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BACKGROUND: Workers' compensation reporting represents an important source of data on occupational injuries and illnesses. The Compensation Information System (WCIS), operated by California Division of Workers’ Compensation (DWC) Workers’, has been collecting workers' compensation claim data since March 2000. Occupational health surveillance must utilize industry and occupation (I&O) to identify risk factors for injuries. However, only approximately 25% of WCIS claims have a NAICS industry code, 49% have a SIC code, and some are coded to incorrect industries. In California, the time and resources necessary to manually code WCIS claims is cost-prohibitive. The objectives of this project were: to create a computerized method of industry coding WCIS claims to Census Industry Codes (CICs), to calculate industry-specific rates of carpal tunnel syndrome (CTS), and to use the National Industry and Occupation Computerized Coding System (NIOCCS) to code occupation for industries with high rates of CTS.
METHODS: We previously developed a case definition for CTS that categorized cases as probable, possible, or uncertain. Possible and Probable cases with an injury date between 2006-2011 were included in our dataset. We reviewed original industry coding, employer information, class code, and narrative fields to aid in industry coding the dataset. To calculate rates, the American Community Survey was used for industry denominators. The ten industries with the highest rates of CTS were uploaded to NIOCCS for computer-assisted occupation coding.
RESULTS: 90,660 cases of carpal tunnel syndrome were included in our dataset. We developed a 12-step program for coding claims that included crosswalks; used employer name, class code, and occupation to identify specific industries; aggregated and clarified problematic industries; and assigned industry based on other claims with the same employer name or FEIN. We assigned a CIC code to 83% of claims in our dataset. This time-consuming process elucidated the barriers to coding claims for I&O, and the limitations of I&O coding for identifying risk factors for CTS. Government workers, telecommunications and power companies, HMOs, banks, and defense contractors were particularly difficult to code. NIOCCS auto-coded between 34-60% of the occupations of top 10 industries, and manual coding was necessary for the remainder of occupations.
CONCLUSIONS: There is differential misclassification of industry coding in WCIS. Some industries were particularly vulnerable to incorrect industry coding due to a large, single employer being coded incorrectly. Recommendations for improvement of I&O coding are suggested to allow use of WCIS for routine occupational surveillance.