Descriptive evaluation of methods for identifying work-related emergency department injury visits

Emergency department visits for heat-related illness among workers: Occupational health surveillance using Washington syndromic surveillance data

BACKGROUND

Information on worker occupation and industry is critical to understanding the occupational risks of heat-related illness (HRI), yet few syndromic surveillance systems capture these key data elements. This study evaluates the work data reported through Washington syndromic surveillance for its utility in characterizing HRI ED visits by industry and occupation.

METHODS

Standard industry and occupation codes were assigned to employer name and occupation descriptions reported in Washington ED visit records maintained within the state's syndromic surveillance system, for visits involving HRI in 2020-2022. HRI ED visits involving workplace heat exposure were identified based on discharge diagnoses or on keywords in the triage note or chief complaint fields. HRI ED visits were summarized by patient characteristics, and visit rates were calculated by industry and occupation.

RESULTS

Employer name or occupation descriptions were reported in 21.5% of HRI ED records among patients age 16 and older, and in 41.2% of records with mention of heat exposure at work. Twice as many records were classified for industry as for occupation. Agriculture, forestry, fishing, and hunting and transportation and warehousing had the highest rates of HRI ED visits. Specific industries with the highest rates included support activities for agriculture and forestry, the postal service, and fruit and vegetable preserving and specialty food manufacturing.

CONCLUSION

Syndromic surveillance data are a valuable source of occupational health surveillance information when work characteristics are reported, enhancing our understanding of the occupational risks of injuries and illnesses.

Utilization of Automated Keyword Search to Identify E-Scooter Injuries in the Emergency Department

Background and objective Accurate identification and categorization of injuries from medical records can be challenging, yet it is important for injury epidemiology and prevention efforts. Coding systems such as the International Classification of Diseases (ICD) have well-known limitations. Utilizing computer-based techniques such as natural language processing (NLP) can help augment the identification and categorization of diseases in electronic health records. We used a Python program to search the text to identify cases of scooter injuries that presented to our emergency department (ED). Materials and methods This retrospective chart review was conducted between March 2017 and June 2019 in a single, urban academic ED with approximately 80,000 annual visits. The physician documentation was stored as combined PDF files by date. A Python program was developed to search the text from 186,987 encounters to find the string "scoot" and to extract the 100 characters before and after the phrase to facilitate a manual review of this subset of charts. Results A total of 890 charts were identified using the Python program, of which 235 (26.4%) were confirmed as e-scooter cases. Patients had an average age of 36 years and 53% were male. In 81.7% of cases, the patients reported a fall from the scooter and only 1.7% reported wearing a helmet during the event. The most commonly injured body areas were the upper extremity (57.9%), head (42.1%), and lower extremity (36.2%). The most frequently consulted specialists were orthopedic and trauma surgeons with 28% of cases requiring a consult. In our population, 9.4% of patients required admission to the hospital. Conclusions The number of results and data returned by the Python program was easy to manage and made it easier to identify charts for abstraction. The charts obtained allowed us to understand the nature and demographics of e-scooter injuries in our ED. E-scooters continue to be a popular mode of transportation, and understanding injury patterns related to them may inform and guide opportunities for policy and prevention.

Identification of work-related injury emergency department visits using International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes

INTRODUCTION

Emergency department (ED) visit discharge data are a less explored population-based data source used to identify work-related injuries. When using discharge data, work-relatedness is often determined by the expected payer of workers' compensation (WC). In October 2015, healthcare discharge data coding systems transitioned to the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM). ICD-10-CM's structure offers potential new work-related codes to enhance work-related injury surveillance. This study identified work-related ED visits using relevant ICD-10-CM work-related injury codes. Cases identified using this method were compared with those identified using the WC expected payer approach.

METHODS

State ED visit discharge data (2016-2019) were analysed using the CDC's discharge data surveillance definition. Injuries were identified using a diagnosis code or an external cause-of-injury code in any field. Injuries were assessed by mechanism and expected payer. Literature searches and manual review of ICD-10-CM codes were conducted to identify possible work-related injury codes. Descriptive statistics were performed and assessed by expected payer.

RESULTS

WC was billed for 87 361 injury ED visits from 2016 to 2019. Falls were the most frequent injury mechanism. The 246 ICD-10-CM work-related codes identified 36% more work-related ED injury visits than using WC as the expected payer alone.

CONCLUSION

This study identified potential ICD-10-CM codes to expand occupational injury surveillance using discharge data beyond the traditional WC expected payer approach. Further studies are needed to validate the work-related injury codes and support the development of a work-related injury surveillance case definition.

Evaluating Oregon's occupational public health surveillance system based on the CDC updated guidelines

Background

The Oregon Occupational Public Health Program (OOPHP) monitors occupational health indicators (OHIs) to inform occupational safety and health (OSH) surveillance. In 2018, OOPHP evaluated the performance of the OSH surveillance system and identified areas for future improvement.

Methods

Following the Centers for Disease Control and Prevention (CDC) updated guidelines for evaluating public health surveillance systems, the OOPHP evaluation team engaged internal and external stakeholders using a mixed‐methods approach. Operational measures for ten surveillance attributes were developed. Multiple data collection methods resulted in credible evidence for evaluation conclusions. Analyses included summary statistics and qualitative analysis of interviews, a focus group, and online surveys.

Results

Twenty stakeholders took part in this evaluation, with an average participation rate of 55%. Results showed the Oregon OSH surveillance system was simple, flexible, and highly accepted by its stakeholders. Funding security presents challenges for stability. A lack of timeliness of OHIs, low relevance of OHIs to local OSH issues, and the system's ineffectual data dissemination all limit the usefulness of the OSH surveillance system. A review of key data sources for the system showed good data quality and predictive value positive, but relatively poor sensitivity and representativeness.

Conclusions

The evaluation team successfully adapted attributes and examples in the CDC guidelines to this Oregon OSH surveillance evaluation. The evaluation findings have informed the development of recommendations for improvements to OOPHP's OSH surveillance. Future research is needed to develop guidance specific to OSH surveillance evaluation.