Falls are the leading cause of unintentional injuries in the U.S.; however, national estimates for all community-dwelling adults are lacking. This study estimated the national incidence of falls and ...fall-related injuries among community-dwelling U.S. adults by age and gender and the trends in fall-related injuries across the adult life span.
Nationally representative data from the National Health Interview Survey (NHIS) 2008 Balance and Dizziness supplement was used to develop national estimates of falls, and pooled data from the NHIS was used to calculate estimates of fall-related injuries in the U.S. and related trends from 2004-2013. Costs of unintentional fall-related injuries were extracted from the CDC's Web-based Injury Statistics Query and Reporting System.
Twelve percent of community-dwelling U.S. adults reported falling in the previous year for a total estimate of 80 million falls at a rate of 37.2 falls per 100 person-years. On average, 9.9 million fall-related injuries occurred each year with a rate of 4.38 fall-related injuries per 100 person-years. In the previous three months, 2.0% of older adults (65+), 1.1% of middle-aged adults (45-64) and 0.7% of young adults (18-44) reported a fall-related injury. Of all fall-related injuries among community-dwelling adults, 32.3% occurred among older adults, 35.3% among middle-aged adults and 32.3% among younger adults. The age-adjusted rate of fall-related injuries increased 4% per year among older women (95% CI 1%-7%) from 2004 to 2013. Among U.S. adults, the total lifetime cost of annual unintentional fall-related injuries that resulted in a fatality, hospitalization or treatment in an emergency department was 111 billion U.S. dollars in 2010.
Falls and fall-related injuries represent a significant health and safety problem for adults of all ages. The findings suggest that adult fall prevention efforts should consider the entire adult lifespan to ensure a greater public health benefit.
•Manual classification of the cause/events leading to injury is useful for injury prevention but can be prohibitive for large batches of narratives.•Human-machine learning ensemble approaches ...maximize the accuracy of the machine-assigned codes allowing strategic filtering for manual review.•If resources are constrained at a low level, the best approach for accuracy is to combine manual coding with codes assigned by the LR algorithm alone.•An ensemble approach to filtering affords more confidence in classifications if models make predictions in fundamentally different ways.•Coding rarer events accurately requires sophisticated filtering or integration of highly tailored resource- intensive methods such as NLP.
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. These algorithms are known to do well classifying narrative text and are fairly easy to implement with off-the-shelf software packages such as Python. We propose human-machine learning ensemble approaches which maximize the power and accuracy of the algorithms for machine-assigned codes and allow for strategic filtering of rare, emerging or ambiguous narratives for manual review. We compare human-machine approaches based on filtering on the prediction strength of the classifier vs. agreement between algorithms.
Regularized Logistic Regression (LR) was the best performing algorithm alone. Using this algorithm and filtering out the bottom 30% of predictions for manual review resulted in high accuracy (overall sensitivity/positive predictive value of 0.89) of the final machine-human coded dataset. The best pairings of algorithms included Naïve Bayes with Support Vector Machine whereby the triple ensemble NBSW=NBBI-GRAM=SVM had very high performance (0.93 overall sensitivity/positive predictive value and high accuracy (i.e. high sensitivity and positive predictive values)) across both large and small categories leaving 41% of the narratives for manual review. Integrating LR into this ensemble mix improved performance only slightly.
For large administrative datasets we propose incorporation of methods based on human-machine pairings such as we have done here, utilizing readily-available off-the-shelf machine learning techniques and resulting in only a fraction of narratives that require manual review. Human-machine ensemble methods are likely to improve performance over total manual coding.
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. Medical costs were lower in states with more workers receiving Social Security Disability, and in areas with lower MHI, but this varied in magnitude and direction among neighborhoods. Medical costs were higher in more urban, more racially diverse, and lower education neighborhoods.
Conclusions
Regional SE disparities in medical costs and LOD occur even when health insurance, health care availability, and indemnity benefits are similar. Results suggest opportunities to improve care and disability outcomes through targeted health care and disability interventions.
Length of Disability and Medical Costs in Low Back Pain Shraim, Mujahed; Cifuentes, Manuel; Willetts, Joanna L. ...
Journal of occupational and environmental medicine,
2015-December, Volume:
57, Issue:
12
Journal Article
Peer reviewed
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. Limiting the option to change providers and having a fee schedule were associated with longer LOD, except that allowing a one-time treating provider change was associated with lower medical costs and shorter LOD.
CONCLUSIONS:WC policies about wage replacement and medical treatment appear to be associated with WC LBP outcomes, and might represent opportunities to improve LOD and reduce medical costs in occupational LBP.
Public health surveillance programs in the U.S. are undergoing landmark changes with the availability of electronic health records and advancements in information technology. Injury narratives ...gathered from hospital records, workers compensation claims or national surveys can be very useful for identifying antecedents to injury or emerging risks. However, classifying narratives manually can become prohibitive for large datasets.
The purpose of this study was to develop a human–machine system that could be relatively easily tailored to routinely and accurately classify injury narratives from large administrative databases such as workers compensation. We used a semi-automated approach based on two Naïve Bayesian algorithms to classify 15,000 workers compensation narratives into two-digit Bureau of Labor Statistics (BLS) event (leading to injury) codes. Narratives were filtered out for manual review if the algorithms disagreed or made weak predictions.
This approach resulted in an overall accuracy of 87%, with consistently high positive predictive values across all two-digit BLS event categories including the very small categories (e.g., exposure to noise, needle sticks). The Naïve Bayes algorithms were able to identify and accurately machine code most narratives leaving only 32% (4853) for manual review. This strategy substantially reduces the need for resources compared with manual review alone.
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.
A retrospective cohort of 59,360 LBP cases from 49 states, filed between 2002 and 2008, and examined LOD as the outcome. LBP cases with at least 1 day of work disability were identified by reviewing indemnity service records and medical bills using a comprehensive list of codes from the International Classification of Diseases, Ninth Edition (ICD-9) indicating LBP or nonspecific back pain, excluding medically complicated cases.
We found significant between-state variations in the negative impact of eMRI on LOD ranging from 3.4 days in Tennessee to 14.8 days in New Hampshire. Higher negative impact of eMRI on LOD was mainly associated with female gender, state workers' compensation (WC) policy not limiting initial treating provider choice, higher state orthopedic surgeon density, and lower state MRI facility density.
State WC policies regulating selection of healthcare provider and structural factors affecting quality of medical care modify the impact of eMRI not adherent to guidelines. Targeted healthcare and work disability prevention interventions may improve work disability outcomes in patients with occupational LBP.
We compared the risk of injury for multiple job holders (MJHs) with that for single job holders (SJHs).
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.
Approximately 8.4% of those employed reported working more than 1 job in the week before the interview. The rate of work and nonwork injury episodes per 100 employed workers was higher for MJHs than for SJHs (4.2; 95% confidence interval CI = 3.5, 4.8; vs 3.3; 95% CI = 3.1, 3.5 work injuries and 9.9; 95% CI = 8.9, 10.9; vs 7.4; 95% CI = 7.1, 7.6 nonwork injuries per 100 workers, respectively). When calculated per 100 full-time equivalents (P < .05), the rate ratio remained higher for MJHs.
Our findings suggest that working in multiple jobs is associated with an increased risk of an injury, both at work and not at work, and should be considered in injury surveillance.
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. We used data from the Bureau of Labor Statistics US American Time Use Survey (ATUS) pooled over a 9-year period (2003-2011). We found that MJHs had significantly reduced sleep duration compared with SJHs due to a number of independent factors, such as working longer hours and more often late at night. Male MJHs, working in their primary job or more than one job on the diary day, also had significantly shorter sleep durations (up to 40 minutes less on a weekend day) than male SJHs, even after controlling for all other factors. Therefore, duration of work hours, time of day working and duration of travel for work may not be the only factors to consider when understanding if male MJHs are able to fit in enough recuperative rest from their busy schedule. Work at night had the greatest impact on sleep duration for females, reducing sleep time by almost an hour compared with females who did not work at night. We also hypothesize that the high frequency or fragmentation of non-leisure activities (e.g. work and travel for work) throughout the day and between jobs may have an additional impact on the duration and quality of sleep for MJHs.
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.
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.
The MJHs often worked 2 part-time jobs (20%), long weekly hours (27% worked 60+ hours), and on weekends. The MJHs working multiple jobs on the diary day averaged more than 2 additional work hours (2.25 weekday, 2.75 weekend day; P < .05), odd hours (more often between 5 pm and 7 am), with more work travel time (10 minutes weekday, 9 minutes weekend day; P < .05) and less sleep (-45 minutes weekday, -62 minutes weekend day; P < .05) and time for other household (P < .05) and leisure (P < .05) activities than SJHs.
Because of long work hours, long daily commutes, multiple shifts, and less sleep and leisure time, MJHs may be at heightened risk of fatigue and injury.
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.
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.
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 semiautomatic classification of injury narratives which are accurate, efficient and meaningful. The human-machine learning approach described in the case study achieved high sensitivity and PPV and reduced the need for human coding to less than a third of cases in one large occupational injury database.
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.
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