Industry and Occupation in the Electronic Health Record: An Investigation of the National Institute for Occupational Safety and Health Industry and Occupation Computerized Coding System

Journals

1. Kim J, Langlois P, Herdt-Losavio M, Agopian A. A Case-Control Study of Maternal Occupation and the Risk of Orofacial Clefts. Journal of Occupational & Environmental Medicine 2016;58(8):833 View
2. Simmons J, Liebman A, Sokas R. Occupational Health in Community Health Centers: Practitioner Challenges and Recommendations. NEW SOLUTIONS: A Journal of Environmental and Occupational Health Policy 2018;28(1):110 View
3. Buckner‐Petty S, Dale A, Evanoff B. Efficiency of autocoding programs for converting job descriptors into standard occupational classification (SOC) codes. American Journal of Industrial Medicine 2019;62(1):59 View
4. Gorucu S, Weichelt B, Redmond E, Murphy D. Coding agricultural injury: Factors affecting coder agreement. Journal of Safety Research 2020;75:111 View
5. Freeman M, Pollack L, Rees J, Johnson C, Rycroft R, Rousseau D, Hsieh M. Capture and coding of industry and occupation measures: Findings from eight National Program of Cancer Registries states. American Journal of Industrial Medicine 2017;60(8):689 View
6. Silver S, Tsai R, Morris C, Boiano J, Ju J, Scocozza M, Calvert G. Codability of industry and occupation information from cancer registry records: Differences by patient demographics, casefinding source, payor, and cancer type. American Journal of Industrial Medicine 2018;61(6):524 View
7. Kim J, Langlois P, Mitchell L, Agopian A. Maternal occupation and the risk of neural tube defects in offspring. Archives of Environmental & Occupational Health 2018;73(5):304 View
8. Harber P, Leroy G. Informatics Approaches for Recognition, Management, and Prevention of Occupational Respiratory Disease. Clinics in Chest Medicine 2020;41(4):605 View
9. Scott E, Hirabayashi L, Graham J, Krupa N, Jenkins P. 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. Injury Epidemiology 2021;8(1) View
10. Varga A, Gruß I, Ritzwoller D, Bradley C, Sterrett A, Banegas M. Characterizing employment of colorectal cancer survivors using electronic health records. JAMIA Open 2021;4(3) View
11. Kelmenson S. Between the farm and the fork: job quality in sustainable food systems. Agriculture and Human Values 2023;40(1):317 View
12. Montoya‐Barthelemy A, Leniek K, Bannister E, Rushing M, Abrar F, Baumann T, Manly M, Wilhelm J, Niece A, Riester S, Kim H, Sellman J, Desai J, Anderson P, Bovard R, Pronk N, McKinney Z. Using advanced racial and ethnic identity demographics to improve surveillance of work‐related conditions in an occupational clinic setting. American Journal of Industrial Medicine 2022;65(5):357 View
13. Roberts B, Shkembi A, Smith L, Neitzel R. Beware the Grizzlyman: A comparison of job- and industry-based noise exposure estimates using manual coding and the NIOSH NIOCCS machine learning algorithm. Journal of Occupational and Environmental Hygiene 2022;19(7):437 View
14. Bunn T, Liford M, Turner M, Bush A. Driver injuries in heavy vs. light and medium truck local crashes, 2010–2019. Journal of Safety Research 2022;83:26 View
15. Russ D, Josse P, Remen T, Hofmann J, Purdue M, Siemiatycki J, Silverman D, Zhang Y, Lavoué J, Friesen M. Evaluation of the updated SOCcer v2 algorithm for coding free-text job descriptions in three epidemiologic studies. Annals of Work Exposures and Health 2023;67(6):772 View
16. Dixon N, Goggins M, Ho E, Howison M, Long J, Northcott E, Shen K, Yeats C. Occupational models from 42 million unstructured job postings. Patterns 2023;4(7):100757 View