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Rosemary D. Cress, DrPH Research Program Director

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. Introduction.

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Rosemary D. Cress, DrPH Research Program Director

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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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