Scott Erika, Hirabayashi Liane, Graham Judy, Krupa Nicole, Jenkins Paul
Northeast Center for Occupational Health and Safety in Agriculture, Forestry, and Fishing, Bassett Medical Center, One Atwell Road, Cooperstown, NY, 13326, USA.
Bassett Research Institute, Bassett Medical Center, Cooperstown, NY, USA.
Inj Epidemiol. 2021 Feb 15;8(1):6. doi: 10.1186/s40621-021-00300-6.
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.
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.
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.
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.
虽然州和国家层面存在与职业伤害相关的统计数据，但利用这些数据来了解农业、林业和商业捕鱼业的非致命伤害趋势存在显著困难。本文描述了农业、林业和渔业（AFF）的国际疾病分类第十次修订本临床修正版（ICD - 10 - CM）伤害外部原因编码（E编码）与职业伤害和疾病分类系统（OIICS）之间转换表的开发和测试。通过使用此转换表，研究人员可以有效地处理住院数据，并快速汇总对流行病学跟踪有用的AFF伤害相关病例。
对所有6810个ICD - 10 - CM E编码进行了双重审查，并标记其与AFF的相关性。然后将与AFF相关的编码编入到与OIICS的转换表中。使用SAS9.3对来自纽约、马萨诸塞州和佛蒙特州的医院数据（住院、门诊和急诊科）进行了测试。伤害记录通过事件类型、伤害来源以及使用描述性流行病学的一般人口统计学特征进行描述。
在ICD - 10 - CM方案中的6810个E编码中，最终有263个不同的E编码被归类为1 = 确诊病例，2 = 创伤性/急性且疑似AFF，或3 = AFF且疑似创伤性/急性。在数据集中总共38,412,241条记录中，转换表映射识别出9969条患者记录被确认为或疑似为AFF伤害。其中，963例为农业伤害确诊病例。其余9006例为疑似AFF病例，其E编码不够具体，无法确定记录与工作的相关性。对于真正的农业病例，最常见的组合是接触农业/园艺设备（301）、涉及越野车的非道路事故（222）以及被牛或其他牛科动物撞击（150）。对于疑似农业病例，大多数（68.2%）为与动物相关的伤害。
该转换表提供了一种可重复、低成本、快速的方法，用于从医院数据中识别和编码AFF伤害。使用此转换表最适合识别真正的农业病例；然而，收集农业、林业和渔业伤害的疑似病例也能提供有价值的数据。
