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Enhancing Occupational Cancer Surveillance in Central Cancer Registries

This study focuses on improving the collection and coding of industry and occupation data in cancer registries to enhance research on occupational cancer risk factors. Results show progress in coding I/O information for cancer incidence analysis. Collaboration between NIOSH, CCR, and USC continues to evaluate and code I/O text strings. Standardized rate ratios will be calculated for different industries, with a particular focus on the construction sector. While feasible, challenges remain due to inconsistent and incomplete I/O data. Further education for abstractors is recommended to enhance data collection.

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Enhancing Occupational Cancer Surveillance in Central Cancer Registries

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  1. Improving Occupation Information in Central Cancer Registries for Use in Occupational Cancer Surveillance Rosemary D. Cress, DrPH Research Program Director NAACCR: June 13-19, 2009, San Diego, CA

  2. Introduction • Occupational exposures are an important cancer risk factor, and may contribute to 4-20% of cancer deaths. • CDC/NPCR requires its registries to collect industry and occupation (I/O) on patients • Thus population based cancer registries provide a rich data source for studies of cancer and occupation

  3. Introduction • However, I/O information is collected in narrative form and has not been consistently collected or coded • Although registrars are asked to record usual occupation, nearly half of cancer patients have an occupation listed as “unknown” or “retired”

  4. Introduction • Despite these limitations, a review of studies by Dennis Deapen in LA demonstrated that registry based studies of occupation and cancer obtained results consistent with more expensive studies, even with incomplete data. • Such studies are useful for hypothesis generation.

  5. Introduction • Both the Los Angeles Cancer Surveillance Program and the California Cancer Registry have a long interest in collection of I/O information • Both registries employed I/O coders in the past, and both developed automatic coding programs that were used until 2002

  6. Introduction • However, coding of I/O is highly labor intensive and expensive • With budget cuts and competing priorities, I/O coding of statewide CCR data was abandoned several years ago

  7. Introduction • The CDC National Institute for Occupational Safety and Health (NIOSH) also has a keen interest in identifying occupations that increase cancer risk • In 2007, NIOSH funded the CCR to conduct a pilot study to improve coding of registry I/O data

  8. NIOSH/CCR Pilot Study • The purpose of this study was to assess the feasibility of coding I/O information in the cancer registry, and of using this information as a surveillance tool • The focus of the pilot was on coding construction occupations and assessing cancer incidence among construction workers

  9. NIOSH/CCR Pilot Study • During the first year, NIOSH staff translated the older CCR autocoding program to SAS. The program utilizes a “look-up” table with I/O codes and text • CCR staff utilized the program to evaluate completeness of I/O coding in the CCR database and to identify uncoded text strings

  10. NIOSH/CCR Pilot Study • Uncoded I/O text strings were prioritized by frequency • USC and NIOSH staff coded text strings to the Bureau of the Census 1990 I/O codes • These new codes and text strings were added to the SAS program, and the process was repeated

  11. Preliminary Results • Of 2,258,857 records in the statewide CCR database, 1,114,620 (49.3%) had I&O coded at the beginning of the study. • As of May 2009, 76.4 % of records were coded

  12. Industry Information on CCR cases, 1998-2008(n=2,843,003)

  13. Preliminary Results • Over 50,000 cancer patients with a construction occupation have been identified. • Over 387,000 patients are coded as “retired,” and over 743,000 as “unemployed or unknown” making their records unusable for I/O research • Over 600,000 remain uncoded

  14. Next steps • NIOSH, CCR and USC will continue to work together to code as many I/O text strings as possible • Standardized rate ratios will be calculated for major cancer sites for workers in the construction industry as well as those in other industries.

  15. Conclusions • This study is demonstrating that it is feasible to code I/O text fields and use this information for research into occupational risk factors for cancer • However, inconsistent and incomplete information recorded for I/O in the medical record will limit this activity

  16. Conclusions • Additional education of abstractors on what to look for and how to code “usual” occupation and industry will improve our ability to collect these data for research.

  17. Acknowledgements • CDC/NIOSH • Geoffrey Calvert • Sara Luckhaupt • Pamela Schumacher • Rui Shen • California Cancer Registry, Sacramento • Katrina Bauer • Mark Allen • Los Angeles Cancer Surveillance Program, USC • Dennis Deapen • Shirley Miyashiro

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