1. Using NAPIIA to Improve the Accuracy of Asian Race Code in Registry Data Mei-Chin Hsieh, MSPH, CTR Lisa A. Pareti, BS, RHIT, CTR Vivien W. Chen, PhD NAACCR Conference, Denver, June 2008

2. Background Overall, Asians have lower risk of developing cancer than non-Hispanic whites and blacks For certain types of cancer, such as liver and stomach, Asians have higher incidence rates than other races For a registry with small numbers of Asians, even a few miscoding on these minority races could potentially bias the estimation of incidence rates To ensure Asian races are coded correctly, the Louisiana Tumor Registry implements NAPIIA into its routine data quality procedure

3. Purpose To demonstrate how the NAPIIA can improve the coding accuracy on Asian races To find the misclassification on Asian groups

4. Asian Population in Louisiana RaceCountPercent 04: Chinese7,47413.8 05: Japanese1,5192.8 06: Filipino4,5048.3 08: Korean2,8765.3 09: Asian Indian/Pakistani9,05016.8 10: Vietnamese24,35845.1 11: Laotian1,3342.5 12: Hmong140.0 13: Kampuchean3100.6 14: Thai6801.3 96: Asian, NOS1,9033.5 From Census 2000: 54,022 (1.208%)

5. Methods and Approach Data source: Louisiana Tumor Registry Cases diagnosed in year 1995 to 2005 with race1 (NAACCR item 160) coded to any Asians, other race, unknown race, or non- Asian race with birthplace in Asian country were selected Converted race1 to 96 (Asian, NOS) and applied NAPIIA on records

6. Methods and Approach New Asian codes assigned by NAPIIA were compared with original race1 and manually reviewed when the assigned Asian codes were different from the original race1 codes Research sources utilized for the review: Abstract Text, Accurint Voter Registration, Online Death Certificate, Online Medical Records, and contact hospitals as last resort

7. Results Out of 221,732 cases diagnosed between years 1995 and 2005, 1,711 (0.77%) eligible cases were run through the NAPIIA Non-Asian race with birthplace in Asian country: 58 (3.4%), white 55 and black 3 Specific Asian codes: 837 (48.9%) Asian NOS: 238 (13.9%) Unknown race: 578 (33.8%)

8. Results 21.8% (374) of cases were identified with race coding differing between original race1 and NAPIIA Comparisons Original race vs. NAPIIA NAPIIA vs. reviewed race Original race vs. reviewed race

9. Results: Comparing Original Race with NAPIIA  767 (44.8%) cases had original race unchanged  570 (33.3%) cases had same Asian race codes  374 (21.9%) cases (highlighted in yellow and blue) had inconsistent race code between original race and NAPIIA, which required manually reviewed

10. Results: Comparing Original Race with NAPIIA

11. Results: Distribution of 374 Cases with Inconsistent Race Codes

12. Results: Comparing Reviewed Race with NAPIIA  After manually reviewing, of the 374 inconsistent race code 254 (67.9%) were identified with same race code as assigned by NAPIIA  Of the 89 Filipino codes assigned by NAPIIA, 48 (53.9%) cases were white (mainly Hispanic) after review

13. Results: Comparing Reviewed Race with NAPIIA

14. Results: Comparing Original Race with Reviewed Race  Out of 374, only 34 (9.1%) cases were initially coded correctly  46.3% (19 out of 41) of Asian Indian/Pakistani were recoded to Vietnamese after review

15. Results: Final Race Categories After Review White: out of 52, 37 were actually Asian Black: all remained as black (due to incorrect birthplace) Asian races: out of 91, 79 were misclassified or miscoded Asian NOS: out of 167, 163 were able to be classified with a more specific Asian race Unknown race:  out of 61, 60 were more specifically classified to a correct race code  42 (70%) out of 60 were white

16. Conclusions Through this exercise, we were able to re- assign the correct race on 340 (90.9%) cases out of 374 cases reviewed Miscoding was one of the main reasons for misclassification of race1, other reasons included multiple races and code transposition  Miscoding: code 10 to 09, 04 to 05  Patient with multiple races: white and Asian Indian  Code transposition: code 10 to 01

17. Conclusions NAPIIA was able to more accurately identify Vietnamese race group compare with other Asian race groups Filipino race code had the least improved accuracy among race groups after NAPIIA Reduce the percentage of unknown race and Asian NOS Unknown race: 0.26% to 0.23% Asian NOS: 0.11% to 0.07%

18. Conclusions For a registry with small proportion of Asian cases, NAPIIA seems to be an excellent tool to improve the race coding accuracy on Asian groups NAPIIA also can be applied to race codes other than Asian NOS to enhance registry data quality (with review) A potential additional benefit of using NAPIIA for data quality control is the identification of cases with incorrect birthplace

19. Recommendations Double check race codes to make sure you coded what you intended If race is known, document the race information in the PE text field. For example, Filipino male If race information is obtained from death certificate or other sources, make sure the corresponding NAACCR race code is coded

20. Recommendations Factors that could improve the NAPIIA’s performance  Correct Spelling on last and first name  Maiden name  Birthplace