From a public-health perspective, the occurrence of drug-drug-interactions (DDI) from multiple drug prescriptions is a serious problem, especially in the elderly population. This is true both for individuals and the system itself since patients with complications due to DDI will likely re-enter the system at a costlier level. We conducted an 18-month study of DDI occurrence in Blumenau (Brazil ; pop. 340,000) using city-wide drug dispensing data from both primary and secondary-care level. Our goal is also to identify possible risk factors in a large population, ultimately characterizing the burden of DDI for patients, doctors and the public system itself .  We found  181 distinct DDI being prescribed concomitantly to almost 5 \% of the city population. We also discovered that women are at a 60\% risk increase of DDI when compared to men , while only having a 6\% co-administration risk increase. Analysis of the DDI co-occurrence network reveals which DDI pairs are most associated with the observed greater DDI risk for females, demonstrating that contraception and hormone therapy are not the main culprits of the gender disparity, which is maximized after the reproductive years . Furthermore, DDI risk increases dramatically with age, with patients age 70-79 having a 50-fold risk increase in comparison to patients aged 0-19 . Interestingly, several null models demonstrate that this risk increase is not due to increased polypharmacy with age. Finally, we demonstrate that while the number of drugs and co-administrations help predict a patient's number of DDI (R^2=.413), they are not sufficient to flag these patients accurately, which we achieve by training classifiers with additional data (MCC= .83,F1 = .72 ). These results demonstrate that accurate warning systems for known DDI can be devised for public and private systems alike, resulting in substantial prevention of DDI-related ADR and savings.
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.
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while the number of drugs and co-administrations help predict a patient's number of DDI (R^2=.413), they are not sufficient to flag these patients accurately, which we achieve by training classifiers with additional data
machine learning classifiers accurately predict patients likely to be administered DDI given their history of prescribed drugs, gender, and age
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public and private systems alike, resulting in substantial prevention
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