Title |
Faster Detection of Poliomyelitis Outbreaks to Support Polio Eradication - Volume 22, Number 3—March 2016 - Emerging Infectious Diseases journal - CDC
|
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Published in |
Emerging Infectious Diseases, March 2016
|
DOI | 10.3201/eid2203.151394 |
Pubmed ID | |
Authors |
Isobel M. Blake, Paul Chenoweth, Hiro Okayasu, Christl A. Donnelly, R. Bruce Aylward, Nicholas C. Grassly |
Abstract |
As the global eradication of poliomyelitis approaches the final stages, prompt detection of new outbreaks is critical to enable a fast and effective outbreak response. Surveillance relies on reporting of acute flaccid paralysis (AFP) cases and laboratory confirmation through isolation of poliovirus from stool. However, delayed sample collection and testing can delay outbreak detection. We investigated whether weekly testing for clusters of AFP by location and time, using the Kulldorff scan statistic, could provide an early warning for outbreaks in 20 countries. A mixed-effects regression model was used to predict background rates of nonpolio AFP at the district level. In Tajikistan and Congo, testing for AFP clusters would have resulted in an outbreak warning 39 and 11 days, respectively, before official confirmation of large outbreaks. This method has relatively high specificity and could be integrated into the current polio information system to support rapid outbreak response activities. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
India | 1 | 33% |
Algeria | 1 | 33% |
United Kingdom | 1 | 33% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 2 | 67% |
Practitioners (doctors, other healthcare professionals) | 1 | 33% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Ireland | 1 | 1% |
Switzerland | 1 | 1% |
Unknown | 66 | 97% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Master | 16 | 24% |
Researcher | 14 | 21% |
Student > Bachelor | 7 | 10% |
Student > Ph. D. Student | 6 | 9% |
Lecturer | 4 | 6% |
Other | 10 | 15% |
Unknown | 11 | 16% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 23 | 34% |
Nursing and Health Professions | 8 | 12% |
Agricultural and Biological Sciences | 7 | 10% |
Biochemistry, Genetics and Molecular Biology | 4 | 6% |
Computer Science | 2 | 3% |
Other | 8 | 12% |
Unknown | 16 | 24% |