The reference price system and socioeconomic differences in ... - KCE

KCE Reports 126 Reference Price System 45
4.4.7 Statistical analyses
4.4.7.1 Analysis of the choice between a high cost original and a low cost alternative
For each of the drugs clusters selected in this analysis, the percentage of low costs
prescriptions was computed separately for GPs and specialists (across all specialities).
For the computation of the overall rate of low cost prescriptions (i.e. for GPs and
specialists), a weighted average was used, using double weight for specialists to account
for the difference in the sampling ratios (10% for GPs and 5% for specialists).
Logistic regression models were used to assess associations between patients’ and
physicians’ characteristics and the probability of a prescription of a low cost drug. As
patients usually receive more than one prescription over the year, the method of
Generalized Estimating Equations (GEE) was used to adjust the variance of each
parameter estimate for the clustering of prescriptions within patients. As this method
does not allow adjusting for more than one level of clustering, variance estimates for
physicians’ characteristics, as well as those of small area characteristics might be
underestimated.
All factors, but one, described in the previous section (4.4.5) were included in the final
model, whether statistically significant or not. This choice was made to allow proper
comparisons of effects across all drugs analyzed. Odds ratios and 95%CI were derived
from these regression models. P-values presented are those of the effect of the factor
as a whole (i.e. testing if there is any difference between all levels of the factor), and not
p-values from pairwise comparisons (testing each level of the factor to a reference
category).
Analysis of the model robustness revealed collinearity problems between the two small
area characteristics: income and education. In our sample, correlation between these
two factors equalled 0.6. Sensitivity analyses revealed that the education level was more
discriminatory than the income level, and thus only the education level of each patient’s
small area was used in the final models.
The association of low cost prescription and explanatory factors was not performed
separately for all drugs clusters (because it would make the interpretation very difficult),
nor were all those clusters pooled to perform one global analysis (because the results
would be a mix of different, probably conflicting, effects). Instead, we chose to select a
list of 12 clusters across 7 ATC-1 groups, and we analyzed these 12 clusters in detail.
Most used molecules within a class, or very specific molecules (for instance mostly
prescribed by GPs or by specialists, or not recommended for INN prescription) were
thus chosen for further analysis.
4.4.7.2 Analysis of the less costly molecule(s) within a class of drugs
For this second analysis, the unit of the analysis was the patient and not the
prescription. This observation unit better reflects the therapeutic choice of each
prescriber, because different active ingredients within a class may require a very
different number of prescriptions to achieve a similar therapeutic effect. Patients having
received different active ingredients from the same class of drugs are not included in the
analysis for the same reason. Because the unit of analysis was the patient, each patient
was assigned to one prescriber only, namely the one who prescribed him/her the
highest number of prescriptions. This was done separately for each type of drug class.
All market share information (in DDD) and expenditures (for NIHDI and for patients
per DDD) in 2008 are based on the total Pharmanet data. All distributions of patients
across active ingredients within a class and all regression results are based on our
sample of Pharmanet data.
The same logistic model as mentioned above was used to assess which patient, physician
and small area information is associated with the choice of the less costly molecule(s)
within a class of drugs.