Definitions And Basic Concepts

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Economic studies come in a variety of forms, and economists have applied them to almost every sphere of human activity. An economic study can range in complexity from a simple "back-of-the envelope" calculation to a sophisticated model that requires substantial computer power for its evaluation. An economic study may focus on a single possibility or compare multiple alternatives. Despite the diversity of technique and domains found in the published economic literature, economic studies share several basic attributes.

2.1. Perspective

One of the most important attributes of an economic study is its perspective, which is the person or organization whose point of view or interests determine the costs and benefits considered in the study. For example, a publicly traded company may be primarily interested in protecting shareholder value, and therefore, an economic study commissioned by the company about the impact of a bioterrorist event might include only elements directly relevant to shareholder value and exclude costs of treating sick individuals other than those employed by the company.

The perspective of an economic study is important because available choices, costs, and benefits vary significantly depending on whom and where you are. For example, people in densely populated areas that are vulnerable to bioterrorist attacks may benefit more from biosurveillance than would people in remote rural areas who are less likely to experience such attacks. If the residents in both areas have to pay the same

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amount of taxes (i.e., shoulder equal burdens of paying for the biosurveillance system), the rural residents may be less interested in such a system.

Every economic study should state clearly its perspective: whether it is taking the perspective of an individual, a particular institution or organization, a government body, or society in general. As you might imagine, changing the perspective of a study can drastically alter its composition and results. For any given decision situation, the optimal decision for one individual or organization may differ from that for another individual or organization. The perspective of the economic study should match that of the decision maker.

2.2. Retrospective, Prospective, and Model-Based Analyses

An organization or individual can perform an economic study of an event that has already transpired (retrospective analysis), will soon occur (prospective analysis), or could occur in the future (theoretical or predictive analysis). Each type of study has strengths and limitations and differs in feasibility. Retrospective studies are useful, because the past often repeats itself or helps predict the future. However, current and future situations may not mirror the past, and reconstructing past events can be difficult, especially without accurate and comprehensive data. Prospective studies, which involve collecting data while natural or created situations occur, give an analyst much more control over the situations and the information collected. However, prospective studies can be difficult and expensive to perform and only generate results representing specific situations. In the case of outbreaks, prospective studies may be nearly impossible, because the onset and timing of events are unpredictable and creating such an event would be unethical (and quite damaging to one's career). A limitation shared by both retrospective and prospective studies is that the studies are only feasible if the event has occurred or is likely to occur. Predictive studies overcome this limitation because the analyst builds a mathematical model or computer simulation of hypothetical situations. Because most outbreaks are uncommon and many types have never occurred, many biosurveillance-related economic analyses are at least partially predictive. A predictive analysis is always feasible. Moreover, it provides the analyst more flexibility in manipulating the situation that is being modeled to produce insights about a range of potential situations. The key limitation of a predictive study is that it rests on many modeling assumptions.

Often, answering a question requires a series of economic studies or an economic study that involves retrospective, prospective, and predictive elements. For example, in deciding whether to administer a certain vaccine, retrospective study of previous outbreaks and experience with vaccination programs can provide important quantitative data for a predictive economic model. Performing a prospective study, such as vaccinating a small representative sample of the population and tracking the ensuing costs and rewards, can provide additional estimates that facilitate projections about what would happen if a strategy was applied on a larger scale (e.g., the entire population). Retrospective and prospective analyses often provide data for a predictive analysis, such as a computer model of different potential outbreaks and the effects of vaccination programs.

2.3. Time Horizon

When conducting an economic study, an analyst should choose an appropriate time horizon or period that adequately captures the immediate and longer-term consequences of a decision or action, but does not make the study unrealistic to perform. For example, a study that only measures costs up to one month after a bioterrorist attack may seriously underestimate the impact of that attack because diseases and injuries can have long-term effects. Conversely, if the study attempted to measure the economic impact of that event 300 years into the future, it would clearly run aground on the banks of infeasibility, owing to the many uncertainties whose cumulative effect would be impossible to model. Analysts frequently face a tension between the benefit of increased fidelity from extending the time horizon of an economic study and the difficulty in constructing the model. The ideal balance between the two is often obvious from the specifics of the problem being modeled; nevertheless, it is the rule rather than the exception that economic modelers revise the time horizon as they develop a study.

2.4. Costs

One of the two major data inputs for an economic study is costs, which can be complicated to measure and very difficult to obtain. A cost is the cash value of money, time, and labor spent for goods or services. Although some costs (e.g., purchasing a stretcher) are relatively straightforward, others (e.g., the cost of caring for a tularemia patient) can include many component costs, such as nursing and physician time, diagnostic testing, medications, and emergency and hospital room occupancy. Each of these "subcosts" may be difficult to quantify and subject to error or variability.

2.4.1. Direct and Indirect Costs

Costs can be subtle or hidden. Even seemingly small events can have immediate and long-term effects on many different people and organizations. People do not even have to be present at the time and place of an event for it to affect them. For instance, a death or severe injury may influence the victim's family, friends, and workplace. Therefore, you must carefully account for every person and organization that may be reasonably involved and affected by an event. For example, a single successful small-scale bioterrorist contamination of a commercial building can lead to many direct costs, including the cost of diagnosing and treating the victims and the cost of decontaminating the building. However, it can also entail indirect costs to the company or companies occupying the building, including lost worker productivity, the cost of finding replacement employees, and damage to the company's reputation and worker morale. When summed, these "hidden costs" can be considerable and even outweigh the more obvious direct costs.

2.4.2. Methods of Estimating Costs

Once you have identified the causes and sources of the costs, you must quantify them, which can be challenging. Rarely do items and services have clear price tags. Often, you must do a fair amount of sleuthing to determine what an item or service actually costs. There are many methods of gathering or estimating costs, each with its relative advantages and disadvantages:

Charges. In some cases, the charges for a service or visit found on hospital, clinic, and insurance bills can serve as reasonable proxies for costs. Of course, these charges usually exceed the actual costs; therefore, an economic modeler will convert them to costs by using established cost-to-charge ratios or conversion factors. Moreover, bills may not break down the charges to the level of detail needed. For example, an emergency department visit charge may aggregate many components (e.g., placing an intravenous line, transporting the patient to different locations) of the visit but not identify what fraction of the charge is associated with each component. Finally, charges often do not always accurately reflect the resources consumed or services provided, as some items and services are not billable and some items on a bill may not have been consumed.

Microcosting. Microcosting is typically more accurate than is using charge information, but microcosting is usually expensive and time-consuming to perform. Microcosting identifies every resource used during an event and then assigns a cost to each resource. Time-and-motion studies frequently help microcost. A time-and-motion study may involve following a patient (e.g., tularemia patient) during a given event (e.g., stay in the emergency department) and counting every item used (e.g., medications, catheters, saline, gauze, radiology film) and every service performed (e.g., 30 minutes of a nurse's time, 10 minutes of a patient transporter's time). An analyst would then assign a cost to each item and each fraction of personnel time and sum the costs to compute an overall cost. The use of microcost-ing tends to be limited to simple and well-defined events.

Resource-Unit Use. Another approach, resource-unit use or health-service-resource use, involves measuring resources that are more readily measured (e.g., number of hospitalizations, length of hospital stay, number of radiology procedures) and assigning unit costs (e.g., cost per hospitalization, cost per hospital day, cost per radiology procedure) to each resource. For example, if an anthrax attack resulted in a patient staying 30 days in the hospital, then multiplying the cost of a hospital day by 30 could estimate the per-hospitalized-patient cost of the attack. Of course, the resource-unit use method provides gross estimates, may not account for significant variability (e.g., fluctuation in cost of a day in the hospital), and assumes that the resource unit accurately reflects everything that is being done.

Other Resources for Costing. An analyst may obtain costs from the medical literature, insurance reports, or other publications. Before using these numbers in an economic study, one should ascertain whether the source is credible and the source's circumstances are comparable to the study at hand. For example, the cost of hospitalizing a patient for a simple uncomplicated case of diarrhea may not be applicable to a case of bioterrorist agent-induced diarrhea. If there is more than one source for a particular cost and the numbers vary significantly among the sources, the analyst may use either a simple or a weighted average of the costs.

2.5. Benefits

The other major data inputs for an economic study are the benefits of a successful medical, public health, or policy intervention, such as money saved, lives saved, quality-of-life improvements, productivity increases, suffering prevented, or adverse events avoided. An economic study should include benefits relevant to the interventions or actions under study. For example, measuring the number of lives saved by an acne cream would not be a useful measure of benefit, whereas improvement in quality of life (or number of dates saved) would.

Over the years, analysts have developed many measures of benefits, and new measures will emerge to fit the needs of different kinds of studies. The earliest economic studies expressed all benefit in purely monetary terms, converting all potential benefits of an intervention into dollars, pounds, yen, francs, etc. However, researchers soon found that they could not easily express all benefits in monetary terms. For example, they could not use monetary terms to capture completely the value of saving a life (e.g., a person contributes to society in many nonmonetary ways, such as providing emotional and psychological support to friends and families), so researchers began using "life-years saved" as a reward for some interventions. However, life-years saved could not adequately represent the benefits of some quality-of-life-improving interventions (e.g., pain medications, walking devices) that do not save lives or the suffering caused by non-life-threatening diseases. As a result, researchers developed quality-adjusted life years (QALYs) as a health status measure, with one QALY representing a year of perfect health and less than one QALY representing a year of impaired health. Researchers also have developed and used many other reward measures specific to particular classes of interventions, such as the number of bypass operations prevented to measure the success of cardiac medications.

2.6. Discounting

If the time horizon of an analysis is longer than a year, an analyst will discount future costs and benefits. The practice of discounting recognizes that inflation and opportunity costs (i.e., the value of the next best alternative that you must forego when you make a choice) make a dollar (or any other cost or reward) in the future worth less than a dollar today. An analyst uses discount rates to adjust future costs and rewards to present day values. A discount rate, denoted typically by r, is the rate used to convert future costs or benefits to their present value.

For example, if Cn represents a cost n years from now and r is the discount rate, C0 (i.e., the current or net present value of Cn) is Cn/(1 + r)n. Although typically researchers use a discount rate between 3% to 5%, there is still considerable debate over the exact appropriate rate (van Hout, 1998; Gravelle and Smith, 2001; Brouwer and van Exel, 2004). By using a 3% discount rate in the above formula, an intervention that earns a $100 ten years from now will be worth $100/(1 + 0.03)10 = $74.41 in today's dollars.

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