Utilizing Haddon matrix to assess nonfatal commercial fishing injury factors in Oregon and Washington

BACKGROUND

Commercial fishing is a precarious industry with high fatal and nonfatal injury rates. The Risk Information System of Commercial [RISC] Fishing project at Oregon State University has been tracking both fatal and nonfatal injuries among Oregon and Washington commercial fishermen. We examined the utility of the RISC dataset variables in highlighting injury factors and prevention opportunities.

METHOD

We identified 245 nonfatal commercial fishing injuries in Oregon and Washington (2000-2018) and assessed the top three injury events (contact with objects or equipment, transportation incidents, and slips/trips/falls) using a cross-sectional design. We generated a Haddon matrix for each event type and populated the matrices with injury-associated factors following our a-priori matrix.

RESULTS

We observed 108 nonfatal injuries due to contact with objects. Contact injuries occurred during fishing (40%) with fishing gears (40%), often while hauling the fishing gear (22%). Common injury mechanisms included getting caught in running equipment or machinery (19%) or compressed by shifting objects or equipment (18%). Of the 58 transportation injuries most occurred in catchers (93%) and smaller vessels (1 to 3 crew) (55%). Vessel casualties were common as several vessels struck rocks/bottom (29%) or experienced fire and explosion (19%). The crew was abandoned to water (38%), often due to no raft or raft malfunctions (19%). Slip/trip/fall injuries (n = 43) typically happened during onboard traffic (49%). Such events were largely experienced by the catcher-processors (44%) including large vessels with > 100 crew (28%).

CONCLUSION

The Haddon matrix demonstrated the injury-event timeline and helped to identify potential injury-associated factors. Our injury-specific risk matrices will let commercial fishing stakeholders determine priorities and work with the experts on prevention efforts.

Temporal trends in occupational injuries treated in US emergency departments, 2012-2019

BACKGROUND

Evidence suggests that rates of occupational injuries in the US are decreasing. As several different occupational injury surveillance systems are used in the US, more detailed investigation of this trend is merited. Furthermore, studies of this decrease remain descriptive and do not use inferential statistics. The aim of this study was to provide both descriptive and inferential statistics of temporal trends of occupational injuries treated in US emergency departments (EDs) for 2012 to 2019.

METHODS

Monthly non-fatal occupational injury rates from 2012 to 2019 were estimated using the national electronic injury surveillance system-occupational supplement (NEISS-Work) dataset, a nationally representative sample of ED-treated occupational injuries. Rates were generated for all injuries and by injury event type using monthly full-time worker equivalent (FTE) data from the US Current Population Survey as a denominator. Seasonality indices were used to detect seasonal variation in monthly injury rates. Trend analysis using linear regression adjusted for seasonality was conducted to quantify changes in injury rates from 2012 to 2019.

RESULTS

Occupational injuries occurred at an average rate of 176.2 (95% CI =  ± 30.9) per 10,000 FTE during the study period. Rates were highest in 2012 and declined to their lowest level in 2019. All injury event types occurred at their highest rate in summer months (July or August) apart from falls, slips, and trips, which occurred at their highest rate in January. Trend analyses indicated that total injury rates decreased significantly throughout the study period (- 18.5%; 95% CI =  ± 14.5%). Significant decreases were also detected for injuries associated with contact with foreign object and equipment (- 26.9%; 95% CI =  ± 10.5%), transportation incidents (- 23.2%; 95% CI =  ± 14.7%), and falls, slips, and trips (- 18.1%; 95% CI =  ± 8.9%).

CONCLUSIONS

This study supports evidence that occupational injuries treated in US EDs have decreased since 2012. Potential contributors to this decrease include increased workplace mechanization and automation, as well as changing patterns in US employment and health insurance access.

REDECA: A Novel Framework to Review Artificial Intelligence and Its Applications in Occupational Safety and Health

Introduction: The field of artificial intelligence (AI) is rapidly expanding, with many applications seen routinely in health care, industry, and education, and increasingly in workplaces. Although there is growing evidence of applications of AI in workplaces across all industries to simplify and/or automate tasks there is a limited understanding of the role that AI contributes in addressing occupational safety and health (OSH) concerns. Methods: This paper introduces a new framework called Risk Evolution, Detection, Evaluation, and Control of Accidents (REDECA) that highlights the role that AI plays in the anticipation and control of exposure risks in a worker’s immediate environment. Two hundred and sixty AI papers across five sectors (oil and gas, mining, transportation, construction, and agriculture) were reviewed using the REDECA framework to highlight current applications and gaps in OSH and AI fields. Results: The REDECA framework highlighted the unique attributes and research focus of each of the five industrial sectors. The majority of evidence of AI in OSH research within the oil/gas and transportation sectors focused on the development of sensors to detect hazardous situations. In construction the focus was on the use of sensors to detect incidents. The research in the agriculture sector focused on sensors and actuators that removed workers from hazardous conditions. Application of the REDECA framework highlighted AI/OSH strengths and opportunities in various industries and potential areas for collaboration. Conclusions: As AI applications across industries continue to increase, further exploration of the benefits and challenges of AI applications in OSH is needed to optimally protect worker health, safety and well-being.