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Perspectives on Cost-effective Medicine and the Use of Cost-effectiveness Analyses

Park, K.T.

Journal of Pediatric Gastroenterology & Nutrition: January 2012 - Volume 54 - Issue 1 - p 2–3
doi: 10.1097/MPG.0b013e3182354d50
Invited Commentaries

Division of Pediatric Gastroenterology, Hepatology, and Nutrition, Lucile Packard Children's Hospital, Stanford University, Palo Alto, CA.

Address correspondence and reprint requests to KT Park, MD, MS, Division of Pediatric Gastroenterology, Hepatology, and Nutrition, Stanford University, 750 Welch Rd, Ste 116, Palo Alto, CA 94304 (e-mail:

Received 15 August, 2011

Accepted 30 August, 2011

The author reports no conflicts of interest.

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Our complex health care system reaches beyond any single political, economic, or ethical ideology and debate. To provide perspective, the United States spends approximately $2.4 trillion per year for health care, close to 18% of our gross domestic product and more than any other industrialized nation (1). Average health insurance premiums have more than doubled in the last decade, rising 4 times faster than wages, yet, the average American life expectancy is 0.95 times that of other developed countries. According to the Congressional Budget Office, health care expenditures will reach >35% of gross domestic product by 2025 and close to 50% by 2080, unless drastic changes in health care spending alter this present trajectory (2).

Traditionally, the present health care system does not incentivize health care providers to think along this avenue of cost savings with patient care. For example, a recent study found that US physicians spend 4 times more than Canadian health care providers, citing high administrative costs as a possible reason for this difference (3). Frequently, escalating and adding layers of therapy become the norm, as physicians are often pressured by patients and their families, malpractice concerns, and medical advances that have not yet been proven to be superior to the present standard. Although the United States still enjoys the benefits of leading the world in medical innovation and technology, the present economy warrants physicians to consider alternatives to cut costs without compromising patient outcomes (4).

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In light of this problem, the Patient Protection and Affordable Care Act of 2010 is the government's latest attempt to fix the ever-expanding and complicated health care system. This piece of legislation brought comparative effectiveness research (CER) into the public limelight as an important future modality to improve health and save health care dollars. According to the act, CER should be conducted as “research evaluating and comparing health outcomes and the clinical effectiveness, risks, and benefits of 2 or more medical treatments, services, and items” (5). Specifically, CER aims to compare real-world clinical alternatives for patient care that are most relevant in decision making (6).

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This focus is where CER meets cost-effectiveness analyses (CEAs). If used correctly, CEAs have the potential to advance the overall goal of CER. The Panel on Cost-effectiveness in Health and Medicine describes the purpose of CEAs as the methodology “to assess the comparative effects of expenditures on different health interventions” (7). In essence, CEAs attempt to standardize and compare the health effects gained or lost by competing clinical strategies, given the dollar-cost of each strategy. Health services researchers who use CEAs, within a societal perspective, aim to inform health policy and optimize patients’ health by identifying the most beneficial interventions among the number of therapy options available (8).

CEAs measure incremental effectiveness and costs. The word “incremental” is used to suggest that one intervention is always compared with at least one other alternative. Hence, the primary outcome measure in a CEA is the incremental cost-effectiveness ratio, or ICER. The ICER is the difference in costs between the interventions divided by the difference in effectiveness of the interventions (ΔcostA-B/ΔeffectivenessA-B). The most commonly used measure of effectiveness in CEAs is quality-adjusted life-year. A quality-adjusted life-year is derived by adding utilities, which are measurements of actual patients’ health preferences. Utilities are bound between 0 and 1 (1 being perfect health and 0 being death). Previously collected utilities can be found in the literature, such as a cumulative report by Tengs and Wallance (9), or researchers can independently estimate utilities using traditional methods such as the time trade-off or standard gamble (SG).

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The Panel on Cost-effectiveness in Health and Medicine lays out specific recommendations for researchers to construct methodologically sound CEAs that will most likely inform health policy makers and clinicians (10–12). This 3-part series of manuscripts discusses the goals of a CEA, the consensus-based methodologies in CEAs, and the recommendations for reporting CEAs. Key model-building recommendations from the panel are taking a societal perspective, considering costs and benefits over a set time horizon (eg, a lifetime), and using standard discounting at 3% annually.

In the actual construction of the computer model, there are many considerations. Most of these practical guidelines are important for discussion but extend beyond the scope of the present article. Several take-home points should be noted. First, consider the available evidence. CEAs are only as powerful and informative as the robustness of the published literature. Understandably, any information from large randomized clinical trials, such as the likelihood of disease recurrence on a certain therapy, should be used as key probability rates in the model. Then, prospective observational cohort studies with a substantial sample size and methodologically sound meta-analyses often can supplement model parameters that are less explored by clinical trials (eg, risk of female infertility after a colectomy in severe colitis). A thorough literature search of the available data will determine the feasibility of the study. Second, standardize cost variables to reflect the specific patient population. For example, hospital and drug charges can vary widely from one institution to the next, often dependent on geographical regions and the patients’ payer mix at different institutions. Typically, cost reimbursements from payers, separated by private payers or government subsidy, are more generalizable to the population of interest. Third, every health utility, transition state probability, and cost variable should undergo rigorous sensitivity analyses. Identifying the most sensitive variables may have major future health policy implications. The specified range of values for sensitivity analysis should come from the literature and expert clinical experience. When performing a 1-way sensitivity analysis, the sensitivity range should be broader for variables with greater uncertainty. A probabilistic sensitivity analysis (eg, a Monte Carlo simulation) typically uses literature-derived 95% confidence intervals as the sensitivity range for each variable. This allows the model to repeatedly run patient simulations (eg, 1000 or 10,000 times) bounded by the variability that comes from the 95% confidence intervals of all of the variables.

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The rise of chronic diseases in childhood, such as obesity and its associated diseases, strategically challenges and places burden on today's pediatricians and pediatric subspecialists to make significant contributions in the practice of cost-effective medicine (13–16). Pediatric gastroenterology is especially well positioned to have an effect in this movement. Pediatric gastroenterologists are accustomed to caring for the most chronic patients because their expertise is needed in feeding and nutritional problems, obesity, dysmotility, multiple inflammatory and immune-deficient conditions, transplantation, and genetic diseases. Although it is a privilege to be on the front lines caring for children who require lifelong health care and services, an alternative method is required for the future of medicine given the economic times. To date, a few adult subspecialties, especially cardiovascular medicine and infectious diseases, have established good benchmarks for how decision science research could inform clinical practice guidelines (eg, Centers for Disease Control and Prevention–recommended screening protocols for patients with hepatitis B based on finding from previous CEAs (17,18). In pediatrics, CEAs represent one underused method of ensuring that precious resources are maximized to achieve optimal health for all patients. Ultimately, shaping the future of medicine to become cost-effective will lead to improved health care for a greater number of patients.

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