City-wide electronic health records reveal gender and age biases in administration of known drug–drug interactions

Abstract

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.

Drug interactions in hospital prescriptions in Denmark: Prevalence and associations with adverse outcomes

“…A Danish study of 167 232 patients from 1998 on the island of Funen found that 4.4% of all inhabitants of age above 70 were prescribed drug combinations with a high risk of severe interactions 43 . A recent Brazilian study with ~340 000 patients from primary‐ and secondary‐care hospitals arrived at a similar figure 44 . These estimates are substantially lower than our 14% patients prescribed pDDIs with expected major clinical significance (Table 3), likely because our data are newer than those in the former and include also tertiary hospitals unlike both those studies.…”

“…43 A recent Brazilian study with ~340 000 patients from primary‐ and secondary‐care hospitals arrived at a similar figure. 44 These estimates are substantially lower than our 14% patients prescribed pDDIs with expected major clinical significance (Table 3 ), likely because our data are newer than those in the former and include also tertiary hospitals unlike both those studies.…”

Analysis of electronic health records from three distinct and large populations reveals high prevalence and biases in the co-administration of drugs known to interact

“…The data includes eighteen months (Jan 2014-Jun 2015) of anonymized drug administration and patient demographics retrieved from Pronto. It is the same data used in Correia et al [5] except for the removal of ophthalmological drugs, topical drugs, and vaccines from the analysis. In total, we analyze 140 unique DrugBank IDs dispensed to 133,047 patients.…”

Mining social media data for biomedical signals and health-related behavior

“…More than 30% of ADR are caused by drug-drug interactions (DDI) that can occur when patients take two or more drugs concurrently (polypharmacy) [35]. The DDI phenomenon is also a worldwide threat to public health [1,67], especially with increased polipharmacy in aging populations.…”

“…Due to the widespread digitization of behavioral and medical data, the advent of social media, and the Web's infrastructure of large-scale knowledge storage and distribution there has been a breakthrough in our ability to characterize human social interactions, behavioral patterns, and cognitive processes, and their relationships with biomedicine and healthcare. For instance, electronic health records of entire cities can yield valuable insights on gender and age disparities in health-care [1], and the communication patterns of Twitter and Instagram help us detect the spread of flu pandemics [2], warning signals of drug interactions [3], and depression [4].…”