Medication Regimen Complexity and Polypharmacy as Factors Associated with Unplanned Hospitalization: A Population-Based Cohort Study in Older People in Sweden

Background: Adverse drug events are a leading cause of preventable hospitalization among older people. The possible association between medication regimen complexity and unplanned hospitalizations has not been investigated in population-based studies. Objectives To investigate and compare the associations between medication regimen complexity and polypharmacy with unplanned hospitalization over a three-year period.

Methods: This population-based study analyzed data from 3348 participants aged 60 years and over in the Swedish National Study on Aging and Care in Kungsholmen (SNAC-K). Medication regimen complexity was assessed using the 65-item Medication Regimen Complexity Index (MRCI) in ten-unit steps. Polypharmacy was assessed by using number of medications as a continuous variable. Cox proportional hazard models were used to compute unadjusted and adjusted hazard ratios (HRs) with 95% confidence intervals (CIs) for factors associated with unplanned hospitalization over a three-year period. Receiver operating characteristics (ROC) curves and corresponding areas under the curve (AUCs) were calculated for the associations between MRCI and polypharmacy with unplanned hospitalization.

Results: In total, 1125 people (33.6%) had one or more unplanned hospitalizations over a three year period. When adjusted for age, sex, education, living place, comorbidity, ADLs, MMSE, unplanned admissions in the previous year, self-reported pain, dexterity, and receipt of help to sort medications; medication regimen complexity (HR 1.22; 95% CI 1.14-1.34) and polypharmacy (HR 1.07; 95% CI 1.04-1.09) were both associated with unplanned hospitalization. The AUCs were similar for both medication regimen complexity and polypharmacy.

Conclusion: Medication regimen complexity and polypharmacy were both associated with unplanned hospitalization. Both parameters had a similar ability to predict unplanned hospitalization.