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 .
The occurrence of drug-drug-interactions (DDI) from multiple drug dispensations 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 (18 months) of the DDI phenomenon at the primary- and secondary-care level using electronic health records (EHR) 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 more than 3 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 DDI combinations, likely to result in major adverse reactions with costs estimated to be larger than previously reported in smaller studies . DDI results are integrated into associative networks for inference and visualization, revealing key medications and interactions involved in the DDI phenomenon . 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 dispensed two or more drugs concomitantly. Interestingly, a null model demonstrates that age- and women-specific risks from increased polypharmacy fail by far to explain the observed risks of DDI in those populations. This suggests that social and biological factors are at play. These results demonstrate that considerable gender and age biases exist, but that accurate warning systems for known DDI can be devised for health-care systems and public-health policy management, to reduce DDI-related adverse reactions and health-care costs .
R
prescriptions
dispensations
A
(18 months)
A
(EHR)
R
18
more than 3
D
major
R
very serious adverse reactions and costs we estimate
major adverse reactions with costs estimated
A
in smaller studies
A
involved in the DDI phenomenon
R
prescribed
dispensed
R
age
age-
R
far exceed expectations
fail by far to explain the observed risks of DDI
R
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
These results demonstrate that considerable gender and age biases exist, but
R
leading to substantial reduction of
and public-health policy management, to reduce
R
savings
costs