Codability of industry and occupation information from cancer registry records: differences by patient demographics, casefinding source, payor, and cancer type
Open accessAbstract
Introduction: Industry and occupation (I&O) information collected by cancer registries is useful for assessing associations among jobs and malignancies. However, systematic differences in I&O availability can bias findings. Methods: Codability by patient demographics, payor, identifying (casefinding) source, and cancer site was assessed using I&O text from first primaries diagnosed 2011‐2012 and reported to California Cancer Registry. I&O were coded to a U.S. Census code or classified as blank/inadequate/unknown, retired, or not working for pay. Results: Industry was codable for 37% of cases; 50% had “unknown” and 9% “retired” instead of usual industry. Cases initially reported by hospitals, covered by preferred providers, or with known occupational etiology had highest codable industry; cases from private pathology laboratories, with Medicaid, or diagnosed in outpatient settings had least. Occupation results were similar. Conclusions: Recording usual I&O for retirees and improving linkages for reporting entities without patient access would improve I&O codability and research validity.
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Four studies found that occupational data were not missing at random and that male patients were more likely to have occupational information in their record.
“Four studies found that data were not missing at random and that male patients were more likely to have occupational information in their record.60,62–64”
From The quality of social determinants data in the electronic health record: a systematic review (2021) · cites this paper as [62]
Table I reports codable industry information for 40.4% of male patients versus 34.3% of female patients; the paper says occupation results were similar. The Discussion says availability varies by demographic, reporting, and outcome characteristics, but frames the resulting selection bias as a possibility rather than establishing that these data were not missing at random. This paper also cannot establish the citing sentence’s count of four studies. (LLM, conf=0.86)
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 [62]
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Cites
Improving the quality of industry and occupation data at a central cancer registry
Armenti, K. R., Celaya, M. O., Cherala, S., Riddle, B., Schumacher, P. K., & Rees, J. R. (2010) · American Journal of Industrial Medicine · doi:10.1002/ajim.20851 · Cited by 17 · cited 2× · see citing works
“A New Hampshire study showed a decrease in the number of records judged to have no I&O data from 74% to 14% after detailed records review followed by targeted registrar training in I/O collection.(8) The percentage of records judged to have complete, codable I&O data was only 48% even after review training (in part be…”
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“Still, I&O data are often missing from state registry records, or are recorded in such a way that precludes assignment of standardized codes that are comparable and therefore usable for analyses.(7) A study of I&O availability (categorizing “retired” and “nonworking” as available) in the New Hampshire State Cancer Reg…”
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Cited by
Sorted by citations
The quality of social determinants data in the electronic health record: a systematic review
Cook, L. A., Sachs, J., & Weiskopf, N. G. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab199 · Cited by 153 · see citing works
Availability and accuracy of occupation in cancer registry data among Florida firefighters
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An Occupational Legacy: Malignant Mesothelioma Incidence and Mortality in Wisconsin
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Assessing the Role of Social Determinants of Health in Health Disparities: The Need for Data on Work
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“One difference between occupational and community cancer investigations is the relative lack of data in the MCR about the occupation, industry, or employer of the patient (Freeman et al. 2017; Silver et al. 2018).”
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Colorectal cancer stage at diagnosis in male Florida firefighters
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Remy, L. L., Kaseff, L., Shiau, R., & Clay, M. (2023) · American Journal of Industrial Medicine · doi:10.1002/ajim.23457
- Authors
- Silver, S. R., Tsai, R. J., Morris, C. R., Boiano, J. M., Ju, J., Scocozza, M. S., & Calvert, G. M.
- Affiliations
- National Institute for Occupational Safety and Health (US)
- University of California Davis Medical Center (US)
- Corresponding author
- Silver, Sharon R.
- Venue
- American Journal of Industrial Medicine
- Publisher
- Wiley
- Published
- 2018-03-25
- Volume
- 61
- Issue
- 6
- Pages
- 524-532
- ISSN
- 0271-3586 (ISSN-L)
- 1097-0274
- OpenAlex
- W2792628236
- Cited by
- 11
- References
- 14
- Citations by year
- 2026: 1 · 2025: 2 · 2023: 3 · 2022: 1 · 2021: 1 · 2020: 1 · 2019: 1 · 2018: 1
- FWCI
- 0.38
- Type
- journal article
- Language
- en
- Open access
- green
- escholarship.org
- Locations
- American Journal of Industrial Medicine (publishedVersion)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- No funding
- Topics
- Health Sciences › Medicine › Oncology › Global Cancer Incidence and Screening
- 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 cancer, Medicaid, data quality, California Cancer Registry, occupational etiology
- MeSH terms
- Adult, Aged, California, Demography, Female, Humans, Industry, Insurance, Health, Male, Medical Record Linkage, Middle Aged, Neoplasms, Occupational Diseases, Occupations, Registries, Retirement, Bias, Censuses, Young Adult, Clinical Coding
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