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General practitioners’ income and activity: the impact of multi-professional group practice in France

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Abstract

France has first experimented, in 2009, and then generalized a practice level add-on payment to promote Multi-Professional Primary Care Groups (MPCGs). Team-based practices are intended to improve both the efficiency of outpatient care supply and the attractiveness of medically underserved areas for healthcare professionals. To evaluate its financial attractiveness and thus the sustainability of MPCGs, we analyzed the evolution of incomes (self-employed income and wages) of General Practitioners (GPs) enrolled in a MPCG, compared with other GPs. We also studied the impacts of working in a MPCG on GPs’ activity through both the quantity of medical services provided and the number of patients encountered. Our analyses were based on a quasi-experimental design, with a panel dataset over the period 2008–2014. We accounted for the selection into MPCG by using together coarsened exact matching and difference-in-differences (DID) design with panel-data regression models to account for unobserved heterogeneity. We show that GPs enrolled in MPCGs during the period exhibited an increase in income 2.5% higher than that of other GPs; there was a greater increase in the number of patients seen by the GPs’ (88 more) without involving a greater increase in the quantity of medical services provided. A complementary cross-sectional analysis for 2014 showed that these changes were not detrimental to quality in terms of bonuses related to the French pay-for-performance program for the year 2014. Hence, our results suggest that labor and income concerns should not be a barrier to the development of MPCGs, and that MPCGs may improve patient access to primary care services.

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Source: Appariement Cnam-DGFiP Drees

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Notes

  1. This study is part of a broader evaluation project that aims to address several aspects of MPCGs using a mixed methods approach.

  2. GPs who have fulfilled certain conditions can enter into a contract authorizing them to charge extra fees, which derogates from the standard fee schedule. They account for 10% of the self-employed GPs and will not be discussed further as they will be excluded from our study sample.

  3. 25% of GPs had additional salaried activities in 2014, and the associated income represented on average 20% of their total income.

  4. The decrees of 23 February 2015 and 24 July 2017 endorsed a continuation of the scheme and presented the eligibility frameworks for voluntary structures (Accord Conventionel Interprofessionnel pour les Structures de Santé Pluriprofessionnelles de Proximité).

  5. Technical procedures, such as stitches or imaging, for example, involve specific fees that are added to the regulated fees for the consultation.

  6. Matching with different criteria including pre-treatment outcomes has also been performed to check the robustness of our results. It involved the use of propensity score matching to better deals with the several continuous matching criteria (see Sect. 5.3 for more details).

  7. This assumption was, furthermore, supported by robustness analysis that parametrically tested the existence of differences in trends before 2008 (see Table 4 in Appendix).

  8. More precisely, we used the felm function from the R package lfe with dummy variable for treatment and time (which amount to use xtreg, fe function in stata). We opted for introducing a time dummy over using the two-way fixed-effect estimator (which is equivalent in our setting), in order to acknowledge the trend of the control group.

  9. The pooled DID model, (1), that we estimate by OSL was: \(Y_{it} = \alpha + \alpha_{g} {\text{MPCG}}_{i} + \lambda d14_{t} + \delta MPCG_{i} *d14_{t} + \beta_{1} Z_{i} + \beta_{2} X_{it} + u_{it}\), where \(\alpha_{g}\) captures the permanent difference between the group of treated and control GPS in the spirit of classic DID setting and \(Z_{i}\) is a vector of constant covariates over time. The pooled DID model does not fully exploit our individual panel dataset and, in principle, is less accurate for our purposes. However, it can be used as a benchmark and allows to estimate the interesting effects of time-invariant characteristics such as gender or living areas effects.

  10. Those descriptive analyses have been further confirmed by replications of our parametric analyses to test dynamics differential in terms of the share of patients with chronic diseases, share of patients over 65 years old or under 15 years old and the share of patients with free complementary health insurance (CMU-C) (available upon request).

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Acknowledgements

We thank the Drees (Direction de la recherche, des études, de l’évaluation et des statistiques) for letting us access to their facilities and to the CNAMTS-DGFiP database.

Funding

Funding for the French survey. Research grant from NHI for the evaluation program pilot by IRDES and CESP.

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Authors and Affiliations

  1. Centre for Research in Epidemiology and Population Health, French National Institute of Health and Medical Research (INSERM U1018), Université Paris-Saclay, Université Paris-Sud, UVSQ, 16 Avenue Paul Vaillant Couturier, 94807 Cedex, Villejuif, France

    Matthieu Cassou & Carine Franc

  2. Institute for Research and Information in Health Economics (IRDES), Paris, France

    Matthieu Cassou, Julien Mousquès & Carine Franc

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  1. Matthieu Cassou
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  2. Julien Mousquès
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Appendix

Appendix

Details of CEM criteria

CEM allows us to control imbalances bound between the treated and control groups, while the large size of the non-MGP sample allows us to use exact matching without discarding too many treated GPs. The continuous variable age was broken down into nine categories from 27 to 30-year-old GPs and then by 5-year increments from 30 to 60-year-old GPs (the oldest GP in the study sample was 59). Furthermore, to better control for GPs’ career profiles we also matched on whether GPs had practiced in private practice for more or less than 5 years. Indeed, depending on the year in which they set up in private practice (e.g., after practicing in a hospital), GPs could be exposed to early carrier ramp-up at different ages. With regard to the number of children, we included the GPs with strictly more than 3 in the same category. Finally, we defined a regular additional salaried activity as one that generated more than €4000 per year, which amounted to 5% of the average total income in 2008.

To achieve a balanced sample, we opted for weighting the standard GPs in each subclass, so that their distribution over the subclass corresponded to that of the GPs in MPCGs: all of whom had a weight equal to 1, while the weights of control GPs were normalized to sum up their actual number. More precisely, the CEM and consecutive weighting were computed with the MatchIt package in R, so that the control GPs’ weights within each exact matching’s subclass is (the number of treated within the subclass/the number of controls within the subclass) * (Overall number of control/Overall number of treated). In contrast to selecting a given ratio of control GPs within each subclass, as in m-to-1 matching approach, it enabled us to keep as much information as possible on the control group and to estimate the average treatment effect on the treated (ATT), provided that the same weights were used in the regression analysis.

Robustness analyses

The remainder of the “Appendix” presents selected tables from our robustness analysis (further results from robustness checks are available upon request).

The last two tables (Tables 6 and 7) present the replication of our parametric analysis when the control group was identified through PSM, and additional matching criteria were introduced. The following details more precisely the procedure which we used. The propensity score was estimated in 2008 (pre-reform period) using a logistic model that predicted the likelihood of joining an accredited MPCGs in function of: the socio-demographic and territorial covariates used in our original CEM, case mix details (share of patients according to their age, patients with free complementary health insurance, and patients with a chronic disease), as well as the number of patients seen, the number of medical services, the GP’s gross revenues, self-employed and salaried income, and the share of home visits and procedures in GPs’ gross revenues. The matching uses nearest-neighbor’s criterion to select 3 control GPs for each GP in a MPCG, and balances territorial socio-demographic characteristics, activity (visits and patients), and incomes. After the PSM, there were no significative differences in the pre-reform period in 2008 with regard to all the criteria included in the logistic model (descriptive statistics of the balanced sample are available on request).

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Cassou, M., Mousquès, J. & Franc, C. General practitioners’ income and activity: the impact of multi-professional group practice in France. Eur J Health Econ 21, 1295–1315 (2020). https://doi.org/10.1007/s10198-020-01226-4

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