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
Open accessAbstract
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.
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Most of these studies examined methods for imputing race/ethnicity data, although several looked at imputing geocoded patient addresses, and one looked at imputing occupational data.
“Most of these studies examined methods for imputing race/ethnicity data, although several looked at imputing geocoded patient addresses,67,69,70 and one looked at imputing occupational data.61”
From The quality of social determinants data in the electronic health record: a systematic review (2021) · cites this paper as [61]
The Methods section (‘Testing the crosswalk on hospital data’; page unavailable) describes using injury codes to flag hospital records and append classification codes, which supports the narrower point that the study inferred work-related injury categories from records. It does not describe imputing patients’ occupational data. The Discussion (‘Limitations’; page unavailable) says hospital data cannot definitively assign occupational status; its explicit imputation in Methods concerns median age, not occupation. (LLM, conf=0.82)
Experimental — accuracy not yet measuredSix of the 7 studies (86%) that looked at the quality of occupational data primarily examined completeness, all noting that occupational information is frequently missing from the health record.
“Six of the 7 studies (86%) that looked at the quality of occupational data primarily examined completeness, all noting that occupational information is frequently missing from the health record.10,60–64”
From The quality of social determinants data in the electronic health record: a systematic review (2021) · cites this paper as [61]
The Background (page unavailable) says occupational variables are missing from many injury surveillance systems, and the Discussion’s Limitations section says hospital data alone cannot definitively establish occupational status. The paper does not report the six-of-seven (86%) tally or primarily examine the completeness of occupational information in health records; it tests a coding crosswalk for injury surveillance. (LLM, conf=0.83)
Experimental — accuracy not yet measuredSeveral studies looked at the use of imputation to increase the completeness of datasets and avoid casewise deletion.
“Several studies looked at the use of imputation, also referred to as indirect estimation, to increase the completeness of datasets and avoid casewise deletion.14,22,30,45,46,50,61,65,67–70”
From The quality of social determinants data in the electronic health record: a systematic review (2021) · cites this paper as [61]
The Methods section describes mapping injury codes and appending OIICS codes to hospital records, while the Limitations section discusses incomplete E-code reporting. Neither addresses imputation, indirect estimation, or avoiding casewise deletion; searches for those terms found no supporting passage in our copy. Page numbers were not provided. (LLM, conf=0.75)
Experimental — accuracy not yet measured
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- Authors
- Scott, E., Hirabayashi, L., Graham, J., Krupa, N., & Jenkins, P.
- Affiliations
- Bassett Medical Center (US)
- Corresponding author
- Scott, Erika
- Venue
- Injury Epidemiology
- Published
- 2021-02-15
- Volume
- 8
- Issue
- 1
- Pages
- 6-6
- ISSN
- 2197-1714 (ISSN-L)
- PMC
- PMC7883573
- OpenAlex
- W3129548213
- Cited by
- 13
- References
- 36
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 1 · 2023: 3 · 2022: 1 · 2021: 1
- FWCI
- 0.90
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Injury Epidemiology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institute for Occupational Safety and Health (2U54OH007542)
- Topics
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Agriculture and Farm Safety
- Health Sciences › Health Professions › Health Information Management › Medical Coding and Health Information
- Health Sciences › Health Professions › Occupational Therapy › Occupational Health and Safety in Workplaces
- Keywords
- occupational injury surveillance, agricultural injuries, pedestrian crossings, hospitalization data, injury coding, animal injuries, work-relatedness
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