Title: City-wide Analysis of Electronic Health Records Reveals Gender and Age Biases in the Administration of Known Drug-Drug Interactions,
Abstract: The occurrence of drug-drug-interactions (DDI) from multiple drug
prescriptions is a serious problem, both for individuals and health-care
systems, since patients with complications due to DDI are likely to re-enter
the system at a costlier level. We present a large-scale longitudinal study of
the DDI phenomenon at the primary- and secondary-care level using electronic
health records from the city of Blumenau in Southern Brazil (pop. ~340,000).
This is the first study of DDI we are aware of that follows an entire city
longitudinally for 18 months. We found that 181 distinct drug pairs known to
interact were dispensed concomitantly to 12% of the patients in the city's
public health-care system. Further, 4% of the patients were dispensed major DDI
combinations, likely to result in very serious adverse reactions and costs we
estimate to be larger than previously reported. DDI results are integrated into
associative networks for inference and visualization, revealing key medications
and interactions. Analysis reveals that women have a 60% increased risk of DDI
as compared to men; the increase becomes 90% when only major DDI are
considered. Furthermore, DDI risk increases substantially with age. Patients
aged 70-79 years have a 34% risk of DDI when they are prescribed two or more
drugs concomitantly. Interestingly, a null model demonstrates that age and
women-specific risks from increased polypharmacy far exceed expectations in
those populations. This suggests that social and biological factors are at
play. Finally, we demonstrate that machine learning classifiers accurately
predict patients likely to be administered DDI given their history of
prescribed drugs, gender, and age (MCC=.7,AUC=.97). These results demonstrate
that accurate warning systems for known DDI can be devised for health-care
systems leading to substantial reduction of DDI-related adverse reactions and
health-care savings.