Why does the adverse effect of inappropriate MRI for LBP vary by geographic location? An exploratory analysis.

Background: Early magnetic resonance imaging (eMRI) for nonspecific low back pain (LBP) not adherent to clinical guidelines is linked with prolonged work disability. Although the prevalence of eMRI for occupational LBP varies substantially among states, it is unknown whether the risk of prolonged disability associated with eMRI varies according to individual and area-level characteristics. The aim was to explore whether the known risk of increased length of disability (LOD) associated with eMRI scanning not adherent to guidelines for occupational LBP varies according to patient and area-level characteristics, and the potential reasons for any observed variations.

Regional socioeconomic disparities in outcomes for workers with low back pain in the United States.

Background: Although regional socioeconomic (SE) factors have been associated with worse health outcomes, prior studies have not addressed important confounders or work disability.

Methods: A national sample of 59 360 workers' compensation (WC) cases to evaluate impact of regional SE factors on medical costs and length of disability (LOD) in occupational low back pain (LBP).

Results: Lower neighborhood median household incomes (MHI) and higher state unemployment rates were associated with longer LOD.

Classifying injury narratives of large administrative databases for surveillance-A practical approach combining machine learning ensembles and human review.

Injury narratives are now available real time and include useful information for injury surveillance and prevention. However, manual classification of the cause or events leading to injury found in large batches of narratives, such as workers compensation claims databases, can be prohibitive. In this study we compare the utility of four machine learning algorithms (Naïve Bayes, Single word and Bi-gram models, Support Vector Machine and Logistic Regression) for classifying narratives into Bureau of Labor Statistics Occupational Injury and Illness event leading to injury classifications for a large workers compensation database.

Working multiple jobs over a day or a week: Short-term effects on sleep duration.

Approximately 10% of the employed population in the United States works in multiple jobs. They are more likely to work long hours and in nonstandard work schedules, factors known to impact sleep duration and quality, and increase the risk of injury. In this study we used multivariate regression models to compare the duration of sleep in a 24-hour period between workers working in multiple jobs (MJHs) with single job holders (SJHs) controlling for other work schedule and demographic factors.

Combining statistics from two national complex surveys to estimate injury rates per hour exposed and variance by activity in the USA.

Background: A common issue in descriptive injury epidemiology is that in order to calculate injury rates that account for the time spent in an activity, both injury cases and exposure time of specific activities need to be collected. In reality, few national surveys have this capacity. To address this issue, we combined statistics from two different national complex surveys as inputs for the numerator and denominator to estimate injury rate, accounting for the time spent in specific activities and included a procedure to estimate variance using the combined surveys.

Falls and Fall-Related Injuries among Community-Dwelling Adults in the United States.

Introduction: Falls are the leading cause of unintentional injuries in the U.S.; however, national estimates for all community-dwelling adults are lacking.

Harnessing information from injury narratives 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.

The direct cost burden of 13years of disabling workplace injuries in the U.S. (1998-2010): Findings from the Liberty Mutual Workplace Safety Index.

Introduction: Although occupational injuries are among the leading causes of death and disability around the world, the burden due to occupational injuries has historically been under-recognized, obscuring the need to address a major public health problem.

Methods: We established the Liberty Mutual Workplace Safety Index (LMWSI) to provide a reliable annual metric of the leading causes of the most serious workplace injuries in the United States based on direct workers compensation (WC) costs.

Results: More than $600 billion in direct WC costs were spent on the most disabling compensable non-fatal injuries and illnesses in the United States from 1998 to 2010.

Length of Disability and Medical Costs in Low Back Pain: Do State Workers' Compensation Policies Make a Difference?

Objective: The aim of the study was to examine the impact of state workers' compensation (WC) policies regarding wage replacement and medical benefits on medical costs and length of disability (LOD) in workers with low back pain (LBP).

Methods: Retrospective cohort analysis of LBP claims from 49 states (n = 59,360) filed between 2002 and 2008, extracted from a large WC administrative database.

Results: Longer retroactive periods and state WC laws allowing treating provider choice were associated with higher medical costs and longer LOD.

A practical tool for public health surveillance: Semi-automated coding of short injury narratives from large administrative databases using Naïve Bayes algorithms.

Public health surveillance programs in the U.S. are undergoing landmark changes with the availability of electronic health records and advancements in information technology.

Differences in time use and activity patterns when adding a second job: implications for health and safety in the United States.

Objectives: We compared work and lifestyle activities for workers who work in 1 job with those who work in multiple jobs during a 1-week period.

Methods: We used information from the 2003-2011 American Time Use Survey to classify workers into 6 work groups based on whether they were a single (SJH) or multiple (MJH) job holder and whether they worked their primary, other, multiple, or no job on the diary day.

Results: The MJHs often worked 2 part-time jobs (20%), long weekly hours (27% worked 60+ hours), and on weekends.

Work in multiple jobs and the risk of injury in the US working population.

Objectives: We compared the risk of injury for multiple job holders (MJHs) with that for single job holders (SJHs).

Methods: We used information from the National Health Interview Survey for the years 1997 through 2011 to estimate the rate of multiple job holding in the United States and compared characteristics and rates of self-reported injury (work and nonwork) for SJHs versus MJHs.

Results: Approximately 8.