The following examples demonstrate how economic studies can address many issues important to biosurveillance. In each case, understanding of the above principles of economic analysis will help a reader (a consumer of these published studies) to catch more subtle implications from the study. Rather than accept or reject a final conclusion, one should fully discern the set of steps that led to that conclusion. Even if the results are not applicable to a decision maker's specific situation, components of the analysis may be. In fact, many times the greatest value of a study is not in the answers it provides, but instead in the questions that it raises. A study such as the boil-water CBA in Chapter 29 may focus decision makers on the key parameters or the crux of the matter. Economic studies may identify the need for additional future studies. In addition, there may be multiple approaches in dealing with a given decision or problem. Therefore, for each issue, although the selected examples offer important teaching points, they are not necessarily the best or most definitive studies.
5.1. How Significant Is the Threat?
Corso et al. (2003) conducted a retrospective cost-of-illness study of the 1993 Milwaukee Cryptosporidium outbreak. This study is a good example of a study that used multiple disparate sources to profile the economic impact of an outbreak. The analysts conducted a telephone survey of a random sample of
Milwaukee residents to estimate the total number of people affected by the outbreak, the percentages who sought different levels of medical care, the length of illness for those who did not seek medical care, and the total number of work days missed. The investigators also selected a representative sample of patients who sought medical care and reviewed each patient's medical chart to determine the medical resources (e.g., medications, diagnostic tests, emergency medical services) each patient consumed. They then used billing records to obtain the associated hospital charges. They used the Wisconsin 1993 average urban hospital and emergency department cost-to-charge ratios (0.70 and 0.67, respectively) to convert charges to costs.
As with most economic studies, the investigators made assumptions about information that was not readily available and used standard industry and government sources to estimate some costs. For example, they assumed patients with mild illness would self-medicate themselves with either loperamide or oral rehydration solution for 50% of the duration of illness. They used 1993 retail drug prices to estimate the unit cost of each medication. The City of Milwaukee Health Department provided data on the average cost of a single outpatient physician visit.
The study demonstrated not only that the outbreak had a substantial economic impact (in Milwaukee, $96.2 million) but that decreased worker productivity represented the largest economic consequence of the outbreak ($64.6 million), a finding confirmed by studies of outbreaks of other diseases such as severe acute respiratory syndrome (SARS) (Achonu et al., 2005) and hepatitis A (Sansom et al., 2003). One implication of these studies for biosurveillance economic analyses is that any cost-of-illness study that fails to include productivity losses may seriously underestimate the potential impact of an outbreak. Outbreaks can be devastating to not only the health care system and directly affected individuals but also many businesses and the economy. This result, if further developed, may perhaps persuade individuals and organizations initially uninterested in biosurveillance to reconsider their stance. In fact, the Milwaukee study actually underestimated productivity costs by not accounting for the lost lifetime productivity of those who died from the outbreak, the degree to which the outbreak diverted companies and the government from daily normal operations, and the damage the outbreak had on consumer and public confidence.
5.2. What Investigation and Response Options Are Available?
Cost-of-investigation/response studies can identify which potential investigation and response options are economically feasible. No matter how effective, a prohibitively expensive response option may not be possible. In addition, this type of analysis can guide the structure of more detailed analyses. An analyst may exclude relatively expensive investigation and response options such as mobilizing the Strategic National
Stockpile from a study of the appropriate initial actions for a low probability alert.
Costs-of-investigations/responses have not been well studied; in some cases, cost-of-investigation/response analyses are included almost as afterthoughts in overall cost estimates that focus mainly on cost of illness. In some of these studies, the cost-of-investigation appeared to be relatively small compared with the overall economic burden of an outbreak, accounting for less than 5% of the total costs in a Salmonella outbreak (Cohen et al., 1978) and less than 3% in a New Mexico botulism outbreak (Mann et al., 1983). In other studies, such as Zhorabian et al. (2004) study of the 2002 Louisiana West Nile virus outbreak, the cost-of-response is a sizable percentage of the overall costs ($9.2 million of the $20.1 million total in the West Nile virus study). However, depending on retrospective cost-of-illness studies to determine costs-of-investigation is fraught with problems. The type and degree of response depend on the severity, nature, and location of the problem. Moreover, there is considerable regional and potentially temporal (e.g., varying by time of day, day of week, and month of year) variation in response mechanisms. In addition, some locations have antiquated accounting systems ( Roberts et al., 1989; Bownds et al., 2003), making it difficult to accurately capture all costs. Therefore, there is a need for predictive studies.
An example of a predictive study is the evaluation by Gupta et al. (2005) of whether mass quarantine during a SARS epidemic would be cost saving, life saving, or both. They estimated the direct costs of SARS by the following formula:
Direct Cost of SARS/person =
[(probability of hospitalization) x (Average Hospital Length of Stay) x
[(probability of an intensive care unit stay) x (Average ICU Length of Stay) x (Cost per ICU day)]
They calculated the indirect costs or the lost productivity using the following:
Indirect Cost of SARS/person =
[(Average Hospital Length of Stay) x
[(Probability of Death) x (Years of Potential Life Lost) x (Annual Salary)]
In the second formula (and in many economic studies), the cost of a death is equivalent to the total value of the victim's potential future earnings. (This means that people are only worth as much as they can potentially earn in their lifetimes. Of course, some may take issue with this contention, but that is a discussion best left for another time.) The following formula generated the cost of quarantine:
Total Cost of Quarantine =
[(Number of People Coming into Contact with SARS) x
(Incubation period of SARS) x (Average Daily Wage)] +
As can be seen, lost worker productivity is one component cost associated with quarantine. In the 2003 Toronto outbreak, the fixed administrative costs associated with quarantine were around $12 million. The component owing to lost productivity was $1140 per person quarantined, for a total of $0.2 million for the primary wave, $1 million for the second wave, and $5 million for the tertiary wave of the Toronto outbreak. The following equation yielded the net savings from quarantine:
Net Savings =
[(Total Cost of SARS/person) x (Total Number of Infections-number infected before Quarantine)]
In other words, the net savings is the difference between the number of SARS cases prevented times the total cost of SARS per person and the total cost of quarantine. The investigators found that mass quarantine would not only save lives but also costs: $279 million during the primary wave of a SARS epidemic, $274 million during a secondary wave, and $232 million during a tertiary wave.
5.3. What is the Value of Rapid Response?
As is often said, time is money, especially in biosurveillance, in which delays in response can have significant consequences. Early action can lead to effective containment of a threat, and prompt measures can minimize disruptions in government and business operations. Many treatment options are only effective very early in an outbreak. Because biological agents used by terrorists would be expected to kill quickly, a tardy response can result in substantial morbidity and mortality. A predictive CBA, such as Kaufmann, Meltzer and Schmid's simulation of three types of bioterrorist attacks (with Bacillus anthracis, Brucella melitensis, and Francisella tularensis) over a major city suburb of 100,000 people, can help quantify the cost of such delays.
Because outbreaks of B. anthracis, B. melitensis, and F. tularensis have been rare, the investigators in this study had to make a number of assumptions to create an economic model involving extrapolations from available data. First, the investigators assumed that the spread (e.g., weather conditions would be ideal and the agents would travel with prevailing winds), physical and biologic decay (minimal decay), and infectivity of the agents (ID50 = infectious dose 50% = the number of infectious particles (spores, viral particles, bacteria, etc.) needed to cause disease in 50% of the people who are exposed of 20,000 spores of B. anthracis or 1,000 cells of B. melitensis or F. tularensis) would be uncomplicated.Then, data from the few previous outbreaks of these agents helped forecast how soon patients would develop symptoms and die after exposure. Next, extrapolations from published laboratory and clinical experimental data supplied the clinical efficacy of administering different antibiotic interventions to the exposed population. The investigators also postulated that 90% of the exposed population would participate in the treatments.
The analysts obtained cost estimates from several different sources and used the following formula to calculate the potential economic benefit of each antibiotic intervention program:
Net savings =
Number of Deaths Averted x Present Value of
Expected Future Earnings +
(Number of Days of Hospitalization Averted) x
(Cost of Hospitalization) +
(Number of Outpatient Visits Averted x Cost of Outpatient Visits)
Once again, the cost of a death was equivalent to the present value of the victim's potential future earnings. The investigators derived age- and sex-specific salary data from the U.S. census and adjusted these numbers to match the age and sex distribution of their theoretical suburban population.They used discount rates of 3% (in one set of scenarios) and 5% (in another set) to express all future earnings in current dollars. The cost per day of hospitalization came from multiplying an average single hospital day charge ($875 in 1993 from the National Center for Health Statistics) by the April 1994 New York State cost-to-charge ratio (0.635) and adding a cost of $65 per day for missing work (lost productivity to society), a figure frequently used by health economists. To tabulate outpatient costs, they surmised the number of outpatient visits that victims would require and then used Medicare average allowance data to derive the cost per outpatient visit. The 1996 Drug Topics
Redbook provided prices to calculate the costs of the antibiotic interventions.
The study included multiple scenarios that varied in the period between the initial release of biological agent and antibiotic intervention, and found that a rapid response was the single most important means of preventing significant mortality, morbidity, and accompanying costs. In the absence of any intervention (antibiotic or vaccine), B. anthracis was most costly to society (ranging from $18 billion to more than $26 billion), followed by F. tularensis ($3.8 billion to more than $5.4 billion) and a B. melitensis ($477 million to more than $579 million). The potential cost savings of antibiotic interventions may be huge if initiated during the first day after the attack, but savings exponentially shrink with each passing day. For example, for an anthrax attack, antibiotic prophylaxis could save somewhere between $14 billion and $22 billion if administered on the day of the attack, $12 billion to $20 billion when administered the day after the attack, but only $5 billion to $8 billion when administered three days after the attack.
5.4. How Much Should One Pay for "Insurance" against an Attack or Outbreak?
Investing in biosurveillance (and other types of emergency preparedness) is analogous to taking out an insurance policy for protection against accidents, disability, death, or natural disasters. Similar to an accident, an outbreak or attack may occur at any moment or location. Although on most days, carrying an "insurance policy" may feel like paying a cost without obvious rewards, it is actually protection against that uncommon but potentially catastrophic occurrence. Therefore, to realize what "premium" is fair to pay for such "insurance," one must factor in the risk and the cost of the occurrence as well as the risk reduction that the "insurance policy" provides. CBAs, such as the analysis of Meltzer et al. (1999) of vaccination responses to a simulated U.S. influenza epidemic, can help ascertain the fair premium for such insurance.
In this economic study, investigators used prior clinical studies, charge data, and salary information to evaluate the economic benefit of employing different mass vaccination strategies to curtail a theoretical influenza pandemic. The analysts identified the diagnosis codes (International Classification of Diseases, Ninth Edition [ICD-9]) associated with each possible clinical sequela of influenza, such as pneumonia, bronchitis, and exacerbations of pre-existing conditions (e.g., heart disease), and searched health insurance claims data to calculate the average charges associated with each code. Previous clinical studies furnished the risk of each outcome for each age and risk category (high risk versus not high risk for contracting influenza). Age- and sex-weighted average wage data helped estimate lost productivity for each outcome. As before, the economic cost of a death was equal to the present value of how much the victim would have earned in his or her remaining lifetime. By use of this procedure, the investigators estimated that without large-scale immunization, an influenza epidemic would cost somewhere between $71.3 billion and $166.5 billion. The majority of these costs would come from deaths, suggesting that vaccine strategies should target those patients most likely to die from influenza.
The investigators then computed the economic value of different vaccination strategies (ranging from vaccinating specific populations to vaccinating the entire population), while varying the influenza attack rates, vaccine effectiveness, the rates of people vaccinated (i.e., compliance), and vaccine costs ($21 and $62). The investigators calculated the net returns of vaccinating each different age and risk category by the following formula:
Net Returnsage, risk group =
Savings from Outcomes Averted in Populationage riskgroup
Cost of Vaccination of Populationage, risk group
The "Savings from Outcomes Averted" for each age and risk group came from
Savings fr°m Outcomes Avertedage, risk group =
Number with outcomes before interventionage risk group x
Compliance x Vaccine EffectivenessOutcomes x
Value of Outcome Prevented
The "Cost of Vaccination of Population" for each age and risk group came from
Cost of Vaccination of Populationage
, risk group
$Cost/Vaccine x Populationage, risk group X Complianceage, risk group
The cost per vaccinated person included the cost of the vaccine, the distribution and administrative costs, patient travel, time lost from work, and side effects, including Guillain-Barre syndrome.
According to the study, the amount of "insurance premium" to spend on maintaining proper influenza preparedness ranges from $48 million to $2,184 million annually. The investigators calculated this premium by using the following formula:
Annual Insurance Premium =
Net returns from an intervention x Annual probability of a pandemic
The results of this study suggest that the United States should be willing to spend somewhere between $48 million and $2 billion per year to prevent an influenza pandemic, depending on which assumptions one uses. Moreover, although the influenza attack rate, vaccine effectiveness, and compliance all affect this premium, the probability of the pandemic was the most important driving factor. This implies that ongoing monitoring and threat assessment is important, as determining the risk of pandemic will help determine the appropriate level of vaccination.
5.5. What Is the Economic Value of an Intervention?
Often, the economic benefits and penalties of an intervention are not necessarily obvious, and economic studies can better elucidate the true value of the intervention. For example, the CUA by Khan et al. (2005) compared different response strategies to a hypothetical SARS outbreak in New York City. Because SARS can be very difficult to distinguish from other illnesses (e.g., caused by influenza, respiratory syncytial virus, Bordetella pertussis, Legionella pneumophilia) that cause respiratory symptoms and fever, i.e., febrile respiratory illnesses (FRI), the investigators wanted to see the value of home isolation versus testing (for SARS or other common diseases) of patients with FRI. Their analysis included a variety of costs (such as transportation, laboratory tests, influenza vaccination, antimicrobial agents, hos-pitalization, public health investigation, and patient time) and used the Health Utilities Index Mark 3 (HUI) to estimate the changes in health-related quality of life from different situations such as home isolation. The study revealed that using a test (multiplex polymerase chain reaction [PCR] assays) to diagnose other common respiratory infections1 would save $79 million and 8,474 quality-adjusted life-years over home isolation.1 Adding SARS testing to the multiplex PCR assays would actually cost $87 million more and decrease utility. The explanation for this less-testing-is-better result is that causes of FRIs other than SARS are much more common than is SARS; therefore, SARS testing would generate false positives, resulting in more patients without SARS erroneously isolated. This study is an excellent example of how additional information provided by testing could actually be suboptimal.
5.6. What Factors Affect the Economic Value of an Intervention?
Because the right choice in some situations can be the wrong choice in others, economic studies can help determine what factors affect the relative values of different interventions. An example is a CUA conducted by Fowler et al. (2005) that evaluated the incremental cost-utility of four different postanthrax attack strategies (doing nothing, vaccination, administering antibiotics, and administering both antibiotics and vaccinations) and two preanthrax attack strategies (vaccination versus no vaccination). The investigators created a hypothetical cohort of a large metropolitan population with a similar age and sex distribution to New York City and obtained costs, probabilities, and rewards from a variety of sources, including the published literature, Centers for Medicare and Medicaid Services data, and the 1998 Statistical Abstract of the United States. The analyses showed that administering vaccine and antibiotics offered more utility (21.36 QALYs) and cost less ($46,099) than did the other three postattack strategies. Of the two preattack strategies, no vaccination was less expensive and resulted in higher QALYs gained per person when the annual risk for attack was 1% and during an attack 10% of the population was infected. However, sensitivity analyses revealed an interesting finding: if the probability of an individual being exposed (i.e., the risk for an attack multiplied by the probability of exposure given an attack) is less than one in 200, then the ICER drops below $50,000 per QALY. In health economics, an ICER of $50,000 per QALY is often used as an arbitrary threshold, as researchers consider anything below this threshold cost-effective. These findings imply that probability of exposure is pivotal in deciding whether to mass vaccinate a population preemptively, another important implication for biosurveillance.
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