BACKGROUND: Workplace violence is a substantial occupational hazard for healthcare workers in the United States. METHODS: We analyzed workplace violence injury surveillance data submitted by hospitals participating in the Occupational Health Safety Network (OHSN) from 2012 to 2015. RESULTS: Data were frequently missing for several important variables. Nursing assistants (14.89, 95%CI 10.12-21.91) and nurses (8.05, 95%CI 6.14-10.55) had the highest crude workplace violence injury rates per 1000 full-time equivalent (FTE) workers. Nursing assistants' (IRR 2.82, 95%CI 2.36-3.36) and nurses' (IRR 1.70, 95%CI 1.45-1.99) adjusted workplace violence injury rates were significantly higher than those of non-patient care personnel. On average, the overall rate of workplace violence injury among OHSN-participating hospitals increased by 23% annually during the study period. CONCLUSION: Improved data collection is needed for OHSN to realize its full potential. Workplace violence is a serious, increasingly common problem in OHSN-participating hospitals. Nursing assistants and nurses have the highest injury risk.

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.

In 2013, one in five reported nonfatal occupational injuries occurred among workers in the health care and social assistance industry, the highest number of such injuries reported for all private industries. In 2011, U.S. health care personnel experienced seven times the national rate of musculoskeletal disorders compared with all other private sector workers. To reduce the number of preventable injuries among health care personnel, CDC's National Institute for Occupational Safety and Health (NIOSH), with collaborating partners, created the Occupational Health Safety Network (OHSN) to collect detailed injury data to help target prevention efforts. OHSN, a free, voluntary surveillance system for health care facilities, enables prompt and secure tracking of occupational injuries by type, occupation, location, and risk factors. This report describes OHSN and reports on current findings for three types of injuries. A total of 112 U.S. facilities reported 10,680 OSHA-recordable* patient handling and movement (4,674 injuries); slips, trips, and falls (3,972 injuries); and workplace violence (2,034 injuries) injuries occurring from January 1, 2012-September 30, 2014. Incidence rates for patient handling; slips, trips, and falls; and workplace violence were 11.3, 9.6, and 4.9 incidents per 10,000 worker-months,dagger respectively. Nurse assistants and nurses had the highest injury rates of all occupations examined. Focused interventions could mitigate some injuries. Data analyzed through OHSN identify where resources, such as lifting equipment and training, can be directed to potentially reduce patient handling injuries. Using OHSN can guide institutional and national interventions to protect health care personnel from common, disabling, preventable injuries.

BACKGROUND: Person-years analysis is a fundamental tool of occupational epidemiology. A life table analysis system (LTAS), previously developed by the National Institute for Occupational Safety and Health, was limited by its platform and analysis and reporting capabilities. We describe the updating of LTAS for the Windows operating system (LTAS.NET) with improved properties. SOFTWARE DEVELOPMENT PROCESS: A group of epidemiologists, programmers, and statisticians developed software, platform, and computing requirements. Statistical methods include the use of (indirectly) standardized mortality ratios, (directly) standardized rate ratios, confidence intervals, and P values based on the normal approximation and exact Poisson methods, and a trend estimator for linear exposure-response associations. SOFTWARE FEATURES: We show examples using LTAS.NET to stratify and analyze multiple fixed and time-dependent variables. Data import, stratification, and reporting options are highly flexible. Users may export stratified data for Poisson regression modeling. CONCLUSIONS: LTAS.NET incorporates improvements that will facilitate more complex person-years analysis of occupational cohort data.