Different health care systems with similar levels of resources per capita
often show wide variations in population health outcomes. One possible
explanation for this is that different health care systems choose to use the
resources available to them in different ways, some of which may be suboptimal.
Sub-optimal resource use can lead to inefficiencies in health care
provision and poorer health outcomes. In an attempt to address the question
of quality of care, the WHO  and the OECD  both published reports
focusing on assessing the performance of health care systems in different
countries. However, neither addressed the issue of cost-effectiveness, or,
more specifically, which countries had the more cost-effective health care
systems. Using data published by the WHO relating to health care outcomes
and expenditure, we attempt to assess how health care performance indicators
are related to wealth and health care expenditure, and to provide an indication
of which countries may be providing the most cost-effective care.
Data from the OECD and WHO
The WHO has recently made publicly available an extensive database containing
a range of health-related, demographic and economic variables
across a number of years . The World Health statistics 2007 presents the
most recent health statistics for the WHO's 193 member states and are collated
from publications and databases produced by the WHO's technical programmes
and regional offices. A core set of health-related indicators was
selected for the database on the basis of their relevance to global health, the
availability and quality of the data, and the accuracy and comparability of
estimates. The statistics for the indicators are derived from an interactive
process of data collection, compilation, quality assessment and estimation
occurring among WHO's technical programmes and its member states.
The health care indicators (HCIs) that were collated for use in this study are
listed in Table 1. These were chosen as they can be viewed as representing
quality of life, and less subject to endogenous factors within particular countries.
Male-to-female ratios were used to aggregate the health care indicators
(HCI) across gender. In addition, values for the per capita gross domestic
product (GDP), and the per capita health-care expenditure (HCE) in both US
and international dollars were also collated. An International dollar is the
hypothetical unit of currency which locally has the same purchasing power
that one US dollar has in the US.
Data were collated for the 30 OECD countries listed in Table 2 . These
countries were chosen as they represented the countries which are most
economically developed and likely to have the most advanced health care
systems. They also represented countries for which the full range of data
was available. Including data from less developed countries would have
introduced unreasonable heterogeneity.
Once the data was collated, a series of analyses were performed. Univariable
regression analyses was conducted to explore the linear association between
each HCI and either GDP or HCE. This was repeated using the logarithms of
GDP and HCE, in US and international dollars. The proposed explanatory variable
with the best predictive power was then used as a predictor of the HCI.
Correlations Between Health Care Indicators And
The correlation coefficients (r) between the HCIs and the logged economic
variables in US dollars are shown in Table 3. Correlation with international
dollar economic variables was generally less statistically significant than with
US dollars, and these results are not shown. The correlations shown are all
statistically significant after adjusting for multiplicity (P<0.05). The HCIs
which were more strongly correlated with GDP than HCE were infant mortality
rates, maternal mortality ratios, neonatal mortality rates and the probability
of dying under five years of age. HCE was more strongly correlated than
GDP with healthy life expectancy (HALE) at birth, average life expectancy at
birth and the probability of dying between ages 15 and 60 years old.
The strongest correlation found was between healthy life expectancy (HALE)
and the logarithm of HCE, for which the correlation coefficient was 0.83
(P<0.001, Table 3). A regression model fitted to the logarithm of the HCE data
is shown in Figure 1. The mean cost was US$2400 and the mean health life
expectancy at birth was 69.1 years. The majority of data from countries fall
within the 95% confidence intervals. Outliers in this correlation were Japan,
Spain and Sweden, which have a higher than expected HALE, and the USA
and Hungary which have a lower than expected HALE for their given HCE.
Discussion and Conclusions
In general there are high levels of correlation between economic and health
care indicators in economically developed countries. In particular there is a
strong correlation between total per capita health care expenditure and
healthy life expectancy, with lower than expected values of healthy life
expectancy given the health-care expenditure in Hungary and the USA. Whilst
this might indicate that the health care systems in these countries are under
performing, this does not imply a causal relationship.
There are a number of other factors that may influence health care indicators,
such as education, nutrition or public spending. To derive robust estimates
of the cost-effectiveness of different health care systems, these factors
would need to be accounted for. In effect this would require deriving a standardised
control group against which the health-care systems within different
countries could be compared.
Further data and analysis would help identify the costs and benefits of different
health care systems to estimate the cost-effectiveness of health care
systems between countries. Such an analysis would be technically challenging,
but potentially yield huge benefits. By identifying the most cost-effective
health care system, best practice could be identified and replicated, thereby
potentially leading to improvements across different health care systems
within different countries.
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3. World Health Statistics 2007, Geneva, WHO, 2007 http://www.who.int/whosis/en/index.html Last
accessed September 2007.