Embed Size (px)
Citation preview
Nat
iona
l Can
cer
Inst
itute
Prevalence Projections: The US Experience
State of Art Methods for the Analysis of Population-
Based Cancer Data
Nat
iona
l Can
cer
Inst
itute
U.S. DEPARTMENT
OF HEALTH AND
HUMAN SERVICES
National Institutes
of Health
Angela Mariotto
Based Cancer Data
January 22-23, 2014
Ispra, Italy
Outline
� Temporal prevalence projections
� PIAMOD application to SEER data to estimate 2020 projections
of US prevalence by phases of care.
� Geographic prevalence projections
� Evaluation of biases in projecting SEER prevalelence
proportions to other areas
Temporal Projections: Relevance
� Prevalence estimates (complete or limited duration) lag
few years behind current calendar year. In the US we
have January 1, 2010 estimates.
� Projections are needed:
� To estimate current year prevalence
� To project into the future for planning and resources allocation
Temporal Projections: Relevance
� Questions
� What is projected economic burden of cancer care in the US?
NIH Office of the Director
� What is the demand for oncologists in 2020? American Society
of Clinical Oncology (ASCO)of Clinical Oncology (ASCO)
� What will be the number of cancer survivors aged 65+ in 2020?
American Cancer Society (ACS)
� Is the prevalence of melaonam stage IIB-IV smaller than
200,000? – Food Drug Administration (FDA) application of the
finantial incentives for drug developement
Projections require modeling: PIAMOD vs. MIAMOD
� PIAMOD: uses incidence and survival
� Easier to control future incidence projections. Incidence projections
done outside software.
� Usually needs to project geographically: registry� national
� MIAMOD: uses mortality data to estimate incidence.
� Mortality data available nationally
� Less control over incidence projections which will be based on the
projected age, period and cohort incidence model.
� A 2 step approach could be used: Estimate incidence using
MIAMOD. Out of MIAMOD project incidence into the future and
input in PIAMOD to estimate prevalence
Projections of the US cancer prevalence through 2020
� Projections of the future number of incident and prevalent
cancer cases, were derived from survival and incidence data
from the 9 registries in the SEER program from 1975-2005
(10% of U.S. population).�Incidence rates were applied � Step1: Estimate US incidence and �Incidence rates were applied to US Census Population projections to estimate the annual number of new cancer cases in the US.
�Survival and US incidence rates were used to estimate prevalence.
� Step1: Estimate US incidence and projections from SEER incidence
� Step 2: SEER Survival and US incidence rates were used to estimate prevalence usingPIAMOD
Input 1: US Population and Projections
40-49
60-69
50-59Male US population born 1945-1954
70-79
80-84
Data source: US Census Bureau
US incidence estimated from SEER data
� 1975-2010 applying SEER cancer incidence rates to the
US populations by age, sex, and race
� CN= (CR / PR )* PN
� 2011-2020 (projections) estimated using different � 2011-2020 (projections) estimated using different
assumptions of future SEER cancer incidence rates
� Base projections: assume future constant incidence rates
� Current trend projections: estimate trend in the last 5 or 10
years of data and continue the trend
Input: Incidence
� SEER rates are applied to
the US population by age
and year to obtain US
cases
Observed
� Projected trend estimated
by applying last 10 year
annual percent change to
future rates
Observed
Base (Constant trend)
Current (Projected trend)
Survival Model Projections: Example Male Colorectal Cancer aged 65-74 years
1-year Observed
Survival is modeled using mixture cure survival model
- - - Trend
Constant5-year
10-year
Year at diagnosis
Constant
Prevalence Projections 2010 and 2020 Under Different Scenarios
Site 2010 Base Incidence Survival Both
All Sites 13,772,000 18,071,000 17,465,000 18,878,000 18,229,000
Female Breast 3,461,000 4,538,000 4,275,000 4,597,000 4,329,000
Prostate 2,311,000 3,265,000 3,108,000 3,291,000 3,132,000
Melanoma 1,225,000 1,714,000 1,971,000 1,724,000 1,983,000
Colorectal 1,216,000 1,517,000 1,327,000 1,575,000 1,376,000
2020
Prevalence (No. of People)
Base=Impact of
changes in
population under
current cancer
control
interventions.
Scenarios
Colorectal 1,216,000 1,517,000 1,327,000 1,575,000 1,376,000
Lymphoma 639,000 812,000 803,000 841,000 831,000
Uterus 586,000 672,000 638,000 667,000 634,000
Bladder 514,000 629,000 576,000 640,000 587,000
Lung 374,000 457,000 392,000 481,000 412,000
Kidney 308,000 426,000 487,000 446,000 511,000
Head & Neck 283,000 340,000 308,000 346,000 313,000
Cervix 281,000 276,000 245,000 277,000 245,000
Leukemia 263,000 340,000 328,000 356,000 342,000
Ovary 238,000 282,000 232,000 296,000 241,000
Brain 139,000 176,000 174,000 185,000 182,000
Stomach 74,000 93,000 80,000 103,000 88,000
Esophagus 35,000 50,000 48,000 62,000 60,000
Pancreas 30,000 40,000 40,000 50,000 50,000
Both=Continuing
trends in
incidence and
survival
interventions.
Increase in prevalence from 2010 to 2020 by annual percent change in incidence rates
40%
50%
60%
70%
Increase inPrevalence (%)
Melanom
a
Kidney
Decreasing trends
-20%
-10%
0%
10%
20%
30%
40%
-6.0 -4.0 -2.0 0.0 2.0 4.0
Trend in Incidence (APC)
Melanom
a
Kidney
Cervix
Ovary
Prevalence by Phase of Care
� More useful than overall prevalence for planning,
resources allocation and costs of cancer
� Both care and costs vary drastically in the initial and last year of
life phases of care compared to the phase in between
(continuing care)(continuing care)
� Prevalence by time since diagnosis, e.g. prevalence of
patients diagnosed 0-2, 2-5 and 5+ years from diagnosis
can be a surrogate for prevalence by phases of care.
Mariotto, Yabroff et al. JNCI, 2011
Mariotto et al. Cancer Causes and Control, 2006
Estimates of Average Annual Costs
Head/Neck
Other
Colorectal
Lymphoma
Lung
Stomach
Esophag…
Pancreas
Brain
0 50 100 150 200 250 300 350
Melanoma
Prostate
Bladder
Leukemia
Kidney
Head/Neck
Net Costs in Thousands (2010 US Dollars)
Initial
Continuing
Cancer Death
Other Cause Death
Percent of Survivors in Each Phase of Care in 2010
Leukemia
Prostate
Colorectal
Kidney
Head & Neck
Stomach
Esophagus
Lung
Pancreas 30,000
374,000
74,000
283,000
35,000
308,000
1,216,000
2,311,000
0% 20% 40% 60% 80% 100%
Cervix
Other sites
Uterus
Ovary
Breast
Melanoma
Brain
All Sites
Lymphoma
Bladder
LeukemiaInitial
Continuing
End of life
Discussions/Conclusions
� In the US population changes have the largest effect on
the 2020 prevalence projections compared to changes in
incidence and survival.
� PIAMOD attractive because allows for different scenarios
of population dynamics, incidence and survivalof population dynamics, incidence and survival
� Micro-simulation models (type of CISNET) may provide
prevalence projections based on assumptions of future
trends for particular interventions
Geographical Projections
� Collaboration with Daniela Pierannunzio (lead), Roberta De Angelis
� When cancer registries do not have national coverage: national cancer
prevalence can be estimated by applying cancer registry prevalence
proportions to the respective populations
� C = (C / P )* P� CN= (CR / PR )* PN
� This method accounts for differences in age, sex and race between
registry and nation, but do not account for other factors, such as
socioeconomic status, that may biased national prevalence estimates
� Objective: evaluate biases in prevalence estimates obtained using this
“naïve” extrapolation method to estimate prevalence in different
geographic areas, e.g., county and state level.
Data and Methods
� Data from SEER-18 Registries at county level
� 5-yrs and 10-yrs limited duration prevalence al 1/1/2010 modelled
using different ecological Poisson regression models by county, age
sex and cancer site (all sites, prostate, breast, colorectal and lung) and
socioeconomic (SES) variablessocioeconomic (SES) variables
� Model 1: Prevalence ~ Mortality + SES
� Model 2: Prevalence ~ County SEER naïve projected prevalence + Mortality +
SES
� Model 3: Model 2 by age
� One round of cross-validation splitting the SEER-18 counties in 2
datasets (training and validation) to assess and compare models
accuracy
Data: SES, age-adjusted mortality in US and SEER-17
US
Training
counties
Validation
counties
(N=200) (N=200)
At least bachelors degree (%) 16.5 17.5 17.4 17.6
Median fam. income 42,154 43,988 44,416 43,560
Persons below poverty (%) 14.2 14.2 13.9 14.4
Unemployed (%) 5.8 6.2 5.9 6.4
Non-white (%) 12.2 8.7 8.5 8.9
SEER-17
2000 Socio-economic
attributes (N=3142)
All
counties
(N=400)
Non-white (%) 12.2 8.7 8.5 8.9
Black (%) 9.0 4.7 4.5 4.9
Minority (incl. white Hispanic) (%) 18.0 17.3 17.2 17.3
Urban (%) 40.1 46.3 47.2 45.4
Foreign born (%) 3.5 5.2 5.2 5.1
Mortality all causes 870.3 844.9 843.3 846.4
Mortality all malignant cancers 200.2 195.8 195.3 196.3
Colon and rectum mortality 19.8 19.4 19.2 19.6
Lung mortality 61.2 59.5 59.1 60.0
Breast mortality 13.0 13.0 12.9 13.1
Prostate mortality 12.8 12.9 12.8 13.0
Data set N Extrapolated Model 1 Model 2 Model 3
Validation 200 3,721 7,494 1,479 1,268
Training 200 3,720 6,905 1,467 1,404
All counties 400 7,441 14,399 2,946 2,672
Data set N Extrapolated Model 1 Model 2 Model 3
Validation 199 1,405 862 422 448
All sites Males
Lung Male
Prevalence counts
are compared to
the respective
observed number
of cases using a
goodness of fit
Preliminary Results Validation: Goodness of fit
Validation 199 1,405 862 422 448
Training 198 1,523 745 421 397
All counties 397 2,928 1,608 843 845
Data set N Extrapolated Model 1 Model 2 Model 3
Validation 194 688 1,351 382 454
Training 186 681 1,010 376 393
All counties 380 1,369 2,361 759 847
Data set N Extrapolated Model 1 Model 2 Model 3
Validation 191 3,631 5,135 1,841 1,575
Training 186 3,663 4,928 1,382 1,273
All counties 377 7,295 10,063 3,224 2,848
Prostate
Colorectal Male
2ˆ[ ]
ˆj j
j Area j
C C
C⊂
−
∑
goodness of fit
indicator
Geographic Projections
� Mortality and SES can improve the extrapolated
prevalence estimates using a Poisson regression model.
� Modeling by age can be done for more common cancer � Modeling by age can be done for more common cancer
sites, with small cells of zero prevalence counts
Acknowledgements
� National Cancer Institute
� Rocky Feuer, Robin Yabroff, Joan WarrenJulia Rowland
� IMS
� Steve Scoppa, Mark Hachey, Ken Bishop� Steve Scoppa, Mark Hachey, Ken Bishop
� Istitute Superiore di Sanità, Rome, Italy
� Roberta de Angelis, Daniela Pierannunzio, Riccardo
Capocaccia, Lucia Martina
