Evidence map›Paper›PMID 41872863›Full record

ArticleBMC public health2026

Syndromic surveillance systems to detect outbreaks of gastroenteritis in high-income countries: a scoping review.

Faris Adel Mutawalli, Amalie Dyda, Satyamurthy Anuradha, Sheleigh Lawler, Phu Cong Do, Rehab Meckawy, Simon Reid

Abstract readScoping Review
In one paragraph

Article in BMC public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Drug sales data for gastrointestinal infections surveillance: a systematic review up to 2025.Archives of public health = Archives belges de sante publique · 2026
    Pooled it
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Faris Adel MutawalliSchool of Public Health, The University of Queensland, Herston, QLD, Australia. f.mutawalli@student.uq.edu.au.
Amalie DydaSchool of Public Health, The University of Queensland, Herston, QLD, Australia.
Satyamurthy AnuradhaSchool of Public Health, The University of Queensland, Herston, QLD, Australia.
Sheleigh LawlerSchool of Public Health, The University of Queensland, Herston, QLD, Australia.
Phu Cong DoSchool of Public Health, The University of Queensland, Herston, QLD, Australia.
Rehab MeckawyCommunity Medicine Department, Faculty of Medicine, Alexandria University, Alexandria, Egypt.
Simon ReidSchool of Public Health, The University of Queensland, Herston, QLD, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSyndromic surveillance systems leverage non-diagnostic data sources to enable the early detection of infectious gastroenteritis outbreaks. However, their use in high-income countries (HICs) remains underexplored. This scoping review explores published literature on syndromic surveillance for gastroenteritis outbreaks in HICs, focusing on the types of data used, their effectiveness, and reported outcomes.

methodsA systematic search was conducted in PubMed, Embase, Scopus, and CINAHL for studies published from 2000 onwards. This scoping review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. Data extraction was conducted using a structured form adapted from the Joanna Briggs Institute (JBI) guidelines and the Centers for Disease Control and Prevention (CDC) framework for evaluating surveillance systems was used to systematically guide the extraction. Studies using diagnostic data or in-person healthcare visit data (e.g., GP or ED presentations) were excluded, in line with the syndromic surveillance purpose of early detection in HICs.

resultsTen studies met the inclusion criteria. The most common data sources were over-the-counter (OTC) medication sales (n = 5) and telehealth consultations (n = 4), followed by web-based queries (n = 2) and school absenteeism reports (n = 1). OTC and telehealth data provided timely, sensitive, and representative outbreak signals. However, challenges (e.g., seasonal stockpiling, demographic variability) affected OTC sales, while shifts in health-seeking behaviour influenced telehealth data reliability. Web-based queries showed early warning potential but were limited by geographic and demographic biases. School absenteeism data were useful for child-focused settings but had reporting limitations. Most studies assessed timeliness and sensitivity, with limited evaluations of other key attributes such as representativeness, acceptability, and stability. No studies explicitly considered health-seeking behaviour as a rationale for data selection.

conclusionsThis scoping review underscores the potential of syndromic surveillance for detecting gastroenteritis outbreaks in high-income countries. While most data sources could provide a signal for early outbreak detection, the findings highlight the need for more comprehensive evaluations, improved data representativeness, and the integration of health-seeking behaviour considerations in syndromic surveillance system design. These areas should be the focus of future research to enhance the effectiveness of syndromic surveillance for gastroenteritis in high-income countries.

Indexed as

Developed CountriesDisease OutbreaksGastroenteritisPopulation SurveillanceHumansEarly outbreak detectionGastroenteritis outbreaksHealth-seeking behaviourHigh-income countriesOver-the-counter medicationSchool absenteeismSyndromic surveillanceTelehealthWeb-based queries

Identifiers

PMID41872863
PMCPMC13011730

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.