THE EMPIRICAL EVIDENCE
MSAs The empirical evidence
One
of the main goals of medical savings accounts (MSAs) and other forms of cost
sharing is to reduce the welfare loss of health insurance. If, however, the
moral hazard of health insurance is not significant, then it may not be
necessary to introduce cost sharing or MSAs. If it is significant, then MSAs
have the potential to improve our health care system.
The effectiveness of MSAs and cost sharing in encouraging more appropriate health care consumption is directly related to the price elasticity17 of demand for health care: the more reactive individuals are to changes in the price of health care, the more effective MSAs will be at reducing health care expenditures. Therefore, empirical studies that examine the effect of price on the demand for heath care are instrumental in the assessment of MSAs, and studies that have looked more specifically at the effect of catastrophic insurance and MSAs on health care expenditures are also important.
Even
if people are price conscious, it does not necessarily follow that total health
care expenditures will decrease if they are given incentives to use the health
care system more prudently. Those at a lower income will have more resources
with which to purchase health care services and may, therefore, increase their
use of the system despite the incentives not to do so. Or, the existence of
prices may lead to lower use of the health system, which may affect individuals'
health status, which in turn may increase health care costs in the future. The
poor are particularly at risk, and it has often been argued that the poor stand
to lose if cost sharing or MSAs are introduced. There are several empirical
studies that examine the effect of cost sharing on health outcomes and on the
poor while others look at the significance of public health care spending on
this segment of the population.
Estimates
of the welfare loss
of health insurance
One
of the main goals of cost sharing is to reduce the welfare loss associated with
health insurance. People purchase insurance to lessen the financial impact on
themselves of an unforeseen event like an illness. As well, society benefits
from the availability of insurance because the risks and costs of a catastrophic
event occurring are shared by many people. However, there is moral hazard
associated with insurance because those who are insured against an event, say, a
car accident, will behave differently from those who, if in an accident, have to
pay the full costs of reparation. They will perhaps drive a little less
carefully, knowing that they are protected from the full cost of an accident.
Feldstein's
study (1973) is the most widely cited study on the welfare loss of health
insurance. Feldstein estimates the welfare loss of excess insurance by looking
at the welfare effects of increases in co-insurance rates, and by using time
series data for individual American states to estimate the demand for hospital
insurance. The welfare effects are calculated by estimating the gross gain from
reduced price distortion--with less insurance, prices more accurately reflect
the true cost of the services--and the gross loss from increased risk
bearing--with less insurance, individuals are at greater risk of paying more if
an accident or illness occurs. Feldstein finds that reducing health insurance
produces significant welfare gains. These results and the fact that public
insurance and non-hospital care are excluded (which understates the welfare
loss) lead Feldstein to conclude that United States could significantly benefit
from a reduction in health insurance--by more than $4 billion (1969 US$).
Since
there is a welfare loss associated with insurance, it follows that to maximize
social welfare one must try to maximize the benefits of risk pooling of
insurance while minimizing the welfare loss.18 Manning and Marquis (1996)
estimate the demand for health insurance and the demand for health services as a
function of co-insurance rates, deductibles, and upper limits on out-of-pocket
expenditures (or maximum dollar expenditure [MDE]19) using experimental data
from the RAND Health Insurance Experiment (HIE). They find a welfare loss of
approximately $480 per family (1995 US$) associated with insurance.
The
empirical evidence reaches a general consensus that there is a trade-off between
risk pooling and moral hazard. The welfare loss of health insurance can be
significant and, therefore, comprehensive coverage or ``free care'' is not
optimal. Deductibles, co-insurance and user fees can reduce the moral hazard of
health insurance. To date, these cost sharing mechanisms have been rejected in
Canada because they erect a barrier to care. MSAs, however, may provide
individuals with a financial incentive to restrain their use of health care
services without imposing such a barrier. MSAs will reduce the moral hazard of
health insurance to the extent that they can curtail consumption, which depends
on the effect of cost sharing on the use of health care services.
Cost
sharing, MSAs and the
use of medical services
The
RAND Health Insurance Experiment
In
the mid-1970s, the RAND Corporation20 began what turned out to be the most
significant medical care insurance study ever accomplished: the Health Insurance
Experiment (HIE). The central focus of the HIE was to study the effect of cost
sharing on medical service use and health status. More than 7,000 non-elderly
families from six different regions of the United States participated in the
experiment; no one above 65 years of age was included in the study. Participants
were assigned to one of 14 fee-for-service insurance plans or to a prepaid group
practice and were studied closely for a period ranging from three to five years.
All of the insurance plans had a limit on out-of-pocket expenditure (maximum
dollar expenditure [MDE]).21 The plans were as follows:
1.
One plan with zero co-insurance (free care).
2.
Three plans with 25% co-insurance and MDEs of 5, 10, or 15% of family
income, to a maximum of $1,000.
3.
Three plans with 50% co-insurance and MDEs of 5, 10, or 15% of family
income, to a maximum of $1,000.
4.
Three plans with 95% co-insurance and MDEs of 5, 10, or 15% of family
income, to a maximum of $1,000.
5.
Three plans with 25% co-insurance for all services except out-patient
mental health and dental, which were subject to 50% co-insurance and MDEs of 5,
10, or 15% of family income, to a maximum of $1,000.
6.
One plan with 95% co-insurance for out-patient services and zero percent
co-insurance (free) for in-patient services and an MDE of $150 per person,
subject to a maximum of $450 per family. (This plan is known as the individual
deductible plan.)
Four
different dependent variables were used in the HIE's analysis of the effects of
cost sharing on the use of medical services and on health:
1.
probability of using medical services;
2.
medical expenditures (includes all services except dental and out-patient
mental health expenditures);
3.
annual number of physician visits;
4.
hospital admission rates.
The
insurance plans were grouped into five categories:
1.
free care;
2.
25% co-insurance rate including the plans with 50 percent co-insurance
for dental care and mental health;
3.
50 percent co-insurance rate;
4.
95 percent co-insurance rate;
5.
individual deductible.
There
was no differentiation made between the levels of MDEs because it was found that
variations in the MDEs were not significant. Factors such as age, gender, race,
family income, and family size were included in the analysis, as were four
different measures of health used to account for differences in initial health
status:
1.
a General Health Index;
2.
the presence of a physical limitation;
3.
chronic disease status;
4.
a Mental Health Index.
The
demand for medical services was then estimated using two different econometric
models, which yielded results that were quite similar. The results of estimates
derived from the multi-equation model are summarized in table 3. When
individuals have access to free medical care, there is an 86.7 percent chance
that they will use the health care system in a given year (table 3, row 1,
column 1). As cost sharing increases from 0 percent (free) to 95 percent, there
is a significant decline both in the probability that medical services will be
used and in the medical expenses incurred per person in the population. The
column ``t vs. free'' lists the results of statistical significance tests on the
differences in probabilities and expenses between the free plan and the three
cost sharing plans. These ``t-tests'' show that the differences are all
significant.22
The
last column in table 3 represents the total spending of each plan as a ratio of
the free plan. On average, individuals on the 25 percent plan spend 19 percent
less than individuals on the free plan; individuals on the 50 percent plan spend
25 percent less, while the ones on the 95 percent spend 33 percent less. Medical
expenses per person fell from an average of US$1,019 (free plan) to as low as
US$700 (95 percent co-insurance plan). The demand for all types of services
falls with cost sharing although some services are more affected than others.
For example, not shown in table 3 is the fact that children's hospital
admissions are less responsive to changes in cost sharing while mental health
services are more responsive.
The
findings of the HIE challenge the claim that heavy cost sharing raises overall
health care costs because of its incentive to delay seeking care. Total
expenditures in the high co-insurance group (95 per cent) were well below those
in the free-care plan. It appears that incentives to delay seeking care were
outweighed by other incentives. In addition, the different sizes of MDE--5, 10,
and 15 percent of income up to a maximum of US$1,000 per family ($500 to $600
per individual)--did not lead to significant changes in medical use (i.e.
spending). As well, the HIE estimates indicate that the risk associated with a
higher MDE is not significant. For these reasons, the HIE results seem to
indicate that the MDE should be set at the high end of the different sizes
examined (Newhouse et al. 1993).
As
a result of having an MDE, the difference in the various co-insurance plans is
much less than is suggested by the difference in the nominal co-insurance rates.
For example, the average cost-sharing rate was 16 percent in the 25 percent
plans, and 31 percent in the 95 percent co-insurance plans (table 4). The lower
average co-insurance rates result from there being a diminishing number of
people who are subject to the co-insurance rate for the whole period as the
co-insurance rate increases. While the nominal co-insurance rate may be 95
percent, so many people reach the deductible (at which point care becomes
``free'') that, on average, the co-insurance rate is only 31 percent over a
specified period.
Recall
that increases in the co-insurance rate have two separate effects: individuals
have to pay more, thus reducing use, and, as the co-insurance rate increases,
the likelihood of exceeding the MDE increases. That is, when the co-insurance
rate is high, people are contributing more out-of-pocket to the cost of their
medical care, therefore, they will reach the MDE faster than those people with a
lower co-insurance rate. Since health care is free once the MDE has been
exceeded, more individuals will have access to free care when the co-insurance
rate is high. Keeler et al. (1977) have stressed the importance of examining
deductibles and co-insurance has part of a sequence and not in isolation, and
the HIE results support such an argument.
Although
the RAND HIE was performed almost 20 years ago and in the United States, it is
not clear why Canadians should see the trade-off between health and money
differently than their American counterparts. As well, the HIE has been used to
study the effect of cost sharing in China and the results were similar to those
of the American experiment (Sine 1994). It is important to note, however, that
the HIE looks only at the non-elderly population and that, therefore, the
results may not be readily applicable to the elderly.
Price elasticities
The
results of the RAND HIE can be expressed in terms of elasticities, i.e. how
individuals change the amount of medical care they use when the price of care
changes. Table 5 produced by Manning et al. (1987) shows that as the
co-insurance rate increases from the range 0 percent to 25 percent to the range
25 percent to 95 percent, the elasticity for all acute medical and out-patient
care increases. That is, people at the higher co-insurance level will reduce
their use of medical care services more than people at the lower level of
co-insurance when the price of care increases.
Prior
to the RAND HIE, several studies had attempted to estimate the price elasticity
of demand for medical services using non-experimental data.23 Phelps and
Newhouse (1974) use data from various sources to calculate the elasticities of
several services including hospitals, physicians, prescription drugs, and
average stay in hospital. Table 6 summarizes these findings. Newhouse, Phelps
and Marquis (1980) argue that inherent statistical problems make the
interpretation of these results difficult. As Phelps points out; ``Perhaps the
only agreement in the literature by the mid-1970s was that `price mattered'''
(1992: 119). On the other hand, Feldstein and Gruber (1994) argue that RAND HIE
elasticities most likely underestimate the true values.24
While
there is no consensus on the true price elasticity of demand for health care
services, all of the studies reviewed conclude that the price elasticity of
health care services is greater than zero--an increase in the price of health
care services leads to a reduction in use. Most elasticity measures are between
zero and one, where a price increase will reduce the demand for health care by
less than the percent increase in the price. There are exceptions: the category
of out-patient visits (Davies-Russell 1972), with an elasticity of one, shows a
one-to-one relation between increase in price and decrease in demand, and the
upper range of elasticities obtained in the Rosett-Huang study of all physician
and hospital expenses reduce the demand for health care by more than the percent
increase in the price.
Catastrophic
insurance
The
impact of catastrophic insurance on individuals' use of health care services is
important because high-deductible catastrophic insurance is an integral part of
the way in which medical savings accounts are organized. Catastrophic insurance
and medical savings accounts differ in how they require individuals to pay for
medical services before the insurance threshold is reached. With Feldstein's
catastrophic insurance, for example, individuals face a deductible and possibly
a co-insurance rate while MSAs make use of contributions from government,
employers, or individuals to pay for health care.
Feldstein
and Gruber (1994) study the potential effects of implementing major risk
insurance (MRI) in the United States.25 They examine the catastrophic insurance
policy that Feldstein proposed in 1971--a 50 percent co-insurance rate with a
maximum out-of-pocket limit of 10 percent of income (50/10 proposal; 1971a)--and
attempt to answer four questions
1.
Would a major risk insurance (MRI) policy reduce excessive spending?
2.
How does MRI affect different income groups?
3.
What are the welfare effects of shifting to MRI?
4.
Could a publicly provided MRI be financed by eliminating the current
favourable tax treatment of health insurance premiums paid by employers?
The
first three questions have implications for the Canadian health care system.
Since
the size of the effect on health spending depends heavily on the price
elasticity of demand, Feldstein and Gruber examine the outcomes of elasticities
ranging from zero to 0.5 (Feldstein and Gruber 1994: 7, n.10). Using data from
the National Medical Expenditure Survey, Feldstein and Gruber determine that the
proposed MRI would affect 89 percent of people with insurance, who collectively
represent 36 percent of total health expenditures.
Table
7 shows the effects of MRI on health care spending at both the individual and
national levels. The first plan, ``Original,'' shows health care spending prior
to the implementation of MRI. The average spending per insurance holder (family
or individual) is $3,985 out of which $747 is out-of-pocket and $3,238 is
insurance. The MRI plan is the 50/10 plan proposed by Feldstein (1971a). In a
zero elasticity scenario, the split of costs between out-of-pocket and insurance
is different but the total costs are the same as in the original plan. With a
price elasticity of 0.33, total average spending decreases by $728 to $3,257;
with a price elasticity of 0.50, spending decreases from $3,985 to $2,758 (for a
savings of $1,227). At the aggregate level, reductions in consumption would
produce savings of $60 billion to more than $100 billion depending on the
elasticities. Even though higher cost sharing would apply to only 36 percent of
spending and the price elasticity is a conservative 0.33, the MRI policy still
reduces annual aggregate spending by an estimated 18 percent.
Since
the insurance threshold is set at 10 percent of income, the MRI policy pays for
more of the health care of lower income families than it does for higher income
families. Table 8 illustrates the effects of Feldstein's 50/10 proposal on four
different income groups.26 The results show that average out-of-pocket spending
under MRI increases with income irrespective of the elasticity, except for those
people below the poverty line. Higher price elasticities diminish the strength
of this effect but not the fact that it is greater than in the original plan.
Higher income individuals reduce their total spending on health care
significantly more than do lower income individuals as the elasticity increases.
A 50/10 MRI policy reduces total spending by all income groups. Lower income
groups spend more on insurance but much less on health care out-of-pocket so
that their total spending decreases. Higher income groups spend more
out-of-pocket but less on insurance so that their total spending also decreases.
Feldstein
and Gruber estimate the welfare effects of introducing an MRI policy by
calculating the effects of more prudent health care consumption and changes in
the distribution of risk separately. Feldstein and Gruber's findings support the
conclusion of the empirical literature reviewed earlier, that there is a
trade-off between risk pooling and the moral hazard of insurance. Their results
are extremely relevant to MSAs as they indicate that the combination of a
catastrophic insurance with a co-insurance rate (0.33 to 0.50) can reduce health
care spending and improve total welfare. Like a co-insurance scheme, MSAs
combine a catastrophic insurance with financial incentives to restrain
consumption of health care services. Feldstein and Gruber also show that by
setting the catastrophic insurance threshold at 10 percent of income, a 50/10
MRI policy need not hurt less wealthy individuals.
Medical
Savings Accounts
Keeler
et al. (1996) explore the impact that implementing MSA legislation for all but
the elderly could have on health care costs in the United States. Their study is
based on the RAND HIE Simulation Model and examines 23,157 sampled households.
The legislation that is tested allows all Americans who purchase catastrophic
health insurance to set up a tax-exempt MSA, which could then be used to pay
medical bills up to the point where the insurance threshold is reached and the
catastrophic insurance begins. Four different health insurance plans are
examined:
an
employee-funded MSA;
an
employer-funded MSA;
a
fee-for-service (FFS) policy;
a
health maintenance organization (HMO) plan.
In
addition, high and low deductible MSA plans are examined. The insurance
threshold at which the catastrophic insurance begins is set at $1,500 for an
individual and $3,000 for a family in the low-deductible MSA and at $2,500 for
an individual and $5,000 for a family in the high-deductible MSA.
To
examine the impact of MSAs a behavioral simulation model is used to estimate the
change in health spending if all Americans (except the elderly, i.e. those of
age 65or older) abandoned their present health insurance plans and adopted an
MSA plan. Then, a plan-selection model is used to estimate the change in health
expenditures if only the individuals expected to benefit from an MSA plan switch
to one. Keeler et al. provide a model of a market in which three plans are
offered: a fee-for-service policy (FFS), an MSA-catastrophic insurance plan, and
a health maintenance organization plan (HMO). Individuals in this model attempt
to maximize the expected value of the health care they receive and minimize the
amount of out-of-pocket expenditures, risk, and changes in income that occur.
Table 9 summarizes the estimated effects of each plan on spending.
The
design of MSAs affects the results. If all Americans switch from FFS and HMO
plans to the low deductible, employee-funded MSAs, there is no significant
change in health spending. In fact, average spending increases slightly, from
US$5,414 to US$5,437. This should not be surprising, as it reflects the
competing effects of the low deductible and the introduction of cost sharing.
Cost sharing induces individuals to restrain their consumption of medical care
services but once they exceed the deductible there is an incentive for
individuals to spend more on health care. Conversely, if the same individuals
switch to the high deductible employee-funded MSAs, health expenditures decrease
by between 6 and 13 percent. In short, if all Americans except the elderly
adopted MSAs, health care expenditures could decrease by up to 13 percent.
Since,
however, these results may not be a good proxy of what would actually happen if
MSA legislation were enacted because the legislation would not oblige all
Americans to switch from their current health care plans to MSAs, Keeler et al.
(1996) repeat the experiment simulating consumer choice among plans. They find
that health expenditures fluctuate by between -2 and 1 percent; i.e. health
expenditures either decrease by 2 percent from what they are currently or they
increase slightly. These results do not support the high expectations that are
placed on MSAs by many of their American advocates. Keeler et al. (1996) contend
that the discrepancy between the expected savings and their estimates is because
MSAs cannot solve the problem of over-insurance caused by tax-subsidies of
employer medical insurance. They show, however, that MSAs have the potential
significantly to change the way in which health care systems operate and have
the potential to generate some cost savings.
Ozanne
(1996) also attempts to estimate the effect of MSAs on health care expenditures
in the United States. He compares an MSA plan with a typical comprehensive
insurance policy. From this comparison he constructs measures of the prices
individuals pay for medical services. Ozanne combines these measures with the
RAND HIE price elasticity estimates in order to predict changes in health care
expenditures. He predicts that if all adults except the elderly switch to MSAs,
medical spending in the United States would decrease by between 2 and 8 percent.
In
addition to the many empirical studies of MSAs and their effects on health care
spending, there are many case studies of successful employer-funded MSAs.
Although these studies suffer from the absence of any control group,27 it is
still useful to assess the experience of employers and employees with MSAs. In
an American employer-funded MSA, the employers purchase a catastrophic insurance
policy for their employees and deposit some of the savings (because high
deductible insurance is cheaper than low deductible insurance) into their
employees' MSAs. The employees use the funds made available by the employer to
purchase medical care services. Once these funds are exhausted, the employees
are responsible for the payment of medical care up to the deductible at which
the catastrophic insurance begins.
Bond
et al. (1996) gather data from 27 Ohio firms that offer MSAs to their
employees.28 All of the firms studied offer MSAs with similar insurance
threshold plans: $1,500 for individual coverage and $2,000 for family coverage.
The average employee's out-of-pocket expenditures are $643 for individuals (a
$1,500 deductible less an $857 MSA) and $833 for families (a $2,000 deductible
less an $1,167 MSA). This is significantly lower than the out-of-pocket expenses
of traditional plans. The average cost to the employer of coverage for families
is 23 percent lower than the cost under traditional family plans while the
average cost of coverage for individuals is 26 percent higher than the cost
under a traditional plan.
The
MSAs examined result in a decrease in the employees' actual out-of-pocket health
expenditures. The employers' expenditures could have been made roughly equal
under both plans if the employers had increased their employees' maximum
out-of-pocket expenditures so that the employees were spending an amount under
the MSA plan that was equal to the amount that they were spending under the
traditional plan. However, the MSA plans of these firms allowed for a decrease
in employees' out-of-pocket health expenditures from what they had been spending
out-of-pocket under their traditional health plans. Despite higher employer
costs for the individual plans, the total average cost of the MSA plans was 12
percent less than that of the traditional plan.
The
data on the Ohio firms did not contain any information on the average amount of
funds remaining in the MSA at year's end. This is unfortunate because it is an
important aspect of an MSA plan that any funds unused at the year's end belong
to the employee and can be used as the employee chooses. Bond et al. (1996) look
at other MSA plans for information on unspent balances and other forms of
savings. For example, employees of Golden Rule Insurance had, on average, $602
remaining in their account at the end of 1993 and $1,002 at the end of 1994, for
a total $1,604 plus interest at the end of two years. Forbes Magazine introduced
MSAs for their employees in 1992; as a result, Forbes' health care costs
decreased by 23 percent ($400,000) and they paid $125,000 in bonuses to its
employees. In total, Bond et al. surveys 17 firms who offer MSAs and found that,
on average, the funds remaining at the end of the coverage year amounts to
roughly $600 for individual coverage and $900 for family coverage.
The
Evergreen Freedom Foundation performed seven extensive case studies of companies
that offer employees health coverage through MSAs (Barchet 1995). All of the
companies surveyed realized significant decreases in costs and showed high
levels of employee satisfaction.29
While
the successes of the employer-funded MSAs examined by Bond et al. and the
Evergreen Freedom Foundation may not be enjoyed by all employers switching to an
MSA plan from traditional insurance coverage, these companies have shown that
MSAs can be conducive to more prudent health spending without compromising
individuals' health. Where they have been adopted, MSAs have resulted in lower
costs to employers and employees, accumulated savings, and high degrees of
employer and employee satisfaction.
The
empirical literature in the United States indicates that MSAs and similar
arrangements have the potential to reduce health expenditures up to 20 percent.
It is worth noting that Americans already face financial incentives with respect
to their use of health care while Canadians, for the most part, do not. One
would predict, therefore, that there would be an even larger decrease in health
expenditures if costv simulations were performed using Canadian data.
Potential
adverse effects of
cost sharing and MSAs
Cost
sharing and health outcomes
While
the effects of cost sharing on the use of health care can be predicted, the
effects on health are less clear. Even if cost sharing manages to decrease use
of medical services, it does not necessarily follow that total expenditure for
health care will decrease. Higher prices that lead to lower use may adversely
affect individuals' health, which may, in turn, increase health care costs. The
RAND HIE is one of a very few studies that examine the effects of cost sharing
on health.
The
Insurance Experiment Group uses 5 measures (see Newhouse et al. 1993: ch. 6) to
examine participants' health: (1) general health (physical, mental and social);
(2) physiological health; (3) health habits; (4) prevalence of symptoms and
disability days; (5) the risk of dying. The predicted values of health are
estimated using several variables, including age, gender, family income adjusted
for family size and composition, and health at enrollment in the experiment. As
well, various insurance plans are examined.
On
the whole, reduced services due to cost sharing have little or no net adverse
effect on health (table 11). In addition, no significant differences in the risk
of dying (for the average person) or measures of pain and worry are found.
Moreover, days of restricted activity dwindle with higher levels of cost
sharing. The most important determinant of health at the end of the experiment
is typically health at enrollment.
The
HIE also looks at the effect of cost sharing on the health of high risk
individuals such as the poor and the sick-poor.30 The health of this
disadvantaged segment of the population is severely affected by cost
sharing--both mortality rates and blood pressure worsen among high risk
individuals. The results indicate that free care can benefit low income groups.
The
HIE also examines the appropriateness of the services that were forgone. Lohr et
al. (1996) conclude that cost sharing reduces both necessary and unnecessary
care. However, the type of cost sharing plan was found to have no effect on most
measures of health and a decrease in necessary care should result in lower
health outcomes. Lohr et al. suggest that this phenomenon occurs because some of
the harm done by inappropriate services is outweighed by the benefits of
appropriate care.
Cost
sharing and the poor
The
RAND HIE examines the effects of income on the demand for medical services.
Table 12 exhibits the differences in the responses of different income groups to
cost sharing. Most of the differences between the income groups are
statistically significant (as is shown by the ``t vs. lower third'' column). The
probability of any use of medical services increases with income. The
probability of any in-patient use, however, shows contrasting results; use of
in-patient care decreases with income for the family plans. Overall, the
percentage reduction in expenditure due to cost sharing did not show any major
differences by income group. However, Newhouse et al. point out that the
``ultimate test of a reduction in use, however, is its effect on [health]
outcomes, and these did differ by income group'' (1993: 340). For example, the
estimated risk of dying was more than twice as high for those classified as poor
than for those in the high income group.
Beck
(1974) studies the effect of user fees upon the poor. In 1968, the government of
Saskatchewan introduced user charges for physician services; for each office or
home visit and for each emergency or hospital out-patient visit. As well,
hospitals introduced a per-day user fee (to a maximum of 90 consecutive days).
These user charges were removed in 1971. Beck finds that the user fees resulted
in a decline in the use of physician services by the average family of
approximately 6 to 7 percent. However, the poor experienced a reduction in
physician services of 18 percent.31 He concludes that the imposition of user
charges introduced a barrier to services to lower income groups.
In
a later study, Beck and Horne (1980) examine the effects of the introduction and
removal of these user charges. The data come from a database of about 40,000
Saskatchewan families and cover the period from 1963 to 1973 for ambulatory
services and 1966 to 1973 for hospital services. Table 13 summarizes the effect
of co-payments on physician services. On average, the use of physicians'
services declined by 5.6 percent per year.
Despite
the decrease in the use of physicians' services, however, gross payments for
medical services increased each year from 1969 to 1971. Two increases in the
service fee schedules during the period when user fees were also being charged
can partially explain this increase in overall costs. It has been argued that a
change in physicians' behavior contributed to the increase in cost but Beck and
Horne find no evidence to support or refute the hypothesis of supply-induced
demand (SID). As well, they find no significant differences in the probability
that patients would be admitted to a hospital, or that their average length of
stay would change with the introduction or removal of user fees.
In
1972, the California State Department of Health Care Services introduced a user
charge on certain Medicaid beneficiaries for the first two physician visits and
the first two drug prescriptions per month. The user charges were imposed only
on Medicaid beneficiaries who had some ``additional financial resources'' (see
Roemer et al. 1975 for more details). Roemer's group was asked by the department
of health to study the effect of these new financial incentives. The Medicaid
beneficiaries were divided into two groups: the ones who faced a user charge
(co-pay) and the ones who did not (no-pay). Due to the design of the experiment,
the co-pay group, by definition, had more financial resources than the no-pay
group. As well, the authors could not control for differences in socio-economic
characteristics nor for the effect of the many administrative changes that were
introduced during the experimental period. It is because of these peculiarities
that it was necessary to follow the demand for services before the user charges
came into effect--data on service use were collected from 6 months before the
user charges were imposed until 12 months after their introduction.
Roemer
et al. find a significant difference between the co-pay and no-pay
groups in visits to physicians at their offices. Members of the co-pay group,
when compared to the members of the no-pay group, significantly reduced their
use of physicians' services. Moreover, there was a significant reduction in
diagnostic tests (e.g. urinalyses), preventive procedures (e.g. Pap smears) and
drug prescriptions when the user fee was introduced. As well, the
hospitalization rate (i.e. the number of hospital admissions) amongst the co-pay
group increased to higher levels than it did amongst those on the no-pay scheme.
In light of these results, Roemer et al. conclude
A
clear cut reduction in diagnostic tests as well as ambulatory treatment ...
could hardly be expected to benefit health status. This is quite aside from the
pain and suffering involved for the low-income patient, who postpones seeking
medical care at early stages of his illness ... In a word, it would appear that
this study of the California Copayment Experiment with Medicaid beneficiaries
that the State government's strategy was penny-wise and pound foolish. (Roemer
et al. 1975: 465-66)
In
1982, the State of California ended its assistance program for its ``medically
indigent'' adults. It no longer provided financial assistance for medical
services for people between the ages of 21 and 65 who were poor and medically
needy and did not receive financial assistance from any federal program. Lurie
et al. (1984, 1986) studied the effects of terminating this program on the
health of the poor. The results were revealing. After only 6 months, a
deterioration in patients' access to care and health was observed. There was a
noticeable and significant increase in uncontrolled hypertension. Hypertensive
patients, with no access to free care, experienced higher blood pressure and
general health decreased. A follow-up study was performed to control for the
possibility that this was only a temporary phenomenon. It found that the
deterioration in health due to the termination of the California benefit program
was not temporary. General health had declined further and blood pressure was
still high. It was even found that lack of access to care played a part in at
least four deaths. This evidence supports the findings of the HIE that those
both sick and poor need to be treated differently from the rest of the
population. In an MSA plan, this would entail fully subsidizing the deductible
of those both sick and poor.
Income
and the demand for health care
It
is often assumed that the poor consume a disproportionate share of health care
and thus benefit more than the wealthy from a health system such as that in
Canada. Phelps (1992) uses the RAND HIE data to calculate income elasticities
for different types of illnesses (table 14). These estimates suggest that income
elasticity of demand for medical services is positive and ranges from 0.12 to
0.23. That is, people tend to demand more health care services as their income
increases.
In
cross-sectional studies such as the HIE, medical technology is held constant but
increases in income can stimulate the demand for new medical services. Since
time-series data and cross-country studies do not take the level of technology
as fixed, they usually generate higher estimates of income elasticity, sometimes
significantly greater than 1.0 (Phelps 1992)--that is, the amount of health care
demanded increases by more than the percent increase in income. However, these
estimates may not apply to Canada because ``both common sense and full use of
economic theory suggest that pure income effects should be small, if not zero,
with full-coverage insurance'' (Phelps 1992: 129).
Several
studies (Forster 1976; Alderson 1970; Le Grand 1978, 1982) examine the
distribution of public spending in Britain. Le Grand finds that the wealthiest
one-fifth of the population receives 40 percent more public money for their
health care than the poorest one-fifth:
In
fact it is difficult to resist the conclusion that there is little the Health
Service can do reduce inequality in its use or in the private cost of that use.
The principal determinants are largely beyond its control. Rather, they stem
from the basic social and economic inequalities in income. (Le Grand 1982: 51)
MSAs
and preventive care
The
RAND HIE studies the effect of cost sharing on preventive care. Preventive care
is defined for children as visits associated with the diagnosis or procedure
codes for well-care examinations, immunizations, or tuberculosis tests. For
adults, it is defined as visits associated with immunizations, annual physical
examinations, administrative examinations, routine gynecological examinations,
and office visits listed as well-care visits (for more details, see Newhouse
1993: 176-80). Although cost sharing reduces the consumption of preventive
services, the differences in use between the free plan and the three cost
sharing plans are only marginal.
It
is often argued that MSAs can make individuals more responsible with respect to
their own health because of the financial incentives they provide. The finding
that there is a slight decrease in the consumption of preventive care when cost
sharing increases, means that MSAs may not result in higher consumption of
preventive medicine. However, if the introduction of MSAs results in less use of
medically desirable preventive medicine, these preventive programs can be
exempted from cost sharing. Such a policy would create an incentive to consume
more preventive medicine simply because its relative price in comparison to
other health care services would be lower.
MSAs
and the poor
The
RAND HIE (Newhouse et al. 1993) finds that the reduction in medical services
used as a result of cost sharing has little or no net adverse effect on health.
Although studies on the effect of cost sharing on health status indicate that
the health status of the poor and the sick may worsen if cost sharing is
introduced or augmented, they do not show that the rest of the population will
necessarily experience a decrease in health status if cost sharing is
introduced. Therefore, if high risk individuals are excluded from any cost
sharing programs, there is little evidence to support the argument that an
increase in cost sharing will lead to a general decline in health.
The
results of the Saskatchewan Experiment and the California Copayment Experiment
may lead one to reject user charges. As well, Beck and Horne (1980) and Roemer
et al. (1975) conclude that user charges may not lead to lower health care
expenditures. However, MSAs need not impose a financial barrier to care. MSAs
allow policy makers to exempt a certain segment of the population (the sick, the
poor, and the sick/poor) while providing financial incentives to wealthier
individuals to either contribute resources to the health care system, or refrain
from using it excessively.
MSAs
and supplier-induced demand
When
physicians and other health care professionals see the number of their patients
and, thus, their revenue decrease because of the introduction of cost sharing
(or MSAs), they have an incentive to encourage the use of medical services in
order to restore their income to its previous level. In other words, the
positive effects of MSAs could be offset by supplier-induced demand (SID).
The
size of the literature about SID renders a complete and detailed review
impossible in this publication. Ferguson (1994), however, provides a basic
review of different interpretations of SID. He divides models of inducement into
four categories:
Market-level models
Ferguson
analyses three types of market-level models. First, he examines models that are
built on the idea that an increase in the number of physicians will increase the
use of health care and thus costs. Essential to this hypothesis is the notion
that this increase in use is not medically necessary (i.e. it will not improve a
patient's health). Studies that examine the relationship between use and the
supply of physicians usually use a basic model that assumes that the number of
medical services demanded is determined by the number of physicians and other
variables such as price, waiting time and income. Studies that use this method
(Fuchs and Kramer 1972; Fuchs 1978; Richardson 1981) are seen as the backbone of
SID theory. Fuchs' results (1978) show that a 10 percent increase in the number
of physicians leads to a 3 percent increase in demand for health care. However,
this type of study has been heavily criticized by both sides of the SID debate.
Second,
Ferguson examines disequilibrium models. It is often argued that because of its
complexities (e.g. public insurance and subsidies) health care markets will
always be in a state of disequilibrium; that is, the supply of health care will
never equal the demand for it. Cromwell and Mitchell (1986) and Ferguson and
Crawford (1989) use disequilibrium models to test the SID hypothesis. Cromwell
and Mitchell find that a 10 percent increase in surgeons per capita leads to a
0.9 percent rise in all surgery per capita and a 1.3 percent increase in
elective procedures per capita.32 Ferguson and Crawford find evidence of
persistent disequilibrium but no support for the SID hypothesis.
Third,
Ferguson (1994) examines models of imperfect competition. Stano (1987) finds
that SID is more important when the local medical market is closer to a monopoly
(i.e. when there are very few physicians providing services). As the supply of
physicians increases, the importance of SID diminishes. Ferguson concludes his
review of market-level models by stating: ``neither the equilibrium nor
disequilibrium market-level models ... give much support to the SID model''
(1994: 73).
Individual-level model
As
opposed to the market-level models which use market-wide data, individual-level
models use micro-level data. Stoddart and Barer (1981) use data from 1,300
British Columbia families who received ambulatory care during 1973/1974. Their
results support the inducement hypothesis. However, there are several serious
econometric problems with the study (Ferguson 1994). For example, Stoddart and
Barer use a test that compares the R2 values of equations with different
variables. (R2 values represent the proportion of the change in the studied
variables that is explained by the other variables in the model of equations.)
Comparing R2 values between equations--let alone those of equations with
different variables--is not considered proper econometric analysis.
Ferguson
(1994) also examines the work of Wilensky and Rossister (1981, 1983), which uses
data from the 1977 US National Medical Care Expenditure Survey. They test
supplier-induced demand by estimating the effect of the availability of
physicians on several variables such as the probability of physician-initiated
visits, the number of visits to the physician, expenditures on services, and the
likelihood of services being used. Wilensky and Rossister's results indicate
that the availability of physicians has a positive but small effect on the
dependent variables:
What
should be clear for even the most casual observer is that the idea is now dead
that a large component of physician self-interested, self-created demand exists
in response to changes in the supply of physicians. It can happen and does
happen; but its policy relevance is small. (Wilensky and Rossister 1987: 626)
Tussing
(1983) and Tussing and Wojtowycz (1986) use a method similar to that of Wilensky
and Rossister. Using 1981 data from a survey of health care use in the Republic
of Ireland, they find support for the SID hypothesis: the supply of physicians
increases the number of physician-initiated doctor visits.
Physician
response to price incentives
The
SID literature has recently devoted particular attention to physician responses
to price incentives (e.g. fees). Ferguson (1994) points out that there is no
consensus in the literature on how to formulate this hypothesis. For example,
some argue that a decrease in fees followed by an increase in the quantity of
services supports the SID hypothesis because physicians are trying to maintain
their level of income. Others argue that an increase in services that follows an
increase in fees is also evidence of SID because physicians now make more money
per visit and, therefore, they induce unneeded visits. Ferguson writes:
Rice
(1984: 156) claims that his results show that physicians induce extra demand in
the face of lower fees, while Krasnik et al. (1990: 1701) argue that their
results show that physicians induce demand in response to higher fees. If we
accept both results, then we are back in the situation of having an untestable
hypothesis, since any response to changes in fees could be taken as evidence of
inducement. (1994: 109-10)
Hickson,
Altemeier, and Perrin (1987) examine the response of medical service providers
to price changes. They constructed an experiment: 18 paediatric resident
physicians in a paediatric clinic were assigned randomly to two group practices
(fee-for-service and salary). The results show that fee-for-service physicians
scheduled more visits, provided better continuity of care, and were responsible
for fewer visits to the emergency room. Salaried physicians missed more visits
recommended by the American Academy of Pediatrics than fee-for-service
physicians. The effect on total costs was not clear because fee-for-service
physicians had increased costs due to more office visits but also reduced costs
from less use of emergency room care.
Small area variation (SAV)
The
literature about small area variation (SAV) examines the reasons why geographic
regions with similar populations and similar incidences of illness use
physicians' services at different rates. Most studies of SAV have found a
significant relationship between the availability of resources and their use.
(For a review of the literature, see Mclaughlin et al. 1989; Paul-Shaheen,
Clarke, and Williams 1987; Joseph and Phillips 1984.) Intuitively, it makes
sense that, if more resources are available to patients, they will take
advantage of them. If a previously unavailable eye laser surgery that can help
patients with glaucoma see better becomes available, it is not surprising that
such patients will opt to have the procedure performed. This positive
relationship between resources and use, however, is often used as evidence of
SID. (See, for example, Folland and Stano 1989; Wennberg, Barnes and Zubkoff
1982; Park et al. 1986; Vayda 1973; McPherson et al. 1981.)
Ferguson
(1994: 124-27), despite reviewing numerous articles, finds no support for the
theory of SID. He also stresses the weak quality of the methodology:
The
methodology of the literature has been surprisingly poor, considering the
importance of the policy implications that have been derived from it ... There
is virtually no testing for [model] misspecification ... Of the literature we
reviewed, the only paper to include a set of misspecification tests is that by
Rochaix (1993) ... In fact, the SID model is virtually never tested ... the few
times this has been done ... SID fails.
Feldman
and Sloan (1988) also perform a review of the SID literature and reject the SID
hypothesis:
This
literature suggests that demand inducement may occur in the market for surgical
services but its extent is less than previously estimated. Little evidence for
demand inducement is found in the primary care physician market. (Feldman and
Sloan 1988: 258.)
Rice
and Labelle (1989) are critical of the conclusion reached by Feldman and Sloan,
arguing that they omitted several important studies that contradict their
conclusions. Rice and Labelle state ``there is a great deal of evidence to
indicate that physicians do induce demand for economic gain'' (1989: 588).33 The
Saskatchewan Experiment discussed above is often presented as evidence that
physicians do, in fact, induce demand. However, Beck and Horne, the authors of
the Saskatchewan study, do not conclude that their findings are necessarily the
result of SID.
Despite
the increasing number of papers dealing with SID, it does not seem that a
consensus is likely. Feldman and Sloan note that ``few participants in the
debate show any sign of changing their positions'' (1988: 239). This lack of
consensus offers little comfort to policy makers who must attempt to estimate
physicians' response to the introduction of financial incentives in the Canadian
health care system. One thing that is certain is that there is a great deal of
uncertainty surrounding the SID hypothesis. As well, Newhouse (1993) suggests
that there is strong evidence that even if physicians induce demand, they will
not be able to fully offset the decrease in demand arising from increased cost
sharing. Finally, Tussing touches a very interesting point: ``Patients are more
likely to resist demand stimulation when their out-of-pocket costs are high''
(1983: 229). In other words, providing individuals with financial incentives may
make it harder for physicians to induce demand.