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Does quality affect patients’ choice of doctors? Evidence from the UK - Discussion
Rita Santos, Hugh Gravelle, Carole Propper, Fabrice Etilé
To cite this version:
Rita Santos, Hugh Gravelle, Carole Propper, Fabrice Etilé. Does quality affect patients’ choice of doc-tors? Evidence from the UK - Discussion. European Workshop of Health Economics and econometrics, 2014, Munich, Germany. 13 p. �hal-01607934�
Does quality affect patients’ choice of
doctors?
Evidence from the UK.
Rita Santos, Hugh Gravelle & Carol
Propper
Discussion by Fabrice Etilé
Main research question
•
Are patients (consumers of health
care) responsive to variations in
doctors/practices quality?
–
In a fixed price/perfect information
setting, a positive quality elasticity is a
necessary (but not sufficient) condition for
competition to improve quality.
–
The quality elasticity is expressed in a
« distance metric »: trade-off between
quality and the patient-practice distance
(value of time?).
–
Earlier studies on patients’ demand for
quality have mainly examined choices
between hospitals.
–
Estimates of the demand curve might be
used in later work to calibrate theoretical
supply-side models, in the spirit of the
empirical IO literature, and address
issues such as the impact of reforms in
practice contracts, entry barriers etc.
Method
•
Random-utility choice model with three key
ingredients:
– dij: distance from patient i to practice j.
– Qj: quality of practice j.
– Ca: set of practices j available for patients i living in LSOA a.
•
Estimation:
– Conditional logit:
– Mixed multinomial logit: more robust to violations of
the IIA. assumption, especially when differences in individual characteristics are not well-controlled.
, , , , ( ) . . . 1 , 1 max a a a a i a j C i ij i j ij ij i i i i i i i a j C i a j j C i a j C V t d Q i i d type Gumbel X X y V V α β ε ε α α α β β β ∈ ∈ ∈ ∈ ∈ ∈ ∈ ∈ = − + + = + = + = ⇔ = : % % ( )i ( ) 0i Var α% =Var β% =
Data (1)
•
Attribution Data Set (ADS): numbers of patients
by age/gender in each practice j (main id: j).
– ADS expanded to get a data-set of patients’ choices
(one line = i living in a, age/gender, with choice yi=j).
– Matched with aggregate neighbourhood statistics for
a (Xi variable).
•
Several data sets giving various measures of
quality for j (Qj):
– QOF: an official and multi-dimensional measure of the
quality of cares.
– Alternative quality measures for robustness tests:
subjective evaluation by patients (not by i!); ASCS emergency admissions.
– Other dimensions of quality: characteristics of doctors
Data (2)
•
Distance dij?
– Approximated by the distance between the nearest surgery
of a practice and the LSOA centroid.
•
Ca?
– All practices within D=10 km of the centroid.
– Play with D to test the robustness of the results.
dja dij
Main results
•
+one std in QOF => +0.83 pp for the probability of
being chosen (+15% patients) / +125 m in distance.
– Effects lower than the impact of +one std in the proportion
of female doctors or EU-trained doctors within the practice.
– Relative effects of various characteristics robust to the
specification of the distance function (t(d)), the potential endogeneity of Q, nonlinearities of the utility function and violations of the IIA assumption (mixed logit) etc.
Discussion/Questions
•
Difference between « objective » quality (QOF)
and subjective perceptions of quality?
– Some qualitative evidence that an increase in QOF in
a given practice may not correspond to an increase in quality as perceived by patients (Chew-Graham et al., 2013, BMC Family Practice) + some important quality aspects are learnt through experience (Gravelle and Masiero, JHE, 2000).
⇒ cross-sectional QOF elasticity may overestimate the
true quality elasticity of demand in a dynamic setting – is it possible to get at least two waves?
Discussion/Questions
•
Difference between « objective » quality
(QOF) and subjective perceptions of
quality?
–
Document the heterogeneity and non-linearity of
the relationship between QOF and a subjective
measure such as « Overall patient satisfaction
2009 » (the CS correlation is only 0.2).
–
High-QOF practices may be more likely to
publicize their performances => reduced
uncertainty for (risk-averse) patients and the
positive QOF effect may partly reflect this.
• Interact QOF and « Practice in different PCT » as
PCTs provide information on the quality of practices within their boundaries.
• What would be the theoretical consequences for
Discussion/Questions
•
Distance:
– Centroid OK if population uniformly distributed.
Otherwise, the distance is measured with important errors: how does this affect the results?
Example: the practice
in green has a larger
market share because
it settled in a densely
populated area. If the
latter are more likely to
be closer to the
centroid => upward
bias on the true
distance effect.
–
Hospitals seldom move while practices
endogenously choose their location to reduce
patients’ transportation costs (railway station
etc.) and to differentiate from competitors:
•
Discussion?
•
Instrumentation using the relative variations in
population density between postcodes in a
same LSOA? Or the distance to other
Discussion/Questions
•
Modelling choices: why treating the data as
micro-data and not aggregate (market-level) micro-data?
– Market = LSOA*Gender*Age Band
– BLP (1995, Ecta): Mixed Multinomial Logit
– Identification through variations of the « market share » of
each practice between markets, with distance d explicitly playing the role of a price.
– Identification of the variance of the distance and quality
effects (Var(αi), Var(βi)) also requires that many different practices are observed to compete on more than a single market: variations in d and variations in the choice sets C for observationally identical individuals.
Discussion/Questions
•
Modelling choices: why treating the data as
micro-data and not aggregate (market-level) micro-data?
– Would perhaps avoid to enter too much in the discussion
about your approximation for d.
– You can easily instrument several variables, as estimation
uses a GMM approach.
Discussion/Questions
•
Various issues:
–
Endogeneity of quality due to patient valuation of
unobserved characteristics (ξij) that may be
correlated with QOF (e.g. presence of a coffee
machine, proximity to a railway station etc.).
• Instrument = average QOF of neighbouring
practices => likely to be correlated with ξij if practices also compete on these unobserved quality aspects.
• I would at least take the average QOF of
neighbouring practices not in the same LSOA (but not valid if the LSOA specific valuation of
unobserved characteristics are correlated across LSOA).
• Other instruments would be useful to tests the
exclusion restriction:
– Sources of variations in inputs required for producing
quality: postcode average rental prices, proximity of complementary health services etc…
– Time variations in practice QOF
– Sum of the values of characteristics (excl. distance &
Discussion/Questions
•
Various issues:
– Distance: value of time? Interact with the average wage rate
in the LSOA if available…
– Link between changes in quality and changes in distance: if
QOF then demand/profit and probability to relocate ↗ ↗ closer to the centroid ?↗
• Attenuate the estimated distance/QOF tradeoffs?
• Document the QOF-distance relationship.
• Structural model for practices’ strategic decisions?
