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Modelling prescription behaviour of general practitioners

  • Šárka Hudecová EMAIL logo , Michal Pešta and Daniel Hlubinka
Published/Copyright: June 7, 2017
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Abstract

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.


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.



(Communicated by Gejza Wimmer)


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Appendix A. Supplementary Tables

Table 3

Doctor-patient characteristics.

CodeName
ageAge of the practitioner (in years)
sexGender of the practitioner
patn# of his/her patients (in hundreds)
female% of female patients
patageAverage age of the patients (in years)
regionBelgian province where his/her office is located (11 factors)
nce% of new chemical entities in DDD of total
magi% of magisterials (medicaments made by retail pharmacist) in € of total
gener% of generic medicaments in DDD of total
Table 4

Belgian provinces.

CodeRegion
1Brussels
2Walloon Brabant
3Flemish Brabant
4Antwerp
5Limburg
6Liege
7Namur
8Hainaut
9Luxembourg
10West Flanders
11East Flanders
Table 5

List of the candidate indicators.

ATC CodeName of the Candidate IndicatorATC CodeName of the Candidate Indicator
AO2Drugs for Acid Related DisordersJ01DFMonobactams
A02BAH2-receptor AntagonistsJ01DHCarbapenems
A02BCProton Pump InhibitorsJ01EATrimethoprim and Derivatives
B01AAVitamin K AntagonistsJ01EDLong-acting Sulfonamides
BOIACPlatelet Aggregation Inhibitors Excluding HeparinJ01EEComb. of Sulfonamides and Trimethoprim, Incl. Der.
B01AB04DalteparinJ01FAMacrolides
B01AB05EnoxaparinJ01FFLincosamides
B01AB06NadroparinJ01GBOther Aminoglycosides
B01AB10TinzaparinJ01MAFluoroquinolones
B01AC04ClopidogrelJ01MBOther Quinolones
B01AC05TiclopidineJ01XAGlycopeptide Antibacterials
C02AntihypertensivesJ01XBPolymyxins
C03DiureticsJ01XENitrofuran Derivatives
C07Beta Blocking AgentsJ01CR02Amoxicillin and Enzyme Inhibitor
C08Calcium Channel BlockersJ01DA01Cefalexine
C09Agents Acting on the Renin-angiotensin SystemJ01DA06Cefuroxime
C10Lipid Modifying AgentsJ01DA08Cefaclor
C01DVasolidators Used in Cardiac DiseasesJ01CF02Cloxacillin
C03ALow-ceiling Diuretics, ThiazidesJ01EA01Trimethoprim
C07BBeta Blocking Agents and ThiazidesJ01CA04Amoxicillin
C07FBeta Blocking Agents and Other AntihypertensivesJ01AA08Minocycline
C08DSel. Ca Channel Blockers with Direct Cardiac EffectsJ01XE01Nitrofurantoin
C09BAce Iinhibitors, CombinationsJ01MA12Levofloxacin
C09DAngiotensin II Antagonists, CombinationsM01AAntiinflam. and Antirheum. Prod., Non-steroids
C03EALow-ceiling Diuretics and Potassium-sparing AgentsM01AHCoxibs
C03DAAldosterone AntagonistsM01AB05Diclofenac
C08CADihydropyridine DerivativesM01AE09Lonazolac
C09AAACE Inhibitors, PlainM01AE01Ibuprofen
C09CAAngiotensin II Antagonists, PlainM01AB01Indometacin
C10AAHMG CoA Reductase InhibitorsM01AE03Ketoprofen
C10ABFibratesM01AE11Tiaprofenic Acid
C01DX12MolsidomineM01AB03Tolmetin
J01Antibacterial for Systematic UseM01AX17Nimesulide
J01ATetracyclinesN02AOpioids
J01CBeta-lactam Antibacterials, PenicillinsN06AANon-selective Monoamine Reuptake Inhibitors
J01DOther Beta-lactam AntibacterialsN06ABSelective Serotonin Reuptake Inhibitors
J01MQuinolone AntibacterialsN02AB03Fluoxetine
J01FMacrolides, Lincosamides and StreptograminsN02AE01Buprenorphine
J01AATetracyclinesR03Drugs for Obstructive Airway Diseases
J01BAAmphenicolsR06AAntihistamines for Systematic Use
J01CAPenicillins with Extended SpectrumR01ADCorticosteroids
J01CEBeta-lactamase Sensitive PenicillinsR03BAGlucocorticoids
J01CFBeta-lactamase Resistant PenicillinsR03ACSelective Beta-2-adrenoreceptor Agonists
J01CRComb. of Penicillins, incl. Beta-lactamase InhibitorsR03BBAnticholinergics
J01DACephalosporins and Related SubstancesR03DCLeukotriene Receptor Antagonists
Table 6

Parameters of the final generalized linear model for drug expenses.

ParameterEstimateStd. Error95% WaldConf. Int.χ2p-value
Intercept15.01992.540410.040819.999034.96< .0001
C090.67550.01380.64850.70242404.84< .0001
C100.56730.01170.54440.59022360.15< .0001
J01D1.64470.12641.39701.8925169.25< .0001
J01M2.15300.15031.85852.4476205.26< .0001
M01A0.50820.02140.46640.5501566.05< .0001
R030.58510.01390.55780.61231771.51< .0001
J01C0.99320.09540.80631.1801108.49< .0001
J01CA−0.36950.1663−0.6954−0.04364.940.0263
J01AA2.63410.15012.33992.9284307.88< .0001
A021.46090.03291.39641.52551966.63< .0001
J01F2.60160.14562.31612.8871319.05< .0001
region2−0.21511.0183−2.21101.78080.040.8327
region3−6.33210.7603−7.8223−4.842069.36< .0001
region4−5.96680.7273−7.3923−4.541367.30< .0001
region5−2.08011.1029−4.24180.08163.560.0593
region63.01261.07600.90375.12147.840.0051
region7−0.87341.4485−3.71251.96570.360.5465
region81.18380.8637−0.50922.87671.880.1705
region94.06742.4310−0.69728.83202.800.0943
region103.58100.93591.74675.415314.640.0001
region11−5.71770.8630−7.4091−4.026243.90< .0001
age−0.19660.0285−0.2524−0.140847.68< .0001
sex1.26850.55410.18262.35455.240.0220
patn−0.56550.0681−0.6990−0.432068.92< .0001
female0.18370.02090.14280.224677.46< .0001
patage−0.07670.0355−0.1462−0.00724.680.0306
nce−0.31120.0536−0.4163−0.206233.71< .0001
magi0.74070.05200.63870.8426202.74< .0001
gener0.20040.08030.04310.35786.230.0125
Scale41.69110.586340.557742.8561

Received: 2015-1-21
Accepted: 2015-8-25
Published Online: 2017-6-7
Published in Print: 2017-6-27

© 2017 Mathematical Institute Slovak Academy of Sciences

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