Prevalence and Predictors of Potential Drug-Drug Interactions in the Elderly: A Cross-Sectional Study in the Brazilian Primary Public Health System

Purpose. The primary objective of this study was to investigate the prevalence of clinically important potential drug-drug interactions (DDIs) in elderly patients attending the public primary health care system in Brazil. The secondary objective was to investigate possible predictors of potential DDIs. Methods. A cross-sectional study was carried out in 5 Brazilian cities located in the Ourinhos Micro-region, Sao Paulo State, between November 2010 and April 2011. The selected sample was divided according to the presence (exposed) or absence (unexposed) of one or more potential DDIs (defined as the presence of a minimum 5-day overlap in supply of an interacting drug pair). Data were collected from medical prescriptions and patients' medical records. Potential DDIs (rated major or moderate) were identified using 4 DDI-checker programs. Logistic regression analysis was used to study potential DDI predictors. Results. The prevalence of clinically important potential DDIs found during the study period was 47.4%. Female sex (OR = 2.49 [95% CI 2.29-2.75]), diagnosis of = 3 diseases (OR = 6.43 [95% CI 3.25-12.44]), and diagnosis of hypertension (OR = 1.68 [95% CI 1.23-2.41]) were associated with potential DDIs. The adjusted OR increased from 0.90 [95% CI 0.82-1.03] in patients aged 60 - 64 years to 4.03 [95% CI 3.79 - 4.28] in those aged 75 years or older. Drug therapy regimens involving = 2 prescribers (OR = 1.39 [95% CI 1.17-1.67]), = 3 drugs (OR = 3.21 [95% CI 2.78-3.59]), = 2 ATC codes (OR = 1.19 [95% CI 1.12-1.29]), = 2 drugs acting on cytochrome P450 (OR = 2.24 [95% CI 2.07-2.46]), and ATC codes B (OR = 1.89 [95% CI 1.05-2.08]) and C (OR = 4.01 [95% CI 3.55-4.57]) were associated with potential DDIs. Conclusion. Special care should be taken with the prescription and therapeutic follow-up of patients who present characteristics identified as predictors. Knowledge of potential DDI predictors could aid in developing preventive practices and policies that allow public health services to better manage this situation.