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Syndromic Surveillance Implementation During Disaster Events

Published online by Cambridge University Press:  30 November 2023

Eyad F. Alkhattabi Open the ORCID record for Eyad F. Alkhattabi [Opens in a new window] ,
Alexander Hart ,
Fadi Issa ,
Attila Hertelendy Open the ORCID record for Attila Hertelendy [Opens in a new window] ,
Yasir Alrusyani ,
Amalia Voskanyan  and
Gregory Ciottone
Eyad F. Alkhattabi*
Affiliation:
BIDMC Disaster Medicine Fellowship, Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA Ministry of Interior, Saudi Arabia Harvard Medical School, Boston, MA, USA
Alexander Hart
Affiliation:
BIDMC Disaster Medicine Fellowship, Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA Department of Emergency Medicine, Hartford Hospital, Hartford, CT, USA University of Connecticut School of Medicine, Farmington, CT, USA
Fadi Issa
Affiliation:
BIDMC Disaster Medicine Fellowship, Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA Harvard Medical School, Boston, MA, USA
Attila Hertelendy
Affiliation:
BIDMC Disaster Medicine Fellowship, Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA Department of Information Systems and Business Analytics, College of Business, Florida International University, Miami, FL, USA
Yasir Alrusyani
Affiliation:
BIDMC Disaster Medicine Fellowship, Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA Harvard Medical School, Boston, MA, USA Ministry of Health, Saudi Arabia
Amalia Voskanyan
Affiliation:
BIDMC Disaster Medicine Fellowship, Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
Gregory Ciottone
Affiliation:
BIDMC Disaster Medicine Fellowship, Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA Harvard Medical School, Boston, MA, USA
*
Corresponding author: Eyad F. Alkhattabi; Email: alkhattabieyad@gmail.com.
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Abstract

Introduction:

Disease surveillance is an integral part of public health. These systems monitor disease trends and detect outbreaks, whereas they should be evaluated for efficacy. The United States Centres for Disease Control and Prevention publish Guidelines for Evaluating Surveillance Systems to encourage efficient and effective use of public health surveillance that are accepted worldwide.

Objective:

This study reviews syndromic surveillance during natural and man-made disasters internationally. It aims to (1) review the performance of syndromic surveillance via pre-specified attributes during disaster and to (2) understand its strengths and limitations.

Methods:

PubMed was systematically searched for the articles assessing syndromic surveillance during a disaster. A narrative review was carried out based on those articles. Updated Guidelines for Evaluating Public Health Surveillance Systems were used to review performance of systems.

Results:

5,059 studies from PubMed were evaluated, and 16 met inclusion criteria. The majority of these studies considered the implementation of syndromic surveillance useable during disaster events. Studies described systems giving relevant and timely information. Simplicity and timeliness were the most highlighted attributes.

Conclusion:

Syndromic surveillance is simple, flexible, useful and usable during a disaster. Timely data can be obtained, but the quality of this type of data is sensitive to incomplete and erroneous reporting; because of this, a standardized approach is necessary to optimize these systems.

Keywords

syndromic surveillancedisasterpublic health emergencyearly warning

Information

Type
Research Letters
Information
Disaster Medicine and Public Health Preparedness , Volume 17 , 2023 , e542
Copyright
© The Author(s), 2023. Published by Cambridge University Press on behalf of Society for Disaster Medicine and Public Health, Inc

Syndromic surveillance is defined as “a method of surveillance that uses health-related data based on clinical observations rather than laboratory confirmation of diagnoses” is different from traditional by collecting information from nontraditional sources rather than being based on official diagnoses or lab results, thus being helpful in detecting outbreaks and monitoring the health impact of disaster in their early stages. 1

The United States Centers for Disease Control and Prevention (CDC) published Guidelines for Evaluating Surveillance Systems in 1988 to encourage the efficient and effective use of public health surveillance consisting of 6 tasks. 1 The guideline given by CDC provides an assessment tool to evaluate surveillance systems that is supposed to be the most comprehensive compared to other countries.

None of previous studies has reviewed the implementation of syndromic surveillance attributes across multiple events and countries. Therefore, to fill this gap in the literature, this study aims to review syndromic surveillance attributes and performance attributes in accordance with CDC’s guidelines during natural and man-made disasters internationally by (a) reviewing syndromic surveillance implementation by means of prespecified attributes during disaster events and (b) understanding its main strengths and limitations.

Methods

Data Collection

A narrative review of applicable articles found in PubMed database was carried out. The literature search used both Medical Subject Headings (MeSH) terms and PubMed keywords (Table 1). Studies met the inclusion criteria if they evaluated the implementation during disasters and examined system’s applicability to address health-related problems, infectious diseases, injuries, or bioterrorism. The exclusion criteria were non-English studies, those on nonhuman diseases or evaluating system outside of a disaster event.

Table 1.

List of MeSH and keyword search terms

Data Analysis

The titles and abstracts were initially screened for the relevancy of a topic, and then the full-text was checked for addressing clinical question. Data were analyzed by creating a spreadsheet in Google Sheets (Google LLC, Mountain View, CA) with the surveillance system’s attributes included in the reviewed studies. The parameters of evaluated implementation were the surveillance system’s level of simplicity, flexibility, data quality, acceptability, representativeness, timeliness, and stability according to CDC’s Updated Guidelines.

Results

A total of 5059 studies from the PubMed search were initially reviewed by title and abstract. This left 52 articles for the full text review, 16 of which finally met inclusion criteria. Some syndromic surveillance systems were studied according to their deployment in mostly natural disasters.

Table 2 demonstrates the findings of the article review. The fully met CDC’s criteria are labeled as fulfilled. ‘Unfulfilled’ indicates failure to adhere to CDC’s guidelines, and ‘undefined’ indicates that the attributes were not described. No article included relevant information on sensitivity and positive predictive value.

Table 2.

Summary of syndromic surveillance attributes in eligible articles

a ‘Unfulfilled’ indicates failure to adhere to CDC’s guidelines.

b Some of the attributes were not assessed due to the lack of sufficient information, as required by CDC’s methodology.

Of the mentioned parameters, simplicity is defined by ease of data entry and storage, while flexibility refers to the ability to integrate across different sites with different needs. Data quality reveals the ability a system to represent data comprehensively and in standardized form without missing required parameters. Meanwhile, acceptability defines the overall willingness, and thus likelihood of use by, participants involved in syndromic surveillance system. The purpose of a sensitivity measurement is to identify the ability of a system to truly detect the disease under surveillance, while the positive predictive value (PPV) determines the proportion of cases detected by the system that are truly related to the disease under surveillance. Representativeness encompasses the overall diagnostic and treatment capacities of health facilities employed in the previous studies. Timeliness refers to speed of reporting within systems and between them, while stability describes the capability of them for quick and efficient distribution of information along with safe preservation of it (Table 2).

Discussion

This study reviewed syndromic surveillance implementation regarding predefined by CDC’s attributes. Some of the articles found the system simple due to feasible and applicable data collection and dissemination. However, during Typhoon Haiyan, it was found to be too complex due to limitations in logistics and transportation supply that nonetheless can be overcome by using electronic-coded data. Reference Salazar, Law and Pesigan2,Reference Mandl, Overhage and Wagner3

Flexibility of application was detected in 6 articles. However, a limitation was noted in the flexibility of the SPEED system to investigate new case definitions and the type of injury in an armed conflict in Philippines. Reference Salazar, Law and Winkler4 Another syndromic surveillance application was flexible enough to adapt to specific health-related events at local level, but had limitations when applied nationally. Reference Ziemann, Rosenkötter and Riesgo5

Overall, maintaining data quality was challenging, incomplete reporting with missing data was an issue, and low-quality and erroneous data could represent an increase in infection rate caused by pseudo-outbreak, while automated reporting can compensate for it. Reference Mandl, Overhage and Wagner3,Reference May, Chretien and Pavlin6

Overall, there was limited information on end-user’s acceptance of syndromic surveillance systems, while during earthquake in Japan, the majority responded positively. Reference Griffith, Yahata and Irie7 The ease of data entry and short timeframe required to yield results were supposed to increase willingness to participate during an emergency.

Situational awareness could be assessed dependent on the setting where the data were reported, while natural disaster could limit representativeness due to non-operational facility. That was the case in the implementation of syndromic surveillance by SPEED during Typhoon Haiyan. Reference Salazar, Law and Pesigan2

Timeliness is considered functional when it provides real-time data, allowing rapid and timely response enhanced by electronic notification. Administrative issues, such as in the case of 2011 Japan tsunami, when the hours of operation in the studied evacuation center and public health office did not match, can produce challenges. Reference Griffith, Yahata and Irie7 Additionally, data validation, privacy issues, and irrelevant information can be associated with social media. Reference Gupta and Katarya8

Stability of the system can be affected by the availability and reliability of the collected information that is illustrated by the case of Hurricane Katrina, when power outages and a shortage of information technology (IT) staff limited the system’s functionality. Reference Fleischauer, Young and Mott9

Fragmented health-care system caused by armed conflict interfered substantially with implementation in Syria, whereas in Philippines, the SPEED system was overall unaffected. Reference Salazar, Law and Winkler4,Reference Ismail, Abbara and Collin10

The reviewed studies were only in English and mostly reported self-assessments of systems, which could incorporate biases, and not all the system attributes were addressed in the eligible articles. Future studies should investigate methods to enhance syndromic surveillance capabilities, and social media and Internet should be considered.

Overall, this review suggests that syndromic surveillance is timely, useful, and applicable during a disaster event.

References

Centers for Disease Control and Prevention (US). A primer for understanding the principles and practices of disaster surveillance in the United States. September 7, 2020. http://www.cdc.gov/nceh/hsb/disaster/default.htm Google Scholar
Salazar, MA, Law, R, Pesigan, A, et al. Health consequences of Typhoon Haiyan in the eastern Visayas region using a syndromic surveillance database. PLoS Curr. 2017;9. doi: 10.1371/currents.dis.4a3d3b4474847b2599aa5c5eefe3a621 CrossRefGoogle Scholar
Mandl, KD, Overhage, JM, Wagner, MM, et al. Implementing syndromic surveillance: A practical guide informed by the early experience. J Am Med Inform Assoc. 2004;11(2):141-150. doi: 10.1197/jamia.M1356 CrossRefGoogle ScholarPubMed
Salazar, MA, Law, R, Winkler, V. Health consequences of an armed conflict in Zamboanga, Philippines using a syndromic surveillance database. Int J Environ Res Public Health. 2018;15(12):2690. doi: 10.3390/ijerph15122690.CrossRefGoogle ScholarPubMed
Ziemann, A, Rosenkötter, N, Riesgo, LG-C, et al. Meeting the International Health Regulations (2005) surveillance core capacity requirements at the subnational level in Europe: the added value of syndromic surveillance. BMC Public Health. 2015;15:107. doi: 10.1186/s12889-015-1421-2 CrossRefGoogle ScholarPubMed
May, L, Chretien, J-P, Pavlin, JA. Beyond traditional surveillance: applying syndromic surveillance to developing settings-opportunities and challenges. BMC Public Health. 2009;9:242. doi: 10.1186/1471-2458-9-242 CrossRefGoogle ScholarPubMed
Griffith, MM, Yahata, Y, Irie, F, et al. Evaluation of an ad hoc paper-based syndromic surveillance system in Ibaraki evacuation centres following the 2011 Great East Japan Earthquake and Tsunami. West Pac Surveill Resp J. 2018;9(4):21-27. doi: 10.5365/wpsar.2017.8.3.006 Google Scholar
Gupta, A, Katarya, R. Social media based surveillance systems for healthcare using machine learning: a systematic review. J Biomed Inform. 2020;108:103500. doi: 10.1016/j.jbi.2020.103500 CrossRefGoogle ScholarPubMed
Fleischauer, AT, Young, S, Mott, J, et al. Disaster surveillance revisited: passive, active and electronic syndromic surveillance during hurricane Katrina, New Orleans, LA – 2005. Adv Dis Surveill. 2007;2:153.Google Scholar
Ismail, SA, Abbara, A, Collin, SM, et al. Communicable disease surveillance and control in the context of conflict and mass displacement in Syria. Int J Infect Dis. 2016;47:15-22. doi: 10.1016/j.ijid.2016.05.011 CrossRefGoogle ScholarPubMed
Figure 0

Table 1. List of MeSH and keyword search terms

Figure 1

Table 2. Summary of syndromic surveillance attributes in eligible articles