tetano
Editor, Senior Moderator
J Infect Chemother. 2015 Aug 25. pii: S1341-321X(15)00171-3. doi: 10.1016/j.jiac.2015.07.007. [Epub ahead of print]
[h=1]Proposition of real-time precise prediction model of infectious disease patients from Prescription Surveillance using the National Database of Electronic Medical Claims.[/h] Nakamura Y[SUP]1[/SUP], Kawanohara H[SUP]2[/SUP], Kamei M[SUP]3[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] The incidence of common pediatric infectious diseases has been monitored officially at sentinel medical institutions in Japan. However, the numbers of affected patients are not provided. Prescription Surveillance (PS), which infers the number of patients with influenza, varicella, and gastrointestinal infections from data related to prescriptions at external pharmacies, provides estimates to the public the following morning. This study assessed the prediction ability of the incidence of common pediatric infectious diseases from PS information using the National Database of Electronic Medical Claims (NDBEMC): the number of patients prescribed neuraminidase inhibitors, anti-herpes virus drugs, antibiotic drugs, antipyretic analgesics, and multi-ingredient cold medications. The diseases include RS virus infection, pharyngoconjunctival fever, hand, foot and mouth disease, erythema infectiosum, exanthem subitum, pertussis, herpangina, influenza, varicella, and gastrointestinal infection. For comparison, we used the estimated number of patients who were prescribed neuraminidase inhibitor in PS, which had been confirmed already for precision, and provided estimates to the general public via the internet. The discrepancy rates of all considered diseases between the reported number in NDBEMC and the predicted numbers of patients from PS were less than the value in NI counts and the coefficients of determination in the estimation were from .8109 to .9825. These predictions were sufficiently precise to provide to the general public.
Copyright ? 2015. Published by Elsevier Ltd.
[h=4]KEYWORDS:[/h] Pediatric infectious disease; Pharmacy; Prescription Surveillance
PMID: 26320387 [PubMed - as supplied by publisher]
[h=1]Proposition of real-time precise prediction model of infectious disease patients from Prescription Surveillance using the National Database of Electronic Medical Claims.[/h] Nakamura Y[SUP]1[/SUP], Kawanohara H[SUP]2[/SUP], Kamei M[SUP]3[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] The incidence of common pediatric infectious diseases has been monitored officially at sentinel medical institutions in Japan. However, the numbers of affected patients are not provided. Prescription Surveillance (PS), which infers the number of patients with influenza, varicella, and gastrointestinal infections from data related to prescriptions at external pharmacies, provides estimates to the public the following morning. This study assessed the prediction ability of the incidence of common pediatric infectious diseases from PS information using the National Database of Electronic Medical Claims (NDBEMC): the number of patients prescribed neuraminidase inhibitors, anti-herpes virus drugs, antibiotic drugs, antipyretic analgesics, and multi-ingredient cold medications. The diseases include RS virus infection, pharyngoconjunctival fever, hand, foot and mouth disease, erythema infectiosum, exanthem subitum, pertussis, herpangina, influenza, varicella, and gastrointestinal infection. For comparison, we used the estimated number of patients who were prescribed neuraminidase inhibitor in PS, which had been confirmed already for precision, and provided estimates to the general public via the internet. The discrepancy rates of all considered diseases between the reported number in NDBEMC and the predicted numbers of patients from PS were less than the value in NI counts and the coefficients of determination in the estimation were from .8109 to .9825. These predictions were sufficiently precise to provide to the general public.
Copyright ? 2015. Published by Elsevier Ltd.
[h=4]KEYWORDS:[/h] Pediatric infectious disease; Pharmacy; Prescription Surveillance
PMID: 26320387 [PubMed - as supplied by publisher]