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Using Massachusetts Workers’ Compensation Data to Identify Priorities for Preventing Occupational Injuries and Illnesses Among Private Sector Workers: Findings from an Analysis of Massachusetts Workers’ Compensation Lost Wage Claims, 2014–2016



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    Introduction: Work-related (WR) injuries and illnesses continue to be a significant problem in Massachusetts, imposing substantial costs to workers and their families, employers and society at large. These injuries and illnesses are preventable, and better information about where and how individuals are injured or made ill at work is essential to focus prevention resources where they are needed most. Workers' Compensation (WC) claims records, maintained by the Massachusetts Department of Industrial Accidents (DIA), are a valuable source of information about WR injuries and illnesses that can be used to target prevention efforts. But until now, these administrative records could not be readily analyzed to describe patterns of WR injuries and illnesses across industries throughout the state. With a grant from the federal government, DIA collaborated with the Departments of Public Health and Labor Standards to prepare the WC claims data for analysis. This report, based on these claims data, presents previously unavailable information about patterns of WR injuries and illnesses among private sector workers in Massachusetts. The central aims of this analysis were to identify common types of events leading to WR injury or illness and to prioritize industries for targeting prevention efforts and research. Findings are based on an analysis of WC lost wage claims filed for private sector workers who were injured or made ill at work during 2014-2016. (Most claims were for injuries, therefore hereinafter, when the term "injury" is used it encompasses both injuries and illnesses unless otherwise noted.) In Massachusetts, workers are eligible for WC lost wage benefits when an injury results in five or more lost work days. Thus the findings in this report reflect more serious WR injuries. Methods: New information processing techniques were applied to mine narrative text in the claims records to identify events leading to injury, such as falls, overexertion in lifting, pushing, etc., and violence. Counts and rates of all WC claims by industry were calculated. Rates are expressed as the number of claims per 1,000 full time workers. The WC claim rate indicates the probability or risk of a worker being injured. In a large industry, many workers can be injured but the rate may be low. Conversely, in a small but high risk industry, the number of workers injured can be low but the rate - or risk - can be high. Both rates and counts of claims need to be taken into consideration in targeting prevention efforts. Accordingly, we used a "Prevention Index" approach, which takes both counts and rates into account, to prioritize industries for prevention and research in this report. A high Prevention Index (PI) rank (1,2,3..) indicates a greater burden of injury and a higher priority for prevention. Industries were classified according to the North American Industry Classification System (NAICS) into sectors (2-digit NAICS), subsectors (3-digit NAICS) and more detailed industry groups (4-digit NAICS). PIs based on all WC claims filed were used to rank 19 industry sectors and 78 subsectors for prevention. In order to inform allocation of prevention resources within the broad sectors, such as transportation and manufacturing, we ranked detailed industry groups by PI within each of the sectors. To provide information about where attention should be focused to prevent specific types of events, such as falls and violence, PIs based on claims for specific events were used to rank industry subsectors for prevention within nine common event categories. The PI approach is most useful when compelled to compare findings across many industries as we have done in this report. It is best thought of as a framework for considering different approaches to prevention rather than a stand-alone priority score. An industry's specific rank is less important than where the industry falls in the range of ranked industries. Ultimately many factors go into determining prevention priorities - but the counts and rates, coupled with information about events leading to injury in this report, are a critical starting point. A final section of this report presents the rate of WC claims by city/town where the injured workers lived (i.e., claims per 1,000 employed residents), providing information that can inform public health planning and preventive action at the community level. These findings illustrate another way in which the WC claims data can now be used to target interventions to reduce WR injuries. Preventing WR injuries and illnesses in Massachusetts and the associated human and economic costs requires the combined effort of many stakeholders -- employers, trade associations, workers, unions, insurers, health and safety professionals, and community organizations -- as well as government agencies. The findings presented in this report are intended to guide and promote these efforts. Lessons learned from this project have led to steps for sustained improvements in the WC data system to allow routine use of the claims data to generate actionable information on WR injuries and illnesses to protect the health of workers. Input from stakeholders is welcome on the types of reports that would be useful in the future. [Description provided by NIOSH]
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  • Location:
  • Pages in Document:
    1-91
  • NIOSHTIC Number:
    nn:20059162
  • Citation:
    Boston, MA: Massachusetts State Department of Public Health, 2019 Nov; :1-91
  • Federal Fiscal Year:
    2020
  • Performing Organization:
    Massachusetts State Department of Public Health
  • Peer Reviewed:
    False
  • Start Date:
    20150701
  • Source Full Name:
    Using Massachusetts Workers' Compensation data to identify priorities for preventing occupational injuries and illnesses among private sector workers: findings from an analysis of Massachusetts Workers' Compensation lost wage claims, 2014-2016
  • End Date:
    20180630
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  • Main Document Checksum:
    urn:sha-512:4eb4094fbd1dc15bbe3c51b6ba305396ae9453f1858815e02431bef289e5a8d680e5fc56edb0ad020f02750c74e9ed16ead4af6b7b25bd8fdd76a82bd62ac922
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    Filetype[PDF - 3.06 MB ]
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