Prevalence and differences in the co-administration of drugs known to interact: an analysis of three distinct and large populations

Abstract

Background: The co-administration of drugs known to interact greatly impacts morbidity, mortality, and health economics. This study aims to examine the drug-drug interaction (DDI) phenomenon with a large-scale longitudinal analysis of age and gender differences found in drug administration data from three distinct healthcare systems.

Methods: This study analyzes drug administrations from population-wide electronic health records in Blumenau (Brazil; 133 K individuals), Catalonia (Spain; 5.5 M individuals), and Indianapolis (USA; 264 K individuals). The stratified prevalences of DDI for multiple severity levels per patient gender and age at the time of administration are computed, and null models are used to estimate the expected impact of polypharmacy on DDI prevalence. Finally, to study actionable strategies to reduce DDI prevalence, alternative polypharmacy regimens using drugs with fewer known interactions are simulated.

Results: A large prevalence of co-administration of drugs known to interact is found in all populations, affecting 12.51%, 12.12%, and 10.06% of individuals in Blumenau, Indianapolis, and Catalonia, respectively. Despite very different healthcare systems and drug availability, the increasing prevalence of DDI as patients age is very similar across all three populations and is not explained solely by higher co-administration rates in the elderly. In general, the prevalence of DDI is significantly higher in women - with the exception of men over 50 years old in Indianapolis. Finally, we show that using proton pump inhibitor alternatives to omeprazole (the drug involved in more co-administrations in Catalonia and Blumenau), the proportion of patients that are administered known DDI can be reduced by up to 21% in both Blumenau and Catalonia and 2% in Indianapolis.

Conclusions: DDI administration has a high incidence in society, regardless of geographic, population, and healthcare management differences. Although DDI prevalence increases with age, our analysis points to a complex phenomenon that is much more prevalent than expected, suggesting comorbidities as key drivers of the increase. Furthermore, the gender differences observed in most age groups across populations are concerning in regard to gender equity in healthcare. Finally, our study exemplifies how electronic health records' analysis can lead to actionable interventions that significantly reduce the administration of known DDI and its associated human and economic costs.

Keywords: Drug–drug interactions; Electronic health records; Multimorbidity; Polypharmacy.

Fig. 1

Diagram of co-administration and interaction computation for Catalonia, Blumenau, and Indianapolis. Two hypothetical patient-drug dispensing timelines with three drugs (i, j, and k) are represented. In Catalonia (left), two drugs (i,j) are assumed to be co-administered if they were dispensed and billed during the same month. In Blumenau and Indianapolis (right), two drugs are assumed to be co-administered if they were dispensed for an administration period with an overlap of at least 1 day. Drug administration lengths (in days for Blumenau and Indianapolis, and months for Catalonia) are shown for each dispensation. The three possible pairwise comparisons (i,j), (i,k), and (j,k) between the dispensed drugs are shown with their co-administration overlap marked with backgrounds in either orange (not known DDI) or red (known DDI). Note: medications dispensed together are not necessarily taken together, they may be distributed throughout the day to avoid certain interactions

Fig. 2

Prevalence of co-administration and interaction by age during the first 18 months of the studies. Green, red, and blue lines denote measurements for Blumenau, Catalonia, and Indianapolis, respectively. a Prevalence of co-administration of drugs. b Prevalence of co-administration of drugs known to interact. c–e Prevalence of interactions against the respective null model in c Blumenau, d Catalonia, and e Indianapolis. Circles denote the values obtained with the real data, while the asterisks denote the values obtained using the null model. The associated relative risk is shown above the points. Asterisks denote significant differences (Fisher’s exact test)

Fig. 3

a–c Prevalence of drug co-administrations and (d-f) interactions by age and gender for Blumenau, Catalonia, and Indianapolis in the first 18 months of administration. Red and blue colors denote the prevalence in women and men, respectively. Relative risks of co-administration and interaction for women per age group are displayed above the points. Asterisks denote significant differences (Fisher’s exact test)

Fig. 4

Catalonia DDI network. Nodes denote drugs involved in at least one co-administration known to be a DDI. Only nodes connected via edges with a strength of interaction larger than 0.18 are shown for clarity. Node color represents the highest level of primary action class, as retrieved from drugs.com. Node sizes are proportional to the probability of patients being affected by a DDI involving the drug (P(U_i^Φ)). Edge weights denote the strength of interaction (co-administration length). Edge colors denote relative risk (RR) for women (red) or men (blue). Color intensity for relative risks varies in [1, 5]; that is, values are clipped at 5 for clarity

Fig. 5

Top 20 drug interactions with the highest difference between DDI prevalence in women and men. Colors denote a higher prevalence of interactions in women (red) and men (blue). Markers (+ and −) denote significantly higher prevalence of DDI administrations in the respective gender after correcting for multiple testing (FDR ≤ 0.05). Note the color scale is different across populations, as the maximum and minimum differences in DDI prevalence are different between populations