Like you I heard it and was also "confused". Anyway the following WHO press
release may help clarify the criteria for judging health systems.
Dr. Mohga Kamal Smith
Health policy advisor
Policy Department
Oxfam GB
274 Banbury Rd
Oxford OX2 7DZ
Tel + 44 (0) 1865 312290
Fax +44 (0) 1865 312245
E mail [log in to unmask]
PressRelease
21June2000
WORLD HEALTH ORGANIZATION
ASSESSES THE WORLD'S HEALTH SYSTEMS
The World Health Organization has carried out the first ever analysis of the
world's health systems. Using five performance indicators to measure health
systems in 191 member states, it finds that France provides the best overall
health care followed among major countries by Italy, Spain, Oman, Austria and
Japan.
The findings are published today, 21 June, in The World Health Report 2000 -
Health systems: Improving performance.
The U. S. health system spends a higher portion of its gross domestic product
than any other country but ranks 37 out of 191 countries according to its
performance, the report finds. The United Kingdom, which spends just six percent
of gross domestic product (GDP) on health services, ranks 18th . Several small
countries - San Marino, Andorra, Malta and Singapore are rated close behind
second- placed Italy.
WHO Director-General Dr Gro Harlem Brundtland says: "The main message from this
report is that the health and well-being of people around the world depend
critically on the performance of the health systems that serve them. Yet there
is wide variation in performance, even among countries with similar levels of
income and health expenditure. It is essential for decision- makers to
understand the underlying reasons so that system performance, and hence the
health of populations, can be improved."
Dr Christopher Murray, Director of WHO's Global Programme on Evidence for Health
Policy. says: "Although significant progress has been achieved in past decades,
virtually all countries are underutilizing the resources that are available to
them. This leads to large numbers of preventable deaths and disabilities;
unnecessary suffering, injustice, inequality and denial of an individual's basic
rights to health."
The impact of failures in health systems is most severe on the poor everywhere,
who are driven deeper into poverty by lack of financial protection against ill-
health, the report says.
"The poor are treated with less respect, given less choice of service providers
and offered lower- quality amenities," says Dr Brundtland. "In trying to buy
health from their own pockets, they pay and become poorer."
The World Health Report says the main failings of many health systems are:
Many health ministries focus on the public sector and often disregard the
frequently much larger private sector health care.
In many countries, some if not most physicians work simultaneously for the
public sector and in private practice. This means the public sector ends up
subsidizing unofficial private practice.
Many governments fail to prevent a "black market" in health, where widespread
corruption, bribery, "moonlighting" and other illegal practices flourish. The
black markets, which themselves are caused by malfunctioning health systems, and
low income of health workers, further undermine those systems.
Many health ministries fail to enforce regulations that they themselves have
created or are supposed to implement in the public interest.
Dr Julio Frenk, Executive Director for Evidence and Information for Policy at
WHO, says: "By providing a comparative guide to what works and what doesn't
work, we can help countries to learn from each other and thereby improve the
performance of their health systems."
Dr Philip Musgrove, editor-in-chief of the report, says: "The WHO study finds
that it isn't just how much you invest in total, or where you put facilities
geographically, that matters. It's the balance among inputs that counts - for
example, you have to have the right number of nurses per doctor."
Most of the lowest placed countries are in sub-Saharan Africa where life
expectancies are low. HIV and AIDS are major causes of ill-health. Because of
the AIDS epidemic, healthy life expectancy for babies born in 2000 in many of
these nations has dropped to 40 years or less.
One key recommendation from the report is for countries to extend health
insurance to as large a percentage of the population as possible. WHO says that
it is better to make "pre-payments" on health care as much as possible, whether
in the form of insurance, taxes or social security.
While private health expenses in industrial countries now average only some 25
percent because of universal health coverage (except in the United States, where
it is 56%), in India, families typically pay 80 percent of their health care
costs as "out-of- pocket" expenses when they receive health care.
"It is especially beneficial to make sure that as large a percentage as possible
of the poorest people in each country can get insurance," says Dr Frenk.
"Insurance protects people against the catastrophic effects of poor health. What
we are seeing is that in many countries, the poor pay a higher percentage of
their income on health care than the rich."
"In many countries without a health insurance safety net, many families have to
pay more than 100 percent of their income for health care when hit with sudden
emergencies. In other words, illness forces them into debt."
In designing the framework for health system performance, WHO broke new
methodological ground, employing a technique not previously used for health
systems. It compares each country's system to what the experts estimate to be
the upper limit of what can be done with the level of resources available in
that country. It also measures what each country's system has accomplished in
comparison with those of other countries.
WHO's assessment system was based on five indicators: overall level of
population health; health inequalities (or disparities) within the population;
overall level of health system responsiveness (a combination of patient
satisfaction and how well the system acts); distribution of responsiveness
within the population (how well people of varying economic status find that they
are served by the health system); and the distribution of the health system's
financial burden within the population (who pays the costs).
"We have created a new tool to help us measure performance," says Dr Murray. "As
we develop it further and strengthen the raw data used for these measures in the
years to come, we believe this will be an increasingly useful tool for
governments in improving their own health systems."
Other findings in the annual WHO report include:
In Europe, health systems in Mediterranean countries such as France, Italy and
Spain are rated higher than others in the continent. Norway is the highest
Scandinavian nation, at 11th .
Colombia, Chile, Costa Rica and Cuba are rated highest among the Latin American
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Subject: Re: Is it worth it?
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Precedence: list
On 22-Jun-00 Mike McDowall wrote:
> Toby Lipman wrote :
>> we have
>> not adequately addressed the translation of population effects into
>> individual applicability.
>
> This is an issue I queried the list about some time ago (to
> deafening silence). We have developed models for prediction of
> outcome after stroke but are not comfortable that they can easily
> be mis-applied to individuals, where they are only really applicable
> to populations. I am afraid this is a fundamental flaw of statistics in
> general. When using Evidence from statistical analysis, you only
> have a probability (often not defined) that the evidence will be
> applicable in that particular case.
>
> So how will we fulfil Lynda Jackson's dream ?
>
> Yours sincerely,
> Mike McDowall (Mr.).
This is an important issue, but I would dispute that it represents
"a fundamental flaw of statistics in general", at any rate where
the principles are concerned.
There is almost always a difference between the characteristics
of a population and those of an individual, and in what is known
about these, and it leads to a paradoxical dilemma.
I have preached the following on various occasions, and I would like
to give it an airing now.
The Administrator and the Administratee have different (or potentially
different) "populations" in mind in questions of chance and statistics.
Their priorities (OK, utilities if you like) differ, and their decisions
and their beliefs (if any) can legitimately differ radically in the face
of the same apparent facts.
A "medical" example (quotes used because simplistic and using round-number
pseudo-risks) concerns breast cancer screening.
Suppose a test (e.g. mammography) has a 90% chance of giving the
correct answer (+ or -) if applied to any given subject (this
chance arising through random events in the operation of the
test procedure).
Suppose that, amongst the population of women referred for mammography,
1 in 20 have the disease (could be even less if GPs get paranoid
about failing to take proper care).
Then, of those referred, the proportions with a positive test result
will be 9/200 who have the disease, and 19/200 who do not.
The proportion of those with a positive result who have the disease
is 9/28, ~= 1/3. So a woman with a positive result probably does
not have the disease -- says the Administrator.
The woman who gets her own personal positive result may well view
the matter differently: "They did a test which has 90% chance of
getting it right, and this test says I have the disease. So I probably
have it. The numbers of other women who have/don't have it shouldn't
have anything to do with whether I have it."
Well, you can dispute the last sentence, since she did have her own
prior (historical) chance of getting it, if only one knew what it was.
One estimate of that could be the cross-sectional population proportion
(stratified appropriately by identifiable categories such as family
history, personal life factors, etc.), to the extent that her history
is "typical" -- or, in that splendidly deceptive word -- "representative"
of the population in her stratum..
Her own true chance mechanism is the historical line of her own exposure
to risk factors (including both ancestral events and exposures
during her own lifetime); and, if one knew these and the risk
mechanisms, then an estimate of her personal prior probability
could in principle be made. This one could call a "longitudinal
risk estimate" personalised for her.
As far as she is aware, she has the _possibility_ of being one
of those for whom the longitudinal prior risk is high, and who
is in a position to tell her different? (I think Bayesians might
wish to intervene here and impose a higher-level prior on this
question, but that opens another dimension of discussion).
So her last sentence could be interpreted as "why should my
longitudinal risk be the same as the cross-sectional risk of
the population of women 'like' me?"
This might be the case if one could make an assumption (what
in mathematical terms might be called an "ergodic hypothesis")
that "long-term longitudinal risk equals large-population cross-
sectional risk". Whether that is legitimate or not is a matter
of fact according to the situation being considered, and the
known facts about the particular patient: does her personal
history "sample the risks" in the same proportions as the
instantaneous population "samples the risks"?
Otherwise, we have a situation where the Administratee has
a different reference population for her chances than the
Administrator (the doctor). In that case, there is no reason
why they should agree about the "probability" that she has the
disease, since they are considering different things. (Of course,
doctors try to incorporate what they happen to know of the patient's
personal history, and thereby redress the balance between individual
and collective risk; but even then they may lack the information
which would help them to interpret what they know in terms
of objective risk).
They also have different utilities. For the woman, having
the disease would be a personal disaster and very little else
would count: if it's at all likely, let's go all the way.
The doctor falls between two stools: one the one hand, having
concern for the patient and to that extent sharing her
view; also, having his/her own back to cover and not wanting
to get a reputation for unduly overlooking real cases.
On the other hand, the doctor is the decision-maker for whether
to refer, so is the front-line custodian of NHS and Practice
resources, and will also feel that responsibility.
And this gets worse as the Administrator gets more elevated:
a real NHS Administrator may say "we're wasting our resources
on 2/3 of the cases that this test shows as positive, not to
mention the 190/200 costly tests with negative results. Let's
tone down this screening program." This Administrator officially
can only look at the cross-sectional probability, otherwise
his job gets out of hand, since it is only cross-sectional
data that he can at all readily consider.
However, the Administrator is also entitled to take "humane utilities"
into account: The function of the NHS is supposed to be to relieve
distress of medical origin, and it could be that ignoring a 1/3
chance (cross-sectional) of breast cancer is politically unacceptable.
But that is a matter of negotiation.
There is an analogous situation in the administration of criminal law.
There are very few crimes in the English statute book where the
criminality of others is proof of of your own (the only one that
springs to mind is Riotous Assembly -- mere presence at a riotous
assembly makes you guilty by definition). Otherwise, association
with criminals is not direct evidence of guilt, which has to be
proved (in criminal law) "beyond reasonable doubt" by evidence
directly bearing on the individual accused.
Nevertheless, a particular individual who associates with criminals
has a personal ("longitudinal") probability of becoming involved
with them in a process leading up to a crime. If this probability
could be reliably evaluated (and came out to a near-certainty) then
it could legitimately lead to a conviction, even though there was
no objective (e.g. forensic, witness) evidence showing that X was
indeed there and indeed did this and that. Usually, the law of evidence
inhibits this line of proof because of the uncertainties usually
associated with it and in order to isolate the jury from prejudicial
influence.
The principle of "proof beyond reasonable doubt" is an administrative
filter whose purpose is to protect the innocent, even if this
means that a fair few guilty get off. The consequence is that
people who are "probably guilty" often do not get convicted.
A good example of legal cross-sectional versus longitudinal evidence
would be the case of drink-driving. Merely being on the road
at pub closing-time on a Saturday night raises your cross-sectional
probability of being over the limit. In certain places at certain
times this could even rise to "much more probable than not".
Yet your longitudinal probability depends on your own drinking habits
(from zero for teetotallers to pretty high for some people) as
well as on what you happened to be doing that evening.
Little of this gets into the law as it stands: You provide a sample,
breath or blood or urine, which is scientifically analysed and,
depending on the result, you are charged (and most likely convicted)
or not. (People do give evidence that they "only had one glass of
whisky so the breathalyser was probably wrong", but it doesn't usually
cut much ice).
Yet there is enough error in the laboratory analysis to lead to
a fairly wide "grey area". This is allowed for, at present, by
subtracting a margin of error such that a positive outcome is
very unlikely unless the person really is guilty (in statistical
terms, this is a pure classical hypothesis test; and as it
happens this is the same logic as the mammography woman applied
to herself).
However, could the circumstances of the case (e.g. when and where
driving and with whom) be used to resolve some cases in the grey area?
11 o'clock on a Saturday night in the town centre, with inebriated
companions, and you get pushed up. 10 am on a Monday morning, alone and
miles from anywhere, and you get pushed down, maybe. But the law
ignores this. Nevertheless, the frequency of correct decision
(viewed cross-sectionally) could be improved by taking it into account.
This may lead to persistent injustice to one individual whose
circumstances are regularly unfortunate. The principle of "proof
beyond reasonable doubt" protects him. But it also allows others
to escape justice.
Other legal administrations (no names, no pack-drill) may take
the view that "we will get all the criminals we can, and never mind
the sufferings of the innocent". In the limit this can lead to
prevalent "guilt by association"; in the case of the sample
taken from the driver, this could mean that you _add_ the margin of
error to the lab result, just in case by chance it had come out low.
Whichever way it's done, taking circumstances into account amounts to
adopting a Bayesian philosophy (with or without a numerical calculation);
and this is also basically the Administrator's approach (though,
as it happens, not that of administrators of the Criminal Law who are
Hypothesis testers; in Civil cases, however, it tends to get much
more "Bayesian" because of the "balance of probabilities" approach).
Well, this has gone on long enough, I dare say.
In summary: there are probabilities ("cross-sectional") which apply
to populations, and probabilities ("longitudinal") which apply to
individuals. These may be similar or different, in any case
depend on different "reference sets", and one (usually the
"cross-sectional") may be known much better than the other
(which may not be known at all).
Individuals and administrators (at different levels) may legitimately
vary in how they evaluate probabilities (indeed, they are in fact
looking at different information even if the data may look the
same on paper), and may (almost certainly will) have different
criteria (utilities) when it comes to determining what should be
done.
There is a distinction between a "hypothesis testing" philosophy
(which avoids incorporation of prior probability) and a "Bayesian"
philosophy (which tries to take it into account).
In the case of the Administrator, who is looking at populations,
there is a reference "cross-sectional" population within which
"Bayesian prior frequencies" can be identified. Administrators
are [closet] Bayesians.
In the case of the individual, the prior probability that should
really be taken into account depends on their "longitudinal"
history, and may be unknown: In that case, it can be argued
(and in fact I believe it is appropriate) that the prior-free
"hypothesis-testing" philosophy is appropriate.
I believe this is an inevitable dilemma. To some extent it can
be alleviated if improved knowledge can lead to the two
sides having a wider basis of common ground in the information
they use. In any case, it is resolved in practice not "scientifically"
but by negotiation -- i.e. political means.
Best wishes to all,
Ted.
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Date: 22-Jun-00 Time: 13:45:52
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