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

Abstract

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.

The quality of social determinants data in the electronic health record: a systematic review

“…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 Imputation is a way to infer missing data, and there are many imputation methods that can be used to generating substitute values to fill in missing 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.…”

“…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 …”

Lessons in Linkage: Combining Administrative Data Using Deterministic Linkage for Surveillance of Sports and Recreation Injuries in Florida, United States

“…Future work will illustrate the methodology using ICD-10-CM and how we approached linking data across ICD versions. Few, if any, data sources to date have converted older data in ICD-9-CM to ICD-10-CM or ICD-10 to ICD-11 though some crosswalks exist for these specific purposes [45][46][47][48].…”

Associations between body mass index and all-cause and CVD mortality in agriculture, forestry, and fishing occupations: A prospective cohort study using NHANES data (1999–2014)

“…Workers in these occupations may exhibit diverse BMI distributions influenced by the strenuous physical demands of their jobs, dietary habits, and other lifestyle factors. Additionally, exposure to environmental hazards, such as agricultural pesticides or extreme weather conditions in fishing, can introduce additional complexities to the BMI-mortality relationship [ 22 , 23 ].…”