124 Journal of the National Cancer Institute Monographs, No. 46, 2013
DOI:10.1093/jncimonographs/lgt011 © The Author 2013. Published by Oxford University Press. All rights reserved.
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Comparing Cancer Care, Outcomes, and Costs Across Health
Systems: Charting the Course
Joseph Lipscomb, K. Robin Yabroff, Mark C. Hornbrook, Anna Gigli, Silvia Francisci, Murray Krahn, Gemma Gatta, Annalisa Trama,
Debra P . Ritzwoller, Isabelle Durand-Zaleski, Ramzi Salloum, Neetu Chawla, Catia Angiolini, Emanuele Crocetti, Francesco Giusti,
Stefano Guzzinati, Maura Mezzetti, Guido Miccinesi, Angela Mariotto
Correspondence to: Joseph Lipscomb, PhD, Department of Health Policy and Management, Rollins School of Public Health, Rm 720, 1518 Clifton Road, NE,
Atlanta, GA 30322 (e-mail: firstname.lastname@example.org).
J Natl Cancer Inst Monogr 2013;46:124–130
This monograph highlights the multiple payoffs from comparing
patterns of cancer care, costs, and outcomes across health systems,
both within a single country or across countries, and at a point in
time or over time. The focus of comparative studies can be on the
relative performance of systems in delivering quality cancer care,
in controlling the cost of cancer care, or in improving outcomes,
such as reducing mortality rates and improving survival. The focus
also can be on comparing the effectiveness, cost, or cost-effective-
ness of competing cancer prevention and control interventions
within a given system or across systems, while taking into account
variations in patient characteristics, disease incidence and severity,
resource availability, unit costs, and other factors influencing sys-
Two recurring themes in this monograph are: 1) the oppor-
tunities for cross-system analysis, learning, and improvement are
enormous and just beginning to be tapped; and 2) the empirical
and methodological challenges in realizing this potential are like-
wise enormous, but real progress is being made. In this concluding
article, we revisit and illustrate both themes, with the aim of sug-
gesting a research agenda for enhancing capacity to conduct strong
empirical cross-system analyses in cancer care delivery. To focus
the inquiry, we limit consideration to those cancer care systems,
whether within or across countries, sufficiently developed to have
access to registries that not only can document cancer incidence
and mortality but, through linkage to additional data sources,
can serve as platforms for patterns-of-care, costing, or other in-
depth studies. This necessarily puts the spotlight on developed
nations; and among these, we concentrate on those in Europe and
North America represented at the September 2010 workshop,
“Combining Epidemiology and Economics for Measurement of
Cancer Costs,” in Frascati, Italy (1).
We distinguish between population-level studies, designed to
compare the performance of health systems across countries or
within a single country along specified dimensions, and patient-
level studies, designed to investigate the effectiveness, cost, or cost-
effectiveness of specific interventions and programs for individual
patients (or individuals at risk for cancer) either within a given
health-care system or across systems. In population-level studies,
the outcome of interest might be summary measures of cancer
mortality, survival, or other prominent patient outcome–oriented
indexes of performance that are feasible to measure across systems
for defined populations. Patient-level studies will often investigate
the determinants of variations in patterns of care, costs, or out-
comes, or apply economic evaluation methods to examine whether
specific interventions offer good value for money. Although most
patient-level studies to date are within-country or within-system,
we note important examples of cross-country or cross-system
In the next section, we highlight some examples of population-
and patient-level studies. This sets the stage for the subsequent
sections discussing a range of options, including some already in
progress, for strengthening the data, methods, and organizational
infrastructure to support policy-relevant comparative research on
cancer outcomes and costs.
Comparisons Across Health Systems:
Informative but Difficult
The methods for conducting empirically sound cross-national
comparisons of cancer incidence, mortality, and survival are
relatively well developed. In recent years, important and frequent
collaborative contributions have been made by research teams
organized by the International Agency for Research on Cancer
(IARC) of the World Health Organization and the International
Association of Cancer Registries (IACR) (2), as well as by the
EUROCARE (European Cancer Registry–based Study on Survival
and Care) study group (3,4). Growing out of EUROCARE-3 was
the CONCORD study, which provided survival estimates for about
1.9 million adults diagnosed with female breast, colon, rectum, or
prostate cancers during 1990–1994, and followed up to 1999 (5).
Projects led by EUROCARE and EUROPREVAL have analyzed
cancer prevalence within and across European countries (4).
Although these and other prominent studies (6) have compared
disease incidence, prevalence, mortality, and survival (singly or
jointly), there are evidently no recent cross-national studies on can-
cer cost, whether overall or by disease site. Although Organization
for Economic Cooperation and Development (OECD) compiles
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Journal of the National Cancer Institute Monographs, No. 46, 2013 125
and publishes country-specific data on health expenditures and its
components, it does not produce cross-national cost estimates by
disease class or specific cancer diagnoses (7).
There are noteworthy examples of within-country efforts to
monitor health system performance on cancer metrics over
time. In Canada, Cancer Care Ontario (CCO) supports the
Ontario Cancer System Quality Index (8). In the United States,
the Agency for Healthcare Research and Quality publishes each
year the National Health Care Quality Report (9), and several
US cancer agencies and organizations collaborate to produce an
annual “report to the nation” on incidence, mortality, survival, and
selected special topics (10).
Patient-Level Comparative Studies
The substantial diversity of health-care delivery systems across
countries, and indeed within any country, creates significant
opportunities for policy-relevant research comparing alternative
approaches to care delivery along the cancer continuum: pre-
vention, detection, treatment, survivorship, and end-of-life care
(11,12). By observing how seemingly similar individuals either
at risk for cancer or with the disease are treated in different sys-
tems, we have the opportunity in principle of benefitting from what
amounts to quasi-natural experiments in care delivery (13). This
could allow for benchmarking of “high quality” or “high value” ser-
vices and identifying best (and less than best) practices.
One cross-national comparison is well illustrated in the study
of colorectal cancer treatment patterns in Italy and the United
States reported herein by Gigli and colleagues (14), who found
clear between-country differences in use of adjuvant therapy, open
abdominal surgery and endoscopic procedures, and hospitalization.
Similarly, Warren and colleagues (15) compared end-of-life care
for non–small cell lung cancer patients aged 65 and over in Ontario
and the United States, finding significantly greater use of chemo-
therapy in the United States, but higher rates of hospitalization in
the last 30 days of life in Ontario. Each study was feasible because
the participating countries could link high-quality cancer registry
data with administrative files to identify similar cancer patients and
then track receipt of services over time.
In cross-national settings where insurance or other administra-
tive data files are not available or accessible, alternative strategies
for augmenting cancer registry data can be pursued. An instructive
case in point is the “high resolution” analyses reported by Gatta
and colleagues (16), examining the impact of guideline-recom-
mended care on survival in samples of patients diagnosed with
breast, colorectal, or prostate cancer across a number of European
countries. Building on earlier EUROCARE studies (17–20), these
analyses brought together cancer registry data enhanced with
additional clinical detail from multiple participating registries and
countries (eg, for breast cancer, data from 26 registries in 12 coun-
tries). Included as determinants of cross-country survival differ-
ences were such macro-level variables as total spending on health
care and the relative availability of such inputs as computed tomog-
raphy, magnetic resonance imaging, and radiotherapy equipment.
Several implications flow from these cross-system studies. For
valid and reliable analyses of cancer care, outcomes, and costs
across geographical boundaries, high-quality registry data (or its
clinical equivalent) are necessary, but generally not sufficient. Such
data must be augmented with either administrative files or addi-
tional clinical information to provide an accurate time profile of
patient-level diagnoses, services and procedures received, and out-
comes, as well as patient, provider, and health system variables. For
any given health system comparison, all pertinent variables should
be defined and measured in the same way, or at least measure the
We are far from achieving widespread international “interoper-
ability” in measurement and reporting of cancer care use and costs.
The resulting challenges in being able to draw valid cross-country
inferences from existing studies are well illustrated in our review
here of economic studies in colorectal cancer, as conducted primar-
ily in countries with well-developed networks of cancer registries
(21). In the main, studies from different countries yielded estimates
of direct medical costs in ways that precluded a sound comparison
across studies. Few studies estimated direct nonmedical costs (eg,
patient or caregiver time) or the productivity costs associated with
disease and treatments. Indeed, aggregate and patient-level cost
estimates varied in so many ways across countries that meaningful
comparisons now are almost impossible. A broadly similar conclu-
sion emerges from the review of colorectal cancer patterns of care
studies from across Europe, Australia, and New Zealand (22) and in
comparisons between Canada and the United States (23).
That challenges in conducting micro-level analyses can arise
across health-care systems within a country is underscored by
Fishman and colleagues (24). They describe the data system hur-
dles in conducting comparative effectiveness research in samples
of elderly US cancer patients when some are enrolled in Medicare
for-for-service (FFS) plans and others in Medicare-managed care
plans that include health maintenance organizations (HMOs).
As one direct response to the issue of data comparability within
Medicare, Rosetti and colleagues (25) developed a “Standardized
Relative Resource Cost Algorithm” (SRRCA) to assign standard-
ized (comparable) relative costs to cancer patients in HMOs and
Such innovative fixes as the SRRCA represent important, yet
incremental, steps toward addressing a more fundamental issue in
conducting sound comparative effectiveness research within the
United States. With its strong cancer registry networks but vast
array of administrative data systems and non-interoperable elec-
tronic health informatics systems, how does the country advance
toward a “national cancer data system,” as advocated by the
Institute of Medicine in 1999 (26) and echoed by multiple cancer
policy makers since then? (27).
Building Capacity for Comparative Studies
Across Health Systems
Enhancing the Empirical Base
High-quality sources of data to support scientifically sound
population-based studies of cancer care, outcomes, and costs have
emerged most often from partnerships involving some combination
of government agencies, professional and provider organizations,
and researchers. The empirical infrastructure required for
comparative analyses will not simply emerge on its own, as the
product somehow of “natural market forces” in the health-care
arena. Little disagreement arises among payers, providers, and
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126 Journal of the National Cancer Institute Monographs, No. 46, 2013
consumers of cancer care surrounding the contention that decision
making about competing interventions should be informed by solid
evidence on effectiveness and costs. But only rarely does any single
or combination of these private stakeholders have the financial
and organizational wherewithal, or indeed an adequate incentive,
to take on the full task of building and sustaining a population-
level database for cancer research. Now, if by some means the
necessary empirical infrastructure does emerge, one would want
to encourage its broad and rapid application, not only by the
parties that paid for it but by qualified researchers everywhere, and
assure that its use by one set of researchers does not diminish its
availability or utility to others. In this sense, the data infrastructure
needed to support population-level cancer research could well be
characterized as a type of public good, with the implication that it
will be underproduced in the absence of collective action organized
and supported by public agencies.
This line of argument (or at least aspects of it) has been well
recognized in both the North American and European arenas for
population-level cancer research (28). As noted, the EUROCARE
project, based in Milan and Rome, has developed the capacity
to draw survival and other surveillance data from over 80 pub-
licly supported cancer registries in 21 European nations cover-
ing about 36% of their combined populations (16). In Canada, the
health services research program jointly sponsored by CCO and
the Institute for Clinical Evaluative Sciences (ICES) has devel-
oped publicly available datasets linking clinical and administrative
information on cancer care, outcomes, and resource utilization in
the province of Ontario (29), and now most Canadian provinces
have similar linked datasets. Most recently, Ontario and British
Columbia researchers teamed up to examine pre- and post-diag-
nosis cancer-related costs for multiple tumor sites (30). In the
United States, the SEER–Medicare linked database represents a
partnership involving the National Cancer Institute (NCI), the
Centers for Medicare and Medicaid Services (CMS), and the fed-
erally supported SEER registries covering roughly 28% of the US
population (31,32). The Cancer Research Network has developed
standardized tumor, clinical, utilization, and cost data for large
HMOs in the United States, all of which have electronic medi-
cal record systems (33,34). The Centers for Disease Control and
Prevention (CDC), in collaboration with seven state cancer reg-
istries and multiple university-based researchers, have supported
the Breast and Prostate Cancer Data Quality and Patterns of Care
Study, creating large population-based samples to study quality-
of-care and survival outcomes (35).
Current collaborative efforts, however, fall short of provid-
ing cancer researchers and policy makers with the data platforms
required for population-based studies encompassing all geographi-
cal regions, all population groups, and the full range of clinical,
patient-reported, and cost-related outcomes that can inform deci-
sion making. Specific research initiatives such as the NCI-created
Cancer Care Outcomes Research and Surveillance (CanCORS)
Consortium (36) have rendered proof of concept that primary data
collection and multiple datasets linked together can effectively
support a range of important innovative studies (37,38). But such
initiatives alone are not intended to address the larger matter of
how to develop and sustain the empirical base for population-based
cancer research over time. What are the prospects for building
sustainable data platforms that are accessible and affordable to a
broad swath of individual researchers and policy makers? A com-
prehensive pursuit of this mammoth topic would require its own
monograph, but we highlight some notable examples.
European Partnership for Action Against Cancer and Other
European Confederations. The European Partnership for Action
Against Cancer (EPAAC) is a confederation of over 30 public and
private sector organizations that seeks to work closely with the
European Union, the IARC, the European Network of Cancer
Registries (ENCR), the EUROCARE project, the OECD, and
others to advance an ambitious agenda for cancer prevention and
control research (39). Among EPAAC’s objectives is a “European
Cancer Information System” that would draw on multiple part-
nerships to develop harmonized population-based data on cancer
incidence, survival, prevalence, mortality, and also high-resolution
studies to examine the impact of medical resource availability,
patient-level variables including lifestyle factors, and specific inter-
ventions on outcomes. In a complementary development, IARC
and ENCR announced in 2012 the creation of a European Cancer
Observatory to provide easier access to basic surveillance data from
over 40 European countries (40). Although not disease-focused,
the “EUnetHTA” is a network of government-appointed organiza-
tions, regional agencies, and nonprofit organizations established in
2008 to harmonize and improve the quality of health technology
assessment across Europe (41). As such, its work could eventually
inform the evaluation efforts in specific domains, including cancer.
CCO–ICES and Other Provincial Partnerships in Canada.
Potentially well positioned to create and sustain data platforms for
cancer care, cost, and outcomes research is Canada, at least on a prov-
ince-by-province basis, as the CCO–ICES health services research
initiative in Ontario is beginning to demonstrate (29). A particularly
strong feature of this system is the capability of linking cancer reg-
istry data with additional clinical information and service provision
data from the province’s publicly funded universal health-care sys-
tem. As a result, it is possible to track medical services rendered, the
corresponding resources consumed, and survival outcomes over time
on a population basis.
American College of Surgeons and American Society of Clinical
Oncology. In the United States, there are several parallel initia-
tives underway to strengthen the capability for monitoring and
improving the quality of cancer care. These include the American
College of Surgeons (ACoS) Commission on Cancer’s (CoC) Rapid
Quality Reporting System (42), already adopted in over 20% of the
CoC’s 1500 approved cancer programs, and the new “CancerLinQ”
information system under development by the American Society of
Clinical Oncology (ASCO) (43). Both of these far-reaching initia-
tives are aimed at providing near real-time feedback to care pro-
viders and eventually at strengthening the basis for comparative
effectiveness research of cancer therapies. As currently configured,
neither appears readily geared to support population-based cost or
cost-effectiveness analyses of care across the cancer continuum.
SEER–Medicare: Building on the Concept. A key to making fur-
ther progress on the economic analysis front is pursuit of a strategy
that is simple in concept but complex in execution: Expand the
SEER–Medicare linked dataset “model” to cover virtually 100%
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Journal of the National Cancer Institute Monographs, No. 46, 2013 127
of the US population—in partnership with the CDC’s National
Program of Cancer Registries—and to include linkages with admin-
istrative data from Medicaid and as many major private insurance
plans and managed care organizations as possible. If data elements
were standardized and harmonized across payers, the result would
be linked cancer registry–claims data yielding population-repre-
sentative samples across all ages, geographical areas, and types of
health plans. Clearly, a number of major organizational, financial,
and perhaps even legal hurdles would have to be cleared for such
an ambitious plan to take flight and become sustainable over time.
Extracting Maximal Value From the Empirical Base: The
Essential Role of Modeling
At the core of any epidemiologically based analysis of health out-
comes and cost is a model (44) and a number of associated tasks.
The tasks can be viewed as falling under two headings: 1) using
the available data to assign values (either point estimates or prob-
ability distributions) to all the variables deployed in the analysis
and then investigating each of the hypothesized causal connec-
tions, for example, impact of intervention A on health outcome X,
or the impact of Y on cost outcome C, or both, after adjusting for
confounding; and 2) combining these estimated variables, and their
inferred causal connections, into some form of decision model
to investigate the impact of alternative intervention strategies on
the outcomes of interest (eg, health outcomes, cost, or cost-effec-
tiveness) for some selected target population. The decision model
becomes the analytical platform for posing compelling “what if”
questions. For example, how costs are expected to shift if interven-
tion X′ is selected rather than X? At the same time, the decision
model is the vehicle for evaluating policy options (X versus X′) to
optimize some designated criterion, for example, cost per quality-
adjusted life year. The pivotal point is that in studying the impact
of X versus X′ in the selected target population, the analyst is not
necessarily constrained by data availability or data quality limita-
tions within that population. Rather, the aim is to make the deci-
sion model appropriate to the question at hand by bringing to bear
the best available data from all feasible sources.
Statistical Inference and Prediction
Whatever the outcome being investigated, the within-country or
cross-country context, or the strengths and limitations of the cor-
responding empirical base, paying close attention to strategies for
both statistical inference and decision modeling is foundational.
We briefly call attention to three problems of statistical inference
(among many) that are especially pertinent: (a) appropriately char-
acterizing the distributional features of the outcome of interest (a
particular concern when cost is the dependent variable); (b) adjust-
ing for patient-related and other selection effects that otherwise
can lead to biased inferences about the impact of factors on out-
comes, costs, or both; and (c) recognizing that cancer care inter-
ventions may be complex, multilevel, and delivered in geographical
and clinical environments characterized by the statistical phenom-
enon of “clustering.”
Over the past two decades, considerable progress has been made
in coping with (a), especially in the area of cost, where robust gener-
alized modeling approaches have been developed (45–47). Regarding
(b), the threat of selection bias in the estimation of outcomes,
including cost, has long been recognized in the econometrics lit-
erature. In recent years, two basic approaches to bias reduction have
been pursued, with applications in the health-care arena accelerat-
ing over the past decade: propensity score matching or weighting
(48) and instrumental variable (IV) methods (49–54), which seek to
identify and remove biasing effects arising from observable or unob-
servable influences on the dependent variable of interest. Likewise,
developing cost estimation and prediction models that jointly handle
problems (a), (b), and (c) by recognizing the frequently hierarchical
nature of interventions is a prime area for further work (54–56).
Consider the following policy questions:
•? What are the relative contributions of screening and adjuvant
therapy to achieving reductions in mortality from breast cancer?
•? What is the effect of rising chemotherapy costs on the possible
cost savings from colorectal cancer screening?
•? What is the cost-effectiveness of human papillomavirus vaccina-
tion and cervical cancer screening in women older than 30?
•? How may one estimate the clinical benefits, harms, and cost
implications of a particular cancer screening program prior to
its widespread adoption so as to inform decision making about
optimal screening policy?
These seemingly diverse inquiries in cancer prevention and con-
trol have certain important features in common. They are complex,
involving many clinical and economic considerations. The time
horizon over which clinical benefits, harms, and costs flow at the
patient level will not be measured in months but years and, indeed,
may span the remainder of the individual’s life, from the point of
intervention going forward. It is highly unlikely that either experi-
mental or observational data would be available for any one cohort
in sufficient detail and duration to include direct observations on
all the variables involved in the multiperiod investigation.
There is one more feature in common: Each of these four
questions has already been investigated in impressive detail using
some form of decision modeling (57–60), most typically a variant
of micro-simulation. However strong or deficient the empirical
base for population-based cancer research within a health system
or across health systems, adopting a decision modeling strategy
provides the additional flexibility to bring the best available data to
bear (whatever the source) on the problem at hand.
The central challenge in conducting technically sound comparative
analyses of cancer care patterns, outcomes, or costs across health-
care systems is marshaling the skill, the will, and the fiscal and
administrative resources to develop and sustain the necessary data
infrastructure that can support strong (and frequently team-based)
research. Whether for cross-national studies or within-country
studies, the task is made all the more difficult because most of the
component building blocks for national, regional, or state cancer
data systems—including insurance and other administrative data
sources, medical records systems, and even cancer registries—were
not originally designed to support research.
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128 Journal of the National Cancer Institute Monographs, No. 46, 2013
Nonetheless, the empirical base needed for a given investi-
gation can frequently be created through some combination of
dataset cleaning and updating (eg, re-abstracted registry records);
dataset linkages (eg, registry data with claims files, or registry data
with medical records); and/or dataset creation (eg, surveys to col-
lect individual-level data on cancer risk-increasing or risk-reducing
behaviors, time costs, or patient-reported outcomes, in some cases
using the cancer registry to establish the sampling frame). Indeed,
some projects have linked both secondary and newly created
sources to provide a rich longitudinal picture of the cancer patient
experience over time, from diagnosis, through treatment, and into
the survivorship period (36).
Population-based cancer registries, whether covering a city,
state, province, region, or entire country, are the bedrocks not
only of epidemiological investigations of disease trends but also
trends in cancer patterns of care and economic cost. As a result
of sustained work by tumor registries and their affiliated experts
worldwide, a consensus is emerging about the international rules-
of-the-road for cancer surveillance data definition, collection, and
analysis (2) (pp. 67–71). Over time, disparate registry operations
have developed operational definitions and criteria for appraising
data completeness, accurate identification of true-positive cancer
cases, and approaches to computing and reporting statistics on
incidence, prevalence, mortality, and survival (61,62). This stan-
dardization supports current and future efforts to foster compara-
tive analyses of cancer care, outcomes, and costs.
Yet to date and to our knowledge, no country-level compara-
tive studies of the cost of cancer have been published, either in the
aggregate or by disease site. What is lacking, to be sure, is not the
methodological wherewithal, but the data on cancer care resource
consumption and prices that have historically been well beyond the
scope of registries. Without some systematic, technically feasible,
affordable, and sustainable strategy for augmenting registry data on
an ongoing basis with additional sources of information on cancer
care delivery and resource use, it is difficult to see how country-
level comparisons of cancer costs can be estimated directly, that is,
from the ground up. As suggested earlier, a viable alternative strat-
egy is to deploy epidemiologically grounded economic modeling,
bringing to bear the most appropriate data for cost inferences from
multiple information sources.
The policy significance of comparative investigations across
health systems has recently been underscored in a report issued by
the US National Research Council and the Institute of Medicine
finding that US males and females at all ages (up to 75) have greater
rates of disease and injury, and shorter life expectancies, than in
16 other wealthy nations (63). The report’s recommendations to
improve the quality and consistency of data, as well as analytic
methods and study designs, highlight a growing consensus about
the importance of building capacity for sound comparative analy-
ses. That such comparative analyses can highlight successes, as well
as failures, in pursuit of the “triple aim” of better health, better
health care, and lower cost is well illustrated in a recently published
series of papers (64).
In sum, progress in producing scientifically strong, policy-rel-
evant comparative analyses of cancer care, health outcomes, and
costs within and across systems requires continuing investments
on three fronts: database development, statistical inference and
prediction, and decision modeling. They go hand in hand. What
would be the payoffs for such an investment? What are some of
the compelling questions and issues that could be more effectively
addressed through stronger cancer data systems and research
methods? The list is long, but would surely include:
•? Assessing the effects on downstream outcomes and costs of spe-
cific cancer prevention and screening strategies.
•? Investigating the impact of existing high-cost anticancer agents
and emerging technologies and interventions (eg, genomics-
guided targeted therapies) on outcomes and the costs faced by
patients, health-care systems, and governments.
•? Evaluating alternative patient management strategies after the
initial therapy, including surveillance during the survivorship
period and end-of-life care.
•? Studying the cost and cost-effectiveness of interventions
at any point along the cancer continuum and including the
direct medical costs, as incurred typically within health-care
systems, direct nonmedical costs (eg, capturing the value of
patient and caregiver time), and the cost of disease-related lost
1. Francisci S, Yabroff KR, Gigli A, Mariotto A, Mezzetti M, Lipscomb J.
Advancing the science of cancer cost measurement: challenges and oppor-
tunities. Ann Ist Super Sanita. 2013;49(1):73–78.
2. International Agency for Research in Cancer and International Association
of Cancer Registries. Cancer Incidence in Five Continents, Vol. IX. IARC
Scientific Publication No. 160. Lyon, France: IARC; 2007.
3. Sant M, Allemani C, Santaquilani M, et al. EUROCARE-4: survival of
cancer patients diagnosed in 1995–1999. Eur J Cancer. 2009;45:931–991.
4. EUROCARE. Survival of Cancer Patients in Europe. http://www.
eurocare.it. Accessed February, 16, 2013.
5. Coleman MP, Quaresma M, Berrino F, et al. Cancer survival in five con-
tinents: a worldwide population-based study (CONCORD). Lancet Oncol.
6. Forouzanfar MH, Foreman KJ, Delossantos AM, et al. Breast and cervi-
cal cancer in 187 countries between 1980 and 2010: a systematic analysis.
7. Organization for Economic Cooperation and Development. Health
expenditure. In: OECD Factbook 2011–2012: Economic, Environmental and
Social Statistics. http://dx.doi.org/10.1787/factbook-2011-112-en. Accessed
June 11, 2013.
8. Cancer Care Ontario. Quality and Performance Improvement. https://
www.cancercare.on.ca/ocs/qpi/. Accessed February 13, 2013.
9. Agency for Healthcare Research and Quality. National Healthcare Quality
Report. http://www.ahrq.gov/qual/nhqr11/nhqr11.pdf. Accessed February
10. Eheman C, Henley SJ, Ballard-Barbash R, et al. Annual Report to the
Nation on the status of cancer, 1975–2008, featuring cancers associ-
ated with excess weight and lack of sufficient physical activity. Cancer.
11. Yabroff KR, Francisci S, Mariotto A, Mezzetti M, Gigli A, Lipscomb J.
Advancing comparative studies of patterns of care and economic out-
comes in cancer: challenges and opportunities. J Natl Cancer Inst Monogr.
12. Karanikolos M, Ellis L, Coleman MP, McKee M. Health systems perfor-
mance and cancer outcomes. J Natl Cancer Inst Monogr. 2013;46(1):7–12.
13. Ritzwoller DP, Carroll N, Delate T, et al. Patterns and predictors of first-
line chemotherapy use among adults with advanced non-small cell lung
cancer in the Cancer Research Network. Lung Cancer. 2012;78(3):245–252.
14. Gigli A, Warren JL, Yabroff KR, et al. Initial treatment of newly diagnosed
elderly colorectal cancer patients: patterns of care in Italy and the United
States. J Natl Cancer Inst Monogr. 2013;46(1):88–98.
by guest on September 8, 2015
Journal of the National Cancer Institute Monographs, No. 46, 2013 129
15. Warren JL, Barbera L, Bremner KE, et al. End-of-life care for lung
cancer patients in the United States and Ontario. J Natl Cancer Inst.
16. Gatta G, Trama A, Capocaccia R. Variations in cancer survival and patterns
of care across Europe: roles of wealth and health-care organization. J Natl
Cancer Inst Monogr. 2013;46(1):79–87.
17. Allemani C, Storm H, Voogd AC, et al. Variation in “standard care” for
breast cancer across Europe: a EUROCARE-3 high resolution study. Eur
J Cancer. 2010;46(9):1528–1536.
18. Gatta G, Zigon G, Aareleid T, et al. Patterns of care for European colorec-
tal cancer patients diagnosed 1996–1998: a EUROCARE high resolution
study. Acta Oncol. 2010;49(6):776–783.
19. Gatta G, Zigon G, Buemi A, et al. Prostate cancer treatment in Europe at
the end of 1990s. Acta Oncol. 2009;48(6):867–873.
20. Allemani C, Sant M, Weir HK, Richardson LC, Baili P, Storm H. Breast
cancer survival in the USA and Europe: a Concord high-resolution study.
Int J Cancer. 2013;132(5):1170–1181.
21. Yabroff KR, Borowski L, Lipscomb J. Economic studies in colorectal
cancer: challenges in measuring and comparing costs. J Natl Cancer Inst
22. Chawla N, Butler EN, Lund J, Warren JL, Harlan LC, Yabroff KR.
Patterns of colorectal cancer care in Europe, Australia, and New Zealand.
J Natl Cancer Inst Monogr. 2013;46(1):36–61.
23. Butler EN, Chawla N, Lund J, Harlan LC, Warren JL, Yabroff KR.
Patterns of colorectal cancer care in the United States and Canada: a sys-
tematic review. J Natl Cancer Inst Monogr. 2013;46(1):13–35.
24. Fishman PA, Hornbrook MC, Ritzwoller DP, O’Keeffe-Rosetti MC,
Lafata JE, Salloum RG. The challenge of conducting comparative effec-
tiveness research in cancer: the impact of a fragmented US health-care
system. J Natl Cancer Inst Monogr. 2013;46(1):99–105.
25. O’Keefe-Rosetti MC, Hornbrook MC, Fishman PA, et al. A standardized
relative resource cost model for medical care: application to cancer control
programs. J Natl Cancer Inst Monogr. 2013;46(1):106–116.
26. Institute of Medicine. Ensuring Quality Cancer Care. Hewitt M, Simone JV,
eds. Washington, DC: National Academy Press; 1999.
27. Lipscomb J, Gillespie TW. State-level cancer quality assessment and
research: building and sustaining the data infrastructure. Cancer J.
28. Carpenter WR, Meyer AM, Abernethy AP, Stürmer T, Kosorok MR. A
framework for understanding cancer comparative effectiveness research
data needs. J Clin Epidemiol. 2012;65(11):1150–1158.
29. Ontario Institute for Cancer Care Research. Health Services Research.
services-research. Accessed February 16, 2013.
30. de Oliveira C, Bremner KE, Pataky R, et al. Understanding the cost of
cancer care before and after diagnosis for the 21 most common cancers in
Ontario: a population-based descriptive study. CMAJ Open. 2013;1(1):E1–
31. National Cancer Institute. SEER-Medicare: Brief Description of the
SEER-Medicare Database. http://healthservices.cancer.gov/seermedicare.
Accessed February 16, 2013.
32. Ambs A, Warren JL, Bellizzi KM, Topor M, Haffer SC, Clauser SB.
Overview of the SEER–Medicare Health Outcomes Survey linked dataset.
Health Care Financ Rev. 2008;29(4):5–21.
33. Hornbrook MC, Hart G, Ellis JL, et al. Building a virtual cancer research
organization. J Natl Cancer Inst Monogr. 2005;35:12–25.
34. Ritzwoller DP, Carroll N, Delate T, et al. Validation of electronic data on
chemotherapy and hormone therapy use in HMOs [published online ahead
of print April 23, 2012]. Med Care. doi:10.1097/MLR.0b013e31824def85.
35. Centers for Disease Control and Prevention. Breast and Prostate Cancer
Data Quality and Patterns of Care (PoC-BP) Study. http://www.cdc.gov/
cancer/npcr/research/poc_studies/poc_bp.htm. Accessed February 16, 2013.
36. Ayanian JZ, Chrischilles EA, Fletcher RH, et al. Understanding can-
cer treatment and outcomes: the Cancer Care Outcomes Research and
Surveillance Consortium. J Clin Oncol. 2004;22(15):2992–2996.
37. Hassett MJ, Ritzwoller DP, Taback N, et al. Validating billing/encounter
codes as indicators of lung, colorectal, breast, and prostate cancer
recurrence using 2 large contemporary cohorts [published online ahead of
print December 6, 2012]. Med Care. doi:10.1097/MLR.0b013e318277eb6f.
38. Bowles EJ, Wellman R, Feigelson HS, Onitilo AA, Freedman AN, Delate
T; Pharmacovigilance Study Team. Risk of heart failure in breast cancer
patients after anthracycline and trastuzumab treatment: a retrospective
cohort study. J Natl Cancer Inst. 2012;104(17):1293–1305.
39. European Partnership for Action Against Cancer (EPAAC). Cancer Data
and Information. http://www.epaac.eu/cancer-data-and-information.
Accessed February 17, 2013.
40. International Agency for Research on Cancer. European Cancer
Observatory. http://eco.iarc.fr/. Accessed February 17, 2013.
41. EUnetHTA. http://www.eunethta.eu/. Accessed February 17, 2013.
42. American College of Surgeons, Commission on Cancer. Rapid Quality
Reporting System (RQRS). http://www.facs.org/cancer/ncdb/rqrs.html.
Accessed February 17, 2013.
43. American Society of Clinical Oncology. CancerLinQ – Building a Trans-
formation of Cancer Care. http://www.asco.org/CancerLinQ. Accessed
February 17, 2013.
44. Eddy D. Bringing health economic modeling to the 21st century. Value
45. Basu A, Manning WG, Mullahy J. Comparing alternative models: log vs
Cox proportional hazard? Health Econ. 2004;13(8):749–765.
46. Manning WG, Basu A, Mullahy J. Generalized modeling approaches to risk
adjustment of skewed outcomes data. J Health Econ. 2005;24(3):465–488.
47. Mullahy J. Econometric modeling of health care costs and expenditures:
a survey of analytical issues and related policy considerations. Med Care.
2009;47(7 Suppl 1):S104–S108.
48. Rosenbaum PR, Rubin D. The central role of the propensity score in
observational studies of causal effects. Biometrika. 1983;70:41–55.
49. Angrist J, Imbens G, Rubin D. Identification of causal effects using instru-
mental variables. J Am Stat Assoc. 1996;91:444–455.
50. Terza JV, Basu A, Rathouz PJ. Two-stage residual inclusion estimation:
addressing endogeneity in health econometric modeling. J Health Econ.
51. Basu A, Heckman JJ, Navarro-Lozano S, Urzua S. Use of instrumental
variables in the presence of heterogeneity and self-selection: an applica-
tion to treatments of breast cancer patients. Health Econ. 2007;16(11):
52. Hadley J, Yabroff KR, Barrett MJ, Penson DF, Saigal CS, Potosky AL.
Comparative effectiveness of prostate cancer treatments: evaluating statis-
tical adjustments for confounding in observational data. J Natl Cancer Inst.
53. O’Malley AJ, Frank RG, Normand SL. Estimating cost-offsets of new
medications: use of new antipsychotics and mental health costs for schizo-
phrenia. Stat Med. 2011;30(16):1971–1988.
54. Garrido MM, Deb P, Burgess JF Jr, Penrod JD. Choosing models for
health care cost analyses: issues of nonlinearity and endogeneity. Health
Serv Res. 2012;47(6):2377–2397.
55. Basu A, Manning WG. Issues for the next generation of health care cost
analyses. Med Care. 2009;47(7 Suppl 1):S109–S114.
56. Taplin SH, Clauser SB, Chollette V, Prabhu Das I, Edwards H, Foster
M. Understanding and influencing multilevel factors across the cancer
continuum. J Natl Inst Monogr. 2012;44:1–134.
57. Berry DA, Cronin KA, Plevritis SK, et al.; Cancer Intervention and
Surveillance Modeling Network (CISNET) Collaborators. Effect of
screening and adjuvant therapy on mortality from breast cancer. N Engl J
58. Lansdorp-Vogelaar I, van Ballegooijen M, Zauber AG, Habbema JD,
Kuipers EJ. Effect of rising chemotherapy costs on the cost savings of
colorectal cancer screening. J Natl Cancer Inst. 2009;101(20):1412–1422.
59. Kim JJ, Ortendahl J, Goldie SJ. Cost-effectiveness of human papillomavi-
rus vaccination and cervical cancer screening in women older than 30 years
in the United States. Ann Intern Med. 2009;151(8):538–545.
60. Etzioni R, Durand-Zaleski I, Lansdorp-Vogelaar I. Evaluation of new
technologies for cancer control based on population trends in disease inci-
dence and mortality. J Natl Cancer Inst Monogr. 2013;46(1):117–123.
61. Jensen OM, Parkin DM, Maclennan R, Muir CS, Skeet RG. Cancer
Registration: Principles and Methods. IARC Scientific Publication No. 95.
Lyon, France: IARC; 1991.
62. Parkin DM, Chen VW, Ferlay J, Galceran J, Storm HH, Whelan SL.
Comparability and quality of data. In: Parkin DM, Whelan SL, Ferlay J,
by guest on September 8, 2015
130 Journal of the National Cancer Institute Monographs, No. 46, 2013 Download full-text
Teppo L, Thomas DB, eds. Cancer Incidence in Five Continents, Vol. VIII.
IARC Scientific Publication No. 155. Lyon, France: IARC; 2002.
63. National Research Council and Institute of Medicine. U.S. Health in
International Perspective: Shorter Lives, Poor Health. Washington, DC:
National Academy Press; 2013.
64. Dentzer S. The “triple aim” goes global, and not a minute too soon. Health
Aff (Millwood). 2013;32(4):638.
This work was supported by the National Cancer Institute (contract
HHSN261201100370P and 5P30CA138292, the Cancer Center Support Grant
to Winship Cancer Institute of Emory University).
Affiliations of authors: Rollins School of Public Health and Winship Cancer
Institute, Emory University, Atlanta, GA (JL); Health Services and Economics
Branch, Applied Research Program (KRY, NC), and Data Modeling Branch,
Surveillance Research Program (AM), Division of Cancer Control and
Population Sciences, National Cancer Institute, Bethesda, MD; The Center
for Health Research, Kaiser Permanente Northwest, Portland, OR (MCH);
Institute of Research on Population and Social Policies, National Research
Council, Rome, Italy (AG); National Center for Epidemiology, Surveillance and
Health Promotion, Italian National Health Institute, Rome, Italy (SF); Toronto
Health Economics and Technology Assessment Collaborative (THETA),
Department of Medicine and Faculty of Pharmacy, University of Toronto,
Toronto, ON (MK); Evaluative Epidemiology Unit (GG) and Department
of Predictive and Preventive Medicine (AT), Fondazione IRCSS, Istituto
Nazionale dei Tumori, Milan, Italy; Institute for Health Research, Kaiser
Permanente Colorado, Denver, CO (DPR); AP-HP URCEco and Hộpital Henri
Mondor, Paris, France (ID-Z); Department of Health Policy and Management,
Gillings School of Global Public Health, University of North Carolina at Chapel
Hill, Chapel Hill, NC (RS); Medical Oncology Unit, Oncology Department,
Azienda Sanitaria, Florence, Italy (CA); Clinical and Descriptive Epidemiology
Unit, Institute for Cancer Study and Prevention, Florence, Italy (EC, FG, GM);
Veneto Institute of Oncology - IOV IRCCS, Padua, Italy (SG); Department of
Economics and Finance, University of Rome “Tor Vergata” , Rome, Italy (MM).
by guest on September 8, 2015