Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Mathematica Slovaca

Editor-in-Chief: Pulmannová, Sylvia

6 Issues per year

IMPACT FACTOR 2017: 0.314
5-year IMPACT FACTOR: 0.462

CiteScore 2017: 0.46

SCImago Journal Rank (SJR) 2017: 0.339
Source Normalized Impact per Paper (SNIP) 2017: 0.845

Mathematical Citation Quotient (MCQ) 2017: 0.26

See all formats and pricing
More options …
Volume 67, Issue 3


Modelling prescription behaviour of general practitioners

Šárka Hudecová
  • Department of Probability and Mathematical Statistics Faculty of Mathematics and Physics Charles University in Prague Sokolovská 83 CZ–186 75 Prague 8 Czech Republic
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Michal Pešta
  • Department of Probability and Mathematical Statistics Faculty of Mathematics and Physics Charles University in Prague Sokolovská 83 CZ–186 75 Prague 8 Czech Republic
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Daniel Hlubinka
  • Department of Probability and Mathematical Statistics Faculty of Mathematics and Physics Charles University in Prague Sokolovská 83 CZ–186 75 Prague 8 Czech Republic
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2017-06-07 | DOI: https://doi.org/10.1515/ms-2017-0010


The health care costs have been rapidly rising in recent years and the public health insurance companies are highly interested in prescription policies of general practitioners. In our contribution, a complex model for prescription behaviour of Belgian general practitioners is built using the structural equation modelling (SEM) framework. The model involves a large number of prescribed medicament groups as well as doctors’ and patients’ characteristics. As one of the results, a relatively small number of medicament groups, which effectively describe the prescription behaviour of a given doctor, is obtained. These indicators are consequently used in a generalized linear model for predicting the drug expenses per patient. Such a model can be used as a useful guideline for the expenses’ assessment of a particular practitioner.

Keywords: drug expense; generalized linear model; prescribing indicator; structural equation modelling; variable selection

MSC 2010: Primary 62J12; 62P10; 62P25; Secondary 92B10; 62H25

This paper was written with the support of the Czech Science Foundation project “DYME – Dynamic Models in Economics” No. P402/12/G097. Research supported by the IAP research network grant No. P7/06 of the Belgian government (Belgian Science Policy) is also gratefully acknowledged. We are thankful to Philippe Van Wilder and RIZIV/INAMI organization (Belgium), the Department for Health Care Services for providing the data.


  • [1]

    Boj, E.—Claramunt, M. M.—Fortiana, J.—Vidiella, A.: The use of distance-based regression and generalized linear models in the rate making process. An empirical study. In: Mathematics Preprint Series, Institut de Matemática de la Universitat de Barcelona, 305, 2002, pp. 1–22.Google Scholar

  • [2]

    Bollen, K. A.: Structural Equations with Latent Variables, Wiley, New York, 1989.Google Scholar

  • [3]

    Cattell, R. B.: The scree test for the number of factors, Multivariate Behavioral Research 1 (1966), 245–276.CrossrefGoogle Scholar

  • [4]

    Ekedahl, A.—Andersson, S. I.—Hovelius, B.—Mölstad, S.—Liedholm, H.—Melander, A.: Drug prescription attitudes and behaviour of general practitioners, European Journal of Clinical Pharmacology 47 (1995), 381–387.CrossrefGoogle Scholar

  • [5]

    Fahrmeir, I.—Tutz, G.: Multivariate Statistical Modelling Based on Generalized Linear Models, Springer, New York, 1994.Google Scholar

  • [6]

    Fox, J.: An introduction to structural equation modelling – Lecture notes, socserv.mcmaster.ca/jfox/Courses/Oxford-2006/SEMs-notes.pdf, 2006, [Accessed on 24th February 2013].Google Scholar

  • [7]

    Gönül, F. F.—Carter, F.—Peterova, E.—Srinivasan, K.: Promotion of prescription drugs and its behavior on physicians’ choice behavior, Journal of Marketing 65 (2001), 79–90.CrossrefGoogle Scholar

  • [8]

    Guttman, L.: Some necessary conditions for common factor analysis, Psychometrica 19 (1954), 149–161.CrossrefGoogle Scholar

  • [9]

    Hardin, J. W.—Hilbe, J.: Generalized Linear Models and Extensions, Stata Press, College Station, TX, 2007.Google Scholar

  • [10]

    Harman, H. H.: Modern Factor Analysis, University Of Chicago Press, Chicago, 1976.Google Scholar

  • [11]

    Iglesias, C.—Nixon, J.—Cranny, G.—Nelson, E. A.—Hawkins, K.—Phillips, A.—Torgerson, D.—Mason, S.—Cullum, N. and on behalf of the PRESSURE Trial Group: Pressure relieving support surfaces (PRESSURE) trial: cost effectiveness analysis, British Medical Journal, 333 (2006), 1413–1415.Google Scholar

  • [12]

    Johnson, R. A.—Wichern, D. W.: Applied Multivariate Statistical Analysis, Prentice-Hall, Englewood Cliffs, NJ, 2002.Google Scholar

  • [13]

    Jöreskog, K. G.—Sörbom, D.: LISREL 8: User’s Reference Guide, Scientific Software International, Chicago, 1996.Google Scholar

  • [14]

    Kaiser, H. F.: The varimax criterion for analytic rotation in factor analysis, Psychometrika 23 (1958), 187–200.CrossrefGoogle Scholar

  • [15]

    Kaiser, H. F.: A note on Guttman’s lower bound for the number of common factors, British Journal of Statistical Psychology 14 (1961), 1–2.CrossrefGoogle Scholar

  • [16]

    Manchanda, P.—Chintangunta, P. K.: Responsiveness of physician prescription behavior of salesforce effort: An individual level analysis, Marketing Letters 15 (2004), 129–145.CrossrefGoogle Scholar

  • [17]

    McCullagh, P.—Nelder, J. A.: Generalized Linear Models, CRC Press, Boca Raton, FL, 1989.Google Scholar

  • [18]

    Miller, R. H.—Lufi, H. S.: Managed care plan performance since 1980: A literature analysis, Journal of the Americal Medical Association 271 (1994), 1512–1519.Google Scholar

  • [19]

    Myers, R. H.—Montgomery, D. C.—Vining, G. G.: Generalized Linear Models with Applications in Engineering and the Sciences, Wiley, New York, 2002.Google Scholar

  • [20]

    RIZIV/INAMI: Global analytical report on the content of Pharmanet – singular pathway 2005, Pharmaceutical Policy Management Unit of the Healthcare Department of Belgium’s National Institute for Health and Disability Insurance, www.riziv.fgov.be/drug/nl/index.asp, 2005, [Accessed on 27th July 2007].

  • [21]

    Rokstad, K.—Straand, J.—Fugelli, P.: General practitioners’ drug prescribing practice and diagnoses for prescribing: The Mϕre & Romsdal prescription study, Journal of Clinical Epidemiology 50 (1997), 485–494.CrossrefGoogle Scholar

  • [22]

    Schonebaum, A. D.—Boyd, J. K.—Dudek, K. J.: A comparison of competitive employment outcomes for the clubhouse and PACT models, Psychiatric Services 57 (2006), 1416–1420.CrossrefGoogle Scholar

  • [23]

    Sharma, S.—Mukherjee, S.—Kumar, A.—Dillon, W.: A simulation study to investigate the use of cutoff values for assessing model fit in covariance structure models, Journal of Business Research 58 (2005), 935–943.CrossrefGoogle Scholar

  • [24]

    Watkins, C.—Harvey, I.—Carthy, P.—Moore, L.—Robinson, E.—Brawn, R.: Attitudes and behaviour of general practitioners and their prescribing costs: A national cross sectional survey, Quality and Safety in Health Care 12 (2003), 29–34.Google Scholar

About the article

Received: 2015-01-21

Accepted: 2015-08-25

Published Online: 2017-06-07

Published in Print: 2017-06-27

Citation Information: Mathematica Slovaca, Volume 67, Issue 3, Pages 785–802, ISSN (Online) 1337-2211, ISSN (Print) 0139-9918, DOI: https://doi.org/10.1515/ms-2017-0010.

Export Citation

© 2017 Mathematical Institute Slovak Academy of Sciences.Get Permission

Comments (0)

Please log in or register to comment.
Log in