Relating calls to US poison centers for potential exposures to medications to Centers for Disease Control and Prevention reporting of influenza-like illness. (15th March 2016) - Beauchamp, Gillian A.; McKeown, Nathanael J.; Rodriguez, Sergio; Spyker, Daniel A.
Abstract: Context : The Centers for Disease Control (CDC) monitors influenza like illness (ILI) and the National Poison Data System (NPDS) warehouses call data uploaded by US poison centers regarding reported exposures to medication.Objective : We examined the relationship between calls to poison centers regarding reported exposures to medications commonly used to treat ILI and weekly reports of ILI.Materials and Methods : The CDC reports ILI, by age group, for each of 10 Health and Human Services (HHS) regions. We examined NPDS summary data from calls reported to poison centers regarding reported exposures to acetaminophen, cough/cold medications, and promethazine, for the same weeks, age groups, and HHS regions for influenza seasons 2000–2013. ILI and NPDS exposures were examined using graphical plots, descriptive statistics, stepwise regression analysis, and Geographic Information Systems (GIS).Results : About 5, 101, 841 influenza-like illness cases were reported to the CDC, and 2, 122, 940 calls regarding reported exposures to medications commonly used to treat ILI, were reported by poison centers to the NPDS over the 13 flu seasons. Analysis of stepwise models of the linear untransformed data involving 24 NPDS data groups and for 60 ILI measures, over the 13 influenza seasons, demonstrated that reported exposures to medications used to treat ILI correlated with reported cases of ILI with a median R 2  =   0.489 (min R 2  =   0.248, max R 2  =   0.717), with mean ± SD of R 2  =   0.494 ± 0.121. Median number of parameters used (degrees of freedom – 1) was 7.Conclusions : NPDS data regarding poison center calls for selected ILI medication exposures were highly correlated with CDC ILI data. Since NPDS data are available in real time, it provides complimentary ILI monitoring. This approach may provide public health value in predicting other illnesses which are not currently as thoroughly monitored.