Characterizing Injuries in Maine Logging and Forestry Operations: A Novel Dataset Extracted from Pre-Care Records (2008-2022).

BACKGROUND: Logging consistently ranks among the most hazardous occupations in the United States, yet non-fatal injury patterns remain under-characterized. This study uses a novel time-series dataset of pre-hospital care records (PCRs) to identify trends in non-fatal injuries within the Maine forestry sector, with a focus on demographic and temporal changes in injury patterns in an increasingly mechanized occupational sector. METHODS: We identified 471 forestry-related injuries from a statewide dataset of PCRs from 2008 to 2022, excluding 2017 and 2018, and performed detailed injury coding using the Occupational Injury and Illness Classification (OIICS) coding system. We analyzed frequencies of OIICS subcodes for Primary Injury Source, Event/Exposure, Nature of Injury, and Body Part classifications. Injury characteristics and rates per year (per 1,000 at risk) were summarized across three periods: 2008-2011, 2012-2016, and 2019-2022, by age category and season. We used mixed-effects Poisson regression to assess the association between age category and injury counts per year, and quasi-Poisson models stratified by period to estimate mean injury counts and confidence intervals by age category and season. Chi-square tests were used to assess differences in injury source, event, nature and body part injured across age category, season, and period. RESULTS: A significant decline in the annual number of injuries and the overall injury rate was observed over the 15-year study period for all age categories. There is little consistent seasonality in injury rates across periods. Workers in prime working age category (ages 31 to 60) were most frequently injured, with older workers (ages 61+) also more frequently injured than the reference level (age 30 or less). The most common injury event was being struck by an object, with trees being the most frequent injury source and legs the most affected body part. CONCLUSION: Injury data classified and described from PCRs provides a valuable vehicle for injury surveillance in the forestry sector. Insights gained can guide the development of targeted safety interventions to protect this high-risk demographic.