Evidence map›Paper›PMID 22249906›Full record

ArticleJournal of medical Internet research2012

Real-time prescription surveillance and its application to monitoring seasonal influenza activity in Japan.

Tamie Sugawara, Yasushi Ohkusa, Yoko Ibuka, Hirokazu Kawanohara, Kiyosu Taniguchi, Nobuhiko Okabe

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

15 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  13. Evaluation of a national pharmacy-based syndromic surveillance system.Canada communicable disease report = Releve des maladies transmissibles au Canada · 2015
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Tamie SugawaraNational Institute of Infectious Diseases, Infectious Disease Surveillance Center, Tokyo, Japan. tammy@nih.go.jp
Yasushi Ohkusa
Yoko Ibuka
Hirokazu Kawanohara
Kiyosu Taniguchi
Nobuhiko Okabe

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundReal-time surveillance is fundamental for effective control of disease outbreaks, but the official sentinel surveillance in Japan collects information related to disease activity only weekly and updates it with a 1-week time lag.

objectiveTo report on a prescription surveillance system using electronic records related to prescription drugs that was started in 2008 in Japan, and to evaluate the surveillance system for monitoring influenza activity during the 2009-2010 and 2010-2011 influenza seasons.

methodsWe developed an automatic surveillance system using electronic records of prescription drug purchases collected from 5275 pharmacies through the application service provider's medical claims service. We then applied the system to monitoring influenza activity during the 2009-2010 and 2010-2011 influenza seasons. The surveillance system collected information related to drugs and patients directly and automatically from the electronic prescription record system, and estimated the number of influenza cases based on the number of prescriptions of anti-influenza virus medication. Then it shared the information related to influenza activity through the Internet with the public on a daily basis.

resultsDuring the 2009-2010 influenza season, the number of influenza patients estimated by the prescription surveillance system between the 28th week of 2009 and the 12th week of 2010 was 9,234,289. In the 2010-2011 influenza season, the number of influenza patients between the 36th week of 2010 and the 12th week of 2011 was 7,153,437. The estimated number of influenza cases was highly correlated with that predicted by the official sentinel surveillance (r = .992, P < .001 for 2009-2010; r = .972, P < .001 for 2010-2011), indicating that the prescription surveillance system produced a good approximation of activity patterns.

conclusionsOur prescription surveillance system presents great potential for monitoring influenza activity and for providing early detection of infectious disease outbreaks.

Indexed as

Drug PrescriptionsPopulation SurveillanceSeasonsDrug Utilization ReviewHumansInfluenza, HumanJapan

Identifiers

PMID22249906
PMCPMC3846340

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.