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Junior doctors’ medical specialty and practice location choice: simulating policies to overcome regional inequalities

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Abstract

There are nowadays over 1 million Portuguese who lack a primary care physician. By applying a discrete choice experiment to a large representative sample of Portuguese junior doctors (N = 503) in 2014, we provide an indication that this shortage may be addressed with a careful policy design that mixes pecuniary and non-pecuniary incentives for these junior physicians. According to our simulations, a policy that includes such incentives may increase uptake of general practitioners (GPs) in rural areas from 18% to 30%. Marginal wages estimated from our model are realistic and close to market prices: an extra hour of work would require an hourly wage of 16.5€; moving to an inland rural setting would involve an increase in monthly income of 1.150€ (almost doubling residents’ current income); a shift to a GP career would imply an 849€ increase in monthly income. Additional opportunities to work outside the National Health Service overcome an income reduction of 433€. Our simulation predicts that an income increase of 350€ would lead to a 3 percentage point increase in choice probability, which implies an income elasticity of 3.37, a higher estimation compared to previous studies.

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Fig. 1

Source: own elaboration using Tableau Software® (NYSE: DATA) with data from Statistics Portugal (INE; https://www.ine.pt)

Fig. 2
Fig. 3

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Notes

  1. In this study, we use the terms “GP” and “family doctor” interchangeably.

  2. In this hyperlink we provide an animated visualization with the population-adjusted geographic dispersion of medical doctors in Portugal, according to their medical specialty. (http://public.tableau.com/profile/pedro.jorge.saldanha.ramos#!/vizhome/DispersoGeogrficadeMdicos/Planilha1). Data Source: Health Human Resources Database, Statistics Portugal, 2011.

  3. Some legal documents that have enforced these policies are [19, 20].

  4. Primary care is publicly financed and publicly delivered, which means there are no GP cooperatives or independently working physicians financed by the State. There is a legal framework for (private) physician cooperatives to contract primary care services for a specific population, yet this model (known as Family Health Unit type C) has never been implemented.

  5. This is the base salary doctors receive in the NHS, for a 35-40 h/week contract.

  6. The IMF issued a report last year where it presented some figures about doctors’ and nurses’ wages in Portugal and a comparison with other European countries. The report was severely criticized by the PMA and there is no compelling evidence for using those estimates in our study.

  7. β coefficients were set to zero in the pilot survey since information about the direction of preferences in the Portuguese context is nonexistent.

  8. Minimizing D-error is the most common criterion for evaluating DCEs [40].

  9. This 10th choice scenario was only used as an exclusion criterion and not for estimating the model.

  10. Some legal documents that regulate territorial nomenclature for statistical purposes are [41, 42].

  11. Interacting these attributes with other socio-demographic or medical education variables in a standard logit is unlikely to fully capture preference heterogeneity since some of it may be dependent only on unobservable characteristics (e.g., prejudice or prestige related to a specific specialty/location, specific moral values, etc.).

  12. Standard logit is a specific case where the mixing distribution is degenerate at 0 and 1.

  13. There is an ongoing debate over the benefits and problems of estimating WTP in preference space (described in the text) and in WTP space (where the model’s coefficients directly represent the WTP measures). We compared our model estimates in preference and WTP spaces and found no significant differences between them (data not shown, available upon request). Our models in preference scale have produced very realistic WTP estimates (see below), so we stick with this more widespread methodology [44].

  14. The model presented here is a tractable version of the full GMNL presented in Fiebig et al. [46]. It was estimated in Stata using the user-written gmnl command [47].

  15. We used this method to account for any asymmetric WTP distributions [48].

  16. We chose to apply on-site paper questionnaires instead of the easier online modality in order to increase our response rates. We are grateful to our colleagues working at each hospital for helping us with the logistics behind administering a national-level paper-based survey.

  17. In the supplemental information, we provide the Stata code for this simulation procedure.

  18. In a separate model (data not shown, available upon request), we included “private work” and “income” as correlated variables, to test whether junior doctors pictured those variables as substitutes. We did not find a statistically significant correlation, which implies that the utility gained from having more “private work” is not just related to income factors, but has a value per se.

  19. In Table 5, we show that the MWTP for “Work hours” is 66€. Converting the monthly salary into a weekly amount, we have 66€/4 = 16,5€.

  20. On Table 7 we show that Policy I predicts that an income increase of 350€ would lead to a 3 p.p. increase in choice probability. Therefore \((\partial { \Pr }\;({\text{Rural GP}})/\partial \;{\text{income}}) \, ({\text{income}}/{ \Pr }({\text{Rural GP}}) = (0.0 3/ 3 50) \times ( 2 7 50/0.0 7) = 3. 3 7.\)

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Acknowledgements

We are deeply indebted to our colleagues and friends at several medical schools and hospitals across the country for helping us with the logistics for distributing and collecting the questionnaires, particularly to the following MDs: Ana Rita Ramos, André Graça, André Tojal, Bernardo Matias, Carina Mendonça, Carolina Cardoso, Carolina Carneiro, Diogo Dias, Eduardo Palha Fernandes, Elisabete Ribeiro, Flávio Costa, João Felgueiras, João Lopes, João Neves, João Rego, João Sousa, José Pedro Pinto, Luís Coutinho, Luís Magalhães, Luísa Graça, Manuel Abecasis, Margarida Bernardo, Mariana Carrapatoso, Nélson Cunha, Nídia Ramos, Nuno China, Nuno Morais, Ricardo Reis, Ricardo Veiga, Rita Ferreira, Rui Coelho, Rui Malheiro, Rui Lopes. We are also thankful to Antonio Monforte for his initial guidance with the SAS algorithm, and to Professor Rita Gaio for the help with the model analysis. We are also grateful to ACSS, I.P. for valuable statistics of the population of junior doctors our sample represented. We acknowledge valuable feedback from the participants at the 14th Health Economics’ Portuguese Conference and at the European Health Economics Association conference (EuHEA 2016).

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Authors and Affiliations

  1. Medical Director Office, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, Universidade de São Paulo, Av. Dr. Enéas de Carvalho Aguiar, 255, Cerqueira César, São Paulo, SP, 05403-000, Brazil

    Pedro Ramos

  2. Faculty of Medicine of the University of Porto, University of Porto, Porto, Portugal

    Pedro Ramos, Hélio Alves & Maria A. Ferreira

  3. Bank of Portugal, Porto, Portugal

    Paulo Guimarães

  4. Faculty of Economics, University of Porto, Porto, Portugal

    Paulo Guimarães

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  1. Pedro Ramos
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Correspondence to Pedro Ramos.

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Appendix

Appendix

See Tables 8 and 9.

Table 8 List of variables and description
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Table 9 Model for junior doctors who referred they were considering a future in Primary Health Care
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Ramos, P., Alves, H., Guimarães, P. et al. Junior doctors’ medical specialty and practice location choice: simulating policies to overcome regional inequalities. Eur J Health Econ 18, 1013–1030 (2017). https://doi.org/10.1007/s10198-016-0846-6

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