Comparing Cancer Care, Outcomes, and Costs Across Health Systems: Charting the Course

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-effectiveness 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 system performance. Two recurring themes in this monograph are: 1) the opportunities 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 likewise enormous, but real progress is being made. In this concluding article, we revisit and illustrate both themes, with the aim of suggesting 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 outcomes, 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 analyses. 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. 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 cancer cost, whether overall or by disease site. Although Organization for Economic Cooperation and Development (OECD) compiles 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). 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: prevention, 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 systems, 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” services 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 chemotherapy 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 administrative 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-recommended 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 countries). Included as determinants of cross-country survival differences were such macro-level variables as total spending on health care and the relative availability of such inputs as computed tomography, 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 additional clinical information to provide an accurate time profile of patient-level diagnoses, services and procedures received, and outcomes, 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 same construct. We are far from achieving widespread international “interoperability” 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 primarily 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 conclusion 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 hurdles 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 standardized (comparable) relative costs to cancer patients in HMOs and FFS plans. 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 electronic 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). 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 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 publicly supported cancer registries in 21 European nations covering 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 developed 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-diagnosis 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 federally 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 medical record systems (33,34). The Centers for Disease Control and Prevention (CDC), in collaboration with seven state cancer registries 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 providing cancer researchers and policy makers with the data platforms required for population-based studies encompassing all geographical regions, all population groups, and the full range of clinical, patient-reported, and cost-related outcomes that can inform decision 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 comprehensive pursuit of this mammoth topic would require its own monograph, but we highlight some notable examples. 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site. 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That such comparative analyses can highlight as well as in pursuit of the of health care, and cost is well illustrated in a of In progress in scientifically policy-relevant comparative analyses of cancer care, health outcomes, and costs within and across systems on database and and decision They in What would be the payoffs for such an What are some of the and that could be more effectively through cancer data systems and research The is but would the on outcomes and costs of specific cancer prevention and the impact of existing and and interventions (eg, on outcomes and the costs by health-care systems, and alternative patient strategies the therapy, including surveillance during the and end-of-life the cost and cost-effectiveness of interventions at any point along the cancer and including the direct medical costs, as within health-care systems, direct nonmedical costs (eg, the value of patient and caregiver and the cost of This was supported by the National Cancer Institute and the Cancer to Cancer Institute of

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