Les enseignements théoriques et pratiques des microsimulations en économie de la santé (version anglaise)

Pascale Breuil-Genier

Description

En matière de santé peut-être encore plus que dans d'autres domaines des sciences sociales, le terme de microsimulation est employé pour désigner des modélisations très différentes : application d'un nouveau barème de remboursement à des données de dépenses de santé, agrégation de comportements individuels théoriques en information imparfaite, modélisation des interactions entre environnement socioéconomique, santé et soins, etc. Le type de modèle construit dépend évidemment de l'objectif poursuivi, et donc en général de l'utilisateur présumé du modèle. Pour schématiser, les organismes proches des pouvoirs publics privilégient le calcul de l'incidence d'une réforme sur les dépenses de santé socialisées, les statisticiens utilisent la microsimulation pour mettre en cohérence les données dont ils disposent ou éventuellement pour générer celles qui leur manquent, les épidémiologistes modélisent la survenue d'une maladie et parfois son traitement clinique, les économistes théoriciens s'appuient sur la microsimulation pour lier comportements individuels et agrégats macroéconomiques. Un examen de cette littérature, centrée sur les modèles analysant plutôt la demande de soins, permet de faire ressortir l'intérêt de ces diverses utilisations et la richesse des résultats qui peuvent d'ores et déjà en être dégagés.

Sujets

• INSEE
• Santé
• Économie

Informations

• Publié par: insee
• Nombre de lectures: 45
• Langue: English

Extrait

Theoretical and Practical
Lessons of Microsimulations in
Health Economics*
Pascale
Breuil Genier** In health matters, perhaps more than any other field of social sciences, the term
microsimulation is used to designate very different simulations, such as the
application of a new reimbursement schedule to health care expenditure, the
aggregation of individual behaviours under imperfect information, the modelling
of interactions within the socio economic environment, health and health care, etc.
Models are built according to their purpose and thus generally to suit the
presumed user of the model. Roughly speaking, government organisations focus
on calculating the impact of reforms on public health care expenditure,
statisticians use microsimulation to explain available data or else to generate
unavailable data, epidemiologists model the occurrence of disease, along with its
clinical treatment in some cases, and theoretical economists use microsimulation to
connect individual behaviours to macroeconomic aggregates.
A literature survey focussing primarily on models that analyse health care
* Originally published as demand reveals how beneficial these various uses are and the wealth of results that
“Les enseignements
théoriques et pratiques are already being produced.
des microsimulations en
économie de la santé,”
Économie et Statisti
que, no. 315, 1998 5.
** Pascale Breuil Genier
is the head of the Health
Economics Office at
the Social Security hree contrasts can be used to rank modelsbehaviour models used for more theoretical
Directorate. T by decreasing order of complexity (Mot,work are based on a stylised description ofThe work presented in
this article is part of a 1992). This is done from the outset to place the reality and on assumptions about consumer
research project funded objectives and characteristics of health related rationality. This type of model has been used to
by a grant from France’s
1microsimulation models into context with study the links between insurance and healthGeneral Planning
Commission. The author regard to other microsimulation work appliedcare with a formalisation of the choice of
would also like to to social policy. The contrasts are between: coverage level to account for potential adverse
express her heartfelt
selection effects (see Box 1) and health carethanks to everyone who
helped her in conducting -exogenous vs. endogenous behaviour : since consumption patterns at given coverage levels
this survey, with special reactions to a change in public policy are being to deal with the issue of moral hazard issue (see
thanks to L. Gatewood
assessed, endogenous behaviour models seem Box 1). These models stress the mechanismsand D. Blanchet.
The names and dates a priori to be preferable. However, their main
in parentheses refer to drawback is that they are more complex and
the bibliography at the
need to be based on assumptions that areend of the article.
1not universally accepted. Most endogenous For a general definition of microsimulation, see Box 5.
INSEE Studies no. 36, June 1999 1and not the macroeconomic results (or -closed ended vs. open ended financing:
forecasts). Microsimulation exercises that are microsimulation models rarely incorporate a
limited to applying spreadsheet methods to closed ended financial equilibrium condition.
exogenous expenditure data are a useful Models applied to health economics are
adjunct for assessing redistributive effects ex open ended since they do not introduce any
ante. element of macroeconomic financial
Box 1
INSURANCE OR UNCERTAINTY ECONOMICS
Imperfect information: adverse selection beneficiaries to pay less attention to preventive
and moral hazard behaviour, but this is actually contrary to observed
behaviours (Caussat and Glaude, 1993; Genier
Some markets are characterised by asymmetrical and Jacobzone, 1998; Menahem, 1997). Or else it
information between sellers and buyers. Not would lead them to engage in more r isky
sharing information can lead to opportunistic behaviours, such as practising dangerous sports.
behaviour on the part of one or the other party. However, given the non financial consequences of
Insurance analysis has focused on two of these health problems, this type of behavioural
behaviours in particular: adverse selection and response still seems unlikely.
moral hazard.
The ex post moral hazard in health insurance leads
Adverse selection to greater consumption of health care in case of
illness. In fact, all the empirical research concurs
Adverse selection comes into play when a voluntary that beneficiaries of complementary health
insurance policy is purchased. Individuals who think coverage use more ambulatory care services than
they have a high probability of making a claim non beneficiaries, even though the structure of their
("poor risks") have a greater incentive to take out consumption is quite different.
insurance (Rotschild and Stiglitz, 1976). An
insurance company that offers a contract that has Utility under uncertainty: risk aversion
been designed to fit the average r isk of the
population is therefore likely to incur losses, since Under uncertainty, economic agents’ expected utility
the average risk of its clients is greater than that ofdepends on their ut ility level (Von
the general population. However, empirical findings Neumann Morgenstern utility function) in each of
show that adverse selection effects are relatively the possible states and the probabilities of entering
limited where complementary health coverage is each of these states. For example, if two states are
concerned in France. Even though the links possible depending on whether or not a risk with a
between insurance and state of health usually seem probability p causing loss d occurs, agents’
to be significant, differences between the health of expected utility, which is assumed to depend solely
beneficiaries and non beneficiaries are still small. on income R, can be written as:
The amplitude or these differences, and even their
sign, sometimes vary depending on the health E(U)=p*U(R d)+(1 p)*U(R).
indicators or the populations being considered
(Genier, 1998). Agents are said to be risk neutral if their utility U is
an affine function of its argument r (e.g. U(r)=r). In
From the insurer’s point of view, the existence of this case, expected utility is equal to expected
sub populations with different risk levels may be an income, or R pd. Agents are just as w illing to pay
incentive to select potential clients so that only for insurance as they are to go without coverage, if
"good risks" are covered. Selection techniques may the premium is actuarially fair, which means equal
include medical questionnaires, targeted advertising to the expected loss ( pd), since they have the same
or exclusion clauses in policies. In this case we level of expected utility ( R pd) in both cases.
speak of cherry picking or risk selection.
On the other hand, if U is a concave function,
Moral hazard agents are said to be "risk adverse" and this makes
buying insurance advantageous for them. Insurance
Once a contract has been signed, beneficiaries enables them to reach an expected uility level oft
may, unobserved by the insurer, engage in certain U(R pd). By virtue of the properties of concave
behaviours that are likely to influence the probability functions, this utility is strictly greater than:
that a claim will be made ( ex ante moral hazard) or
to increase the value of claims made ( ex post moral P*U(R d)+(1 p)*U(R).
hazard) (Ehrlich and Becker, 1972). These
behaviours are assumed to reflect the fact that Risk aversion is therefore linked to the concavity of
beneficiaries are less inclined to prevent losses if function U. The an absolute index of risk aversion is
they know they are covered. In the case of health defined (for a point r) as the value of the ratio of the
insurance, ex ante moral hazard would lead derivatives U"/U’ at the point r.
2 INSEE Studies no. 36, June 1999equilibrium, although it sometimes appears inmodels. Some of them can be used for
the form of a financial equilibrium constraint simultaneous simulation of health and health
imposed on insurers. Individuals’ behaviour care.
affects their health spending and thus
influences the premiums paid by all. No claim is made as to the exhaustiveness of
Individuals then take premiums into account our survey of existing microsimulation models.
when making their own decisions relating to We cite a selection here merely to illustrate the
insurance. variety of applications and the contribution that
these methods can make to planning health care
-dynamic vs . static models: in static models, systems. However, we did chose to cite only
aggregated results are obtained by reweighting models dealing with health care demand, use
observations according to benchmark and financing, to the exclusion of models
2information from outside the model. Such dealing with health care supply.
models are often preferred to dynamic models
where changes in the population are simulated
Spreadsheet models endogenously period after period. However,
time is not a key variable in health matters, iand public acceptancen
contrast to pension plan simulations for
example, and static models offer the advantageThe sheer volu