• Gillian A Beauchamp, Nathanael J McKeown, Sergio Rodriguez, and Daniel A Spyker.
• a Emergency Medicine, Oregon Health & Science University , Portland , OR , USA ;
• Clin Toxicol (Phila). 2016 Mar 1; 54 (3): 235-40.

ContextThe 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.ObjectiveWe 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 MethodsThe 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).ResultsAbout 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.ConclusionsNPDS 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.

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