Rion B. Correia1,2,3, Luciana Araujo Kohler4, Mauro M Matos4, Luis M. Rocha1,2,*

1School of Informatics, Computing, and Engineering, Indiana University, Bloomington IN, USA
2Instituto Gulbenkian de Ciencia, Portugal
3CAPES Foundation, Ministry of Education of Brazil
4Universidade Regional de Blumenau (FURB), Blumenau, SC, Brazil
* To whom correspondence she be addressed.

Citation: R.B. Correia, L.P. de Araújo, M.M. Mattos, L.M. Rocha [2019]. City-wide Analysis of Electronic Health Records Reveals Gender and Age Biases in the Administration of Known Drug-Drug Interactions. NPJ Digital Medicine. 2:74 . DOI: 10.1038/s41746-019-0141-x. The open-source full text pdf and the ArXiv:1803.03571 preprint are also available.


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 reenter 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). 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 drug pairs that are likely to result in major adverse drug reactions (ADR)—with costs estimated to be much larger than previously reported in smaller studies. The large-scale analysis reveals that women have a 60% increased risk of DDI as compared to men; the increase becomes 90% when considering only DDI known to lead to major ADR. 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 statistical null model demonstrates that age- and female-specific risks from increased polypharmacy fail by far to explain the observed DDI risks in those populations, suggesting unknown social or biological causes. We also provide a network visualization of drugs and demographic factors that characterize the DDI phenomenon and demonstrate that accurate DDI prediction can be included in health care and public-health management, to reduce DDI-related ADR and costs.

Keywords: Electronic Health Records, Drug-Drug Interactions, complex systems, complex networks, public health, digital medicine