International Comparisons of Health Care Output
and Productivity
Hui Duan and Kam Yu∗
May 18, 2004
Abstract
Measuring the output and productivity of the health care sector is important but difficult, and it has been a long-standing policy concern. Statistical
agencies in some countries such as U.K., Eurostat, Australia, and the U.S. have
done substantial research to investigate various approaches to measure the output. This paper reviews recent developments in the economic measurement
of health care output and compares the results by applying different methods
to the health care sector in Canada. Our initial results show that different
approaches result in significant different output measurement. Health care
analysts and policy makers should be aware of these differences in comparing
output and productivity across countries. JEL: C43, H51
1
Introduction
The definition of health care sector adopted by the Bureau of Labour Statistics
(BLS) in the U.S. is “to focus on the treatments of diseases or the relief of pain and
the suffering” (Cutler and Berndt, 2001). Thus, its major function is to improve the
health conditions of the general population. Obviously, the health care industry is
largely based on services. Measuring the output of the service industries has long
been regarded difficult. Because we do not know exactly what is being transacted
and what services correspond to the payments made to their providers, conceptual
problems arise. Among the hard-to-measure services, the health care sector has been
perceived as one of the most difficult.
However, measuring health care output is an important component in health and
welfare economics. For example, in order to evaluate the productivity of the health
care sector, we usually conduct cost-effective analysis. In such an analysis, we need
∗
Department of Economics, Lakehead University, Thunder Bay, Ontario, Canada, P7B 5E1.
Phone: 807-343-8229, Fax: 807-346-7936, E-mail: Kam.Yu@lakeheadu.ca.
1
to compare what we can get out of the health care sector with its expenditures. To
accurately answer the question, we require measuring health care output. This is
the problem faced by both health care analysts and national income accountants.
In national income accounts, we divide nominal spending growth into changes in
prices and changes in quantities. But without an accurate measure of health care
industry output, we can estimate neither prices nor quantities. There are two major
difficulties in implementation: First, “Measuring health status is difficult, and attributing changes in health status to a particular factor such as health care is even
more challenging” (Cutler and Berndt, 2001).
Second, another difficulty rests on the measurement of real output. For most
industries in most countries, real output measurement is accomplished by deflation,
that is, dividing data on revenues (values) by a price index to obtain a measure of
real output. Such an approach requires reliable revenue data and a price index. In
a country where the market prices are available, this method can be implemented,
although other measuring problems still exist. However, for countries such as Canada
where health care goods and services are provided by the government without direct
charge or with only nominal direct charges, the revenue data may not be available
or may be irrelevant. In such cases the problem of measuring health care output
is combined with the problem of measuring the output of the government sector
(Berndt et al., 2001).
In this paper, we mainly focus on the issues of measurement in public health
care systems. Section 2 below reviews the basic concept in output measurement and
the difficulties encountered in measuring government services. The traditional cost
approach is described in section 3. Various newly developed direct measurement
techniques are reviewed in section 4. In section 5 we apply the various techniques to
the Canadian health sector for the case of health disease treatment and show that
different methods gives significant different GDP measurement. Section 6 concludes
and recommends further studies.
2
The Index Number Problem
Consider one of the most important identities in index number theory:
P (p1 , p2 , q 1 , q 2 )Q(p1 , p2 , q 1 , q 2 ) =
p2T q 2
.
p1T q 1
(1)
P is the price index function of four vectors, namely, p1 is the price vector of N
goods and services in period 1 (base period), p2 is the prices in period 2 (comparison
period), q 1 and q 2 are the corresponding quantityP
vectors. Superscript T denotes
the transpose of a matrix or vector. Thus pT q = N
i=1 pi qi is the inner product of
the vectors p and q. Q is the quantity index function. The right hand side of (1)
is the ratio of the total values of the N products in the two period. This identity
2
is sometimes called the product test. It requires the product of the price index
and the quantity index to be equal to the total value ratio. If there is only one
product, that is, N = 1, then the price index P is simply p21 /p11 and quantity index
Q = q12 /q11 . Identity (1) is trivially satisfied and there is no index number problem.
For N > 1, we have to find a way to sum up the prices and quantities of different
goods so that the resulting indices will satisfy (1). In other words, we try to compare
apples with oranges, and then add them up together. Mathematically speaking we
decompose the value ratio into two factors P and Q. In the economic approach to
index numbers literature, P is interpreted as a cost of living index. It compares the
cost of consuming the N goods and services to achieve the same standard of living
in both period (Konüs, 1939). Q, on the other hand, measures the change in the
standard of living. Recently there has been a lot of discussion about adapting the
economic approach in the construction of the consumer price index (CPI).1 Some
countries such as the U.S. have adopted the concept as the objective of the CPI.
Although the objectives and goals of the CPI may be different from those of the
SNA, the economic approach can provide a theoretical foundation for both. The
goal of the SNA is to measure the quantities of goods and services produced and
consumed in the economy, which then serves as a partial social welfare indicator.
In principle this objective is compatible with the conceptual framework of a cost-ofliving price index. For example, if Q in (1) is the constant dollar GDP per capita,
then it can be interpreted as the SNA-economic welfare indicator. Consequently,
identity (1) stipulates that the implicit GDP deflator P is indeed a cost-of-living
index. In addition, the economic approach does not conflict the other purposes of
the SNA in economic analysis, decision-taking, and policy-making.
In practice we often deflate the current dollar value of a product (or a product
group) with a proper price index to obtain a constant dollar volume index.2 That
is, on a subindex level, we implicitly defined the Konüs quantity index as
1 p2T q 2
QK =
PK p1T q 1
where
PK =
and
C(p, u) = min
q
(2)
C(u, p2 )
C(u, p1 )
(
X
)
pi qi : U (q) ≥ u
i
PK is the Konüs cost-of-living (sub)index. It is defined as the ratio of the cost
functions in periods 2 and 1 when a consumer faces prices p2 and p1 . The consumer
1
A detailed discussion justifying the economic approach is given by Triplett (2001).
The name volume index is used interchangeably with the term quantity index. See SNA93,
p. 381.
2
3
is supposed to maximize her utility U (q) in both periods. The cost function C is
the minimum amount of money the consumer needs to achieve the same reference
standard of living or utility u in both periods. The choice of u is of course very
important because PK and consequently QK depend on it. It can be shown, however,
that there exists a utility level u∗ between the utility levels of the consumer in periods
1 and 2 such that PK lies between the values of the Laspeyres price index PL and
the Paasche price index PP . That is, there is a u∗ = λu1 + (1 − λ)u2 for 0 ≤ λ ≤ 1
such that3
PP ≤ PK ≤ PL or
(3)
PL ≤ P K ≤ PP .
In fact the implicit Konüs quantity index is a special case of a more general index,
known as the Allen index:
QA (q 1 , q 2 , p) =
C(U (q 2 ), p)
.
C(U (q 1 ), p)
(4)
Here we directly compare the cost function of the utility or standard of living in
periods 2 and 1, using a reference price vector p. In the literature of cost benefit
analysis, C(U (q), p) is called a money metric measure of quantity vector q. Another
commonly used theoretical index is the Malmquist quantity index:
QM (q 1 , q 2 , q) =
D(U (q), q 2 )
D(U (q), q 1 )
(5)
where
D(u, q t ) = max k : U (q t /k) ≥ u, k > 0
k
is called a distance or deflation function. D measures the maximum value that can
deflate the quantity q t such that the resulting utility level is at least as high as u. In
(5) q is a reference quantity vector that defines the reference utility level u = U (q).
And QM is the ratio of the deflators for q 2 and q 1 with respect to U (q).
So far we have defined three theoretical quantity indices. In principle if the
consumer’s utility function is known or can be estimated, all three indices can be
computed using duality theory. In practice, of course, this is seldom done. These indices are used to derive simple non-parametric index formulae. A natural question is
which one should be used to develop our quantity index in practice. It turns out that
if we make further assumptions about the utility function, the three indices become
the same. In particular, if we assume that consumers’ preferences are homothetic,
then
QK = QA = QM = U (q 2 )/U (q 1 ).
Technically, a homothetic preference structure means that the utility function is an
increasing transform of a linearly homogeneous function. That is, U (q) = f (Û (q))
3
For a proof and other discussions on economic approach to index number see Diewert (1981).
4
where f is a single-variable increasing function, i.e., f 0 > 0, and Û (αq) = αÛ (q)
for all α > 0. It implies that a consumer has constant expenditure share for each
commodity even when total expenditure varies. Although this assumption is very
restrictive, it provides us with a guideline for the search of the index formulae. For
example, a frequently used utility function in economic analysis is the Cobb-Douglas
function
N
Y
U (q) =
qiαi
i=1
where
PN
i=1
αi = 1. It is easy to show that for this utility function
QK = QA = QM =
N 2 α i
Y
q
i
i=1
qi1
.
That is, the three theoretical indices all equal to the weighted geometric mean of the
quantity ratios, which is often called the weighted geometric index. In this case we
say that the weighted geometric index is exact for the Cobb-Douglas utility function.
This example shows that different assumptions on the utility function give rise to
different index number formulae. In applied economic analysis a group of functions
called flexible functional forms is often used for empirical estimation of demand
and supply functions. The advantage is that they approximate an arbitrary utility
function to the second degree in a Taylor expansion. For example, the square root
quadratic utility function defined as
U (q) = (q T Aq)1/2 ,
(6)
where A is a symmetric N × N square matrix of coefficients, is a commonly used
flexible functional form. It can be shown that the Fisher quantity index, which is the
geometric mean of the Laspeyres index and the Paasche index, is exact for the square
root quadratic function. An index which is exact to a flexible functional form is called
a superlative index. Critics of superlative indices often argue that the homotheticity
assumption is too restrictive and unrealistic.4 This argument is misleading in light
of (3) because the result does not invoke the homotheticity assumption. Being a
geometric mean of PL and PP , the Fisher price index is good approximation for PK .
Based on the above concept, international comparisons of output and productivity can be studied using the economic approach (Diewert 1996). In measuring
government outputs, however, there are no prices, quantities are difficult to define,
and some products and services undergo constant quality change. Nevertheless, the
value of government services has to be estimated and then added to the value of
marketed goods to get the GDP. How we add apples to oranges when oranges have
no price tag has long been a vexing problem for economic statisticians.
4
See, for example, Hillinger (2002).
5
3
The Cost Approach
Because of the service orientation of the health care sector, some statistical agencies in countries with public health care systems think of health care as a kind of
government services to be included into the System of National Accounts (SNA).
They simply take the sum of factor costs (inputs) as the outputs.5 This implies that
productivity growth for this sector is always zero.
Generally, we can divide an economic process into four phases: factor inputs,
activities, outputs and outcomes. Factor inputs include labour, capital as well as
other intermediate products consumed in the production process. The intermediate
steps that contribute to a final product are activities. Outputs are the products and
services that the consumer purchase from the producers. The effects of the final
products on the consumers are considered as outcomes. In the case of health care,
physicians, nurses and medical investments are input factors. Activities can be the
number of admitted or discharged patients, number of days in hospital, or number
of operations performed for different diseases. Output may be the number of cured
heart attacks or strokes, or cases of diabetes treated. The physical well-being or the
welfare of the patients can be treated as outcome. If we assume the whole health
care sector as a large producer in a market economy and take the factor inputs as
outputs, then there is no productivity change or growth reflected in the national
accounts since any added-value is neglected.
Despite some criticisms, taking inputs as outputs in the health care sector is justifiable for some reasons. First, it is the most convenient way to include non-market
output in the SNA. Second, intermediate transactions and products generated in
the sector are difficult to be included in the SNA. Actually, if we consider the problem from microeconomic perspective, we may consider the output prices p to be
the marginal costs ∇q C(w, q). The inner product of quantities and marginal costs
q T ∇q C(w, q) should equal total cost C(w, q).6 Therefore, cost can be the “correct”
value for health care output. And we may split the value of volume into price and
quantity components using the vector of marginal costs.
Although the cost approach is justifiable, there are still some problems with it,
as mentioned before. Therefore, we still need to find alternative methods to evaluate
health care output more accurately for policymaking reasons.
4
Direct Output Measurement Approaches
Due to the inherit drawbacks of input method, some health economists and statisticians have recommended the improvements of output measurement in public health
5
In fact, Statistics Canada follows the exact method to calculate the output of Canadian health
care sector.
6
We wish to thank Erwin Diewert for this point.
6
care systems. In recent years, a lot of research has been done to investigate the
feasibility of direct output measurement. In particular, statistical agencies in some
countries and regions have examined output measurement methods. Among them
are the Office for National Statistics (ONS) in the U.K., European Statistics (Eurostat), and the Australian Bureau of Statistics (ABS).
4.1
The U.K. Methods
From 1998 to 2001, the ONS in the U.K. allocated resources to develop approaches
of measuring government output and non-market services. The researchers at ONS
made some suggestions to improve the current SNA system and applied the direct
measurement approaches to some public services.
As mentioned earlier, for non-market production it is impossible to measure the
output by looking at the prices paid by final consumers, as there may be no price
information available, and a convention is needed to value the output. British national accountants previously used the costs of production as a measure of the value
of output. These costs include the compensation of the employees, the value of goods
and services purchased to produce the output (the intermediate consumption), and
the depreciation of capital items (capital consumption).
Historically, in the U.K., the measure of volume is done by adding the volumes
of labour, intermediate consumption and capital consumption to get the total volume. As Caplan (1998) indicated, “there is a serious deficiency in this approach
as there is an implicit assumption that there is no change in labour productivity”.
He used an example to illustrate the underlying deficiency. In the example, there
is a clerical process of writing cheques. As printing technology improves, this process becomes automated so the cheques are printed by a computer rather than hand
written. As a result, fewer clerks will be employed. However, if we look at the the
national accounts, the volume of output would be recorded as having fallen because
the decline in the volume of labour used will not be offset by the increases in capital
consumption. In fact, we can see that the output remains at the same level as the
same number of cheques are being written. This is actually a problem of measuring
the productivity of the public sector and technology in general. For example, Baumol (1967) examined the relationship between “unbalanced growth”7 and the urban
crisis. The corresponding cost increase and limited source of tax funds, which are
associated with “unbalanced growth”, might lead to financial problems of a large
city.
7
The growth of activities with technological progress and those with sporadic productivity increase.
7
4.1.1
Concerns and Concepts
In recent years, there has been growing concern for the ONS that the implicit assumption of no change in productivity was unreasonable, and such productivity
improvements should be recorded in the national accounts. The ONS was committed to producing national accounts compatible with the 1995 European System of
Accounts (ESA). The ESA recommended that “direct measures of the output of
non-market services should be used in preference to input measures”. Additionally,
at the European level, there is a coordinated attempt to improve estimates of GDP
at constant prices (Caplan, 1998).
ONS developed the approach based on the idea that “it is better to measure
the right thing approximately than the wrong thing precisely” (Caplan, 1998). This
idea implies that any new indicators would not necessarily be perfect or beyond
criticism, but at least, would be more appropriate than the existing input approach.
Output “proxies” for market output was considered by the ONS. A proxy itself
cannot necessarily measure the output but it measures something reasonably thought
to move in a similar way to output.8
To carry out the approach, the ONS defined the output and how it could be measured. The four definitions (inputs, activities, output and outcome) are the same as
those mentioned in Section 3. However, the measures of output should be used rather
than outcome, which is recommended by the ESA: “. . . , it is of prime importance to
adopt the principle that the production and consumption of non-market services, like
the production and consumption of goods and market services, must be defined in
terms of the actual flows of these goods and services and not in terms of final results
obtained from their use. As these results depend on several other factors as well, . . . ”
(Caplan, 1998). But it is sometimes difficult to distinguish between an outcome and
an output, and in practice, output is impossible to be measured in some cases, so
activities would be more can reasonably be taken as proxies for output. Therefore,
the measurement of economic activity has an important distinction.
4.1.2
Approaches
In the U.K., health services are broken down into two categories: Family Health Services (FHS, or primary care) and Hospital and Community Health Services (HCHS,
or secondary care). For HCHS, there has been a long established indicator of activities called the Cost Weighted Activity Index (CWAI). The general procedure for
this technique are as follows (Ashaye, 2001):
1. Identify an area of services where activities are to be measured
8
For example, the output of the banking sector may be measured by counting the number of
transactions. This figure is regarded to reflect changes in output although it does not measure the
results directly.
8
2. List all activities of the area
3. Find a volume measure for each activity (number of incidents or cases, etc.)
4. Form a volume ratio for each activity between the comparison period and the
base period
5. Aggregate the volume ratios using cost shares as weights to produce a subindex
for the area
The FHS covers services provided by family doctors, opticians, etc. Each activity
indicator is identified for each category of services (e.g, the number of consultations
with general practitioners, the number of NHS sight tests). After counting the
numbers, they are aggregated using each service’s expenditure or cost as weights
(like the CWAI method for HCHS).
The methods described above is in fact a measurement of activities instead of
output. However, they are regarded as an improvement on the input or cost method.
Such methods can also be applied to measure the output of health services in countries with public health care systems, as there are similarities between these services
and services provided by the government.
4.2
The Eurostat Methods
From 1998 to 2000, the European Statistics established two task forces to investigate
the “embryonic” output methods used in the volume measurement of Section N of
NACE.9 According to the definition of ESA, non-market output is “output that is
provided free, or at prices that are not economically significant, to other units”.
Further, “economically significant” is defined as “any sums recovered through sales
cover less than 50% of the production costs” (Eurostat, 1998, p.7).
4.2.1
Classifications of Health Services
In Europe, there are two classifications of health services to serve different objectives:
the NACE and the COICOP.10 NACE classifies the production of health services by
activity, while COICOP classifies the expenditure of individual consumption. “Nonmarket output at current prices is defined as the sum of its production costs, and
the expenditure on non-market output is defined as the output of their producers,
that is NPISHs11 and general government, less receipts from sales” (Eurostat, 1998,
p.7). Accordingly, Section N of NACE Group includes the following main activities:
9
The “Nomenclature générale des Activités économiques dans les Communautés Européennes”
(NACE - General Industrial Classification of Economic Activities within the European Communities)
10
Classification of individual consumption by purpose
11
Non-profit institutions serving households
9
• Hospital activities.
• Medical practice activities.
• Dental practice activities.
• Other human health activities.
In COICOP, the individual health expenditures are classified as follows:
• Hospital services.
• Outpatient medical services.
• Outpatient dental services.
• Outpatient paramedical services.
• Public health services.
• Other health services.
However, it is not a one-to-one correspondence between the two classifications, so it is
necessary to ensure consistent coverage and treatments of individual health services
on both sides of the accounts (Eurostat, 1998, p.6).
4.2.2
Recommendation Criteria
Like the ONS in the U.K., the task forces made the following definition of output:
The health output is the quantity of health care received by patients,
adjusted to allow for the qualities of service provided, for each type of
health care. The quantities should be weighted together using data on
the costs or prices of the health care provided. (Eurostat, 1998, p.9)
More importantly, the task forces suggested: “the quantity of health care received
by patients should be measured in terms of treatments.” As we may notice, this is
a different measure from the one used by the ONS of U.K., and it is an “output”
measure in strict sense. A treatment should also be measured in entirety, taking into
account the whole bundle of complementary services (e.g., the medical services, laboratory and radiological services, and non-medical services in hospitalization). But
in practice, the degree of fragmentation of the services in a treatment can affect the
feasibility of measuring complete treatments. For example, there is not a strong linkage between the inpatient services and outpatient services for a transferred patient
before and after hospitalization.
10
The task forces also indicated the output measure should reflect the quality
changes. Generally, the quality of treatments may depend on the amount of resources used for each patient (including the number and mix of medical and paramedical staff, the sophistication of the equipment and the availability of the drugs and
medicines) (Eurostat, 1998, p.9). But sometimes quality changes are complicated
and difficult to measure because there are combined effects (both positive and negative).12
In order to ensure the consistency between the constant price estimates of market
output and non-market output, the task forces suggested an output price method
for market output and an output indicator method used for either market output
or non-market output. The best output price method should use the ideal price
index, which can produce the ideal volume index when used to deflate the output
at current prices. And the best output indicator method would be the one which
uses the ideal volume index. To be considered “ideal”, the prices/quantities used
to construct indices should meet the following six practical requirements (Eurostat,
1998, p.10):
1. Direct relation to output: They should relate directly to complete treatments
and not to contributory activities or any primary inputs.
2. Sufficient stratification: Different prices/quantities should be available for all
different product groups comprising output.
3. Sufficient homogeneity: If there is more than one product within a product
group, the composition of the product group does not change over time.
4. Sufficient representation: It is ideal if every item of the product group has
observations of prices/quantities. However, in many cases, there is not enough
available information for the prices/quantities of all products but only those for
a sample group of products. In these circumstances, changes in the observed
prices/quantities should represent changes in the prices/quantities that are not
observed.
5. Changes in the quality: The prices/quantities for a product group should account for changes in the quality. Moreover, changes in values from quality
changes should be included in the volume index rather than the price index
(i.e., the volume index = quantity index × quality index).
6. Different weights: The value of output in time period t should be used as
weights to aggregate prices, while the value (output for market production and
12
For example, a reduction in the length of stay in hospital can imply a gain in quality due to
technological advances in drugs and surgeries; but it is also possible that the patient now receive
post-operative services from family and visiting paramedics rather than hospitals, then it indicates
a quality loss.
11
total costs for non-market production) at period t − 1 be used as weights to
aggregate quantities.
The above requirements apply to both prices and quantities. However, there are two
exceptions:
1. Quantities may have less strict sufficient representation requirement than prices
because price changes in a sample can represent the whole population more
often than quantity changes in the same sample.
2. The price indices should be Paasche indices (using weights of the current period) and the volume indices should be Laspeyres indices (using weights of the
base period).
Based on the extent to which a method satisfies the six requirements, the task forces
divided different methods into three classifications: A method (recommended),
B method (reasonable) and C method (avoided). Obviously, the volume index
constructed using an A method is the most approximate to the ideal volume index,
and the volume index of a C method differs most from the ideal volume index.13
Therefore, the A, B and C methods should have the following properties:
• A method: It takes account both quantity and quality changes in output.
Quantity measures number of complete treatments. The quality indicator covers both changes in the physical characteristics of products and in the product
mix. It is possible to analyze productivity changes using an A method.
• B method: It may be used when we cannot apply an A method. It has similar
properties of an A method except that it can only reflect the quality changes
resulting from changes in the product mix.
• C method: It is neither an A method nor a B method. The volume index
obtained by this method is an unacceptable approximation of the ideal volume
index.
4.2.3
Recommendations
Considering the proposed criteria, the task forces made recommendations on the
measurement of the output of health services. An A method or B method was
suggested according to these requirements.
First, the indicators and weights should both relate directly to output in order
to satisfy the requirement for an A method. Hospital output is expected to be
the total number of discharged patients by kind of complete treatments, using the
13
However, the actual ideal volume index is unknown, so in practice, the method choice should
be determined by statistical analysts.
12
corresponding cost in period t − 1 as weight. Output indicators for consultations and
dental services may adopt similar methods. However, in cases where the information
on prices or costs for weights is unknown, allocating costs of nursing days (or surgical
operations etc.) to complete treatments by cross-tabulation. Then the cost of each
treatment can be used as approximate weight. This method is considered to meet the
B method requirement since there is no direct data and assumptions are needed.14
Second, to be considered an A method for the output of hospitals, a detailed
classification of complete treatments (or treated patients), which accounts for the
differences between diseases and treatment methods, should be used. Generally, the
use of DRG 15 or ICD 16 systems can meet the requirements of sufficient stratification
and homogeneity for A and B methods (if the product group is homogeneous, the
number of patient days or activities can be used).
Third, regarding sufficient representation, generally, we need large samples for
output indicators to be sufficiently representative of the whole population, if we
apply both A and B methods. However, when the volume index can only represent
part of the group, we may use the implicit-price index to represent the remaining
based on the assumption that price indicators from incomplete observations have
more representative ability than the associated volume indicators (Eurostat, 1998,
p.15).
The above work was done by the first task force (TF I). To further investigate the
key issues of direct output measurement for health, the Eurostat formed another task
force (TF II) in November 2000. The task force II distinguished the inpatient and
outpatient treatments. “Inpatient treatment is by definition provided by hospitals
only, whereas outpatient treatments are provided both by hospitals and by general
practitioners and specialists outside hospitals.” A detailed overview on inpatient
and outpatient treatments was provided on the basis of a cross-classification.
It also examined the issue of re-admissions in both hospital and other medical
and dental practice services. It showed that for hospitals, the percentage of readmissions in the total number of discharges is relatively low, and do not affect the
output indicators significantly. Therefore, this indicator may be thought of to qualify
for an A method even without the correction for re-admissions (Eurostat, 2000, p10).
For medical and dental services, some countries (such as the Netherlands) use the
number of “first visits” as an indicator. But the task force II only recommended “first
visits” as an ideal indicator for dental practice services because there are differences
between specialist services and those services provided by a general practitioner.
14
But the task forces still had opinion that a volume index based on numbers of contributory
activities could not fulfil the A or B method requirements because of the mixed effects of those
activities on output and quality.
15
Diagnosis Related Groups, a classification system for hospital stays in general and specialized
hospitals, used in the U.S. and some European countries.
16
World Health Organisation’s (WHO) International Statistical Classification of Diseases and
Health Related Problems.
13
Regarding the weights, the task force II recommended four possible solutions:
1. Best solution: proper output values or cost weights based on full (exhaustive)
coverage.
2. Second best: proper cost weights based on representative samples. In France
and Norway, the costs in a sample of hospitals are used as weights for DRG
groups.
3. Third best: indirect cost weights (e.g., cross-classifying the expenditure by
sub-sectors and diagnosis groups).
4. Fourth best: expert guesses if the requirement of precision for weights is not
“100% correct”.
Both the best and second best solutions can be acceptable for an A method.
Finally, due to lack of enough time, the task force II recommended the ratio of
qualified staff (man year)/occupant days as a quality indicator in hospital services
be further investigated (Eurostat, 2000, p.17).
The methods recommended by the European Statistics improved those used by
the ONS of U.K. and contained useful details about the output measurement in the
health care sector (rather than general government and non-market sectors).
4.3
The ABS Methods
In 1997, the Australian Bureau of Statistics (ABS) undertook a major project to
improve the measurement of output and productivity for the government sector
and other service industries in Australia. As part of the project, the Methodology
Division of ABS proposed a research program with the aim of trying to improve
the measures for the health services industry. Researchers in the program presented
their report of “measuring the outputs and productivity for Australian public acute
care hospitals” at the 19th Australian Conference of Health Economists.
4.3.1
Issues and Requirements
The researchers realized that the output of health services is more difficult to measure than the medical goods producers because of intangibility and heterogeneity.
Problems exist when measuring the output both in a given year and across years. If
we try to measure the output in a given year, it is not straightforward to distinguish
the difference between two treatments involving the “same” service; and we also have
to construct a composite output measure by aggregating the numbers of different
types of services.
14
Because of the heterogeneity of health services, the quality changes cannot be
easily identified in the measures across several years, and “the change in productivity
may be under-estimated or over-estimated” (Daniel et al., 1997).
Like the ONS and the Eurostat, ABS also recognizes that it should focus on the
measure of “output” rather than “outcome” to conform to the standards of U.N.
SNA, i.e., measure the services provided (consumption) rather than their effects on
people’s health conditions and life expectancy (utility or welfare).17 It is also because
the goal of the project is to enhance and improve the measurement of health services
in the Australian national accounts.
Viewing outputs as the final products rather than the component processes or intermediate parts, the ABS developed a similar classification system called “casemix”,
which classifies the patient care episodes. A casemix should have the following characteristics:
1. Clinical meaning: that is, patients in the same category should have similarities
in terms of clinics.
2. Resource homogeneity: the costs of treating patients in the same class should
be almost the same.
3. Right number of classes: it should correspond to the number of service types.
The acute care hospitals (both public and private) in Australia adopt a DRG-like
casemix classification: Australian National Diagnosis Related Groups (AN-DRG).
4.3.2
Indicators and Indices
Based on the activity data from the National Hospital Morbidity Database, the ABS
has the following activity indicators for inpatient care available in the version 3.0 of
the AN-DRG casemix (Daniel et al., 1997, p.7):
• Separations: including the cases when a patient is discharged, transferred to
another institution, dies while in care, changes status or leaves hospital for a
period of seven days or more
• Occupied bed days
• Same day separations
• Average length of stay
• Average cost, Relative cost weight and Cost by volume
17
“The objective is to measure the quantities of services actually delivered to households. This
should not be confused with the benefits or utility derived from those services.” SNA93 (para.
16.135)
15
The researchers in the project program adopted the number of separations as the
primary indicator of activity.18
In Australia, non-inpatients refer to patients who are treated on the hospital
site (including emergency patients, outpatients and day program patients). The
researchers took the total number of occasions of service as the activity measure
for non-inpatient care in public acute hospitals. But there are only limited activity
indicators and the information is available only from HUCS.19 The HUCS historically
counted treatments as occasions of service. Each attendance at a non-inpatient clinic
is counted by HUCS as a treatment and a patient may have more than one treatment
in hospital.20
Different from the other two agencies, the ABS takes into account the research
and education activities that hospitals engage in. Although some argued that research and education activities should be treated as part of the output since it “will
improve procedures and health outcomes or reduce costs and increase efficiencies”,
the researchers do not recommend this. First, it is impossible to isolate expenditure
on non-patient-care products given the available data.” Moreover, in the national
accounts, in-house research and education services are not treated as an output of
hospitals, but as an intermediate input.
For non-market production, two “quality change” measurement approaches are
identified by the ABS. The first approach is to assess the consequences or effects
of the service variations on people’s utility. The other focuses on the degree to
which the providers conform with the the existing “good practice” procedures, while
ignoring whether these standards or criteria are correct or not. The second approach
suggests that the ABS assumes desirable results from the procedures.
In the Australian health services industry, there is a tendency to incorporate the
two approaches to adjust quality change. For example, the Department of Health
and Family Services identifies quality-of-care as a measure of “the extent to which
the resources allocated to a patient”, and “the processes by which those resources
were applied to a patient”. The First National Report on Health Sector Performance
Indicators suggests to combine both the quality-of-care and the patient satisfaction
Indicators in future development (Daniel et al., 1997).
When it comes to the aggregation needed to produce a composite output measure, the ABS puts forward a problem of consistency. Because there is significant
proportion of non-market output in the public acute care hospitals, no “current
price” value is available. If we treat the cost of production as “current price”, there
is “a zero net operating surplus”. An alternative to this approach is to use pseudo
18
However, we note that, according to the definition of “separation” by ABS, it is neither an
“activity” nor a “complete treatment” from the perspective of ONS and Eurostat.
19
The Hospital Utilization and Costs Surveys, a comprehensive Australian national dataset containing information on activity and other data for health care institutions.
20
We may also notice that, this is different from the “first visits” indicator recommended by the
Eurostat.
16
market price (e.g., imputing the data from similar services in the market sector).
However, the aggregated volume measure using the pseudo price is inconsistent with
the SNA convention, although there is a non-zero net operating surplus. The researchers finally suggested to use the cost data as weights for aggregation due to
the imputing difficulties and the aim of the research project (for the use in national
accounts).
Using the data from the National Hospital Morbidity Database and the National
Costing Study developed by the Department of Health and Family Services of Australia, the researchers developed the following weighting system:
Si0 × Average Total Costi0
wi0 = P
,
i Si0 × Average Total Costi0 )
where wi0 and Si0 are respectively the cost share and the number of separations of
group i in the AN-DRG and t = 0 refers to the base period. The output index Qt is
based on cost shares as the fixed weights:
X
Sit
.
Qt =
wi0
Si0
i
As we may notice that, the output (volume) index proposed by ABS is consistent
with Eurostat’s recommendation: “volume indices should be Laspeyres indices (using
weights of the base period)”.
For the purpose of compiling a composite output index involving both inpatient
and non-inpatient services, the researchers used another set of fix weights called
inpatient fraction of costs (IFRAC).21 A composite output index can be constructed
by aggregating the subindices for inpatient and non-inpatient services using the
IFRACs.
5
An Empirical Study Using Canadian Data
We now compare the different approaches to measure the output of treating heart
attacks from 1994 to 1999 in Canada. The results can be used as references for
measuring other types of diseases, and the whole health care sector.
A heart attack (myocardial infarction) is an acute event which results from a
blockage of arteries supplying blood to the heart. It will become life-threatening if
not diagnosed and treated early and effectively. Because of the high mortality (30%)
among elderly people, nearly everyone who suffers from a heart attack will receive
medical treatment (Cutler, 2002).
21
A ratio reflecting the relative cost of non-inpatient to inpatient care episodes (usually in terms
of occupied bed days (OBD)), with the assumption that “inpatient services account for a fixed
share (77.6%) of input costs”.
17
A diagnostic procedure called “cardiac catheterization” is usually used to detect
the extent of arterial blockage. Then physicians may decide to perform either of
the following two revascularization procedures: bypass surgery (creating a blood
flow around occluded arteries) or angioplasty (inserting a balloon catheter into the
arteries to remove the blockage and restore blood flow). We use the number of
procedures for the treatment of heart attack as an output indicator.
The data source is mainly from Canadian Institute for Health Information (CIHI).
We obtained the number of cases and number of patient days for each province from
CIHI’s Hospital Morbidity Database (HMDB). These data can be freely downloaded
from CIHI’s official web site. Based on the numbers, simple output indices can be
constructed by applying the following formula (1994 is treated as the base year):
Case Indext =
Number of Casest
,
Number of Cases1994
and similarly,
Patient Day Indext =
Number of Patient Dayst
,
Number of Patient Days1994
where t stands for the year (1994-1999). Table 1 illustrates the number of heart
attack cases, number of patient days and their corresponding indices for Canada.
We see a generally increasing trend in the number of cases. While the number of
patient days increased during 1995 and 1996, it declined afterwards to a number
that is close to the base year (1994) level. The appendix includes output indices
using “direct output measurement” for each province/territory. However, for some
provinces/territories (e.g., Newfoundland and Northwest Territories), these indices
have large fluctuations probably due to small sample sizes.
The “number of cases” indicator is based on “complete treatments”, and can
be considered “output measure”, while the “number of patient days”, which is an
“activity” indicator, does not reflect output in strict sense. However, if it has direct
relation to output, we may regard it as a proper “proxy” to an output indicator,
given its sufficient representation and homogeneity.22
For comparison, Table 2 shows the indices constructed using the cost approach,
which is adopted by Statistics Canada. Due to the unavailability of specific data for
heart attacks, it is the hospitals GDP. However, it may reflect the general trend of
heath services output to some extent and 1994 is still regarded as the base year. As
we may notice, contrary to the “patient days” indices, the “cost” indices declines
around 4% during 1996 and 1997, and increases after that period.
An activity-based method is to calculate the number of procedures (operations).
To construct this type of indices, four major CMG codes, which correspond to the
22
If each patient has to stay nearly the same number of days while receiving heart attack treatment, then it has direct relation to output.
18
Table 1: Number of Heart Attack Cases,
Cases
Year No. Index
1994 53685 1.00
1995 57146 1.06
1996 59622 1.11
1997 59250 1.10
1998 59003 1.10
1999 61015 1.14
Patient Days and Indices; Canada
Patient Days
No.
Index
500433 1.00
513942 1.03
518060 1.04
500236 1.00
491959 0.98
504652 1.01
Table 2: Hospitals GDP; Canada; 1997 Constant Dollars (Millions)
Year Cost (GDP) Index
1994
$20,939
1.00
1995
$20,444
0.98
1996
$20,101
0.96
1997
$20,002
0.96
1998
$20,301
0.97
1999
$20,599
0.98
above-mentioned treatments for heart attacks are identified.23 They are:
• CMG 178: coronary bypass with heart pump with cardiac catheterization
• CMG 179: coronary bypass with heart pump without cardiac catheterization
• CMG 188: percutaneous transluminal coronary angioplasty with complicating
cardiac conditions
• CMG 189: percutaneous transluminal coronary angioplasty without complicating cardiac conditions.
Because the medical records on some provinces such as Quebec and Manitoba are
not included in CIHI’s DAD database, we are unable to construct indices for those
provinces.
As for weights, “Resource Intensity Weights” (RIW) in 2003 from CIHI are used
to aggregate the number of each CMG procedure.24 A procedure-based indicator
23
CMG is a classification system designed to aggregate acute care inpatients with similar clinical
and resource utilization characteristics. It assigns acute care inpatients to groups based on clinical
and administrative data collected via CIHI’s Discharge Abstract Database (DAD).
24
The RIW system is a relative resource allocation methodology for estimating a hospital’s
inpatient-specific costs for both acute and day procedure care. In this case, averaged expected
length of stay (ELOS) across all age groups and complexity levels is the RIW for each CMG code.
19
Table 3: Cost Per Case and RIW For Each CMG Code
CMG Cost Per Case RIW
178
$22,858.75
16.31
179
$14,031.53
8.42
188
$9,447.85
8.39
189
$4,921.61
5.23
Table 4: Number of Procedures and Indices; Canada (provinces in DAD database)
Year CMG178 CMG179 CMG188 CMG189 CPC Index RIW Index
1994
642
8096
7257
927
1.00
1.00
1995
643
8308
7707
935
1.04
1.04
1996
3098
7579
3634
6510
1.21
1.24
1997
2949
9776
4323
10773
1.48
1.54
1998
3183
10078
5473
11615
1.60
1.69
1999
3100
9971
6471
12443
1.65
1.76
can be calculated using a method that is similar to the CWAI proposed by ONS in
the U.K.:
P
Number of Proceduresit × RIWi
,
RIW Indext = P i
i Number of Proceduresi1994 × RIW i
where i stands for each of the CMG code. The RIW indices (for provinces that have
medical records in CIHI’s DAD) are also included in the appendix. Although the
weights used to construct RIW indices are not “costs”, we can still treat them as
proxies if “expected length of stay” is proportional to the cost of each procedure.
Fortunately, “cost per case” (CPC) in 2002 for each of the specified CMG code can be
requested from Ontario Case Costing Initiative (OCCI).25 Table 3 shows the weights
(both RIW and “cost per case”) used to aggregate the numbers of procedures. Then
we may apply the CWAI method directly to measure the output of heart attack
treatments in Ontario. Accordingly,
P
Number of Proceduresit × CPCi
CPC Indext = P i
.
i Number of Proceduresi1994 × CPCi
Together with the cost(GDP) index (see tables 4–6), five different kinds of output
indices for Ontario can be constructed based on the data from CIHI and OCCI. The
results for the 1994–1999 period are illustrated in table 8 and figure 1. To look at the
trend at national level, table 9 and figure 2 show these indices for Canada.
These
tables and figures show that, generally for each year, RIW Index > CPC Index >
25
OCCI is an under-taking of the Ontario Ministry of Health and Long-Term Care. It was created
to continue the work started by the Ontario Case Costing Project (OCCP).
20
Year
1994
1995
1996
1997
1998
1999
Table 5: Number of Procedures and Indices; Ontario
CMG178 CMG179 CMG188 CMG189 CPC Index RIW Index
371
4638
3651
497
1.00
1.00
365
4851
3821
481
1.04
1.04
1630
4558
1710
2625
1.18
1.19
1620
5933
2307
3868
1.46
1.48
1509
6037
2820
4442
1.52
1.56
1523
5940
3213
4978
1.57
1.63
Table 6: Number of Heart Attack Cases,
Cases
Year No. Index
1994 21457 1.00
1995 22427 1.05
1996 23999 1.12
1997 23996 1.12
1998 24176 1.13
1999 25036 1.17
Patient Days and Indices; Ontario
Patient Days
No.
Index
187544 1.00
190112 1.01
196622 1.05
193343 1.03
192151 1.02
201687 1.08
Table 7: Hospitals GDP; Ontario; 1997 Constant Dollars
Year Cost (GDP) Index
1994
$7,837.80
1.00
1995
$7,555.20
0.96
1996
$7,343.70
0.94
1997
$7,162.50
0.91
1998
$7,368.30
0.94
1999
$7,506.00
0.96
Year Case Index
1994
1.00
1995
1.05
1996
1.12
1997
1.12
1998
1.13
1999
1.17
Table 8: Output Indices for Ontario
Patient Day Index CPC Index RIW Index
1.00
1.00
1.00
1.01
1.04
1.04
1.05
1.18
1.19
1.03
1.46
1.48
1.02
1.52
1.56
1.08
1.57
1.63
21
Cost Index
1.00
0.96
0.94
0.91
0.94
0.96
Figure 1: Output Indices for Ontario
Year
1994
1995
1996
1997
1998
1999
Table 9: Output Indices for Canada
Case Index Patient Day Index CPC Index RIW Index
1.00
1.00
1.00
1.00
1.06
1.03
1.04
1.04
1.11
1.04
1.21
1.24
1.10
1.00
1.48
1.54
1.10
0.98
1.60
1.69
1.14
1.01
1.65
1.76
22
Cost Index
1.00
0.98
0.96
0.96
0.97
0.98
Figure 2: Output Indices for Canada
Case Index > Patient Day Index > Cost Index. We may notice that the cost index
is always less than 1, which reflects the fact that both federal and provincial cut
budget for health care during the 1994–1999 period, since Statistics Canada simply
takes the health care budget as output (GDP).
When the case index is compared with patient day index, we can see an obvious
difference: patient day index remains almost constant (less than 1.10) in the period,
while the case index increases slightly. The gap between the two indices becomes
bigger after the base year (1994). Such difference shows decreasing average days of
stay in hospital for a single complete heart attack treatment, which may be regarded
as an indicator of “quality change” or“productivity improvement” in the treatment.
As for the “procedure-based” indices (CPC and RIW), they increase much more
than other indices after 1996. The underlying reason for the “dramatic increase”
is the “big sudden jump” of the numbers of some CMG procedures. Figure 3 and
figure 4 illustrate the changes graphically for both Ontario and Canada. From 1995
to 1996, the procedure numbers of CMG 178 and CMG 189 increased more than
five times, while those figures of CMG 179 and CMG 188 moved in the opposite
direction, which may imply substitutions in these procedures. However, this effect
cannot offset the increasing effect from CMG 178 and CMG 189, thus leading to a
small upward change in both CPC and RIW indices in the period. But from 1996 to
23
Figure 3: Number of CMG procedures for Ontario
1997, nearly all the numbers of four CMG procedures increased substantially, which
generated an even bigger “jump”.
One possible explanation for these big movements is that the diagnostic procedure
“cardiac catheterization” showed significant technological improvement in 1996 and
could detect the extent of arterial blockage more accurately. Therefore, patients
tended to rely more on such procedure before undergoing coronary bypass surgeries.
Furthermore, using the “RIW” weights leads to higher output indices than using
“CPC” to aggregate the procedure numbers. It is probably because “CPC” system
places less weight on CMG 189 than “RIW”, and as we can see from figure 3 and
figure 4, the number of CMG 189 procedures had the largest increasing rate in the
period. As a result, the more increase in the number of CMG 189 procedures by
percentage, the bigger difference between these two indices.
The “output” (case) index might be misleading if we look at the measure from
the perspective of “activity” (procedure). Although the case index has a slightly
upward trend, both the “CPC” and “RIW” indices increased far more significantly
in the same period. That is, in each case, the patient had much more procedures
than before while receiving the treatment. Therefore, the simple number of cases is
unable to account for the quality change or improvement in the treatment of heart
attacks.
However, on the other hand, one can speculate: is there indeed any quality improvement? Some evidence gave a negative answer. Health researchers have investigated the outcomes of utilizing such procedures to treat acute myocardial infarction
(heart attack). They compared the use of invasive cardiac procedures and the mor24
Figure 4: Number of CMG procedures for Canada (provinces in DAD database)
tality rates among 224,258 elderly Medicare beneficiaries in the United States and
9444 elderly patients in Ontario, each of whom had a new acute myocardial infarction in 1991. What they found is that the U.S. patients were significantly more likely
than those in Canada to undergo percutaneous transluminal coronary angioplasty
(11.7 percent vs. 1.5 percent), and coronary-artery bypass surgery (10.6 percent vs.
1.4 percent) during the first 30 days after the infarction. In the following 180 days
these differences still persisted although narrowed. U.S. patients had slightly but
significantly lower 30-day mortality rates than Canadian (Ontario) patients (21.4
percent vs. 22.3 percent). But the one-year mortality rates were almost identical
with 34.3 percent in the U.S. vs. 34.4 percent in Ontario (Tu et al., 1997). Therefore, they concluded that the much higher cardiac procedures usage rates do not
bring better long-term survival rates to elderly U.S. patients with heart attacks.
As mentioned earlier, it is sometimes difficult to distinguish “output” from “outcome”. However, as a general rule, quality improvement should be reflected in better
outcomes to some extent. Therefore, how to measure the output with quality being
adjusted becomes a crucial problem. Since it involves outcome measurement and
is out of this paper topic, future research can be carried out to explore possible
solutions.
25
6
Conclusion and Further Studies
In this paper, we choose heart attack as a typical acute clinical disease to compare
the direct output indices constructed on different bases. Theoretically, statistical
agencies can use direct output “proxies” (indicators) to measure the output (GDP)
for the whole health care sector on a disease-based classification system (e.g., stroke,
diabetes). However, as far as Ontario is concerned, it turns out that the choice
of “proxy” has large influence on final output (GDP) results. Different “activity”
proxies (such as patient days and procedures) may result in largely different output
indices. Even for the procedure-based approach, using different weights (shares) can
lead to somewhat slightly different figures.
When it comes to the national level, as mentioned earlier, both the CPC index
and RIW index for Canada do not account for the records of some provinces like
Quebec and Manitoba, and the cost index is based on the GDP for all hospitals
rather than heart attack treatments. Moreover, the CPC index is based on Ontario’s
“costs per case” as weights, and it is assumed that such costs remain the same
for all other provinces, which might not be the case in reality. Furthermore, the
“costs” data may not be from all Ontario hospitals, which can cause an insufficient
representation problem. As a result, strictly speaking, these indices for Canada are
not comparable.26
Therefore, important problems need to be addressed before these approaches can
be implemented. Which method should be used by statistical agencies in practice
requires further investigation. Research may also be necessary to evaluate the properties of different direct approaches or even different weights for the same approach.
The large difference between directly measured output and health care GDP has interesting implications for cross-country (e.g. U.S. and Canada) comparison. Lastly,
direct output-based cost-effectiveness study on a disease treatment across different
ages/provinces, or on the CMG procedures can be innovative future research topics
in this field.
Further studies can extend our results in heart disease treatments to other major
categories. The trends of different measurement methods should be compared and
analyzed to obtain a deeper understanding of the impact on health care output and
productivity measurement.
26
However, for reference purpose, they do reflect the general output trend to some extent.
26
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28
Table 10: Number of Heart Attack Cases, Patient Days and Indices; Alberta
Cases
Patient Days
Year No. Index No. Index
1994 4162 1.00 35842 1.00
1995 4362 1.05 35572 0.99
1996 4548 1.09 36721 1.02
1997 4410 1.06 34803 0.97
1998 4544 1.09 35809 1.00
1999 4787 1.15 37372 1.04
Table 11: Number of Heart Attack Cases, Patient Days and Indices; B.C.
Cases
Patient Days
Year No. Index No. Index
1994 6284 1.00 46066 1.00
1995 6487 1.03 47427 1.03
1996 6730 1.07 47950 1.04
1997 6362 1.01 42591 0.92
1998 6504 1.04 43762 0.95
1999 6806 1.08 45801 0.99
Table 12: Number of Heart Attack Cases,
Cases
Year No. Index
1994 2019 1.00
1995 2062 1.02
1996 2192 1.09
1997 2025 1.00
1998 2026 1.00
1999 2201 1.09
Patient Days and Indices; Manitoba
Patient Days
No. Index
17790 1.00
18626 1.05
19501 1.10
16687 0.94
17190 0.97
20599 1.16
Table 13: Number of Heart Attack Cases, Patient Days and Indices; New Brunswick
Cases
Patient Days
Year No. Index No. Index
1994 1730 1.00 16330 1.00
1995 1790 1.03 16466 1.01
1996 1668 0.96 14187 0.87
1997 1807 1.04 15160 0.93
1998 1744 1.01 17772 1.09
1999 1876 1.08 15121 0.93
29
Table 14: Number of Heart Attack Cases, Patient Days and Indices; Nova Scotia
Cases
Patient Days
Year No. Index No. Index
1994 1951 1.00 20238 1.00
1995 2100 1.08 19812 0.98
1996 2087 1.07 20182 1.00
1997 2212 1.13 20986 1.04
1998 2041 1.05 17768 0.88
1999 2288 1.17 18836 0.93
Table 15: Number of Heart Attack Cases, Patient Days and Indices; NT
Cases
Patient Days
Year No. Index No. Index
1994 17
1.00
94
1.00
1995 37
2.18 232
2.47
1996 25
1.47 152
1.62
1997 42
2.47 346
3.68
1998 52
3.06 352
3.74
1999 36
2.12 216
2.30
Table 16: Number of Heart Attack Cases, Patient Days and Indices; PEI
Cases
Patient Days
Year No. Index No. Index
1994 289 1.00 2613 1.00
1995 286 0.99 2455 0.94
1996 276 0.96 2540 0.97
1997 295 1.02 2364 0.90
1998 395 1.37 3066 1.17
1999 367 1.27 3372 1.29
Table 17: Number of Heart Attack Cases, Patient Days and Indices; Quebec
Cases
Patient Days
Year No. Index
No.
Index
1994 13743 1.00 156612 1.00
1995 14186 1.03 154116 0.98
1996 14510 1.06 150113 0.96
1997 14561 1.06 144839 0.92
1998 14265 1.04 136912 0.87
1999 14293 1.04 135813 0.87
30
Table 18: Number of Heart Attack Cases, Patient Days and Indices; Saskatchewan
Cases
Patient Days
Year No. Index No. Index
1994 2001 1.00 17074 1.00
1995 2133 1.07 16840 0.99
1996 2305 1.15 18327 1.07
1997 2278 1.14 17024 1.00
1998 2060 1.03 15688 0.92
1999 2122 1.06 14720 0.86
Table 19: Number of Heart Attack Cases, Patient Days and Indices; Yukon Territory
Cases
Patient Days
Year No. Index No. Index
1994 23
1.00 181
1.00
1995 27
1.17 161
0.89
1996 21
0.91 100
0.55
1997 37
1.61 215
1.19
1998 26
1.13 104
0.57
1999 35
1.52 208
1.15
Table 20: Number of Procedures and Indices; Alberta
Year CMG178 CMG179 CMG188 CMG189 RIW Index
1994
57
928
775
100
1.00
1995
40
887
866
109
1.01
1996
252
1116
948
1117
1.73
1997
234
1200
871
1496
1.84
1998
336
1145
1138
1518
2.07
1999
313
1264
1166
1530
2.13
Table 21: Number of Procedures and Indices; British Columbia
Year CMG178 CMG179 CMG188 CMG189 RIW Index
1994
89
1304
1619
192
1.00
1995
106
1286
1560
175
0.98
1996
403
1174
522
1717
1.10
1997
439
1476
620
2725
1.44
1998
476
1527
791
2890
1.57
1999
505
1528
1169
3205
1.76
31
Table 22: Number of Procedures and Indices; New Brunswick
Year CMG178 CMG179 CMG188 CMG189 RIW Index
1994
36
214
295
43
1.00
1995
37
216
306
27
1.01
1996
268
53
131
148
1.31
1997
58
38
148
559
1.07
1998
268
173
205
647
2.15
1999
273
165
242
609
2.17
Table 23: Number of Procedures and Indices; Newfoundland
Year CMG178 CMG179 CMG188 CMG189 RIW Index
1994
23
182
214
17
1.00
1995
27
184
165
17
0.91
1996
148
169
24
268
1.43
1997
141
236
21
722
2.17
1998
178
228
35
786
2.43
1999
100
81
20
650
1.55
Table 24: Number of Procedures and Indices; Nova Scotia
Year CMG178 CMG179 CMG188 CMG189 RIW Index
1994
46
505
228
28
1.00
1995
29
559
491
66
1.37
1996
232
307
52
376
1.24
1997
247
540
117
764
1.92
1998
239
547
183
714
1.95
1999
213
560
230
727
1.97
Table 25: Number of Procedures and Indices; Saskatchewan
Year CMG178 CMG179 CMG188 CMG189 RIW Index
1994
20
325
475
50
1.00
1995
39
325
498
60
1.08
1996
165
202
247
259
1.07
1997
210
353
239
639
1.61
1998
177
421
301
618
1.67
1999
173
433
431
744
1.91
32