Cost-benefit analysis CBA Enumerates and compares costs and benefits achieved in monetary terms. A health authority could use a CBA to project whether a vaccination program is likely to lead to an improved cost-benefit outcome versus not implementing the program. Budget impact analysis BIA Estimates effect of an intervention in terms of overall cost to organization or health plan. The study would measure the net cumulative direct costs of treatment for a given number of patients in a specific population.
Arnold Melnick
Discounting is a common practice in multiyear economic analyses. Discounting should not be confused with upward price adjustment for inflation, which is also commonly performed in retrospective studies lasting more than 1 year. A sensitivity analysis assesses the effect of uncertainty on an economic analysis or decision. It is a widely accepted way to verify the strength of the results. In a sensitivity analysis, selected assumptions underlying the analysis are altered to see how this affects overall results.
For example, if the perspective is that of the payer an insurance company or national health care system , the analysis is likely to consider direct medical costs that accrue to the health plan over the short or long term. If the analysis is assessing costs to a hospital, an institutional perspective would be appropriate. Some modeling approaches require a computer and dedicated software, whereas others are more straightforward. The creation of a decision tree is a common first step in model design Figure 1.
A decision tree provides a framework to systematically compare different decision options and subsequent potential patient outcomes. In this example, Figure 1 compares two treatment options for pressure ulcer Treatment A and Treatment B. Immediately after treatment application, three disease states are possible epithelialized, proliferative, or necrotic wound. Over time, three outcomes are possible closure, granulation, or an ongoing chronic wound.
In a more detailed decision tree, probability data for the three final outcomes would also be displayed on the tree branches.
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This image depicts a straightforward decision tree and represents a situation in which treatment options, duration of therapy, and timeframe under consideration are limited. The condition of the wound and the outcomes of treatment can be easily classified into a manageable number of states. This approach is considered the most comprehensive method to evaluate a program from a health economics viewpoint, although it is most applicable where health care is nationalized.
The societal perspective is not widely applied in US studies. This perspective incorporates all the cost considerations discussed above but also considers the direct and indirect treatment-related costs that patients and society must bear, such as copayments and financial losses due to decreased productivity. If the ICER is positive, other factors must be taken into consideration to determine which treatment is preferable in which circumstances.
A decision tree comparing two treatments for pressure ulcers.
In such cases, a more sophisticated approach, such as a Markov model of disease progression, is more appropriate. Markov models are useful in situations in which patient risk is ongoing over time, multiple outcomes are possible, event timing must be considered or cannot be predicted, and disease-related events eg, a myocardial infarction may occur more than once. Within the model, it is assumed that a patient is always in one of a finite number of discrete health states.
Representation of a Markov model of disease progression. Data Sources for Health Economics Analyses The accuracy of any health economics analysis is dependent on the quality and applicability of study data. The primary inputs used in HEOR analyses are clinical outcomes, their associated costs, and patient preference information.
Often, clinical outcomes and cost data are obtained from clinical trials of the treatment in question. Economic evaluations may be conducted simultaneously with clinical trials. However, when this is not feasible, investigators must rely on the published literature, existing databases, and even expert opinion in cases in which limited evidence is available to identify appropriate inputs. One of the greatest challenges in identifying appropriate cost data is determining whether the information available is generalizable to the patient population being evaluated.
Typically, it is expressed as a value between 0 representing death and 1 representing perfect health. As an example of the utility concept, patients facing dual lower-extremity amputation might rank their utility at 0. On the other hand, patients with moderate seasonal allergies might assign a utility of 0. Utilities may also be calculated from another perspective in cases in which input from another source such as the treating physician is relevant. Growth in health-economics—related publications: Numerous journals devoted to the health economics literature have emerged, targeted to managed care plan executives, pharmacy benefit and formulary managers, government officials, and other key decision-makers.
Second, it is not unusual for an HEOR writer to have to fully understand the primary study data. This is because, compared with analyses of clinical trials, health economics analyses are likely to have fewer authors who comprehend the mathematical aspects of the work. Third, a concise writing style is also important, because an additional, documented challenge to preparing HEOR analyses for publication is the need to report cost-related items that would not be required in a purely clinical study in the methods section of a document.
It is also becoming common for pharmaceutical and device companies, as well as health insurance companies, to have internal HEOR departments. Typically, however, a substantial proportion of the writing and other professional HEOR work of these organizations is outsourced. Figure 3 illustrates the growth in HEOR-related medical writing over the past 20 years. Growth has been quite steady, with only publications in and 1, for Health economics writers almost always bring value beyond writing skills to an HEOR project.
Beyond medical writing capabilities, there are some additional skills an HEOR writer should possess. These observations are based on my 7 years of work in this field. First and most important is a willingness to work directly with numbers. Health economics writers are often presented with raw data or data that must be reviewed closely, double-checked, and Number of Articles consider both the positive and negative aspects of treatment on health and quality-of-life outcomes.
PRO is an umbrella term for input provided directly by patients regarding their treatment preferences. Typically, PRO instruments rely heavily on qualitative input for development, but are subsequently translated into a validated numeric scoring system. PROs are increasingly common in economic and clinical research, with multiple instruments both disease-specific and designed to assess general health being developed and validated.
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It ignores resource constraints and has an unreal, wishful quality to it, as though skydivers could defy gravity by cleverly talking their way around it. Perceptions have long existed that US health care resources are not really constrained, although this outlook is changing. There is also substantial political resistance to the use of HEOR in decision-making. It has also been noted that an overemphasis on health economics requirements might harm innovation or affect the development of new medical technology where a higher price typically goes hand-in-hand with advancement. Last, there are genuine concerns that health economic information is not currently being developed in a timely and transparent way, and that many key decision-makers lack the expertise to prepare, review, communicate, or fully understand findings from health economics research.
These discussions are ongoing, with multiple stakeholders trying to determine the best way to make HEOR communications more straightforward. This document consolidates input from multiple prior publications timeframe to into a single standard and provides detailed recommendations and a checklist for the appropriate reporting of HEOR data for publication. Health economics research helps health care purchasers and consumers understand not only the clinical efficacy and safety of treatments, but their overall value in terms of cost and patient preferences.
In this way, health economics is broadening our concept of evidencebased medicine and guiding us toward improved decision-making. HEOR analyses are an increasingly common component of clinical research and an emerging area of specialization in medical writing. It is likely that most medical writers will be exposed to HEOR concepts as part of their professional work; therefore, it is important for them to be familiar with HEOR concepts and terminology.
The author notes that she has no commercial associations that may pose a conflict of interest in relation to this article. Health Care Cost, Quality, and Outcomes. Dix Smith, M, editor. International Society for Pharmacoeconomics and Outcomes Research; Wolters Kluwer Health; Cost-Effectiveness in Health and Medicine.
Oxford University Press; National health spending projections through A menu without prices. Accessed July 16, Markov models in medical decision making: Determining cost values for health care resources in pharmacoeconomic studies. Primer on medical decision analysis: Part 3— estimating probabilities and utilities. Content validity—establishing and reporting the evidence in newly developed patient-reported outcomes PRO instruments for medical product evaluation: Medication compliance and persistence: Health Qual Life Outcomes. Published September 11, Draft definition of comparative effectiveness research for the Federal Coordinating Council.
A primer on comparative effectiveness research methods. Am J of Pharmacy Benefits. What we talk about when we talk about health care costs. N Engl J Med. Medicare and costeffectiveness analysis. International Society of Pharmacoenomics and Outcomes Research website.
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Gain the skills you will need as a writer or editor in health, technology, medicine, nature, space, energy, or other fields. A close look at the sentence raises several important questions about meaning, measurement, statistical analyses, presentation of data, and interpretation of results. Analytical editing requires a knowledge of several topics: Substantive editors who can add analytical editing to their services—the learning curve is not that steep—can greatly increase both their professional skills and their value to authors. However, at least in clinical medicine, such support is often inadequate, even in the United States, and especially in developing countries.
Researchers are not expected to do their own literature searches and so are given access to librarians. They are not expected to do their own data analysis and so are given access to statisticians.
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They are not expected to render their own graphs and drawings and so are given access to medical illustrators. But for some reason, we expect them to do their own writing— to communicate technical information accurately, completely, clearly, and economically, in words, numbers, and images—without specific training and often without the support of professional medical writers and editors. One of the most important lessons I have learned in almost 40 years of editing is that the certainty we believe we have about understanding even a simple, straightforward sentence is often illusory.
Only on closer examination does the illusion become apparent. Furthermore, such sentences are found in most scientific articles, which is to say, these illusions are also a certainty in the scientific literature. I encountered a good example of a sentence in which the actual meaning differs remarkably from its apparent one. In this article, I pose some questions that should be answered if this sentence is to be understood correctly. Try the Kindle edition and experience these great reading features: Share your thoughts with other customers.
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