The Impact of a State-Based Workers' Compensation Insurer's Risk Control Services on Employer Claim Frequency and Cost Rates.

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 to 2017 claims data from 4606 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.

Workers' compensation claim counts and rates by injury event/exposure among state-insured private employers in Ohio, 2007-2017.

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.

Workers' compensation claims for traumatic brain injuries among private employers-Ohio, 2001-2011.

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).

Workers' compensation injury claims among workers in the private ambulance services industry-Ohio, 2001-2011.

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.

Applying Machine Learning to Workers' Compensation Data to Identify Industry-Specific Ergonomic and Safety Prevention Priorities: Ohio, 2001 to 2011.

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.

Development of methods for using workers' compensation data for surveillance and prevention of occupational injuries among State-insured private employers in Ohio.

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).

The effectiveness of insurer-supported safety and health engineering controls in reducing workers' compensation claims and costs.

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.