[NARRATOR] Module 2 discusses economic impact analysis.
Economic impact analysis is sometimes referred to as cost-of-illness analysis.
In the public health model for prevention, cost-of-illness analysis often falls within
measuring the burden of disease or illness.
Measures of disease burden related to health outcomes include mortality, morbidity, life
expectancy, and quality of life; other examples include quality-adjusted life expectancy,
disability-adjusted life expectancy, healthy-days equivalent, and activities of daily living.
Cost-of-illness analysis is another measure of disease burden that incorporates
costs of disease.
In the United States, cost-of-illness analysis typically includes the value of medical care
resources used to treat a disease and the losses in productivity caused by the illness.
Other non-medical costs associated with the illness are sometimes included as well.
Examples of medical costs include inpatient visits, emergency department visits, outpatient
visits, prescription drugs, medical equipment, and home health services.
Examples of non-medical costs include child care and travel expenses associated with receiving
treatment, and special education costs if cognitive function is impaired by the illness.
When assessing productivity losses, we typically use the human capital approach.
This method calculates a person’s production potential based on average wages, with some
adjustments for household productivity.
Although the human capital approach is fairly standard in cost-of-illness analysis in the
United States, other countries may rely on different methods for calculating productivity,
such as the friction cost method, which calculates productivity based on what an employer would
have to pay to replace you as an employee.
More information on assessing societal productivity costs is presented in Module 4.
Examples of productivity losses include missed days of work and other activities associated
with the illness itself or with receiving treatment for the illness.
Just like other measures of disease burden, cost-of-illness analysis can either be prevalence-based
or incidence-based.
The question underlying prevalence-based cost-of-illness analysis is, “How much do we spend
each year to take care of individuals with condition X?”
Prevalence-based cost-of-illness analysis includes the total costs of an illness or
disease within a specified time period, typically 1 year, regardless of when the disease first occurred.
In other words, prevalence-based estimates are a cross-sectional view of costs associated
with the illness.
But prevalence-based estimates don’t tell us how much money prevention can save
in the long term.
They look only at the annual costs of a disease, not at costs of a disease over the course of a life.
In contrast, incidence-based cost-of-illness analysis calculates the value of lifetime
costs for new cases of a disease or illness.
Incidence-based analyses are essential for calculating the value of prevention.
To assess lifetime costs without longitudinal data taken over a lifetime, we may need to
model a synthetic cohort of people with the illness over time.
In modeling this synthetic cohort, we may also need to assume cross-sectional differences
regarding the costs that apply in future years and assume that costs are relatively
stable over time.
Although incidence-based cost-of-illness analyses are better tools for knowing what could be
saved through prevention efforts, these analyses require more assumptions and perhaps even
more sophisticated modeling techniques than other methods.
There are several approaches for measuring cost of illness.
You can total all the medical costs for the population of interest—for example, a group
of people with hypertension.
Or you can add only hypertension-specific medical costs for a group of people with hypertension.
Or, by matching cases to controls or running regression analyses, you can assess the incremental,
or marginal, medical costs for people with hypertension compared to a non-hypertensive group.
Following are more detailed descriptions of these three methods and their pros and cons.
Using hypertension as an example, we could assess the prevalence-based costs associated
with hypertension by identifying all people with hypertension within a specific time period—say,
2015—and then summing up all the medical costs associated with that cohort.
The pros are that this approach is straightforward and easy.
It works well for relative, not absolute, comparisons.
For example, in 2015 there were X people who had hypertension, and their total medical
costs were Y.
In comparison, 10 years earlier, there were A people with hypertension, and their total
health care costs were B. The cons are that we may not properly isolate
the burden associated with a particular disease or identify costs of comorbidities—other
illnesses that might be associated with the disease.
Another problem with this approach is that some of the medical costs included in the
analysis would also be the same for the non-hypertensive group (for example, the costs of
preventive teeth cleanings), so the total medical costs may overinflate the medical resources required
for the hypertensive population.
Alternatively, we could include only those medical costs in the hypertension population
that are explicitly related to hypertension.
This approach allows us to assess the percentage of medical costs attributable specifically
to hypertension and not to other reasons for seeking health care—such as teeth cleaning.
The advantage of this approach is that it’s conservative, representing the lower end of
the range of actual costs, and it can be applied easily to incidence-based models of disease
that assess lifetime costs.
However, this approach may underestimate costs if comorbid events aren’t included.
For example, if people with hypertension also have other conditions, such as cardiovascular
disease, or CVD, then the costs of CVD attributable to hypertension may not be counted in the
hypertension-specific study design.
In the American Heart Association’s 2016 statistical update, the authors reported that
the total annual cost of hypertensive disease in the U.S. in 2012 was $48.6 billion.
This included $45 billion from direct medical costs, such as inpatient hospital stays or
prescribed medications.
It also included $3.6 billion in costs from lost productivity due to premature death resulting
from hypertension.
This estimate is conservative, because it includes only costs from diagnoses of hypertensive
disease and does not include attributable costs from comorbidities.
Thus, it underestimates the total costs attributable to the disease.
To deal with the comorbidity issue, we can use epidemiologic data that show the attributable
fraction of hypertension and CVD.
We can then include that fraction of the total costs of CVD in our calculation.
But epidemiological data may not exist for all comorbidities.
For example, use of mental health resources is an area with limited information on the
attributable fraction for other comorbidities.
But the attributable fraction could vary so much across the population that using averages
might skew the results.
Here is an example from an American Heart Association study that examined the total
costs of CVD in the U.S.
In their analysis, the authors first estimated that, in 2010, the direct medical costs of
treating hypertension alone were $69.9 billion, and indirect costs from lost productivity
were $23.6 billion.
But when they added a portion of the costs of complications associated with hypertension—including
coronary heart disease, stroke, and other forms of CVD—the direct medical costs attributable
to hypertension almost doubled.
Another method for conducting cost-of-illness analysis is to match cases—in this case,
people with hypertension—to controls, then determine the incremental differences in use
and costs of health care.
This approach assumes that all confounding factors, such as age, are observable and accounted
for in matching.
This method may provide more accurate results.
However, we may still overestimate incremental costs if there are other confounding factors
that were not accounted for in matching.
For example, we may not have information on important demographic factors, such as age,
that might affect medical costs.
There are regression methods available to help solve some of the problems associated
with data’s observable and unobservable confounding factors.
Although we won’t get into the details of these methods here, we’ll show an example
in the next few slides.
Balu and Thomas conducted a regression analysis to assess the incremental costs of treating
people with hypertension in the United States.
Balu and Thomas conducted their analysis using a national dataset of medical claims that
had information on current health outcomes.
They compared a population of people reporting one or more hypertension diagnoses to a population
reporting no hypertension diagnoses.
To account for confounding effects, Balu and Thomas controlled for age, sex, ethnicity,
education, and other comorbidities, using the Charlson comorbidity index.
So in this analysis, the authors were conservative in their estimate, because they did not include
the attributable fraction of the costs of the other comorbidities that hypertension
may cause.
The authors concluded that the increased cost in medical expenditures per year for a person
with hypertension was a little more than $1,000, compared to a person without hypertension.
This implies that hypertension alone costs $55 billion per year.
Despite the large estimate reported, there are still many other costs of hypertension
that Balu and Thomas did not include.
First and foremost, productivity losses associated with hypertension were not included in the estimate.
To think about other costs that could be considered in a cost-of-illness analysis, we must examine
the perspective of the analysis.
That is, who is the audience for the study, how will they be using the results, and based
on that, what costs should be considered?
From the perspective of the health care system, costs gleaned from the medical expenditure
panel survey may be enough.
But if you also include the individual perspective, you would want to include the loss in household
productivity and leisure resulting from the illness.
You may also place a value on the loss in quality of life.
From an employer’s perspective, it would be important to include productivity losses,
not only in terms of days missed from work, but also days at work when the person with
hypertension might not be as productive as usual.
From a societal perspective, all these costs should be included.
There are a few hazards in collecting medical costs for use in a cost-of-illness analysis,
either at a patient or at a participant level—or even from national datasets, such as the medical
expenditure panel survey.
First, there are many cases with zero health care costs, and a few cases with extraordinarily
large health care costs.
These two phenomena can skew outcomes, making the use of an “average” cost inaccurate.
Although we won’t get into the details in this presentation, there are methods for dealing
with skewed health care costs.
One approach is to log-transform the cost data.
This can be done only if costs are non-zero, because you can’t log-transform a zero value.
To deal with this problem, you could also conduct multiple-part regression models to
determine average costs and their confidence intervals.
For example, you could first run a regression that incorporates the probability of having
any medical care costs, then add to that a regression that incorporates the probability
of having an inpatient admission. By looking at all
of these regressions simultaneously, you can better assess average costs based
on the variability of total costs predicted by these events.
When conducting cost-of-illness analyses, you’ll want to avoid some common mistakes.
First, if cost data are available from multiple years, you must convert them to the same base-year
dollars for comparison.
Second, in some cases, the market value for some resources may not reflect the true value
of the resources.
For example, if you included the cost of an inpatient admission, you would probably want
to use the hospital cost for that admission and not the charge, since the latter reflects
the negotiated reimbursement rate, depending upon insurance status.
Third, when conducting the analysis, you must decide whether costs will reflect the average
costs of all health care resources for someone with the illness, or the incremental costs
of health care resources when those with the illness are compared to those without the illness.
This approach corresponds to the diagnosis-specific cost approach mentioned earlier.
Finally, if you consider costs that occur in the future, they must be adjusted to reflect
the current value.
To convert costs into the same base year, researchers commonly use the All Items component
or the Medical Care component of the Consumer Price Index.
The CPI is found in the U.S. statistical abstract published by the U.S. Census.
Here’s an example where the cost of a physician visit in 2005—50 dollars—needs to be converted
to 2010 dollars for the cost-of-illness analysis.
To do this, you look up the CPI rate in 2005, divide it by the rate in 2010, then multiply
it by the value in 2005 to determine the corresponding 2010 value: In this case,
60 dollars and nine cents.
To convert hospital charges to costs, several sources are available.
First, hospitals themselves might provide these data.
If that isn’t an option, another source is the Health Care Financing Administration’s
cost-to-charge ratios provided by each state.
Here is an example of the Health Care Financing Administration’s ratios, published for 1996.
Note that the ratios differ by urban or rural location.
The national average suggests that the true cost of a hospital inpatient admission in
1996 was 47 percent lower than the hospital charge for the same time period.
When you are quantifying future costs, you need to adjust those costs into present-day dollars.
This is the present value of future costs.
To convert future costs into present-day dollars, you can use this simple formula: Take the
future value and divide it by one-plus-the-discount-rate to the power of the number of years in the future.
For example, to calculate a cost of 100 dollars that you expect to occur five years from now,
divide it by one-plus-the-discount-rate to the fifth power.
The discount rate typically used in economic analyses of health care interventions is three
percent, although you could use a rate between zero and ten percent in a sensitivity analysis.
Note that the discount rate already accounts for inflation.
In the case of a three percent discount rate, you would divide 100 by 1.03 to the fifth power.
That equals 86 dollars and 28 cents.
So the present value of 100 dollars, five years from now, equals 86 dollars and 28 cents.
If you needed to convert a whole set of common future values to present values—say, 100
dollars every year for the next 10 years—you could use the same formula from the last slide
and simply make the calculation separately for every year.
Alternatively, you could use this similar, but slightly more complicated, formula.
At the end of the cost-of-illness analysis, when you find that, for example, hypertension
costs 108 billion dollars, why and to whom is this value important?
Cost-of-illness estimates have value for public health practitioners who want to highlight
the economic impact of a disease, beyond the morbidity and mortality incidence-and-prevalence statistics.
These data provide additional information to argue for more prevention resources.
Cost-of-illness estimates also have value for policymakers charged with allocating scarce
public health resources.
If they can quantify the economic impact of hypertension relative to other diseases or
illnesses, it provides another argument for allocating these resources.
Finally, cost-of-illness estimates provide policymakers with one piece of a return-on-investment analysis.
Cost-of-illness estimates show what could be saved through successful prevention.
The next step will determine the costs of the prevention program.