| Literature DB >> 24955857 |
Michael Edelstein1, Anders Wallensten2, Inga Zetterqvist2, Anette Hulth2.
Abstract
Norovirus outbreaks severely disrupt healthcare systems. We evaluated whether Websök, an internet-based surveillance system using search engine data, improved norovirus surveillance and response in Sweden. We compared Websök users' characteristics with the general population, cross-correlated weekly Websök searches with laboratory notifications between 2006 and 2013, compared the time Websök and laboratory data crossed the epidemic threshold and surveyed infection control teams about their perception and use of Websök. Users of Websök were not representative of the general population. Websök correlated with laboratory data (b = 0.88-0.89) and gave an earlier signal to the onset of the norovirus season compared with laboratory-based surveillance. 17/21 (81%) infection control teams answered the survey, of which 11 (65%) believed Websök could help with infection control plans. Websök is a low-resource, easily replicable system that detects the norovirus season as reliably as laboratory data, but earlier. Using Websök in routine surveillance can help infection control teams prepare for the yearly norovirus season.Entities:
Mesh:
Year: 2014 PMID: 24955857 PMCID: PMC4067301 DOI: 10.1371/journal.pone.0100309
Source DB: PubMed Journal: PLoS One ISSN: 1932-6203 Impact factor: 3.240
Figure 1Weekly norovirus notifications and Websök searches, Sweden 2006–2013.
Figure 1 represents for each week between week 27 2006 and week 26 2013, the norovirus notifications and online searches for the terms “kräk” (vomiting) and “vinterkräksjuka” (winter vomiting disease) expressed as weekly proportion of the yearly total after smoothing, standardisation and adjusting for trend. The blue line represents laboratory notifications, the red line represents online searches for “kräk” (vomiting) and the green line represents online searches for “vinterkräksjuka” (winter vomiting disease).
Week number when activity crossed the epidemic threshold for norovirus laboratory notifications, and searches for “kräk” (vomiting) and “vinterkräksjuka” (winter vomiting disease) in Websök along with number of weeks gained for each search term compared with laboratory notifications, Sweden, 2006/2007 - -2012/2013.
| Week activity crossed epidemic threshold | Weeks gained | ||||||||||
| Laboratory | Kräk | Vinterkräksjuka | Kräk | Vinterkräksjuka | |||||||
| Prediction interval | 95% | 99% | 95% | 99% | 95% | 99% | 95% | 99% | 95% | 99% | |
| Season | 2006–07 | 45 | 45 | 45 | 45 | 44 | 44 | 0 | 0 | 1 | 1 |
| 2007–08 | 46 | 46 | 44 | 44 | 44 | 44 | 2 | 2 | 2 | 2 | |
| 2008–09 | 46 | 46 | 46 | 46 | 46 | 46 | 0 | 0 | 0 | 0 | |
| 2009–10 | 52 | 52 | 44 | 46 | 44 | 46 | 8 | 6 | 8 | 6 | |
| 2010–11 | 46 | 46 | 45 | 45 | 45 | 45 | 1 | 1 | 1 | 1 | |
| 2011–12 | 49 | 51 | 49 | 49 | 43 | 49 | 0 | 2 | 6 | 2 | |
| 2012–13 | 47 | 47 | 45 | 45 | 43 | 45 | 2 | 2 | 4 | 2 | |
Figure 2Number of weekly norovirus laboratory notifications and Websök searches, Sweden, 2006–2013.
Dots represent the weeks the threshold for norovirus season onset is crossed. Figure 2 presents three separate graphs. The black graph represents laboratory notifications, the green graph represents online searches for the term “vinterkräksjuka” (winter vomiting disease) and the red graph represents online searches for the term “kräk” (vomiting). Each graph presents the number of notifications or searches per week between week 27 2006 and week 26 2013, along with a baseline and the 95% upper prediction interval line of the baseline. For each year, on each graph, the dots represents the week when the number of searches or notifications exceeded the 95% upper prediction interval line of the baseline, thus indicating when the onset of the norovirus season is detected in each case.
Sociodemographic characteristics of Vårdguiden.se users compared with the Swedish population, 2012.
| Vårdguiden users | Sweden | P value | ||||
| Number (n = 3000 | Proportion (%) | Number (n = 9 555 893) | Proportion (%) | |||
|
| 0–10 | 0 | 0 | 1 117 576 | 12 | <0.001 |
| 10–17 | 90 | 3 | 810 545 | 8 | ||
| 18–30 | 570 | 19 | 1 643 022 | 17 | ||
| 31–50 | 1140 | 38 | 2 529 920 | 26 | ||
| 51–65 | 750 | 25 | 1 749 773 | 18 | ||
| >65 | 450 | 15 | 1 705 057 | 18 | ||
|
| Male | 690 | 23 | 4 765 905 | 50 | <0.001 |
| Female | 2280 | 76 | 4 789 988 | 50 | ||
|
| Primary | 240 | 8 | 1399764 | 20 | <0.001 |
| Secondary | 1110 | 37 | 3120099 | 45 | ||
| University | 1590 | 53 | 2374052 | 34 | ||
Totals do not always add up to 3000 due to missing answers
*p value for the chi square goodness of fit test
** out of the 16–74 year old 2012 Swedish population, n = 6893915.
Impact of norovirus outbreaks in hospitals and perception of web-based surveillance data according to 17 Swedish infection control teams, 2012/13.
| Impact of norovirus outbreaks on hospital | Closed wards | 11/16 | 69 |
| Staff members off sick | 11/16 | 69 | |
| Staff restricted to specific wards | 12/15 | 80 | |
| Patients redirected to other hospitals | 2/15 | 13 | |
| Beginning of infection control activities | After learning the winter vomiting season had started | 6/17 | 35 |
| Fixed date every year (October or November) | 3/17 | 18 | |
| After the first outbreak was declared | 6/17 | 35 | |
| Other | 2/17 | 12 | |
| Method infection control team became aware of beginning of norovirus season | Received a warning | 2/16 | 12 |
| actively searched for the information | 9/16 | 56 | |
| Other | 5/16 | 31 | |
| Preventive measures used for hospital norovirus outbreaks | Improved staff hand washing policy | 6/9 | 67 |
| Improved patient handwashing policy | 2/9 | 22 | |
| Modified visiting rules | 0/9 | 0 | |
| Information in media (radio, TV, newspapers, internet | 2/9 | 22 | |
| Posters in hospitals | 1/9 | 11 | |
| Staff training | 3/9 | 33 | |
| Reactive measures to hospital norovirus outbreaks | Improved staff hand washing policy | 14/17 | 82 |
| Improved patient handwashing policy | 1417 | 82 | |
| Modified visiting rules | 12/17 | 71 | |
| Information in media (radio, TV, newspapers, internet | 7/17 | 41 | |
| Posters in hospitals | 13/17 | 76 | |
| Closed wards | 10/17 | 59 | |
| Isolation of infected patients | 17/17 | 100 | |
| Restricting patient movements between departments | 15/17 | 88 | |
| Protective equipment for staff | 16/17 | 94 | |
| Restricting staff to work on specific wards | 9/17 | 53 | |
| Daily communication between hospital and infection control team | 12/17 | 71 | |
| Deep cleaning of affected wards | 1/17 | 6 | |
| General perception of web-based surveillance data | Web based surveillance data always trusted | 2/17 | 12 |
| Web based surveillance data trusted if originates from official source | 6/17 | 35 | |
| Web based surveillance data trusted as complement to laboratory data only | 9/17 | 53 | |
| Perception of web-based surveillance data compared with laboratory data | Web-based data and laboratory data as trustworthy | 7/13 | 54 |
| Web-based data more trustworthy | 1/13 | 8 | |
| Web-based data less trustworthy | 5/13 | 38 | |
| Perception of early warning from web-based surveillance data | Useful information | 15/17 | 88 |
| Helps to direct infection control strategy | 11/17 | 65 | |
| Decreases the number or size of norovirus outbreaks in hospitals | 5/16 | 31 |