Building Capacity Through the Northeast Agricultural Safety and Health Coalition.

Land grant universities in the Northeast (e.g. Delaware, Maryland, New York) have seen changes in their faculty or Extension level positions: Agricultural safety specialist positions have been left vacant or have been merged with another position with other responsibilities, which reduces the amount of time available to address safety. In response to the resulting support gap, Penn State Ag Safety and Health and the Northeast Center for Occupational Health and Safety (NEC) launched the Northeast Agricultural Safety and Health Coalition (NEASHC) in 2014. NEASHC provides extension staff and other individuals interested in safety trainings (e.g. insurers, Farm Bureau staff) with opportunities to network and learn about best practices in agricultural health and safety training. Recognizing the importance of NEASHC's role in filling this service gap in the Northeast, the NEC included support for NEASHC in its last 5-year grant cycle (2016-2022) and has continued its support for the Coalition in their current five-year center grant (2022-2027). The NEC center grant is funded by National Institute for Occupational Health and Safety (NIOSH) Grant #2 U54OH007542. NEASHC is an important component of the NEC's unified approach to worker health and safety, which is based in part on G. Sorensen's conceptual model for integrated approaches to the protection and promotion of worker safety.1 The goals for NEASHC through 2027 are as follows: expand the Coalition's reach by adding states and members; provide professional development to members; (3) offer mini-grants for outreach projects and activities; (4) develop and disseminate new resources; and (5) provide occupational safety and health (OSH) updates for agricultural populations.

Factors associated with injury among Maine logging workers.

INTRODUCTION: Despite dramatic improvements in safety, logging remains one of the most dangerous industries in the United States. The purpose of this study was to explore longitudinal injury trends among Maine logging workers. METHODS: Loggers participated in seven quarterly surveys, over the course of 18 months. Categorical and free text data related to traumatic and acute injury, musculoskeletal disorders (MSD), and chronic pain were exported from REDCap into SAS 9.4, Excel, and NVivo, for quantitative and qualitative analysis, respectively. Time to injury was modeled using two different approaches: (1) time to the occurrence of first injury modeled by proportional hazard regression and (2) an intensity model for injury frequency. Two research team members also analyzed qualitative data using a content analysis approach. RESULTS: During the study, 204 injuries were reported. Of the 154 participants, 93 (60.4%) reported musculoskeletal pain on at least one survey. The majority of injuries were traumatic, including fractures, sprains, and strains. Lack of health insurance was found to be related to increased risk of first injury [HR = 1.41, 95% CI = 0.97-2.04, p = 0.069]. Variables found to be related to injury intensity at the univariate level were: (1) a lack of health insurance [HR = 1.51, 95% CI = 1.04-2.20, p = 0.030], (2) age [HR for 10-year age increase;= 1.12, 95% CI = 0.99-1.27, p = 0.082], and (3) years employed in logging industry [HR for 10-year increase = 1.12, 95% CI = 0.99-1.26, p = 0.052]. Seeking medical attention for injury was not a priority for this cohort, and narratives revealed a trend for self-assessment. A variety of barriers, including finances, prevented loggers from seeking medical attention. DISCUSSION: We found that loggers still experience serious, and sometimes disabling, injuries associated with their work. It was unsurprising that many injuries were due to slips, trips, and falls, along with contact with logging equipment and trees/logs. The narratives revealed various obstacles preventing loggers from achieving optimal health. Examples included geographic distance from healthcare, lack of time to access care, and entrenched values that prioritized independence and traditional masculinity. Financial considerations were also consistently cited as a primary barrier to adequate care. CONCLUSION: There is a continued need to emphasize occupational health and safety in the logging industry. Implementation of relevant safety programs is key, but it is likely that the benefits of these will not be fully realized until a cultural shift takes place within this industry.

Continuous physiological signal measurement over 24-hour periods to assess the impact of work-related stress and workplace violence.

Work-related stress has long been recognized as an essential factor affecting employees' health and wellbeing. Repeated exposure to acute occupational stressors puts workers at high risk for depression, obesity, hypertension, and early death. Assessment of the effects of acute stress on workers' wellbeing usually relies on subjective self-reports, questionnaires, or measuring biometric and biochemical markers in long-cycle time intervals. This study aimed to develop and validate the use of a multiparameter wearable armband for continuous non-invasive monitoring of physiological states. Two worker populations were monitored 24 h/day: six loggers for one day and six ICU nurses working 12-hr shifts for one week. Stress responses in nurses were highly correlated with changes in heart rate variability (HRV) and pulse transit time (PTT). A rise in the low-to high-frequency (LF/LH) ratio in HRV was also coincident with stress responses. HRV on workdays decreased compared to non-work days, and PTT also exhibited a persistent decrease reflecting increased blood pressure. Compared to loggers, nurses were involved in high-intensity work activities 45% more often but were less active on non-work days. The wearable technology was well accepted by all worker participants and yielded high signal quality, critical factors for long-term non-invasive occupational health monitoring.

Ensuring data quality and maximizing efficiency in coding agricultural and forestry injuries: Lessons to improve occupational injury surveillance.

INTRODUCTION: Specialized occupational injury surveillance systems are filling the gap in the undercount of work-related injuries in industries such as agriculture and forestry. To ensure data quality and maximize efficiency in the operation of a regional occupational injury surveillance system, the need for continued dual coding of occupational injury records was assessed. METHODS: Kappa scores and percent agreement were used to compare interrater reliability for assigned variables in 1,259 agricultural and forestry injuries identified in pre-hospital care reports. The variables used for the comparison included type of event, source of injury, nature of injury, part of body, injury location, intentionality, and farm and agriculture injury classification (FAIC). RESULTS: Kappa (κ) ranged from 0.2605 for secondary source to 0.8494 for event and exposure. Individual coder accuracy ranged from medium to high levels of agreement. Agreement beyond the first digit of OIICS coding was measured in percent agreement, and type of event or exposure, body part, and primary source of injury continued to meet levels of accord reaching 70% or greater agreement between all coders and the final choice, even to the most detailed 4th digit of OIICS. CONCLUSIONS: This research supports evidence-based decision making in customizing an occupational injury surveillance system, ultimately making it less costly while maintaining data quality. We foresee these methods being applicable to any surveillance system where visual inspection and human decisions are levied. PRACTICAL APPLICATIONS: Assessing the rigor of occupational injury record coding provides critical information to tailor surveillance protocols, especially those targeted to make the system less costly. System administrators should consider evaluating the quality of coding, especially when dealing with free-text narratives before deciding on single coder protocols. Further, quality checks should remain a part of the system going forward.

The Impact of COVID-19 on Northeast and Appalachian Loggers.

OBJECTIVE: The Northeast Center for Occupational Health and Safety conducted a survey in December 2020 to understand the impact of the coronavirus disease 2019 (COVID-19) and the Androscoggin Mill explosion among loggers in six states: Maine, New Hampshire, Vermont, New York, Pennsylvania, and West Virginia. METHODS: Logger mailing addresses were aggregated with the assistance of state logging organizations. A paper survey, including a self-addressed return envelope was mailed to the loggers in December 2020. RESULTS: The mailed survey had a response rate of 13.3% and captured data on 484 loggers. The majority knew someone (including themselves) who tested positive for COVID-19 (71.9%). Less than half (43%) received employer training about COVID-19 prevention measures, though 73% received some form of COVID-19 personal protective equipment from their employers. The health department, Centers for Disease Control and Prevention (CDC), and health care providers were the most trusted sources of health information. Nearly half acknowledged significant change in their home lives and work responsibilities due to the pandemic. The explosion of the Androscoggin Mill affected the business of 80% of Maine loggers, and 18% of loggers that resided outside Maine. CONCLUSION: The lessons learned can be used to enhance trust in public health institutions and future public health response to this unique occupational group.

Not Quite Out of the Woods: Overall Health and Chronic Disease Risk Factors among Maine Logging Workers.

OBJECTIVE: This research reports on the health status, including chronic disease risk factors, among Maine loggers. METHODS: Loggers completed a survey and health screenings were held across Maine, collecting data on a variety of health endpoints. RESULTS: Seventy-five loggers participated. The majority were men (97.1%) with a median age of 46, and a mean BMI of 30.6 kg/m2 (SD 4.9). Nearly half of those screened (45.9%) had blood pressure at the level of stage II hypertension. Loggers with at least a single joint abnormality were 38.4%. The health screening cohort was similar to the non-health screening cohort for many attributes. CONCLUSIONS: Future research should focus on tailored interventions to improve cardiovascular and musculoskeletal risk factors among loggers.

The modern gut-hammer: Understanding the eating habits of loggers through Photovoice.

"Give Us Your Best Shot" is a Photovoice project designed to shed a light on loggers' food choices, attitudes toward and challenges around eating. This research focused on answering the question: What do you typically eat on a workday and where do you get it (breakfast, lunch, dinner, snacks, and drinks)? Consider: What makes it hard to eat healthier? If you feel you eat healthy, how do you do it? Participants were asked to take a photo to answer our research question at least once a week for a six-week period in autumn 2019. Photos and comments were exported from REDCap and imported into NVivo 12, a qualitative analysis software. Two members of the research team analyzed these data. In total, six male Maine loggers, ages 33 to 64, took part in the Photovoice project. Several themes emerged from these data including, but not limited to, the conflict between stated feelings about diet and health and actual consumption habits, the priority of health among many demands, and perceived healthfulness. Data analysis revealed time and family to be significant influential factors affecting loggers' attitudes and ability to eat healthfully. Modern trends toward processed and pre-made food resulting in less home cooking, impacts how and what loggers eat. This project served to show that food choice and diet are modulated by complex outside forces and that improving diet is not a straightforward task. Maine loggers are coping with the same struggles that many workers face, with the added hardship of dealing with extremely long work hours and commutes, leaving little time for anything else. These factors should be taken into consideration when planning any nutrition related interventions with logging workers.

The development of a machine learning algorithm to identify occupational injuries in agriculture using pre-hospital care reports.

PURPOSE: Current injury surveillance efforts in agriculture are considerably hampered by the limited quantity of occupation or industry data in current health records. This has impeded efforts to develop more accurate injury burden estimates and has negatively impacted the prioritization of workplace health and safety in state and federal public health efforts. This paper describes the development of a Naïve Bayes machine learning algorithm to identify occupational injuries in agriculture using existing administrative data, specifically in pre-hospital care reports (PCR). METHODS: A Naïve Bayes machine learning algorithm was trained on PCR datasets from 2008-2010 from Maine and New Hampshire and tested on newer data from those states between 2011 and 2016. Further analyses were devoted to establishing the generalizability of the model across various states and various years. Dual visual inspection was used to verify the records subset by the algorithm. RESULTS: The Naïve Bayes machine learning algorithm reduced the volume of cases that required visual inspection by 69.5 percent over a keyword search strategy alone. Coders identified 341 true agricultural injury records (Case class = 1) (Maine 2011-2016, New Hampshire 2011-2015). In addition, there were 581 (Case class = 2 or 3) that were suspected to be agricultural acute/traumatic events, but lacked the necessary detail to make a certain distinction. CONCLUSIONS: The application of the trained algorithm on newer data reduced the volume of records requiring visual inspection by two thirds over the previous keyword search strategy, making it a sustainable and cost-effective way to understand injury trends in agriculture.

Using hospitalization data for injury surveillance in agriculture, forestry and fishing: a crosswalk between ICD10CM external cause of injury coding and The Occupational Injury and Illness Classification System.

BACKGROUND: While statistics related to occupational injuries exist at state and national levels, there are notable difficulties with using these to understand non-fatal injuries trends in agriculture, forestry, and commercial fishing. This paper describes the development and testing of a crosswalk between ICD-10-CM external cause of injury codes (E-codes) for agriculture, forestry, and fishing (AFF) and the Occupational Injury and Illness Classification System (OIICS). By using this crosswalk, researchers can efficiently process hospitalization data and quickly assemble relevant cases of AFF injuries useful for epidemiological tracking. METHODS: All 6810 ICD-10-CM E- codes were double-reviewed and tagged for AFF- relatedness. Those related to AFF were then coded into a crosswalk to OIICS. The crosswalk was tested on hospital data (inpatient, outpatient, and emergency department) from New York, Massachusetts, and Vermont using SAS9.3. Injury records were characterized by type of event, source of injury, and by general demographics using descriptive epidemiology. RESULTS: Of the 6810 E-codes available in the ICD-10-CM scheme, 263 different E-codes were ultimately classified as 1 = true case, 2 = traumatic/acute and suspected AFF, or 3 = AFF and suspected traumatic/acute. The crosswalk mapping identified 9969 patient records either confirmed to be or suspected to be an AFF injury out of a total of 38,412,241 records in the datasets, combined. Of these, 963 were true cases of agricultural injury. The remaining 9006 were suspected AFF cases, where the E-code was not specific enough to assign certainty to the record's work-relatedness. For the true agricultural cases, the most frequent combinations presented were contact with agricultural/garden equipment (301), non-roadway incident involving off-road vehicle (222), and struck by cow or other bovine (150). For suspected agricultural cases, the majority (68.2%) represent animal-related injuries. CONCLUSIONS: The crosswalk provides a reproducible, low-cost, rapid means to identify and code AFF injuries from hospital data. The use of this crosswalk is best suited to identifying true agricultural cases; however, capturing suspected cases of agriculture, forestry, and fishing injury also provides valuable data.

Health and safety in the Maine woods: Assemblage and baseline characteristics of a longitudinal cohort of logging workers.

BACKGROUND: Logging remains one of the most hazardous industries in the United States, despite many safety improvements made in the last decades. Currently, we know little about regional trends in health conditions of logging workers, especially in the Northeast. However, the forest products industry is a critical component of the Northeast's economy, especially in the State of Maine. METHODS: This paper reports on the baseline data of a longitudinal cohort study involving Maine loggers, aimed to assess the health and safety of the industry. RESULTS: Three hundred twenty-five are included in these analyses, 246 mechanized loggers, and 79 conventional. On average mechanized loggers worked longer days (11.8 vs 9.7 hours) and had longer commutes from home to the woodlot (72.6 vs 40.7 minutes) than conventional loggers. For health factors, mechanized and conventional loggers had similar responses. Nearly two-thirds of both mechanized and conventional loggers had an annual physical in the previous year, and 36.3% had seen a health specialist during that same time period. The overall work-related injury and illness rate is 6.8 of 100 workers for this cohort. CONCLUSIONS: These factors contribute to a need to work with the community on transforming logging into a safer and healthier profession for the current workforce, as well as the workforce of the future. This study provides the basis for an appropriate intervention, in collaboration with the loggers and industry stakeholders, to improve the lives of these vital workers.

Characteristics of Agriculture Related Motor Vehicle Crashes in Rural New York State.

Objective: The Centers for Disease Control and Prevention (CDC) data have shown that agriculture, forestry, and fishing as an occupational group have the third highest rate of work-related roadway crashes. Agriculture-related crashes have been explored in the Midwest and South; however, we know little about agriculture-related crashes in the Northeast, especially in New York.Methods: To better understand this, researchers obtained motor vehicle crash data from 2010 to 2012 from the New York State Department of Motor Vehicles (NYSDMV). These data were then filtered to agriculture-related cases by both vehicle registration type and vehicle body type.Results: We identified 203 agriculture-related vehicle crashes, involving 381 vehicles and 482 people. Of the agriculture incidents, 91.6% caused property damage, while 36.0% caused injury. The case fatality rate for roadway vehicle crashes was nearly five times as great (2.0/0.4 = 5.00) for agriculture versus non-agriculture crashes (p = 0.0003).Conclusion: Using these data as a supplement to the New York Center for Agricultural Medicine and Health's existing surveillance system provided information useful in setting priorities involving roadway safety.

Accelerometers: Assessing their potential for measuring personal flotation device use on commercial fishing vessels.

BACKGROUND: Falls overboard are the most common cause of fatalities in commercial fishing. As a result, interventions aimed at increasing and measuring the use of personal flotation devices (PFDs) are a high priority. The focus of this study was to explore the use of accelerometers as a means for objectively measuring PFD use on lobster fishing vessels. METHODS: For participating vessels, researchers attached an accelerometer to a PFD worn by a crewmember and another to the vessel's wheelhouse. GoPro videos were also employed to record crewmember activities so these could be synchronized with accelerometer outputs. Accelerometer outputs included two distinct measures, the proportional integration mode (PIM) and zero crossing mode (ZCM). Data were fitted to various equations to identify the best method for predicting PFD use. RESULTS: Seven lobster fishing vessels participated in the trial. Data indicated that accelerometers could predict PFD use with a fairly high degree of accuracy. In particular, a logistic equation incorporating PIM values from the PFD and the absolute value of the difference between the PFD PIM readings and the PIM readings from the stationary accelerometer demonstrated the highest degree of accuracy, with correct classifications for 73.3% to 77.6% of the 10-second data intervals. Accuracy was highest when crew members were moving versus stationary. The predictive value of ZCM was comparatively limited. CONCLUSIONS: PIM accelerometer readings can be used to measure PFD use with a considerably high degree of accuracy, especially for sternmen who are moving regularly and have the highest risk for falling overboard.

Lifejackets and Lobstermen: Giving Safety Equipment a Competitive Advantage.

Background: Falls overboard are the most frequent cause of death in the Northeast lobster fishing industry. National Institute for Occupational Safety and Health (NIOSH) surveillance indicates every victim of a fall overboard who has been found was not wearing a lifejacket. Preliminary research conducted in Maine and Massachusetts indicates lifejacket use is relatively uncommon among lobstermen due to barriers such as comfort, practicality, and social norms.Methods: This study highlights an initiative to: (1) trial various lifejacket designs with lobstermen; (2) identify the most popular designs; and (3) identify other features that could encourage use of lifejackets. In 2017, nine designs were trialed with lobstermen in Maine and Massachusetts during the winter and summer fishing seasons. Participants were recruited dockside, and lifejackets were randomly assigned. Participants completed surveys at 1 week and 4 weeks to assess positive and negative design features and to understand the importance of survival technology that can be used in conjunction with lifejackets.Results: 181 lobstermen in Maine and Massachusetts agreed to participate. Recruitment rates were 90.5%, while the survey completion rate was 88.4%. Survey results identified no clear preference for a specific lifejacket design; however, the ability to choose from many options appeared to be an important factor.Conclusion: Previous studies have indicated that lifejacket preferences are fisheries specific. In the Northeast lobster fishery, however, individual preferences varied. Our research demonstrates that a range of devices covering different buoyancies, wear type, and retrieval systems should be made more available to lobstermen.

Emergency Medical Services Pre-Hospital Care Reports as a Data Source for Logging Injury Surveillance.

Logging workers are at high risk of injury, and although data on fatal injuries exists, less is known about non-fatal injuries. The purpose of this study is to describe initial trends in logging morbidity in Maine and New Hampshire using pre-hospital care reports. Demographics and specifics of the event were recorded for each incident case. Both quantitative and qualitative analyses were employed. Logging injuries (n = 70) were primarily due to trees, heavy equipment, and chainsaws. Interventions focused on heavy equipment safety (particularly slips, trips, and falls), and personal protective equipment use would be warranted based on the findings of this surveillance system. Our methods are able to capture traumatic injury in logging, in sufficient detail to prioritize interventions and to evaluate their outcomes. The system is low-cost and has the potential to be sustained over a long period.

Data processing and case identification in an agricultural and logging morbidity surveillance study: Trends over time.

BACKGROUND: Agriculture and logging are dangerous industries, and though data on fatal injury exists, less is known about non-fatal injury. Establishing a non-fatal injury surveillance system is a top priority. Pre-hospital care reports and hospitalization data were explored as a low-cost option for ongoing surveillance of occupational injury. METHODS: Using pre-hospital care report free-text and location codes, along with hospital ICD-9-CM external cause of injury codes, we created a surveillance system that tracked farm and logging injuries. RESULTS: In Maine and New Hampshire, 1585 injury events were identified (2008-2010). The incidence of injuries was 12.4/1000 for agricultural workers, compared to 10.4/1000 to 12.2/1000 for logging workers. CONCLUSIONS: These estimates are consistent with other recent estimates. This system is limited to traumatic injury for which medical treatment is administered, and is limited by the accuracy of coding and spelling. This system has the potential to be both sustainable and low cost.

Trends in Nonfatal Agricultural Injury in Maine and New Hampshire: Results From a Low-Cost Passive Surveillance System.

OBJECTIVES: Agriculture is a dangerous industry, and although data on fatal injuries exist, less is known about nonfatal injuries. The purpose of this study is to describe trends in agricultural morbidity in Maine and New Hampshire from 2008 to 2010 using a newly established passive surveillance system. This passive system is supplied by injury cases gathered from prehospital care reports and hospital data. METHODS: Demographics and specifics of the event were recorded for each incident case. RESULTS: The average age of injured people in Maine and New Hampshire was 41.7. Women constituted 43.8% of all agricultural injuries. Machinery- (n = 303) and animal- (n = 523) related injuries accounted for most agricultural incidents. Of all injured women, over 60% sustained injuries due to animal-related causes. Agricultural injuries were spread across the two states, with clustering in southern New Hampshire and south central Maine, with additional injuries in the Aroostook County area, which is located in the northeast part of the state. Seasonal variation in agricultural injuries was evident with peaks in the summer months. There was some overlap between the agricultural and logging industry for tree-related work. CONCLUSIONS: Our methods are able to capture traumatic injury in agriculture in sufficient detail to prioritize interventions and to evaluate outcomes. The system is low-cost and has the potential to be sustained over a long period. Differences in rates of animal- and machinery-related injuries suggest the need for state-specific safety prioritization.

Developing Surveillance Methodology for Agricultural and Logging Injury in New Hampshire Using Electronic Administrative Data Sets.

OBJECTIVE: Agriculture and logging rank among industries with the highest rates of occupational fatality and injury. Establishing a nonfatal injury surveillance system is a top priority in the National Occupational Research Agenda. Sources of data such as patient care reports (PCRs) and hospitalization data have recently transitioned to electronic databases. METHODS: Using narrative and location codes from PCRs, along with International Classification of Diseases, 9th Revision, external cause of injury codes (E-codes) in hospital data, researchers are designing a surveillance system to track farm and logging injury. RESULTS: A total of 357 true agricultural or logging cases were identified. CONCLUSIONS: These data indicate that it is possible to identify agricultural and logging injury events in PCR and hospital data. Multiple data sources increase catchment; nevertheless, limitations in methods of identification of agricultural and logging injury contribute to the likely undercount of injury events.