1. Lung 2018
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Using NAACCR CiNA data to estimate blood cancer prevalence in the United States using more complete geographic coverage and to provide local estimates
23 words, 150 characters with spaces
NAACCR/iacr 2019 Conference Presented by:
Vancouver, BC Chris Johnson
June 13, 2019
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2. The Team - coauthors Rick Firth, IMS Steve Scoppa, IMS Andy Lake, IMS
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The Team - coauthors
Rick Firth, IMS
Steve Scoppa, IMS
Andy Lake, IMS
Recinda Sherman, MPH, PhD, CTR, NAACCR
Angela Mariotto, PhD, NCI
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3. Lung 2018
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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. HHSN I and the Centers for Disease Control and Prevention, Department of Health and Human Services, under Cooperative Agreement NU58DP 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, , 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.
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4. Wearing my “NAASCCAR” Hat
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Wearing my “NAASCCAR” Hat
You may note that I’m using a NAACCR slide deck. While I work at the Cancer Data Registry of Idaho, I’m wearing my NAACCR hat for this presentation.
Homage to first CiNA Survival Volume, which used the theme “I just wanna go fast.”
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5. Presentation Objectives
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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.
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6. Lung 2018
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Leukemia and Lymphoma Society (LLS) asked NAACCR to estimate prevalence
The Leukemia & Lymphoma Society, or LLS, is the world's largest voluntary health agency dedicated to blood cancer. The LLS mission is to cure leukemia, lymphoma, Hodgkin's disease, and myeloma, and improve the quality of life of patients and their families.
In 2018, the Leukemia & Lymphoma Society approached NAACCR with a request for assistance on estimating prevalence for each LLS Chapter and state in the United States.
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7. LLS Chapters (N = 57) Lung 2018 10/4/18
LLS Chapters are patient outreach and service delivery areas. Sometimes the Chapters are whole states. For high population states, there may be several chapters, and for low population states, several states may be grouped into one chapter, like the Oregon/Southwest Washington/Idaho/Montana Chapter.
LLS has chapters throughout the country that providing immediate help to patients, families, and supporters.
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8. Lung 2018
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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
Prevalence is a statistic of interest to public health because it identifies the level of burden of disease or health-related events on the population and impact on the health care system. Prevalence represents new and pre-existing cases alive on a certain date, in contrast to incidence which reflects new cases of a condition diagnosed during a given period of time.
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9. Lung 2018
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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
There are a couple of types of cancer prevalence measures.
<read slide>
There are other methods for estimating prevalence, like Back Calculation/Transition Rate Methods , but I’m not going to talk about these.
Source:
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10. Counting Method Illustration
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This is an illustration of the counting method. Each row represents a patient. The left side of each colored bar is the diagnosis date, and the right side is the date of death, or loss to follow-up. You pick a prevalence date, then simply count the cases that were alive on that date. For cases lost to follow-up, you use survival proportions to estimate the number alive on the prevalence date.
Requires population-based incidence and follow-up/death ascertainment (fit for use for survival statistics)
The expected number of cases lost to follow-up who make it to the prevalence date is computed using conditional survival curves
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.
Counting Method Illustration
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11. Lung 2018
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Background
Limited-Duration Prevalence represents 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
The other kind of prevalence is called limited-duration prevalence.
Source:
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12. An aside… Idaho BRFSS vs. CDRI
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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
Using data from the Cancer Data Registry of Idaho (CDRI), we can compute a maximum of 46-year limited-duration prevalence, which likely approximates complete prevalence for the cases diagnosed as Idaho residents. However, Idaho has recently been one of the fastest growing states, and people who move to Idaho with a history of cancer (not diagnosed as Idaho residents) are not included in incidence or prevalence statistics from CDRI. We attempted to also use data from the Idaho Behavioral Risk Factor Surveillance System to estimate prevalence, but found extensive issues with misclassification of disease.
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13. It appears that BRFSS respondents may misclassify:
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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
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14. Lung 2018
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Background
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
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
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15. Lung 2018
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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.
NAACCR has been producing reports of cancer incidence statistics for over 20 years and survival statistics since March 2016.
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16. Lung 2018
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Methods
We estimated 5-year limited-duration prevalence on Jan 1, 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)
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17. Methods CiNA data from November 2017 NAACCR submission
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)
Alabama Chapter (Florida counties), the remainder of Florida (Northern & Central Florida Chapter, Palm Beach Area Chapter, Southern Florida Chapter, Suncoast Chapter), Massachusetts, Michigan (besides counties covered by the Detroit Registry), Kansas, Minnesota, Nevada, North Dakota, South Dakota, part of the National Capital Area Chapter (District of Columbia and Virginia), and the Virginia Chapter
District of Columbia, Florida, Kansas, Massachusetts, Michigan, Minnesota, Nevada, North Dakota, South Dakota, and Virginia

19. Nearest Neighbors Filling in the Gaps GeoDa Lung 2018 10/4/18
GeoDa is free, open source software for spatial data analysis. It has a tool for identifying adjacent polygons, and we used that to fill in the gaps for both LLS chapters and states that did not have survival data. We used age by sex by race prevalence proportions for their adjacent neighbors and imputed prevalence based on those proportions and the population in the state or LLS Chapter.
Rook and queen contiguity derive their names from the movements of chess pieces. The rook can move only to polygons that share a border of some length with its polygon. For queen, the shared border can be as small as one point.
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20. Total LLS Blood Cancer Prevalence
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5-Year LD Prevalence = 552,826
Prevalence Percent = .172%
State Range = .123% %
Darker red = higher prevalence percent
Hatching = imputed (adjacency)
Total LLS Blood Cancer Prevalence
I have a bunch of maps showing the results. For the total U.S. population, including Puerto Rico, we estimated 5-year prevalence of total blood cancers on January 1, 2014 to be 552,826. Total prevalent blood cancer cases by registry ranged from 907 in Alaska and Wyoming to over 56,000 in California.
The map shows patterns of prevalence expressed as a percentage.
State label for Alabama is missing.
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21. Hodgkin Lymphoma Prevalence
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5-Year LD Prevalence = 37,464
Prevalence Percent = .012%
State Range = .007% %
Darker red = higher prevalence percent
Hatching = imputed (adjacency)
Hodgkin Lymphoma Prevalence
I’m going to flip through the maps for the 5 blood cancer types to show that the geographic patterns were similar, but not the same, for each type.
There were 37,464 cases of Hodgkin Lymphoma.
By state, 5-year limited-duration prevalence for Hodgkin lymphoma ranged from 55 cases in Alaska to 3,819 in California.
State label for Alabama is missing.
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22. Non-Hodgkin Lymphoma Prevalence
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5-Year LD Prevalence = 232,931
Prevalence Percent = .073%
State Range = .052% %
Darker red = higher prevalence percent
Hatching = imputed (adjacency)
Non-Hodgkin Lymphoma Prevalence
There were 232,931 cases of non-Hodgkin lymphoma.
5-year limited-duration prevalence for non-Hodgkin lymphoma ranged from 342 cases in the District of Columbia to 24,695 in California.
State label for Alabama is missing.
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23. Leukemia Prevalence 5-Year LD Prevalence = 141,416
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5-Year LD Prevalence = 141,416
Prevalence Percent = .044%
State Range = .031% %
Darker red = higher prevalence percent
Hatching = imputed (adjacency)
Leukemia Prevalence
There were 141,416 cases of leukemia.
5-year limited-duration prevalence for leukemia ranged from 211 cases in the District of Columbia to 14,485 in California.
State label for Alabama is missing.
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24. Myeloma Prevalence 5-Year LD Prevalence = 71,211
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5-Year LD Prevalence = 71,211
Prevalence Percent = .022%
State Range = .014% %
Darker red = higher prevalence percent
Hatching = imputed (adjacency)
Myeloma Prevalence
There were 71,211 cases of Myeloma.
5-year limited-duration prevalence for myeloma ranged from 103 cases in Alaska and Wyoming to 6,780 in California.
State label for Alabama is missing.
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25. Other Blood Cancers Prevalence
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5-Year LD Prevalence = 74,151
Prevalence Percent = .023%
State Range = .011% %
Darker red = higher prevalence percent
Hatching = imputed (adjacency)
Other Blood Cancers Prevalence
And there were 74,151 cases of other blood cancers.
5-year limited-duration prevalence for other blood cancers ranged from 103 cases in Wyoming to 7,388 in California.
State label for Alabama is missing.
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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
States in the northeast and north-central US generally had higher prevalence proportions of blood cancers, and southern states and Alaska had lower prevalence proportions.
* imputed
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27. Patterns Considerable variation in prevalence proportion by state
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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…
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28. Lung 2018
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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
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29. Lung 2018
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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.
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30. Conclusions This is the first use of CiNA data to estimate prevalence.
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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.
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31. Next Steps CiNA Prevalence Volume Coming sometime Fall 2019
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Next Steps
Extending the methods to other cancer sites
CiNA Prevalence Volume
Coming sometime Fall 2019
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