Farming becomes more precarious with age: injury in Maine agricultural communities,2008-2022, via time series analysis.

Background. Fatal occupational injury rates among agricultural workers are within the top ten most dangerous civilian jobs. However, tracking and documenting non-fatal agricultural injuries in Maine, like in other states, has proved challenging. In 2021, we developed a machine learning-based strategy to extract injury cases from pre-hospital free-text records (PCR), which together with final human coding, produces injury time-series records coded by type, severity, location, date, and subject industry. In this paper we explore and summarize novel time-series records for agriculture obtained from PCR from Maine. Methods. From a fully labeled Maine dataset (N = 57,960) comprising coded injuries, we select only agricultural events, yielding 1,604 injuries from 2008 to 2022. We summarize by year, month and age category, and establish seasonality before decomposing time series data, divided into three roughly equal four-year sub-periods, into seasonal, trend and random components using a classical additive model. We investigate associations between age category and injury rate via mixed effects regression, then perform time series regression on differenced monthly injury time series and temperature records to determine if, seasonality aside, temperature extrema are responsible for increased injuries. Finally, we visualize and summarize trend and random components for each study sub-period. Results. Injury rates show strong seasonality with a peak in July-August, and a trough in January or February. Subject age drifts slowly upwards during our study period, and there is a significant and positive association between age category and injury rate for all but the most elderly farm workers. Injury rates in the ages 40 through 81 years categories increase dramatically in between 2016 and the 2019–2022 period, as does the moving average of the injury rates, and the variability of the random component of the time series. Conclusions. There is a significant positive association between increasing age category and injury rate across all periods. While our injury data has strong seasonality, we find no significant associations between monthly temperature extremes and injury rates. Moving average trends for injury rates in the two periods comprising 2008 through 2016 show little change in trend, but injury rate trend shifts upward in 2019 to 2022, almost doubling in mean value.