Download Division of Cancer Control and Population Sciences

National Cancer Institute
Cancer Prevalence in the US:
An Overview and Applications
By Angela Mariotto
National Cancer Institute
Combining Epidemiology and Economics
for Measurement of Cancer Costs
U.S. DEPARTMENT
OF HEALTH AND
HUMAN SERVICES
National Institutes
of Health
September 21-24, 2010
Frascati
Acknowledgements
 Istitute Superiore di Sanità, Rome, Italy
 Roberta de Angelis, Andrea Tavilla, Arianna Simonetti, Arduino
Verdecchia, Riccardo Capocaccia, Lucia Martina
 National Cancer Institute
 Rocky Feuer, Robin Yabroff, Joan Warren, Martin Brown, Julia
Rowland
 IMS
 Steve Scoppa, Mark Hachey, Ken Bishop, Yongwu Shao
Outline
 Overview on cancer prevalence
 Importance and definitions
 Methods
 US prevalence statistics
 Projections of cancer prevalence and applications
 Data/Inputs
 Projections assumptions and method
 Estimating the demand for oncologists in 2020
Importance
 Overall Cancer Burden and Survivorship
 NCI Office of Cancer Survivorship
 Cancer Advocacy Groups
 FDA Office of Orphan Drug Products Development
 Prevalence of less than 200,000 for disease or condition to be
treated
 Health Care Utilization and Costs of Care
 Health planners and policy makers
Prevalence Definitions
 Complete prevalence: living persons ever diagnosed with
cancer (regardless of the current state of their disease)
 Overall measure of cancer burden and survivorship
 Limited duration prevalence - prevalence of persons
diagnosed with cancer in the last x years.
 Easier to estimate from cancer registry data
 Provides prevalence by time since diagnosis
 E.g. 0-5 years, 5-10 years, etc.
Methods to Estimate Prevalence from Cancer
Registry Data
Method
Input
Prevalence
Output
Counting
Method (1)
Incidence & vital status
Limited duration
SEER*Stat
Complete
COMPREV
(childhood cancers)4
Limited duration
Completeness
prevalence, incidence
Index (2-4)
and survival models
Software
MIAMOD (5)
Mortality data and
survival model
Complete, limited
MIAMOD/PIAMOD
duration and projections
PIAMOD (6)
Incidence data and
survival model
Complete, limited
MIAMOD/PIAMOD
duration and projections
1. Feldman et al., N Engl J Med 1986
2. Capocaccia et al. Stat Med 1997
3. Merrill et al., Int J Epidemiol 2000
4. Simonetti et al., Stat Med 2007
5. Verdecchia et al., Stat Med 1989.
6. Verdecchia et al., Stat Med 2002
Cancers Diagnosed in Childhood (age at diagnosis 0-19 yrs)
Age distribution of survivors at 1/1/1997 using 1975-1996 data
Ages 41+
No Information
Estimating Complete Prevalence of
Cancers Diagnosed in Childhood
Simonetti A, et al. Stat Med. 2008.
Estimating Complete Prevalence of
Cancers Diagnosed in Childhood
Estimated Number Cancer Survivors
in the US 1971-2006
12.000.000
10.000.000
8.000.000
6.000.000
4.000.000
2.000.000
0
11.7
million
Percent of US cancer survivors at Jan. 1st,
2006. Percent within each age group.
Percent
(3.8% of the total US population)
35%
30%
25%
20%
Males
Females
15%
10%
5%
0%
Age at Prevalence
Projections of the Cancer Prevalence in the US
 In the past estimation of the costs of cancer care used:
 SEER data to estimate # patients in each phase of care
 SEER-Medicare (claims data) to estimate costs of cancer care
 Projections need modeling: MIAMOD or PIAMOD
 We used PIAMOD to have more control on projection
assumptions
 Applications
 What is projected economic burden of cancer care in the US?
 What is the demand for oncologists in 2020?
Project Cancer Prevalence: Data and Methods
Overview
 Projections of the future number of incident and prevalent
cancer cases, were derived from survival and incidence data
from the nine registries in the SEER program from 1975-2005
(10% of U.S. population).
Incidence
ratesrates
werewere
applied
 Step1:
Incidence
to US Census
Population
applied
to the US
Census
projectionsprojections
to estimatetothe
Population
estimate
annual number of new cancer
the
annual No. of new cancer
cases in the US.
cases in the US
Survival
and US
 Step
2: Survival
andincidence
US incidence
rateswere
wereused
usedto
toestimate
estimate
rates
prevalence.using PIAMOD
prevalence
 Step3. Prevalence by phase
Step 2: PIAMOD Method
Inputs (1975-2020)
•US census population and
projections
•Estimates and projections of
US number of cancer cases
•US mortality and projections
from Berkeley life tables
Modeled Input
Cancer survival model. Age
and year of diagnosis trends.
MIAMOD /PIAMOD
software
Outputs
Predicted
prevalence by
year of
diagnosis, age
and calendar
year
Input: US Populations and Projections from the
Census Bureau
Male US population born 1945-1954
40-49
50-59
60-69
70-79
80-84
Data source: US Census Bureau
Input: SEER Age-Adjusted Incidence Rates
Projections
 Observed and projected
SEER incidence rates
Observed
Flat
Continuing trend
 SEER rates are applied
to the US population by
age and year to obtain
US cases
Input: Survival Modeling
 Modeled and observed
relative survival by site
and year of diagnosis
5-year
10-year
15-year
Survival Model Projections: Example Male
Colorectal Cancer
Observed
Base projection
Males aged 65-74 years
- - - Trend projection
1-year
5-year
10-year
Year at diagnosis
Step 3: Estimation of Prevalence by Phases of
Care in Person-Years
 Person-years in each phase of care:
 Last year of life for people dying of cancer and other causes
(last 12 months alive)
 Initial (12 months after diagnosis)
 Continuing (time between initial and last year of life)
 Calculations are done by combining prevalence and mortality with
survival
1-yr after cancer
diagnosis
Cancer
diagnosis
1-yr prior to death
Cancer
death
Priority order: Last year of life, initial
and continuing
Mariotto et al. Cancer Causes and Control, 2006.
Projections of US cancer prevalence by phases
of care, 2010-2020.
Cancer Prevalence (Number of people in thousands)
Population
Year
All ages
65+ years
Total
Initial
2010
2015
2020
308,936
322,366
335,805
40,244
46,791
54,632
13,772
15,829
18,071
1,080
1,187
1,306
% increase
2010-2020
9%
36%
31%
21%
Continuing
11,791
13,601
15,548
32%
End Year of Life
Other
cause Cancer
603
704
835
298
337
381
38%
28%
Prevalence Projections 2010 and 2020 Under
Different Assumptions
Prevalence (No. of People)
2020
Site
2010
All Sites
Female Breast
Prostate
Melanoma
Colorectal
Lymphoma
Uterus
Bladder
Lung
Kidney
Head & Neck
Cervix
Leukemia
Ovary
Brain
Stomach
Esophagus
Pancreas
13,772,000
3,461,000
2,311,000
1,225,000
1,216,000
639,000
586,000
514,000
374,000
308,000
283,000
281,000
263,000
238,000
139,000
74,000
35,000
30,000
Base
Incidence Survival
Both
18,071,000 17,465,000 18,878,000 18,229,000
4,538,000 4,275,000 4,597,000 4,329,000
3,265,000 3,108,000 3,291,000 3,132,000
1,714,000 1,971,000 1,724,000 1,983,000
1,517,000 1,327,000 1,575,000 1,376,000
812,000
803,000
841,000
831,000
672,000
638,000
667,000
634,000
629,000
576,000
640,000
587,000
457,000
392,000
481,000
412,000
426,000
487,000
446,000
511,000
340,000
308,000
346,000
313,000
276,000
245,000
277,000
245,000
340,000
328,000
356,000
342,000
282,000
232,000
296,000
241,000
176,000
174,000
185,000
182,000
93,000
80,000
103,000
88,000
50,000
48,000
62,000
60,000
40,000
40,000
50,000
50,000
Continuing trends in
Base=
population
change only
Percent of Survivors in Each Phase of Care in
2010
Pancreas
Lung
Esophagus
Stomach
Head & Neck
Kidney
Colorectal
Prostate
Leukemia
Bladder
Lymphoma
All Sites
Brain
Melanoma
Breast
Ovary
Uterus
Other sites
Cervix
0%
Initial
Continuing
End of life
20%
40%
60%
80%
100%
Examples of Applications of Cancer Prevalence
Estimates by Phases of Care
 Total expenditure of cancer care (Robin Yabroff)
 By combining with average annual costs
 Estimates of the future demand for oncologists’
services through 2020
 By combining with estimates of the use of oncologists’
services among current cancer patients
 Warren et al. (JCO, 2008)
Estimates of the future demand for oncologists’
services through 2020
 American Association of Medical Colleges Center for
Workforce Studies, initiated a project to estimate the
future demand for oncologists’ services through 2020
 These estimates were derived by combining two pieces of
information:
 Estimates of the use of oncologists’ services among current
cancer patients
 Projections of the future # of cancer cases
Use of Oncologists’ Services Estimated from
SEER-Medicare Data
 Federal health insurance plan that offers health insurance
for the 65 years and older US population.
 Medicare data contains enrollment and “claims data”
associated with health care paid by Medicare plan.
 Hospitalization, clinic visit, outpatient tests bills
 Information on date, diagnosis codes, procedure codes, and
cost.
 94% of the 65 years and older US population has inpatient and
outpatient coverage
 In this study we only retained claims from oncologists in
this study
Use of Oncologists’ Services Among Current Cancer
Patients
 We calculated the proportion of people who had at
least one visit with an oncologist.
 For persons who had at least one visit, we calculated the
mean number of visits.
 Utilization estimates from the Medicare population were
applied to the projected prevalent cases for the total
U.S. population to determine future demand for
oncologists’ services.
Utilization of oncologists’ services among Medicare
beneficiaries diagnosed with cancer in SEER areas
% Patients seeing an oncologist
100%
80%
60%
40%
20%
0%
Initialof visits among
Continuing
Last oncologist
year of life
Mean number
those who saw an
25
20
15
10
5
0
Initial
Total
Continuing
65-69
70-84
Last year of life
85+
Projected Number of Oncology Visits by Phase
Among Persons in the U.S. with Cancer, 2005-2020
Table 2. Total Number of Annual Oncology Vistits, 2005-2020
Year
2,005
2,010
2,015
2,020
Total Annual
Oncology Visits (in
Millions)
38.4
44.2
50.6
57.5
Millions
Figure 3. Total Annual Oncologists Visits by
Phases of Care (in Millions)
50
45
40
35
30
25
20
15
10
5
0
2005
2010
2015
2020
Initial
Continuing
Last year of life
Estimating Cure, non-Cure and Recurrence
Prevalence
Cancer
Diagnosis
Cure: C(t)
In recurrence R(t)
Not-cured {1-C(t)}
Non in recurrence {1-R(t)}
 Mixture survival cure model to estimate the proportion
cured
 For those “destined” to die of their cancer
estimate recurrence and recurrence free
prevalence
Percent of 29-years survivors in each
prevalence category, 1/1/2004
100%
50%
0%
Cured
Not cured/recurrence-free
Not cured in recurrence
Discussions/Conclusions
 Changes of population have the largest effect on
prevalence projections compared to incidence and
survival.
 The growing number of cases will place a major burden
on the U.S. health care system.
 The demand for oncologists will increase significantly
within the next 15 years.
 Other health professionals may serve as substitutes for
oncologists, especially in the last year of life.
Conclusions
 CISNET models may provide prevalence projections
based on assumptions of future trends for particular
interventions
 Projecting prevalence has proven useful to project costs
and utilization of cancer care
Thank you!!!!
 http://srab.cancer.gov/prevalence/
 [email protected]