucky death certificate data, free-text fields were tokenized and features were created from these tokens using natural language processing (NLP). Word, bigram, and trigram features were created as well as features indicating the part-of-speech of each word. These features were then used to train machine learning classifiers on 2017 data. The resulting models were tested on 2018 Kentucky data and compared to a simple rule-based classification approach. Documented code for this method is available for reuse and extensions: https://github.com/pjward5656/dcnlp. RESULTS:The top scoring machine learning model achieved 0.96 positive predictive value (PPV) and 0.98 sensitivity for an F-score of 0.97 in identification of fatal drug overdoses on test data. This machine learning model achieved significantly higher performance for sensitivity (p<0.001) than the rule-based approach. Additional feature engineering may improve the model's prediction. This model can be deployed on death certificates as soon as the free-text is available, eliminating the time needed to code the death certificates. CONCLUSION:Machine learning using natural language processing is a relatively new approach in the context of surveillance of health conditions. This method presents an accessible application of machine learning that improves the timeliness of drug overdose mortality surveillance. As such, it can be employed to inform public health responses to the drug overdose epidemic in near-real time as opposed to several weeks following events