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Estimating Blood Cancer Prevalence in the United States Using NAACCR CiNA Data

This presentation discusses the use of NAACCR CiNA data to estimate blood cancer prevalence in the United States, providing more complete geographic coverage and local estimates. It explores different statistical approaches and the feasibility of estimating national and local limited-duration prevalence statistics using NAACCR data.

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Estimating Blood Cancer Prevalence in the United States Using NAACCR CiNA Data

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  1. Using NAACCR CiNA data to estimate blood cancer prevalence in the United States using more complete geographic coverage and to provide local estimates NAACCR/iacr 2019 Conference Presented by: Vancouver, BC Chris Johnson June 13, 2019 cjohnson@teamiha.org

  2. The Team - coauthors Rick Firth, IMS Steve Scoppa, IMS Andy Lake, IMS Recinda Sherman, MPH, PhD, CTR, NAACCR Angela Mariotto, PhD, NCI

  3. Acknowledgements • This project was supported by the Leukemia & Lymphoma Society (LLS), IMS, and NAACCR. • Participation of Chris Johnson was funded in whole with Federal funds from the National Cancer Institute, National Institutes of Health, Department of Health and Human Services, under Contract No. HHSN261201800006I and the Centers for Disease Control and Prevention, Department of Health and Human Services, under Cooperative Agreement NU58DP006270 to the Cancer Data Registry of Idaho, Idaho Hospital Association. • The findings and conclusions in this presentation are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention or the National Cancer Institute. • Dataset: SEER*Stat Database: NAACCR Incidence Data - CiNA Analytic File, 1995-2015, for Expanded Races, Custom File With County, Johnson - Prevalence WG (which includes data from CDC’s National Program of Cancer Registries (NPCR), CCCR’s Provincial and Territorial Registries, and the NCI’s Surveillance, Epidemiology and End Results (SEER) Registries), certified by the North American Association of Central Cancer Registries (NAACCR) as meeting high-quality incidence data standards for the specified time periods, submitted December 2017.

  4. Wearing my “NAASCCAR” Hat Homage to first CiNA Survival Volume, which used the theme “I just wanna go fast.”

  5. Presentation Objectives Educate participants about the different types of cancer prevalence statistics and approaches to estimating them. Inform participants about use of NAACCR CiNA data to estimate blood cancer prevalence in the United States, by state, and for the individual Leukemia & Lymphoma Society (LLS) Chapters Demonstrate the feasibility of estimating national and local limited-duration prevalence statistics using NAACCR data.

  6. Leukemia and Lymphoma Society (LLS) asked NAACCR to estimate prevalence

  7. LLS Chapters(N = 57)

  8. Background • Cancer prevalence is the number of persons alive on a certain date who have a history of cancer, so is a function of both incidence and survival. • Information on prevalence can be used for: • health planning • resource allocation • an estimate of cancer survivorship

  9. Background • Complete Prevalence represents the proportion of people alive on a certain day who previously had a diagnosis of the disease, regardless of how long ago the diagnosis was. • Two estimation approaches: • Cross-sectional population-based surveys (self-reporting) • But… underreporting and misclassification of disease • Direct computation (the counting method) • Requires registry data that has been collected over a sufficiently long period of time to capture all prevalent cases of the disease. • In the US, only Connecticut Tumor Registry Source: https://surveillance.cancer.gov/prevalence/complete.html

  10. Counting Method Illustration Mock‐up data are for illustration purposes only. Left side of bars denotes diagnosis date and right side denotes date of death or loss to follow-up*. 5 persons (1, 3, 4, 6, and 7) were known to be alive at the prevalence date. Persons 2 and 5 were deceased prior to the prevalence date. Persons 8 and 9 were lost to follow-up prior to the prevalence date and survival proportions would be applied to estimate their contribution to prevalence.

  11. Background • Limited-Duration Prevalencerepresents the proportion of people alive on a certain day who had a diagnosis of the disease within the past x years • e.g. x = 5, 10 or 20 years • Registries of shorter duration (say, < 40 years) can only estimate limited-duration prevalence • Same two estimation approaches Source: https://surveillance.cancer.gov/prevalence/limited.html

  12. An aside… Idaho BRFSS vs. CDRI • CDRI has collected population-based incidence data since ~1970 (> 45 years for prevalence) • Population Demographics • 1.8 million (2018) • 12% increase since 2010 • For last 2 years, Idaho was fastest-growing state • Behavioral Risk Factor Surveillance System (BRFSS): health-related telephone surveys, partnerships between CDC and states. • Idaho included optional module on Cancer Survivorship in 2016 • Self-reported cancer prevalence estimates, by primary site

  13. BRFSS complete prevalence • CDRI 46-yr LD prevalence • Invasive, in situ, benign & borderline brain/CNS • Ages 20+ • BRFSS total estimate is much higher than CDRI, and it should be due to non-melanoma skin cancers • It appears that BRFSS respondents may misclassify: • Melanoma and non-melanoma skin cancers • Cervical cancer and cervical intraepithelial neoplasia (CIN) [and maybe ovary] • BRFSS asks about most recent cancer dx, so could be ~20% undercount for some sites (multiple primaries) • For LLS blood cancer types, CDRI estimates are always higher

  14. Background • SEER-9 • Cases diagnosed from 1975 through the current data year • Connecticut • Detroit • Atlanta • San Francisco-Oakland • Hawaii • Iowa • New Mexico • Seattle-Puget Sound • Utah • Historically, data from the National Cancer Institute’s SEER-9 registries have been used to estimate U.S. national complete prevalence. • 9.4% population covered, may not be representative

  15. Background • Now that NAACCR CiNA survival statistics cover almost all registries, we can estimate prevalence for the U.S. and also provide local limited duration (LD) prevalence estimates.

  16. Methods • We estimated 5-year limited-duration prevalence on Jan 1, 2014 by LLS Chapter and state for: • Hodgkin lymphoma • non-Hodgkin lymphoma • Leukemia • Myeloma • all other blood cancers* * Myelodysplastic syndrome (ICD-O-3 histology 9989, 9987, 9895, 9986) and Myeloproliferative Disease (9975, 9960, 9961, 9960) both with ICD-O-3 typology C42, C77; Waldenstroms (9761); Polycythemia Vera (9950); Essential thrombocythemia (9962); Myeloid and lymphoid neoplasms with PDGFRA rearrangement (9965/3); Myeloid neoplasms with PDGFRB rearrangement (9966/3 ); Myeloid and lymphoid neoplasms with FGFR1 abnormalities (9967/3)

  17. Methods • CiNA data from November 2017 NAACCR submission • 2009-2013 incidence cases and survival • 41 states and the Detroit registry • ~83% national population coverage

  18. Filling in the Gaps • What about geographic areas not included in the CiNA Survival Volume? • “Borrowed” estimates from nearest neighbors • Twice – once for Chapter analysis, once for state analysis • Stratified by: • age (19 age groups: <1, 1-4, 5-9, 10-14… 80-84, 85+) • sex (male, female) • race (white/unknown, black, other)

  19. Filling in the Gaps Nearest Neighbors GeoDa

  20. Total LLS Blood Cancer Prevalence 5-Year LD Prevalence = 552,826 Prevalence Percent = .172% State Range = .123% - .226% Darker red = higher prevalence percent Hatching = imputed (adjacency)

  21. Hodgkin Lymphoma Prevalence 5-Year LD Prevalence = 37,464 Prevalence Percent = .012% State Range = .007% - .016% Darker red = higher prevalence percent Hatching = imputed (adjacency)

  22. Non-Hodgkin Lymphoma Prevalence 5-Year LD Prevalence = 232,931 Prevalence Percent = .073% State Range = .052% - .098% Darker red = higher prevalence percent Hatching = imputed (adjacency)

  23. LeukemiaPrevalence 5-Year LD Prevalence = 141,416 Prevalence Percent = .044% State Range = .031% - .063% Darker red = higher prevalence percent Hatching = imputed (adjacency)

  24. MyelomaPrevalence 5-Year LD Prevalence = 71,211 Prevalence Percent = .022% State Range = .014% - .030% Darker red = higher prevalence percent Hatching = imputed (adjacency)

  25. Other Blood Cancers Prevalence 5-Year LD Prevalence = 74,151 Prevalence Percent = .023% State Range = .011% - .037% Darker red = higher prevalence percent Hatching = imputed (adjacency)

  26. Patterns • Maine, Massachusetts*, New York, New Hampshire, Wisconsin had the highest prevalence proportions • Alaska, Utah, Puerto Rico, Arizona, Alabama had the lowest prevalence proportions • Some variation by type * imputed

  27. Patterns • Considerable variation in prevalence proportion by state • 1.9 to 2.2-fold, by type • 3.4-fold for “Other Blood Cancers” • Some variation due to demographics (age, sex, race) • These are crude prevalence estimates, not age-adjusted • But…

  28. Discussion • For some blood cancers, there are known issues with reporting delay and potentially missed incidence cases when the person is diagnosed and treated in a physician’s office, but not seen in a hospital, which may underestimate prevalence. “Purification through utilization.” -John Young

  29. Limitations • We are unable to estimate complete prevalence using CiNA data. • Net-migration not accounted for • Prevalence statistics calculated using registry data do not include information on persons with a history of blood cancer who move to a new state. • For states with high population growth, the prevalence would be underestimated.

  30. Conclusions • This is the first use of CiNA data to estimate prevalence. • 83% > 9.4% population coverage • We hope these limited-duration prevalence statistics are useful to states and LLS Chapters for outreach and patient support services. • The success of this project hinged on collaboration between NAACCR, the National Cancer Institute, cancer registries, and Information Management Services, Inc. • This project demonstrated the feasibility of estimating national and local limited-duration prevalence statistics using NAACCR CiNA data.

  31. Next Steps • Extending the methods to other cancer sites • CiNA Prevalence Volume • Coming sometime Fall 2019

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