Introduction: This study aimed to develop a set of broad clinical diagnosis (ClinDx) groups relevant to occupational safety and health. The ClinDx groups are necessary for analysis and interpretation of longitudinal health data that include injury and disease codes from the Ninth and Tenth Revision of the International Classification of Disease, Clinical Modification (ICD-9-CM, ICD-10-CM). Methods: Claims data were analyzed for Ohio Bureau of Workers’ Compensation insured employers from 2011 to 2018. We used interrupted time series regression models to estimate level (frequency) and slope (trend) changes to the percentage of each ClinDx group in October 2015. We created ClinDx groups aligned with ICD-10-CM structure and coding principles. Each ClinDx group was counted once per claim (distinct groups). Monthly percentages were calculated based on the injury date. When present, seasonality was assessed separately for each outcome using an autoregressive-moving average model. Results: The final set of ClinDx groups included 57 mutually exclusive and exhaustive groups. The study population included 661,684 claims, with 959,322 distinct ClinDx groups. Among all claims, 96.27% included injury code(s) and 11.77% included disease(s) codes. At the transition to ICD-10-CM, 33 ClinDx groups lacked any statistically significant (P < 0.05) changes between periods. We observed level changes for 17 ClinDx groups and slope changes for nine groups. Eight ClinDx groups had ≥ 20% (+/-) level changes. Conclusion: While the transition to ICD-10-CM is a break in series, about two-thirds of disease groups and half of injury groups were relatively stable across the transition. These findings also underscore the need for characterizing both injury and disease outcomes when analyzing workers’ compensation data. Practical Applications: The 57 ClinDx groups created in this study may be a practical starting point for other occupational epidemiologic analyses that include a mixture of ICD-9-CM and ICD-10-CM data. © 2024

Background: This study examined age-group differences in the rate, severity, and cost of injuries among construction workers to support evidence-based worker safety and health interventions in the construction industry. Methods: Ohio workers' compensation claims for construction workers were used to estimate claim rates and costs by age group. We analyzed claims data auto-coded into five event/exposure categories: transportation incidents; slips, trips, and falls (STFs); exposure to harmful substances and environments (EHS); contact with objects and equipment (COB); and overexertion and bodily reaction. American Community Survey data were used to determine the proportion of workers by age group. Results: From 2007-2017, among 72,416 accepted injury claims for 166,000 construction full-time equivalent (FTE) per year, nearly half were caused by COB, followed by STFs (20%) and overexertion (20%). Claim rates related to COB and EHS were highest among those 18-24 years old, with claim rates of 313.5 and 25.9 per 10,000 FTE, respectively. STFs increased with age, with the highest claim rates for those 55-64 years old (94.2 claims per 10,000 FTE). Overexertion claim rates increased and then declined with age, with the highest claim rate for those 35-44 years old (87.3 per 10,000 FTE). While younger workers had higher injury rates, older workers had higher proportions of lost-time claims and costs per claim. The total cost per FTE was highest for those 45-54 years old ($1,122 per FTE). Conclusion: The variation in rates of injury types by age suggests age-specific prevention strategies may be useful.

Introduction: Managing and improving occupational safety and health requires evaluating performance. Organizations are encouraged to use both lagging indicators (such as injury rates and costs) and leading indicators (such as questionnaire-assessed safety hazards and management practices) for this purpose, but the association between types of indicators over time can be complex. Longitudinal data can assist in clarifying these associations and increasing indicator utility. Method: Employer data were used to evaluate the reliability and predictive validity of a safety management questionnaire. Employers’ longitudinal questionnaire responses and workers’ compensation (WC) claims data were analyzed using a marginal model with time-dependent covariates. Multivariable Poisson and linear regression analyses with claim rate and logarithmic cost, respectively, as dependent variables were carried out after adjusting for industry sector and size. Questionnaire data were used to evaluate questionnaire scaling properties and to assess generalizability of results. Results: One safety management scale was associated with a better WC outcome as predicted and two scales were unexpectedly associated with poorer WC claim outcomes. Analyses assisted in interpreting the latter results, suggesting that WC outcomes were a stimulus for change in some cases. Twelve hazards assessed on the questionnaire were associated with poorer WC claim outcomes as predicted. Conclusions: This study extends leading indicator research using longitudinal questionnaire and WC claims data from employers. Analyses provided insight into associations between leading and lagging indicators, emphasizing the importance of both for safety improvement. Safety management questionnaire scales were predictive of WC claim outcomes, although support for hazard assessments as leading indicators was stronger. Practical Applications: This study supports the use of employer-completed hazard assessment questionnaires for targeting and prioritizing improvement efforts. Employer-completed safety management scales may be useful for directing improvement efforts, although the conditions under which they are completed, including submission to insurers, require additional consideration. © 2022

OBJECTIVE: This study evaluated the impact of a state workers' compensation (WC) insurer's onsite risk control (RC) services on insured employers' WC claim frequency and cost. METHODS: We used two methods to model 2004-2017 claims data from 4,606 employers that received RC visits over time and compare this claims experience to matching employers that did not receive RC services. RESULTS: Relative total WC claim rates increased slightly after RC services, while relative lost-time claims rates either remained similar or decreased and WC cost rates decreased. The impact of RC services on reducing WC costs was cumulative up to the fourth visit but diminished thereafter. CONCLUSIONS: The insurer RC consultation program was effective in reducing WC cost rates for serviced employers. This is consistent with other research conducted on insurer RC services and related regulatory visits.

Introduction: This study analyzed workers compensation (WC) claims among private employers insured by the Ohio state-based WC carrier to identify high-risk industries by detailed cause of injury. Methods: A machine learning algorithm was used to code each claim by U.S. Bureau of Labor Statistics (BLS) event/exposure. The codes assigned to lost-time (LT) claims with lower algorithm probabilities of accurate classification or those LT claims with high costs were manually reviewed. WC data were linked with the state's unemployment insurance (UI) data to identify the employer's industry and number of employees. BLS data on hours worked per employee were used to estimate full-time equivalents (FTE) and calculate rates of WC claims per 100 FTE. Results: 140,780 LT claims and 633,373 medical-only claims were analyzed. Although counts and rates of LT WC claims declined from 2007 to 2017, the shares of leading LT injury event/exposures remained largely unchanged. LT claims due to Overexertion and Bodily Reaction (33.0%) were most common, followed by Falls, Slips, and Trips (31.4%), Contact with Objects and Equipment (22.5%), Transportation Incidents (7.0%), Exposure to Harmful Substances or Environments (2.8%), Violence and Other Injuries by Persons or Animals (2.5%), and Fires and Explosions (0.4%). These findings are consistent with other reported data. The proportions of injury event/exposures varied by industry, and high-risk industries were identified. Conclusions: Injuries have been reduced, but prevention challenges remain in certain industries. Available evidence on intervention effectiveness was summarized and mapped to the analysis results to demonstrate how the results can guide prevention efforts. Practical Applications: Employers, safety/health practitioners, researchers, WC insurers, and bureaus can use these data and machine learning methods to understand industry differences in the level and mix of risks, as well as industry trends, and to tailor safety, health, and disability prevention services and research. 2021

Overexertion is a leading cause of work-related musculoskeletal disorders (WMSDs) among construction workers. Nearly 90% of construction jobs require manual handling of materials for approximately one half of the worker's time (1). In 2015, overexertion from lifting and lowering materials caused 30% of WMSDs among construction workers; overexertion involving pushing, pulling, holding, carrying, and catching materials caused an additional 37% of WMSDs (1). This study examined the rate and cost of WMSD claims from overexertion among Ohio construction workers during 2007-2017. Workers' compensation claims related to overexertion that were submitted to the Ohio Bureau of Worker's Compensation (OHBWC) by workers in the construction industry for injuries and illnesses occurring during 2007-2017 were analyzed. Rates and costs of allowed claims were measured by age group. Workers aged 35-44 years experienced the highest claim rate: 63 per 10,000 full-time employees (FTEs) for WMSDs from overexertion. However, claims by workers aged 45-54 years and 55-64 years were more costly on average and resulted in more days away from work. Ergonomic design improvements and interventions are needed to ensure that the majority of construction workers can safely perform jobs throughout their careers. Age-specific WMSD prevention and risk communication efforts also might be helpful.

INTRODUCTION: Skilled nursing facilities have one of the highest rates of occupational injury and illness among all industries. This study quantifies the burden of occupational injury and illness in this industry using data from a single state-based workers' compensation (WC) system. METHODS: Ohio Bureau of Workers' Compensation claims from 2001 to 2012 were analyzed among privately owned, state-insured skilled nursing facilities and are presented as claim counts and rates per 100 full-time equivalents (FTE). Worker, employer, incident, and injury characteristics were examined among all claims and by medical-only (medical care expenses and/or less than eight days away from work) and lost-time (eight days or more away from work) claim types. RESULTS: There were 56,442 claims in this population of Ohio skilled nursing facilities from 2001 to 2012. Overexertion and bodily reaction, slips, trips, and falls, and contact with objects and equipment accounted for the majority of all WC claims (89%). Overexertion and bodily reaction, and slips, trips, and falls comprised 85% of the 10,793 lost-time claims. The highest injury event/exposure rates for all claims were for overexertion and bodily reaction (3.7 per 100 FTE for all claims), followed by slip, trips, and falls (2.1), and contact with objects and equipment (1.9). CONCLUSION: Understanding the details surrounding injury events and exposures resulting in WC claims can help better align prevention efforts, such as incorporation of safe patient handling policies and lifting aids, improvement in housekeeping practices, and employee training within skilled nursing facilities to prevent worker injury and mitigate related expenses.

This study evaluated the effectiveness of ergonomic interventions in material handling operations involving 33 employers and 535 employees from 2012 to 2017. Outcomes included employee-reported low back/upper extremity pain and safety incidents at baseline, every three months, and annually for up to two years. A total of 32.5% of employees completed at least one survey, while 13.6% completed all nine surveys over two years. Among highly exposed employees (who reported handling >= 50 lbs. > 33% of the time), upper extremity pain frequency and severity were lower among those who reported using the intervention routinely versus those that reported using their body strength alone to handle objects >= 50 lbs. After excluding from analyses one employer that used anti-fatigue mats, low back pain frequency was also significantly lower among highly exposed intervention users. In conclusion, there was some evidence that the interventions were effective in reducing employee-reported pain for highly exposed employees.

This paper presents a review of 153 case studies of equipment interventions to improve safety and health of construction businesses in Ohio in 2003-2016. These represent $6.46 million (2016 USD) in purchases incentivized through the Ohio Bureau of Workers' Compensation (OHBWC) Safety Intervention Grant (SIG) program. The source data in the review were extracted from employer grant applications and final reports of the case studies. Results were aggregated by type of construction equipment and included the reduction in safety and ergonomic hazards (risk factors for work-related musculoskeletal disorders), and an assessment of the quality of the case studies as determined through criteria established by the authors. Equipment associated with greatest reduction in risk factors and with case studies of higher quality were electrical cable feeding/pulling systems, concrete sawing equipment, skid steer attachments for concrete breaking, and manlifts (boom lifts). This review illustrates challenges in demonstrating efficacy of equipment interventions to improve construction safety/health-even from case studies within a structured health/safety program. The authors are aware of no other systematic review of case studies reporting on experiences with health/safety intervention equipment specific to the construction industry. 2020 American Society of Civil Engineers.

BACKGROUND: The purpose of this analysis was to identify and prioritize high-risk industry groups for traumatic brain injury (TBI) prevention efforts. METHODS: Workers with TBI from 2001 to 2011 were identified from the Ohio Bureau of Workers' Compensation data. To prioritize industry groups by claim type (lost-time (>/=8 days away from work) and total claims) and injury event categories, we used a prevention index (PI) that averaged TBI counts and rate ranks (PI = (count rank + rate rank)/2). TBI rates per 10 000 estimated full-time equivalent (FTE = 2000 h/y) workers were calculated. RESULTS: From 2001 to 2011, 12 891 TBIs were identified among private employers, resulting in a rate of 5.1 TBIs per 10 000 FTEs. Of these, 40% (n = 5171) were lost-time TBIs, at a rate of 2.0 per 10 000 FTEs. Spectator Sports had the highest lost-time TBI rate (13.5 per 10 000 FTEs), whereas General Freight Trucking had the greatest number of lost-time TBIs (n = 293). Based on PIs, General Freight Trucking ranked first for lost-time TBIs for all injury events combined. Several industry groups within Construction, General and Specialized Freight Trucking, Services to Building and Dwellings, Employment Services, and Restaurants and Other Eating Places ranked high across multiple injury event categories for lost-time TBIs. CONCLUSIONS: The high-ranking industry groups identified from our study can be used to effectively direct occupational TBI prevention efforts.

OBJECTIVE: The aim of this study was to describe levels of integration between occupational safety and health (OSH) and workplace wellness programs/practices/policies ("programs") among participants in an insurer-sponsored wellness grant program. METHODS: We analyzed survey responses about year 1 of an insurer-sponsored grant to start a wellness program from 220 small- and medium-sized employers. Responses yielded 25 indicators of OSH-wellness integration, and 10 additional indicators to summarize multiple responses. RESULTS: At least half of the employers (N = 220) reported some level of integration within five of seven categories of OSH-wellness integration. Employers sometimes considered ergonomics, safety, or substance exposure hazards while designing their wellness program (15%) or reduced such hazards to support their wellness program (24%). Few meaningful differences were observed by employer size. CONCLUSION: Although high levels of integration were unusual, some degree of integration was common for most indicator categories.

BACKGROUND: Ambulance service workers frequently transfer and transport patients. These tasks involve occupational injury risks such as heavy lifting, awkward postures, and frequent motor vehicle travel. METHODS: We examined Ohio workers' compensation injury claims among state-insured ambulance service workers working for private employers from 2001 to 2011. Injury claim counts and rates are presented by claim types, diagnoses, and injury events; only counts are available by worker characteristics. RESULTS: We analyzed a total of 5882 claims. The majority were medical-only (<8 days away from work). The overall injury claim rate for medical-only and lost-time cases was 12.1 per 100 full-time equivalents. Sprains and strains accounted for 60% of all injury claims. Overexertion from patient handling was the leading injury event, followed by motor vehicle roadway incidents. CONCLUSIONS: Study results can guide the development or improvement of injury prevention strategies. Focused efforts related to patient handling and vehicle incidents are needed.

OBJECTIVE: This study leveraged a state workers' compensation claims database and machine learning techniques to target prevention efforts by injury causation and industry. METHODS: Injury causation auto-coding methods were developed to code more than 1.2 million Ohio Bureau of Workers' Compensation claims for this study. Industry groups were ranked for soft-tissue musculoskeletal claims that may have been preventable with biomechanical ergonomic (ERGO) or slip/trip/fall (STF) interventions. RESULTS: On the basis of the average of claim count and rate ranks for more than 200 industry groups, Skilled Nursing Facilities (ERGO) and General Freight Trucking (STF) were the highest risk for lost-time claims (>7 days). CONCLUSION: This study created a third, major causation-specific U.S. occupational injury surveillance system. These findings are being used to focus prevention resources on specific occupational injury types in specific industry groups, especially in Ohio. Other state bureaus or insurers may use similar methods.

BACKGROUND: Workers' compensation (WC) claims data may be useful for identifying high-risk industries and developing prevention strategies. METHODS: WC claims data from private-industry employers insured by the Ohio state-based workers' compensation carrier from 2001 to 2011 were linked with the state's unemployment insurance (UI) data on the employer's industry and number of employees. National Labor Productivity and Costs survey data were used to adjust UI data and estimate full-time equivalents (FTE). Rates of WC claims per 100 FTE were computed and Poisson regression was used to evaluate differences in rates. RESULTS: Most industries showed substantial claim count and rate reductions from 2001 to 2008, followed by a leveling or slight increase in claim count and rate from 2009 to 2011. Despite reductions, there were industry groups that had consistently higher rates. CONCLUSION: WC claims data linked to employment data could be used to prioritize industries for injury research and prevention activities among State-insured private employers.

Manually reading free-text narratives in large databases to identify the cause of an injury can be very time consuming and recently, there has been much work in automating this process. In particular, the variations of the naive Bayes model have been used to successfully auto-code free text narratives describing the event/exposure leading to the injury of a workers' compensation claim. This paper compares the naive Bayes model with an alternative logistic model and found that this new model outperformed the naive Bayesian model. Further modest improvements were found through the addition of sequences of keywords in the models as opposed to consideration of only single keywords. The programs and weights used in this paper are available upon request to researchers without a training set wishing to automatically assign event codes to large data-sets of text narratives. The utility of sharing this program was tested on an outside set of injury narratives provided by the Bureau of Labor Statistics with promising results.

BACKGROUND: Stationary sawing machinery is often a basic tool in the wood product manufacturing industry and was the source for over 2,500 injury/illness events that resulted in days away from work in 2010. METHODS: We examined 9 years of workers' compensation claims for the state of Ohio in wood product manufacturing with specific attention to saw-related claims. For the study period, 8,547 claims were evaluated; from this group, 716 saw-related cases were examined. RESULTS: The sawmills and wood preservation sub-sector experienced a 71% reduction in average incidence rate and an 87% reduction in average lost-time incidence rate from 2001 to 2009. The top three injury category descriptions for lost-time incidents within saw-related claims were fracture (35.8%), open wounds (29.6%), and amputation (14.8%). CONCLUSIONS: For saw-related injuries, preventing blade contact remains important but securing the work piece to prevent kickback is also important.

BACKGROUND: This study evaluated the effectiveness of a program in which a workers' compensation (WC) insurer provided matching funds to insured employers to implement safety/health engineering controls. METHODS: Pre- and post-intervention WC metrics were compiled for the employees designated as affected by the interventions within 468 employers for interventions occurring from 2003 to 2009. Poisson, two-part, and linear regression models with repeated measures were used to evaluate differences in pre- and post-data, controlling for time trends independent of the interventions. RESULTS: For affected employees, total WC claim frequency rates (both medical-only and lost-time claims) decreased 66%, lost-time WC claim frequency rates decreased 78%, WC paid cost per employee decreased 81%, and WC geometric mean paid claim cost decreased 30% post-intervention. Reductions varied by employer size, specific industry, and intervention type. CONCLUSIONS: The insurer-supported safety/health engineering control program was effective in reducing WC claims and costs for affected employees.

INTRODUCTION: Tracking and trending rates of injuries and illnesses classified as musculoskeletal disorders caused by ergonomic risk factors such as overexertion and repetitive motion (MSDs) and slips, trips, or falls (STFs) in different industry sectors is of high interest to many researchers. Unfortunately, identifying the cause of injuries and illnesses in large datasets such as workers’ compensation systems often requires reading and coding the free form accident text narrative for potentially millions of records. METHOD: To alleviate the need for manual coding, this paper describes and evaluates a computer auto-coding algorithm that demonstrated the ability to code millions of claims quickly and accurately by learning from a set of previously manually coded claims. CONCLUSIONS: The auto-coding program was able to code claims as a musculoskeletal disorders, STF or other with approximately 90% accuracy. IMPACT ON INDUSTRY: The program developed and discussed in this paper provides an accurate and efficient method for identifying the causation of workers' compensation claims as a STF or MSD in a large database based on the unstructured text narrative and resulting injury diagnoses. The program coded thousands of claims in minutes. The method described in this paper can be used by researchers and practitioners to relieve the manual burden of reading and identifying the causation of claims as a STF or MSD. Furthermore, the method can be easily generalized to code/classify other unstructured text narratives.