Statistical methods

All medicines were aggregated per patient according to the fifth level codes (substance) in the Anatomical Therapeutic Chemical (ATC) Classification System.9 Based on these aggregated data, the medicine changes were computed from the differences in the prescribed medicines at the different time points (baseline, after first visit, follow-up after 4 months, and follow-up after 13 months). If a medicine was still discontinued or prescribed with precisely the same dosage after the first visit and at a follow-up visit, then the medicine change at the first visit was described as persistentOverprescribed medicines were defined as medicines that were discontinued or reduced in dosage at the first visit in the outpatient clinic. Underprescribed medicines were defined as medicines that were prescribed or increased in dosage at the first visit in the outpatient clinic. Rebounds were defined as medicines that were prescribed again following discontinuation.
The number of medicines prescribed at baseline was summarized according to the therapeutic subgroups (second level ATC). The number of underprescribed and overprescribed medicines were then plotted as a function of the total number of medicines at baseline with trend lines using loess regression.10 The medicine changes per group at the first visit were summarized descriptively along with the persistence of these changes. To identify the medicines that were more often discontinued during the medication review, we calculated the absolute difference in the proportions of discontinuations per medicine between groups. Only medicines prescribed to at least ten patients at baseline were included in the calculation. Rebounds were summarized for medicines with at least five discontinuations during the first visit. To compare the proportion of discontinuations and rebounds between groups, we plotted the ratio of the number of medicines prescribed at each time point to the number of medicines prescribed at baseline for pharmacological subgroups (third level ATC). Only subgroups with at least 40 medicines prescribed in both groups at baseline and at least 10 discontinuations in the medication review group during the first visit were included.
The reason(s) for discontinuations in the medication review group were registered prospectively, and based on these the primary reason for discontinuation was determined using the following hierarchy: 1) Treatment not indicated; 2) Treatment with no or poor effect; 3) Safety-related issues; 4) Patient preferences and circumstances; and 5) Unknown reason.
Lastly, to identify factors related to the number of overprescribed medicines, we created two exploratory models using all the subjects from the medication review group. One model included all patient baseline characteristics (to identify patient-related factors) and the other included all medicine groups (ATC fourth level, chemical subgroup) prescribed at baseline (to identify medicines that were associated with overprescribing). As the predicted variable was a count of overprescribed medicines, we fitted generalized linear models with a quasi-Poisson distribution (log link) using R version 3.6.311 with the tidymodel12 and poissonreg13 packages. For both models, we first fitted a full model using all variables excluding variables with near-zero variance. The statistically significant variables (defined as P < 0.05) from the full models were then further explored in univariate models. The models were purely exploratory and confidence limits and P-values were not adjusted for multiple comparisons. To illustrate the results from the models, model predictions using estimated marginal means14 were plotted for all patient baseline characteristics that were statistically significant in both the full and univariate models.