Correction to: BMC Infect Dis (2021) 21:696 https://doi.org/10.1186/s12879-021-06387-y
Following the publication of the original article [1], some errors were identified in the text and in Table 1:
The sentences currently read:
-
the systematic monitoring of phone calls made to health services could have limited the outbreak from 18,500 cases to approximately 2300 cases by detecting the outbreak approximately six days earlier than actually detected
Anzeige
The sentence should read:
-
the systematic monitoring of phone calls made to health services could have limited the outbreak from 18,500 cases to approximately 2300 cases by detecting the outbreak approximately 2.5 months earlier than actually detected.
Furthermore, the correct Table 1 is given in this Correction article.
Table 1
Synthesis of data from the included articles (n = 18)
Data signal | Reference | Timeliness | Sensitivity/Specificity | Pros | Cons |
|---|---|---|---|---|---|
Single data signal SyS system | |||||
Over-the-counter (OTC) sales of pharmacy sales | Edge et al., 2004 [30]* | NI | NI | In situations where infected individuals have symptoms prompting self-medication, OTC sales trend would provide a more sensitive, timely and geographically specific detection tool than monitoring emergency room visits and laboratory-based surveillance | Adaptations to the algorithm will have to be developed to adjust for a number of factors contributing to the general noisiness of these data such as seasonal effects, promotional sales and type of population served. The success of such system will rely on automatic collection, analysis and dissemination of results |
Kirian et al., 2011 [25]* | NI | Sensitivity: 4–14%, specificity: 97–100% | It may capture symptoms in the population before a person with gastrointestinal illness seeks health care | It does not necessarily indicate the buyer’s location, their demographic status, or the reason for the purchase. Those who purchase OTC medications for their illness may not be representative of the sick population as a whole. Hoarding behaviour will also affect the outcome | |
Reimbursement of prescription drugs | Mouly et al., 2016 [20]* | NI | Sensitivity: 6% and 21% for two examined outbreaks | Prescription drug data can be considered for the development of a detection system of waterborne outbreaks given its ability to describe an epidemic signal. It could support authorities in slow developing outbreaks | The algorithm cannot be used directly in other countries because of their different health systems, types and sources of data, and medical practices. The accuracy depends on the medical consultation rate in the impacted population. The accuracy of using health insurance data to describe waterborne outbreaks depends on the medical consultation rate in the impacted population, however, as this is never the case, data analysis underestimates the total number of acute gastrointestinal cases |
Calls to health advice line (‘telehealth’) | Bjelkmar et al., 2017 [21]* | ~ 2.5 months | NI | Comparing call patterns between water distribution areas that were based on groups of postal codes gives timely indication of the underlying cause and therefore substantially increases the chances of effective countermeasures | Tradeoff between sensitivity and specificity in signal detection. Need for a protocol for signal evaluation and validation, especially for regions where the population size is small |
Multiple data signal SyS systems | |||||
Emergency care data; medical dispatch, ambulance medical service, emergency department chief complaints | Balter et al., 2005 [27]* | NI | NI | Emergency department syndromic surveillance might prove useful for detecting a problem and quantifying its magnitude | This system cannot determine the true etiology. If insufficient information exists to initiate an investigation, the decision is often made to observe whether the signal continues the next day, thereby losing syndromic surveillance's theoretical advantage of timeliness |
Ziemann et al., 2014 [24]* | NI | NI | This system could detect changes in local trends and clusters of statistical alarms | It is not likely to detect local gastrointestinal outbreaks with few, mild, or dispersed cases. The probability of detecting an outbreak increases with the outbreak size. The results cannot be generalized to region-level data or very sparse time series | |
Over-the-counter (OTC), web queries, calls to health advice line | Andersson et al., 2014 [19]* | NI | Calls to health advice line: sensitivity: 40–50%, specificity: 99%, web queries and OTC: no signal | SyS can serve as an early warning for waterborne outbreaks, especially with telephone triage data with sufficient temporal and spatial resolution. It may be suited to detecting widespread rises in syndromes and, rarely, small-scale outbreaks | The alarm does not contain information on the cases’ medical status to validate the cause of the alarm. Moderate and low outbreaks (< 1000 cases) are unlikely to be detected. Limitations to the reported results are linked to one of the four outbreaks were not waterborne |
Telehealth, in-hours and out-of-hours GP, ED visits | Smith et al., 2010 [22]* | Peak of calls coincides with outbreak (95% CI) in one area | NI | Multiple syndromic data streams are an advantage | Telehealth may, in general, be driven by media bias |
Chief complaints of patients reporting to emergency departments, over-the-counter and prescription pharmacy sales, and worker absenteeism | Heffernan et al., 2004 [28]* | NI | NI | Syndromic surveillance systems have proved useful for detecting substantial citywide increases in common viral illnesses (e.g. influenza, norovirus and rotavirus) | The studied systems have not detected more contained outbreaks earlier than traditional surveillance |
Combined health, spatial and environmental data | Proctor et al., 1998 [29]* | Timeliness of learning about the peak was 15 days earlier in in monitoring treatment plant effluent turbidity compared to ER’s visits and clinical laboratory | NI | It is noted the value of alternate data sources as early warning systems which can complement laboratory diagnosis | There are weaknesses for all proposed surrogate waterborne surveillance systems. For example, turbidity did not give information on disease causing-organisms; and treated water meeting quality standard could still contain sufficient level of pathogens |
Rambaud et al., 2016 [26]* | NI | NI | Combining two complementary methods protects against false positives, e.g. confusion of cases stemming from exposure from other types of food or swimming, for example | Pilot-study and not tested on a larger scale | |
Coly et al., 2017 [23]* | NI | Detected outbreaks < 100 cases | Increases sensitivity and timely detection of waterborne outbreaks | These systems are expensive in terms of resources and shared expertise in incorporating local knowledge regarding both environmental and health data | |
Simulations | |||||
Method evaluations via simulations of multiple signal SyS systems | Cooper et al., 2006 [36]** | Unlikely to detect local outbreak | NI | It may capture symptoms in the population before seeking health care | The alarm does not contain information regarding the cases’ medical status to validate the cause of the alarm. Moderate and low outbreaks (< 1000 cases) are unlikely to be detected. The detection ability varies seasonally. Telehealth may, in general, be driven by media bias |
Burkom et al., 2011 [31]** | NI | Sensitivity: 80%, specificity: 99% | Use of multiple syndromic data streams is an advantage. The number of false alarms is greatly reduced | Simulation results must generally be improved with real epidemiological data | |
Xing et al., 2011 [35]** | NI | Of the simulated models, the regression method had higher sensitivity (range 6–14% improvement of sensitivity in the surveillance system) | Demonstrates possible improvement in the surveillance system to increase sensitivity | Simulations based on small number of data points | |
Zhou et al., 2015 [34]** | 3.3 to 6.1 days | When reported, the sensitivity ranged from 24 to 77%, and the PPV was 90.5% | Sensitivity and timeliness increase with stratification | Study population perhaps not representative | |
Colón-Gonzales et al., 2018 [33]** | Unlikely to detect outbreaks < 1000 cases | NI | Framework applicable for other SyS systems | The detection ability varies seasonally | |
Mouly et al., 2018 [32]** | NI | Sensitivity: 73%, PPV: 90.5% | Space–time increases the likelihood of detecting outbreaks | The probability of detecting outbreaks increases with the outbreak size | |
The original article [1] has been corrected.
Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.