Helen R. Marucci-Wellman

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. 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. Methods 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. Results 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. Conclusions 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.

Bayesian methods: a useful tool for classifying injury narratives into cause groups

To compare two Bayesian methods (Fuzzy and Naïve) for classifying injury narratives in large administrative databases into event cause groups, a dataset of 14 000 narratives was randomly extracted from claims filed with a worker’s compensation insurance provider. Two expert coders assigned one-digit and two-digit Bureau of Labor Statistics (BLS) Occupational Injury and Illness Classification event codes to each narrative. The narratives were separated into a training set of 11 000 cases and a prediction set of 3000 cases. The training set was used to develop two Bayesian classifiers that assigned BLS codes to narratives. Each model was then evaluated for the prediction set. Both models performed well and tended to predict one-digit BLS codes more accurately than two-digit codes. The overall sensitivity of the Fuzzy method was, respectively, 78% and 64% for one-digit and two-digit codes, specificity was 93% and 95%, and positive predictive value (PPV) was 78% and 65%. The Naïve method showed similar accuracy: a sensitivity of 80% and 70%, specificity of 96% and 97%, and PPV of 80% and 70%. For large administrative databases, Bayesian methods show significant promise as a means of classifying injury narratives into cause groups. Overall, Naïve Bayes provided slightly more accurate predictions than Fuzzy Bayes.

Chronotypes in the US – Influence of age and sex

An individual’s chronotype reflects how the circadian system embeds itself into the 24-h day with rhythms in physiology, cognition and behavior occurring accordingly earlier or later. In view of an increasing number of people working at unusual times and linked health and safety risks, the wide range in human chronotypes may provide opportunities to allow people to work (and sleep) at times that are in synch with their circadian physiology. We aimed at estimating the distribution of chronotypes in the US population by age and sex. Twelve years (2003–2014) of pooled diary data from the American Time Use Survey were used to calculate chronotype based on mid-point of sleep on weekends (MSFWe, n = 53,689). We observed a near-normal distribution overall and within each age group. The distribution’s mean value is systematically different with age, shifting later during adolescence, showing a peak in ‘lateness’ at ~19 years, and shifting earlier thereafter. Men are typically later chronotypes than women before 40, but earlier types after 40. The greatest differences are observed between 15 and 25 for both sexes, equaling more than 50% of the total chronotype difference across all age groups. The variability in chronotype decreases with age, but is generally higher in males than females. This is the first study to estimate the distribution and prevalence of individual chronotypes in the US population based on a large-scale, nationally representative sample. Our finding that adolescents are on average the latest chronotypes supports delaying school start times to benefit their sleep and circadian alignment. The generally wide range in chronotypes may provide opportunities for tailored work schedules by matching external and internal time, potentially decreasing long- and short-term health and safety risks.

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 8.4% 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. CONCLUSIONS 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.

A combined Fuzzy and Naïve Bayesian strategy can be used to assign event codes to injury narratives

Background Bayesian methods show promise for classifying injury narratives from large administrative datasets into cause groups. This study examined a combined approach where two Bayesian models (Fuzzy and Naïve) were used to either classify a narrative or select it for manual review. Methods Injury narratives were extracted from claims filed with a workers compensation insurance provider between January 2002 and December 2004. Narratives were separated into a training set (n=11,000) and prediction set (n=3,000). Expert coders assigned two-digit Bureau of Labor Statistics Occupational Injury and Illness Classification event codes to each narrative. Fuzzy and Naïve Bayesian models were developed using manually classified cases in the training set. Two semi-automatic machine coding strategies were evaluated. The first strategy assigned cases for manual review if the Fuzzy and Naïve models disagreed on the classification. The second strategy selected additional cases for manual review from the Agree dataset using prediction strength to reach a level of 50% computer coding and 50% manual coding. Results When agreement alone was used as the filtering strategy, the majority were coded by the computer (n=1,928, 64%) leaving 36% for manual review. The overall combined (human plus computer) sensitivity was 0.90 and positive predictive value (PPV) was >0.90 for 11 of 18 2-digit event categories. Implementing the 2nd strategy improved results with an overall sensitivity of 0.95 and PPV >0.90 for 17 of 18 categories. Conclusions A combined Naïve-Fuzzy Bayesian approach can classify some narratives with high accuracy and identify others most beneficial for manual review, reducing the burden on human coders.

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. 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.

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. 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.

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

ABSTRACT 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.

Circumstances Of Fall-Related Injuries By Age And Gender Among Community-Dwelling Adults In The United States

Introduction Falls are the leading cause of injury in almost all age-strata in the U.S. However, fall-related injuries (FI) and their circumstances are under-studied at the population level, particularly among young and middle-aged adults. This study examined the circumstances of FI among community-dwelling U.S. adults, by age and gender. Methods Narrative texts of FI from the National Health Interview Survey (1997–2010) were coded using a customized taxonomy to assess place, activity, initiating event, hazards, contributing factors, fall height, and work-relatedness of FI. Weighted proportions and incidence rates of FI were calculated across six age-gender groups (18–44, 45–64, 65+ years; women, men). Results The proportion of FI occurring indoors increased with age in both genders (22%, 30%, and 48% among men, and 40%, 49% and 62% among women for 18–44, 45–64, 65+ age-groups, respectively). In each age group the proportion of indoor FI was higher among women as compared to men. Among women, using the stairs was the second leading activity (after walking) at the time of FI (19%, 14% and 10% for women in 18–44, 45–64, 65+ age groups, respectively). FI associated with tripping increased with age among both genders, and women were more likely to trip than men in every age group. Of all age-gender groups, the rate of FI while using ladders was the highest among middle-aged men (3.3 per 1000 person-year, 95% CI 2.0, 4.5). Large objects, stairs and steps, and surface contamination were the three most common hazards noted for 15%, 14% and 13% of fall-related injuries, respectively. Conclusions The rate and the circumstances of FI differ by age and gender. Understanding these differences and obtaining information about circumstances could be vital for developing effective interventions to prevent falls and FI.

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. 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. CONCLUSIONS 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.