Crosswalks to Convert US Census Bureau Industry and Occupation Codes, 1980-2018.
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ABSTRACT:
SUBMITTER: Beard JD
PROVIDER: S-EPMC10280701 | biostudies-literature | 2022 Mar
REPOSITORIES: biostudies-literature
Publications
Crosswalks to Convert US Census Bureau Industry and Occupation Codes, 1980-2018.
Beard John D JD Verdeja Marco A MA Bonsrah Dick A DA Westfall Susan D SD Steege Andrea L AL Schubauer-Berigan Mary K MK
Epidemiology (Cambridge, Mass.) 20220301 2
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Project description:As there are no well-established modifiable risk factors for prostate cancer, further evidence is needed on possible factors such as occupation. Our study uses one of the largest Canadian worker cohorts to examine occupation, industry, and prostate cancer and to assess patterns of prostate cancer rates. The Canadian Census Health and Environment Cohort (CanCHEC) was established by linking the 1991 Canadian Census Cohort to the Canadian Cancer Database (1969-2010), Canadian Mortality Database (1991-2011), and Tax Summary Files (1981-2011). A total of 37,695 prostate cancer cases were identified in men aged 25-74 based on age at diagnosis. Cox proportional hazards models were used to estimate hazards ratios and 95% confidence intervals. In men aged 25-74 years, elevated risks were observed in the following occupations: senior management (HR = 1.12, 95% CI: 1.04-1.20); office and administration (HR = 1.19, 95% CI: 1.11-1.27); finance services (HR = 1.09, 95% CI: 1.04-1.14); education (HR = 1.05, 95% CI: 1.00-1.11); agriculture and farm management (HR = 1.12, 95% CI: 1.06-1.17); farm work (HR = 1.11, 95% CI: 1.01-1.21); construction managers (HR = 1.07, 95% CI: 1.01-1.14); firefighting (HR = 1.17, 95% CI: 1.01-1.36); and police work (HR = 1.22, 95% CI: 1.09-1.36). Decreased risks were observed across other construction and transportation occupations. Results by industry were consistent with occupation results. Associations were identified for white-collar, agriculture, protective services, construction, and transportation occupations. These findings emphasize the need for further study of job-related exposures and the potential influence of nonoccupational factors such as screening practices.
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Project description:BackgroundNight work has been increasing in the last decades due to new working arrangements for good and services production. Numerous studies have shown that night shift work causes disruptions in circadian rhythms that may affect health. In 2019, night shift work was classified as probably carcinogenic to humans by the International Agency for Research on Cancer, and may contribute to other health disorders. In this context, we assessed the number and proportion of workers exposed to night work today and investigated time trends by occupation and industry in France since 1982 in terms of prevention.MethodsUsing the data on work time schedules collected in the French Labour Force Surveys, sex- and period-specific job-exposure matrices (JEMs) to night work (working between midnight and 5 AM) were developed. After linkage of the JEMs with data of the national censuses of 1982, 1990, 1999, 2007 and 2015, the numbers and proportions of workers usually or occasionally exposed to night work were estimated.ResultsThe number of night workers (usual and occasional) increased from 3.67 million in 1982 to 4.37 million in 2015 (15.8% vs 16.4%). Night work was more common in men than in women (e.g. 22.4% vs 10.0% in 2015), and usual night work largely increased after 2000 (4.4% in 1999, 7.2% in 2007). In 2015, 1.29 million men worked usually at night, including 882,000 workers in the service sector (63%) and 360,000 in the manufacturing and extracting industries (28%). For the same period, 581,000 women were usual night workers, most of them being employed in the service sector (90%). Among women, a 97% increase of usual night work was observed between 1982 and 2015.ConclusionsThis study shows that night work involves a growing number of workers in France, particularly in women in the service sector. These results raise concern about the public health impact of night work and particularly about the numbers of outcomes attributable to this exposure such as breast or prostate cancers.
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Project description:Background On October 1, 2015, the Center for Medicare and Medicaid Services transitioned from the International Classification of Diseases, Ninth Revision ( ICD-9) to the ICD, Tenth Revision ( ICD-10) compendium of codes for diagnosis and billing in health care, but translation between the two is often inexact. Here we describe a validated crosswalk to translate ICD-9 codes into ICD-10 codes, with a focus on complications after carotid revascularization and endovascular aortic aneurysm repair. Methods and Results We devised an 8-step process to derive and validate ICD-10 codes from existing ICD-9 codes. We used publicly available sources, including the General Equivalence Mapping database, to translate ICD-9 codes used in prior work to ICD-10 codes. We defined ICD-10 codes as validated if they were concordant with the initial ICD-9 codes after manual comparison by two physicians. Our primary validation measure was the percent of valid ICD-10 codes out of the total ICD-10 codes obtained during translation. We began with 126 ICD-9 diagnosis codes used for complication identification after carotid revascularization procedures, and 97 ICD-9 codes for complications after endovascular aortic aneurysm procedures. Translation generated 143 ICD-10 codes for carotid revascularization, a 14% increase from the initial 126 codes. Manual comparison demonstrated 98% concordance, with 99% agreement between the reviewers. Similarly, we identified 108 ICD-10 codes for endovascular aortic aneurysm repair, an 11% increase from the initial 97 ICD-9 codes. We again noted excellent concordance and agreement (98% and 100%, respectively). Manual review identified 4 ICD-10 codes incorrectly translated from ICD-9 codes for carotid revascularization, and 3 codes incorrectly translated for endovascular aortic aneurysm repair. Conclusions Algorithms to crosswalk lists of ICD-9 codes to ICD-10 codes can leverage electronic resources to minimize the burden of code translation. However, manual review for code validation may be necessary, with collaboration across institutions for researchers to share their efforts.
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