Impact of Occupational Injuries on Nonworkers' Compensation Medical Costs of Patient-Care Workers

Predicting occupational injury causal factors using text-based analytics: A systematic review

Workplace accidents can cause a catastrophic loss to the company including human injuries and fatalities. Occupational injury reports may provide a detailed description of how the incidents occurred. Thus, the narrative is a useful information to extract, classify and analyze occupational injury. This study provides a systematic review of text mining and Natural Language Processing (NLP) applications to extract text narratives from occupational injury reports. A systematic search was conducted through multiple databases including Scopus, PubMed, and Science Direct. Only original studies that examined the application of machine and deep learning-based Natural Language Processing models for occupational injury analysis were incorporated in this study. A total of 27, out of 210 articles were reviewed in this study by adopting the Preferred Reporting Items for Systematic Review (PRISMA). This review highlighted that various machine and deep learning-based NLP models such as K-means, Naïve Bayes, Support Vector Machine, Decision Tree, and K-Nearest Neighbors were applied to predict occupational injury. On top of these models, deep neural networks are also included in classifying the type of accidents and identifying the causal factors. However, there is a paucity in using the deep learning models in extracting the occupational injury reports. This is due to these techniques are pretty much very recent and making inroads into decision-making in occupational safety and health as a whole. Despite that, this paper believed that there is a huge and promising potential to explore the application of NLP and text-based analytics in this occupational injury research field. Therefore, the improvement of data balancing techniques and the development of an automated decision-making support system for occupational injury by applying the deep learning-based NLP models are the recommendations given for future research.

Workers' Compensation Elements in Different Jurisdictions in the United States

: Over the decades, the workers' compensation system has provided many injured workers with a significant guarantee of both medical and financial support when they have been injured on the job. To be effective, workers' compensation systems at a minimum should include principles that require the addressing of medical causation, determination of an individual's functional ability both pre- and post-injury to include activity restrictions, return-to-work capability and disability, meeting jurisdiction-specific reporting requirements of the workers' compensation reporting requirements, and having knowledge of other perspectives of the various authorities and jurisdictions present in the United States. ACOEM lays out a description of various aspects of workers' compensations systems in the United States, with recommendations for minimal standards and best practices. This paper limits itself to the discussion of jurisdictions within the United States and ACOEM strongly recommends that providers consult directly with the states in which they are working as there are state variations in workers' compensation.

Arc flash macular hole

Macular holes are common. Lightening, direct electric shock induced and laser beam induced macular holes are though rare. Reporting a case of spark flashlight (Arc Flash) induced macular hole in an electrician, which has never been reported. A 19 year old male electrician by profession presented to our clinic with a history of exposure to a bright flash light from spark of wires while at work that led to decrease of vision in his both eyes. Examination revealed a full thickness macular hole in his right eye and loss of foveal photoreceptors in the left eye. Arc flash light exposure in electricians can lead to macular holes too adding a new entity to the already existing types of macular holes.

The relationship of occupational injury and use of mental health care

INTRODUCTION

Symptoms of depression and anxiety are a common consequence of occupational injury regardless of its cause and type. Nevertheless, mental health care is rarely covered by workers' compensation systems. The aim of this study was to assess the use of mental health care post-injury.

METHODS

We used a subsample of patient-care workers from the Boston Hospital Workers Health Study (BHWHS). We matched one injured worker with three uninjured workers during the period of 2012-2014 based on age and job title (nurse or patient-care associate) and looked at their mental health care use pre- and post-injury using medical claims data from the employer sponsored health plan. We used logistic regression analysis to assess the likelihood of mental health care use three and six months post-injury controlling for any pre-injury visits. Analyses were repeated separately by job title.

RESULTS

There were 556 injured workers between 2012 and 2014 that were matched with three uninjured workers at the time of injury (n = 1,649). Injured workers had a higher likelihood of seeking mental health care services than their uninjured counterparts during the six months after injury (OR = 1.646, 95% CI: 1.23-2.20), but not three months post-injury (OR = 0.825, 95% CI: 0.57-1.19). Patient-care associates had a higher likelihood to seek mental health care post-injury, than nurses (OR: 2.133 vs OR: 1.556) during the six months period.

CONCLUSIONS

Injured workers have a higher likelihood to experience symptoms of depression and anxiety based on their use of mental health care post-injury and use is more predominant among patient-care associates; however, our sample has a small number of patient-care associates. Practical Applications: Treating depression and anxiety as part of the workers' compensation system has the potential of preventing further physical ailment and improving the return to work process regardless of nature of injury.

Literature Review of Policy Implications From Findings of the Center for Work, Health, and Well-being

OBJECTIVE

To review the publications of a Total Worker Health Center of Excellence, the Harvard T.H. Chan School of Public Health Center for Work, Health, and Well-being, in order to identify research findings relevant to either organizational or public policies.

METHODS

Two researchers independently reviewed 57 publications from 2011 to 2019 to identify cross-cutting themes that focus on working conditions or related health outcomes and their organizational and public policy implications.

RESULTS

Twelve cross-cutting themes were identified with their respective organizational and public policy implications. Several policy implications cut across work-related themes.

CONCLUSIONS

Policy implications of TWH research will aid in setting priorities to translate this from research into practice in future studies and help identify gaps that we and others can use to plan future TWH research.

Coverage Gaps and Cost-Shifting for Work-Related Injury and Illness: Who Bears the Financial Burden?

The heavy economic burden of work-related injury/illness falls not only on employers and workers' compensation systems, but increasingly on health care systems, health and disability insurance, social safety net programs, and workers and their families. We present a flow diagram illustrating mechanisms responsible for the financial burden of occupational injury/illness borne by social safety net programs and by workers and their families, due to cost-shifting and gaps in workers' compensation coverage. This flow diagram depicts various pathways leading to coverage gaps that may shift the burden of occupational injury/illness-related health care and disability costs ultimately to workers, particularly the most socioeconomically vulnerable. We describe existing research and important research gaps linked to specific pathways in the flow diagram. This flow diagram was developed to facilitate more detailed and comprehensive research into the financial burden imposed by work-related injury/illness, in order to focus policy efforts where improvement is most needed.