Literature DB >> 16956163

Geographic representativeness for sentinel influenza surveillance: implications for routine surveillance and pandemic preparedness.

Hazel Clothier1, Joy Turner, Alan Hampson, Heath Kelly.   

Abstract

OBJECTIVE: To review the guidelines for geographic representativeness applied to sentinel influenza surveillance as proposed in the Framework for an Australian Influenza Pandemic Plan (1999).
METHODS: The number of sentinel practices, participating general practitioners and their consultation rates per 100,000 population, by region, were described for the Victorian sentinel surveillance system for 2003 and 2004. Influenza-like illness rates per 1,000 consultations were calculated for all participating practices and for a subset of regular participators. Indicators of seasonal influenza activity, set according to predefined thresholds, were compared in the two groups.
RESULTS: During these two influenza seasons, a subset of approximately one-quarter (27%) of participating practices provided almost half (45%) of the patient swabs and detected the same level of influenza activity over two influenza seasons as all participating practices. However, this subset of GPs recorded only 0.3% of all GP consultations in Victoria in 2004.
CONCLUSIONS: There should be an updated, evidence-based strategy for interpandemic influenza based on the number of general practice consultations. Requirements for surveillance during various pandemic phases also need to be reviewed.

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Year:  2006        PMID: 16956163     DOI: 10.1111/j.1467-842x.2006.tb00846.x

Source DB:  PubMed          Journal:  Aust N Z J Public Health        ISSN: 1326-0200            Impact factor:   2.939


  8 in total

1.  Detecting the start of an influenza outbreak using exponentially weighted moving average charts.

Authors:  Stefan H Steiner; Kristina Grant; Michael Coory; Heath A Kelly
Journal:  BMC Med Inform Decis Mak       Date:  2010-06-29       Impact factor: 2.796

2.  Optimizing provider recruitment for influenza surveillance networks.

Authors:  Samuel V Scarpino; Nedialko B Dimitrov; Lauren Ancel Meyers
Journal:  PLoS Comput Biol       Date:  2012-04-12       Impact factor: 4.475

3.  Quantifying differences in the epidemic curves from three influenza surveillance systems: a nonlinear regression analysis.

Authors:  E G Thomas; J M McCAW; H A Kelly; K A Grant; J McVERNON
Journal:  Epidemiol Infect       Date:  2014-04-23       Impact factor: 4.434

4.  Geographical spread of influenza incidence in Spain during the 2009 A(H1N1) pandemic wave and the two succeeding influenza seasons.

Authors:  D Gomez-Barroso; M A Martinez-Beneito; V Flores; R Amorós; C Delgado; P Botella; O Zurriaga; A Larrauri
Journal:  Epidemiol Infect       Date:  2014-01-27       Impact factor: 4.434

5.  Deploying digital health data to optimize influenza surveillance at national and local scales.

Authors:  Elizabeth C Lee; Ali Arab; Sandra M Goldlust; Cécile Viboud; Bryan T Grenfell; Shweta Bansal
Journal:  PLoS Comput Biol       Date:  2018-03-07       Impact factor: 4.475

6.  Estimation of influenza vaccine effectiveness from routine surveillance data.

Authors:  Heath Kelly; Kylie Carville; Kristina Grant; Peter Jacoby; Thomas Tran; Ian Barr
Journal:  PLoS One       Date:  2009-03-31       Impact factor: 3.240

7.  Royal College of General Practitioners Research and Surveillance Centre (RCGP RSC) sentinel network: a cohort profile.

Authors:  Ana Correa; William Hinton; Andrew McGovern; Jeremy van Vlymen; Ivelina Yonova; Simon Jones; Simon de Lusignan
Journal:  BMJ Open       Date:  2016-04-20       Impact factor: 2.692

8.  Applying Machine Learning Models with An Ensemble Approach for Accurate Real-Time Influenza Forecasting in Taiwan: Development and Validation Study.

Authors:  Hao-Yuan Cheng; Yu-Chun Wu; Min-Hau Lin; Yu-Lun Liu; Yue-Yang Tsai; Jo-Hua Wu; Ke-Han Pan; Chih-Jung Ke; Chiu-Mei Chen; Ding-Ping Liu; I-Feng Lin; Jen-Hsiang Chuang
Journal:  J Med Internet Res       Date:  2020-08-05       Impact factor: 5.428

  8 in total

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