Background: Few studies have been carried out to estimate the prevalence of polypharmacy in the general population using administrative databases. Different methods and definitions have been proposed, but no comparisons have been provided. The aim of this study is to estimate the prevalence and the determinants of polypharmacy in Rome (Italy).
Methods: Adults (35+; n=331,923) residing in 2008 in the Local Health Authority ‘Roma D’ (southern part of Rome) were included; prescriptions (years 2009-12) were retrieved from a database which collects information on all drugs prescribed. Three algorithms were defined: (1) the number of different drugs prescribed for at least 60 days per year; the number of different drugs prescribed for at least 60 days per quarter per year, using 90-days-fixed- (2), and -mobile-windows (3). Determinants of polypharmacy based on patients’ and general practitioner’ (GP) characteristics were investigated using multilevel logistic regression models.
Results: The prevalence of major polypharmacy (>5 drugs) ranged between 6 and 10%, depending on the algorithm used, yielding estimates similar to those in the existing literature. Algorithm 1 provided higher estimates than Algorithm 2 and 3; a temporal increase of about 3% for each algorithm was observed as well. Multilevel models showed that polypharmacy was more frequent among women, Italian-born-subjects, elderly, patients with ≥ 3 comorbidities, and subjects living in disadvantaged areas. No particular differences were detected by GPs’ characteristics.
Conclusions: Polypharmacy is an emerging public health issue with increasing prevalence. Prevalence estimates were sensitive to the algorithm used. Major polypharmacy was principally related to age and comorbidities, but other patient’s characteristics may play a role as well.
Citation: Fano V, Chini F, Pezzotti P, Bontempi K (2014) Estimating the Prevalence and the Determinants of Polypharmacy Using Data from a Health Administrative Database: A Comparison of Results Obtained Employing Different Algorithms. Adv Pharmacoepidemiol Drug Saf 3:151. doi: 10.4172/2167-1052.1000151