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NAACCR / IACR Combined Annual Conference 2019 Vancouver, British Columbia June 13, 2019

Furthering the Understanding of the Burden of Cancer among the American Indian and Alaska Native Population in Maine:. Assessing the Impact of Case Identification through Indian Health Service Linkage and Differences Due to Case and Regional Inclusion Criteria.

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NAACCR / IACR Combined Annual Conference 2019 Vancouver, British Columbia June 13, 2019

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  1. Furthering the Understanding of the Burden of Cancer among the American Indian and Alaska Native Population in Maine: Assessing the Impact of Case Identification through Indian Health Service Linkage and Differences Due to Case and Regional Inclusion Criteria NAACCR / IACR Combined Annual Conference 2019 Vancouver, British Columbia June 13, 2019 Denise Yob, MPH Lead Cancer Registry Epidemiologist Maine Cancer Registry, Maine CDC, DHHS, Augusta, Maine University of Southern Maine, Portland, Maine

  2. Overview Maine Department of Health and Human Services/ Center for Disease Control and Prevention

  3. Large rural state with diverse geography Sparse population in parts of state Current population density 43.1 per square mile

  4. Maine We are here Maine Department of Health and Human Services/ Center for Disease Control and Prevention

  5. Demographics Maine’s population is older and less racially/ethnically diverse than most states Maine Race and Age, 2010

  6. Preparing Registry Data

  7. Using Maine Cancer Registry Data

  8. Preparing for Analysis – Assessing Dataset Quality

  9. Preparing for Analysis - Data Quality U.S. Cancer Statistics Working Group. U.S. Cancer Statistics Data Visualizations Tool, based on November 2017 submission data (1999–2015): U.S. Department of Health and Human Services, Centers for Disease Control and Prevention and National Cancer Institute; www.cdc.gov/cancer/dataviz, June 2018. NAACCR Fast Stats is an interactive tool for quick access to key NAACCR cancer statistics for major cancer sites by age, sex, and race.  https://faststats.naaccr.org/ *except for small expected differences in US Cancer Statistics Data Visualizations Tool results due to reporting source exclusion of “death certificate only”

  10. Preparing for Analysis - Data Quality U.S. Cancer Statistics Working Group. U.S. Cancer Statistics Data Visualizations Tool, based on November 2017 submission data (1999–2015): U.S. Department of Health and Human Services, Centers for Disease Control and Prevention and National Cancer Institute; www.cdc.gov/cancer/dataviz, June 2018. NAACCR Fast Stats is an interactive tool for quick access to key NAACCR cancer statistics for major cancer sites by age, sex, and race.  https://faststats.naaccr.org/ *except for small expected differences in US Cancer Statistics Data Visualizations Tool results due to reporting source exclusion of “death certificate only”

  11. Preparing for Analysis - Background U.S. Cancer Statistics Working Group. U.S. Cancer Statistics Data Visualizations Tool, based on November 2017 submission data (1999–2015): U.S. Department of Health and Human Services, Centers for Disease Control and Prevention and National Cancer Institute; www.cdc.gov/cancer/dataviz, June 2018.

  12. Same data source, different analysts The Problem – Results do not agree U.S. Cancer Statistics Working Group. U.S. Cancer Statistics Data Visualizations Tool, based on November 2017 submission data (1999–2015): U.S. Department of Health and Human Services, Centers for Disease Control and Prevention and National Cancer Institute; www.cdc.gov/cancer/dataviz, June 2018. NAACCR Fast Stats is an interactive tool for quick access to key NAACCR cancer statistics for major cancer sites by age, sex, and race.  https://faststats.naaccr.org/ *except for small expected differences in US Cancer Statistics Data Visualizations Tool results due to reporting source exclusion of “death certificate only”

  13. Unraveling the Problem – Results do not agree What is known: MCR submits data annually to North American Association of Central Cancer Registries (NAACCR) and National Program of Cancer Registries (NPCR) as part of each organization’s “Annual Call for Data” Assume because all 3 datasets based on the same original dataset (Maine data submission), the cancer incidence data for Maine reflected in national cancer incidence datasets should match the counts and rates produced by our MCR dataset. Since they don’t: What have we done wrong? What are NAACCR and NPCR doing?

  14. Unraveling the Problem – Results do not agree Question Are rate differences due to differences in population or counts (or something else)? Differences due to Population Differences due to Counts *except for small expected differences in US Cancer Statistics Data Visualizations Tool results due to reporting source exclusion of “death certificate only” + Not Available:- Population size not presented in NAACCR FAST STAT results, AA = Age-Adjusted

  15. Unraveling the Problem – Results do not agree Review Results – Compare Populations Are rate differences due to differences in population or counts (or something else)? • MCR and USCS same population • Conclusion, no error in generating correct population file • No differences due to Population • NAACCR Fast Stats population not available on query •  Conclusion, dig deeper Agreement + Not Available:- Population size not presented in NAACCR FAST STAT results

  16. Unraveling the Problem – Differences in Determining the Population A purchased/referred care delivery area (PRCDA) is a geographic area within which purchased/referred care is made available by the Indian Health Service (IHS) to members of an identified Indian community who reside in the area • A PRCDA was formerly a contract health service delivery area (CHSDA) • When producing statistics using SEER Incidence or US Mortality data for American Indians/Alaska Natives, SEER frequently only includes cases that are in a PRCDA • Starting with data through 2016 (November 2018 submission), the PRCDA 2016 variable is used. Prior to the November 2018 submission, • Data from 2010-2015 (November 2012-2017 Submissions) used CHSDA 2012 • Data from 2004-2009 (November 2006-2011 Submissions) used CHSDA 2006 https://seer.cancer.gov/seerstat/variables/countyattribs/static.html#chsda

  17. Unraveling the Problem – Differences in Determining the Population States and Contract Health Service Delivery Area counties by Indian Health Service Region: United States, 2009. CHSDA = Contract Health Service Delivery Area. We learned about CHSDA NAACCR Fast Stats Rates for American Indian or Alaska Native are based on the CHSDA (Contract Health Service Delivery Area) counties Maine had 3 CHSDA counties (1 additional county added as of late 2017) The IHS defines a PRCDA, formerly referred to as a Contract Health Service Delivery Area or Purchased/Referred Care Service Delivery Area, as the geographic area within which PRC will be made available by the IHS to members of an identified Indian community who reside in the area. Am J Public Health. 2014 June; 104(Suppl 3): S286–S294. Published online 2014 June.

  18. Many Native Americans in Maine are known as Wabanakie, or “People of the Dawn” There are five federally recognized Indian tribes in Maine today. Maine's Indian reservations:

  19. Location of Services

  20. Unraveling the Problem – Differences in Determining the Population Question Are rate differences due to differences in population or counts (or something else)? Differences due to Population: MCR and USCS same population  Conclusion, no error in generating correct population file NAACCR Fast Stats population not available on query  Conclusion, dig deeper Read “Fine print”: NAACCR limits AI/AN counts and rates to residence at time of diagnosis in CHSDA (Contract Health Service Delivery Area) counties. (3 of 16 counties in Maine) Refined our analysis to distinguish between CHSDA and Non-CHDSA residence  Conclusion, probably same population Agreement + Not Available:- Population size not presented in NAACCR FAST STAT results Contract Health Service Delivery Area now known as Purchased/Referred Care Delivery Areas (PRCDA)

  21. Unraveling the Problem – Case Counts do not agree Question Are rate differences due to differences in population or counts (or something else)? Differences due to Population  Solved Differences due to Counts  ?? USCS excludes cases identified by “Death Certificate Only (DCO)” - Only 3 AI/AN cases in 2011-2015 identified as “DCO” Conclusion, dig deeper to understand count differences NAACCR count difference not explained by non-CHSDA exclusion  Conclusion, dig deeper to understand count difference Population size not presented in NAACCR FAST STAT results, AA = Age-Adjusted

  22. Unraveling the Problem – Differences in Determining Counts Review Documentation “NPCR-CSS Data Evaluation Report 2017 November (DER 2017 November) “NAACCR Call For Data 2017 - Data Submission Summary For the CINA Publication Data –Maine”, Footnote below the race table on page 2 of the NAACCR report “Race Recode modified by IHS”. “NAACCR Call For Data 2017 - Data Submission Summary For the CINA Publication Data –Maine”

  23. Unraveling The Count Problem – Indian Health Service (IHS) Linkage • “Race Recode modified by IHS”. • Linkage purpose - to identify American Indian and Alaska Native (AI/AN) cancer patients who may have been misclassified as non-Native in cancer registries • The IHS Linkage project team is composed of CDC/Division of Cancer Prevention and Control (DCPC) staff and IHS staff • Link files from participating NPCR registriesand the administrative file from IHS • Registries receive linkage results and contain the variable IHSLINK with value of “1” indicating a match with a record in the IHS patient registration database.

  24. Unraveling The Count Problem – Indian Health Service (IHS) Linkage Review Documentation IHS Linkage Report: “Results from 2017 IHS linkage with Maine Cancer Registry”, CDC Division of Cancer Prevention and Control In collaboration with IHS Division of Epidemiology and Disease Prevention Results of 2018 NPCR-IHS linkage for the Maine Cancer Registry (based on 1995-2017 records) • Of 175,827 individuals, • 490 (0.28 percent) were classified by MCR as having AI/AN ancestry • Corresponding IHS records for 272 (55.5%) of MCR AI/AN cases • Among the remaining 175,337 individuals who were not classified as AI/AN by MCR, the linkage identified 136 that matched with IHS records (0.08%) * American Indian / Alaska Native

  25. Maine Cancer Registry: Original Race, IHS Results, and Race Recode Recode Record Source: hospitals, doctor’s offices, and pathology labs, death certificates Registry Database IHS Linkage Result Analysis IHS Link n= 272 individuals Maine Cancer Registry AI/AN n= 490 individuals NCI ANALYSIS 175,827 Individuals, all malignancies No IHS Link n= 218 individuals Original Records from Case Reporting Sources MCR ANALYSIS Recode results in 626 total AI/AN individuals Original Race IHS Link n= 136 individuals Original Records from Case Reporting Sources Not AI/AN n= 175,377 individuals NAACCR CHSDA use (based on county of residence at time of diagnosis 368 individuals No IHS Link n= 175,141 individuals NAACCR ANALYSIS Original Records from Case Reporting Sources IHS Link Total n= 408 individuals

  26. Solving the Problem – Differences Populations and Counts Question Agreement Are rate differences due to differences in population or counts (or something else)? Differences due to Population: Solved NAACCR Fast Stats differences explained by case restriction to residence at time of diagnosis in CHSDA county Differences due to Counts: Solved Differences explained by use of Race recode: “Race Recode modified by IHS” used in both NAACCR Fast Stats and USCS

  27. Count Problem Solved. Decide on MCR use of Recode • Algorithm - An easy change to make • If Race 1 or Race 2 = AI/AN then modified Race field = AI/AN • IF Race 1 or Race 2 not = AI/AN and IHS Link field = “Yes” then modified Race field = AI/AN “Race Recode modified by IHS”. • Hadn’t considered this before, but what should our registry do? • Various reasons for registry reluctance to “change” data • Uncertain of value of recode • Belief that IHS link field was sufficient for data submission purposes • Previous version of registry software wouldn’t allow a state-added field to store “original” race code • Numbers are small, didn’t expect an impact • No official protocol or requirement from NAACCR or NPCR No specific instruction for cancer registries to create “Race Recode” although NPCR guidance states that “The central cancer registry should utilize linkages to address gaps identified in data quality and completeness or to improve the utility of the data.”

  28. Count Problem Solved. Decide on MCR use of CHSDA Counties • Hadn’t considered this before, but what should our registry do? • Various reasons for using either method • Don’t fully understand how AI/AN people living in non-CHSDA counties may differ from those living in CHSDA counties • No official protocol or requirement from NAACCR or NPCR • Will depend on stakeholder needs and purposes of analysis • In meantime, may produce both statewide and CHSDA related rates “Limiting presentation and analysis to CHSDA counties helped mitigate the effects of race misclassification of AI/AN persons, although a portion of the population was excluded.” Melissa A. Jim et al. “Racial Misclassification of American Indians and Alaska Natives by Indian Health Service Contract Health Service Delivery Area”, American Journal of Public Health 104, no. S3 (June 1, 2014): pp. S295-S302. DOI: 10.2105/AJPH.2014.301933 PMID: 24754617

  29. Impact of Race Recode

  30. Impact of Race Recode • “Race Recode modified by IHS”. • Overall a 28% increase in case counts among AI/AN population in Maine after cases originally classified as non AI/AN and linked to IHS records were recoded to AI/AN • Increases differed by gender, CHSDA designation, and county

  31. Original Results

  32. Impact of Race Recode and CHSDA on Incidence Rates Comparisons AIAN Statewide (Original vs Recoded Race) CHSDA(Original vs Recoded Race) Non-CHSDA (Original vs Recoded Race) 3 CHSDA County (Recoded Race) Comparison to Maine Rate  How does it change conclusions? The AI/AN statewide update similar to NPCR approach The Ai/AN Update similar to NAACCR Fast Stats

  33. Impact of Race Recode and CHSDA Designation Comparisons How does it change conclusions? AIAN Statewide (Original vs Recoded Race) Increase 27% (NS) CHSDA(Original vs Recoded Race) Increase 31% (S) Non-CHSDA (Original vs Recoded Race) Increase 20% (NS) Comparison to Maine Rate 1) Statewide: From significantly lower to no detectable difference 2) CHSDA: From no detectable difference to significantly higher 3 ) non-CHSDA: From significantly lower to no detectable difference CHSDA County (Recoded Race)

  34. Impact of Race Recode and CHSDA Designation Comparisons How does it change conclusions? AIAN Statewide (Original vs Recoded Race) Increase 27% (NS) CHSDA (Original vs Recoded Race) Increase 31% (S) Non-CHSDA (Original vs Recoded Race) Increase 20% (NS) Comparison to Maine Rate 1) Statewide: From significantly lower to no detectable difference 2) CHSDA: From no detectable difference to significantly higher 3 ) non-CHSDA: From significantly lower to no detectable difference 3 CHSDA County (Recoded Race) + 137 records + 92 records + 44 records

  35. Impact by Gender All Cancer SitesFemale MALES VERSUS FEMALEs + 21.4% + 39.9% + 32.7% + 22.7% + 16.9%

  36. Leading Causes of “Recoded” Cancers

  37. Impact by Cancer Site LUNG 26% of “added” cancers among AI/AN males were prostate (15 of 58) 11 of 15 “added” prostate cancers occurred among males living in 1 CHSDA county How does it change conclusions? AIAN Statewide (Original vs Recoded Race) Increase 26% (NS) CHSDA (Original vs Recoded Race) Increase 34% (NS) Non-CHSDA (Original vs Recoded Race) Increase 9% (NS) MALES VERSUS FEMALEs

  38. Impact by Cancer Site LUNG 30% of “added” cancers among AI/AN females were breast (27 of 91) (23 malignant) How does it change conclusions? AIAN Statewide (Original vs Recoded Race) Increase 29% (NS) CHSDA (Original vs Recoded Race) Increase 37.5% (S) Non-CHSDA (Original vs Recoded Race) Increase 16.7% (NS) MALES VERSUS FEMALEs

  39. Impact by Cancer Site LUNG “added” 2 cervical cancers among AI/AN females (1 malignant) How does it change conclusions? AIAN Statewide (Original vs Recoded Race) Increase 12.5% (NS) CHSDA (Original vs Recoded Race) Increase 12.5.5% (S) Non-CHSDA (Original vs Recoded Race) Increase 16.7% (NS) MALES VERSUS FEMALEs

  40. Incidence and Mortality Notes: Incidence, CHSDA/Non-CHSDA began below, elevated after recode, CHSDA > Non-CHSDA. Mortality AI/AN began above state overall, and CHSDA and Non-CHSDA similar Cancer Incidence Cancer Mortality

  41. Summary and Lessons

  42. Acknowledgements Support for the Maine CDC Cancer Registry is provided in part by the National Program of Cancer Registries, Centers for Disease Control and Prevention, Cooperative Agreement number 1NU58/DP006297 and by the Maine Department of Health and Human Services. We also acknowledge the contributions of Molly Schwenn, Former Medical Director of the Maine Cancer Registry (Retired) Sara Huston, Lead Chronic Disease Epidemiologist, Maine CDC Kim E. Haggan, Director and State Registrar, Data, Research, and Vital Statistics within the Division of Public Health Systems Recinda Sherman, Program Manager, Data Use & Research, North American Association of Central Cancer Registries Melissa Jim, Epidemiologist, Division of Cancer Prevention and Control, National Center for Chronic Disease Prevention and Health Promotion The Maine CDC Cancer Registry wishes to thank the cancer registrars and reporters at hospitals throughout Maine as well as Cancer registry staff Kathy Boris and Douglas Light.

  43. THANK YOU

  44. LUNG MALES VERSUS FEMALEs

  45. LUNG Female MALES VERSUS FEMALEs

  46. COLORECTAL MALES VERSUS FEMALEs

  47. COLORECTALmale MALES VERSUS FEMALEs

  48. COLORECTALfemale MALES VERSUS FEMALEs

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