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Cited by 242Salmonella and Campylobacter epidemiologyFood Safety and HygieneDelphi Technique in Research

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World Health Organization Estimates of the Relative Contributions of Food to the Burden of Disease Due to Selected Foodborne Hazards: A Structured Expert Elicitation

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Hald, Tine, Aspinall, Willy, Devleesschauwer, Brecht, Cooke, Roger, Corrigan, Tim, Havelaar, Arie H., Gibb, Herman J., Torgerson, Paul R., Kirk, Martyn D., Angulo, Fred J., Lake, Robin J., Speybroeck, Niko, Hoffmann, Sandra·2016·PLOS ONE
DOI 10.1371/journal.pone.0145839PMC PMC4718673
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Abstract

BACKGROUND: The Foodborne Disease Burden Epidemiology Reference Group (FERG) was established in 2007 by the World Health Organization (WHO) to estimate the global burden of foodborne diseases (FBDs). This estimation is complicated because most of the hazards causing FBD are not transmitted solely by food; most have several potential exposure routes consisting of transmission from animals, by humans, and via environmental routes including water. This paper describes an expert elicitation study conducted by the FERG Source Attribution Task Force to estimate the relative contribution of food to the global burden of diseases commonly transmitted through the consumption of food. METHODS AND FINDINGS: We applied structured expert judgment using Cooke's Classical Model to obtain estimates for 14 subregions for the relative contributions of different transmission pathways for eleven diarrheal diseases, seven other infectious diseases and one chemical (lead). Experts were identified through international networks followed by social network sampling. Final selection of experts was based on their experience including international working experience. Enrolled experts were scored on their ability to judge uncertainty accurately and informatively using a series of subject-matter specific 'seed' questions whose answers are unknown to the experts at the time they are interviewed. Trained facilitators elicited the 5th, and 50th and 95th percentile responses to seed questions through telephone interviews. Cooke's Classical Model uses responses to the seed questions to weigh and aggregate expert responses. After this interview, the experts were asked to provide 5th, 50th, and 95th percentile estimates for the 'target' questions regarding disease transmission routes. A total of 72 experts were enrolled in the study. Ten panels were global, meaning that the experts should provide estimates for all 14 subregions, whereas the nine panels were subregional, with experts providing estimates for one or more subregions, depending on their experience in the region. The size of the 19 hazard-specific panels ranged from 6 to 15 persons with several experts serving on more than one panel. Pathogens with animal reservoirs (e.g. non-typhoidal Salmonella spp. and Toxoplasma gondii) were in general assessed by the experts to have a higher proportion of illnesses attributable to food than pathogens with mainly a human reservoir, where human-to-human transmission (e.g. Shigella spp. and Norovirus) or waterborne transmission (e.g. Salmonella Typhi and Vibrio cholerae) were judged to dominate. For many pathogens, the foodborne route was assessed relatively more important in developed subregions than in developing subregions. The main exposure routes for lead varied across subregions, with the foodborne route being assessed most important only in two subregions of the European region. CONCLUSIONS: For the first time, we present worldwide estimates of the proportion of specific diseases attributable to food and other major transmission routes. These findings are essential for global burden of FBD estimates. While gaps exist, we believe the estimates presented here are the best current source of guidance to support decision makers when allocating resources for control and intervention, and for future research initiatives.

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Figures

Fig 1 Geographical distribution of the number of experts per subregion according to working experience (>3 years). Several experts had experience in more than one subregion. The subregions are defined on the basis of child and adult mortality as described by Ezzati et al. [63]. Stratum A: very low child and adult mortality, Stratum B: low child mortality and very low adult mortality, Stratum C: low child mortality and high adult mortality, Stratum D: high child and adult mortality, and Stratum E: high child mortality and very high adult mortality. AFR = African Region; AMR = Region of the Americas; EMR = Eastern Mediterranean Region; EUR = European Region; SEAR = South-East Asia Region; WPR = Western Pacific Region. A list of countries per subregions can be found in Havelaar et al. (48). The use of the term ‘subregion’ here and throughout the text does not identify an official grouping of WHO Member States, and the ‘subregions’ are not related to the six official WHO regions.
Fig 1 Geographical distribution of the number of experts per subregion according to working experience (>3 years). Several experts had experience in more than one subregion. The subregions are defined on the basis of child and adult mortality as described by Ezzati et al. [63]. Stratum A: very low child and adult mortality, Stratum B: low child mortality and very low adult mortality, Stratum C: low child mortality and high adult mortality, Stratum D: high child and adult mortality, and Stratum E: high child mortality and very high adult mortality. AFR = African Region; AMR = Region of the Americas; EMR = Eastern Mediterranean Region; EUR = European Region; SEAR = South-East Asia Region; WPR = Western Pacific Region. A list of countries per subregions can be found in Havelaar et al. (48). The use of the term ‘subregion’ here and throughout the text does not identify an official grouping of WHO Member States, and the ‘subregions’ are not related to the six official WHO regions.CC BY · PubMed Central
Fig 2 Statistical accuracy versus informativeness of the included experts when using equal weight (blue) or performance weight (red) combinations, respectively.
Fig 2 Statistical accuracy versus informativeness of the included experts when using equal weight (blue) or performance weight (red) combinations, respectively.CC BY · PubMed Central
Fig 3 Subregional estimates of the proportion of foodborne illnesses caused by Campylobacter spp., non-typhoidal Salmonella spp., Shiga toxin-producing Escherichia coli (STEC), Brucella spp. and Shigella spp. Indicated on the line plot are the 2.5th, 5th, 50th, 95th and 97,5th percentiles.
Fig 3 Subregional estimates of the proportion of foodborne illnesses caused by Campylobacter spp., non-typhoidal Salmonella spp., Shiga toxin-producing Escherichia coli (STEC), Brucella spp. and Shigella spp. Indicated on the line plot are the 2.5th, 5th, 50th, 95th and 97,5th percentiles.CC BY · PubMed Central
Fig 4 Subregional estimates of the proportion of foodborne illnesses caused by enteropathogenic E. (EPEC), enterotoxigenic E. coli (ETEC), Cryptosporidium spp. and Giardia spp. Indicated on the line plot are the 2.5th, 5th, 50th, 95th and 97,5th percentiles.
Fig 4 Subregional estimates of the proportion of foodborne illnesses caused by enteropathogenic E. (EPEC), enterotoxigenic E. coli (ETEC), Cryptosporidium spp. and Giardia spp. Indicated on the line plot are the 2.5th, 5th, 50th, 95th and 97,5th percentiles.CC BY · PubMed Central
Fig 5 Subregional estimates of the proportion of foodborne illnesses caused by Salmonella Typhi, Vibrio cholerae, Entamoeba histolytica, Norovirus, and Hepatitis A virus. Indicated on the line plot are the 2.5th, 5th, 50th, 95th and 97,5th percentiles.
Fig 5 Subregional estimates of the proportion of foodborne illnesses caused by Salmonella Typhi, Vibrio cholerae, Entamoeba histolytica, Norovirus, and Hepatitis A virus. Indicated on the line plot are the 2.5th, 5th, 50th, 95th and 97,5th percentiles.CC BY · PubMed Central
Fig 6 Subregional estimates of the proportion of foodborne illnesses caused by Toxoplasma gondii, Ascaris spp., Echinococcus granulosus, and Echinococcus multilocularis. Indicated on the line plot are the 2.5th, 5th, 50th, 95th and 97,5th percentiles.
Fig 6 Subregional estimates of the proportion of foodborne illnesses caused by Toxoplasma gondii, Ascaris spp., Echinococcus granulosus, and Echinococcus multilocularis. Indicated on the line plot are the 2.5th, 5th, 50th, 95th and 97,5th percentiles.CC BY · PubMed Central
Fig 7 Subregional estimates of the proportion of disease caused by foodborne exposure to lead. Indicated on the line plot are the 2.5th, 5th, 50th, 95th and 97,5th percentiles.
Fig 7 Subregional estimates of the proportion of disease caused by foodborne exposure to lead. Indicated on the line plot are the 2.5th, 5th, 50th, 95th and 97,5th percentiles.CC BY · PubMed Central
Fig 8 Subregional estimates (medians) of the proportion of disease caused by exposure to lead through eight different exposure routes.
Fig 8 Subregional estimates (medians) of the proportion of disease caused by exposure to lead through eight different exposure routes.CC BY · PubMed Central
Fig 9 Major transmission routes of human foodborne diseases illustrating two points of attribution: the reservoir level and the exposure level.
Fig 9 Major transmission routes of human foodborne diseases illustrating two points of attribution: the reservoir level and the exposure level.CC BY · PubMed Central

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Research works printed but not linked: 2

  1. B Devleesschauwer, 2015, Methodological framework for estimating the global burden of foodborne diseases, PLOS One
  2. H Gibb, 2015, World Health Organization estimates of the global and regional disease burden of four foodborne chemicals and toxins, 2010, PLOS Medicine

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Metadata

Authors
Hald, T., Aspinall, W., Devleesschauwer, B., Cooke, R., Corrigan, T., Havelaar, A. H., Gibb, H. J., Torgerson, P. R., Kirk, M. D., Angulo, F. J., Lake, R. J., Speybroeck, N., & Hoffmann, S.
Affiliations
  • Technical University of Denmark (DK)
  • University of Bristol (GB)
  • Ghent University (BE)
  • Resources For The Future (US)
  • World Health Organization (CH)
  • Utrecht University (NL)
  • University of Zurich (CH)
  • Australian National University (AU)
  • Centers for Disease Control and Prevention (US)
  • New Zealand Institute for Public Health and Forensic Science (NZ)
  • UCLouvain (BE)
  • Economic Research Service (US)
Corresponding author
Hald, Tine
ORCID
Venue
PLOS ONE
Publisher
Public Library of Science (PLoS)
Published
2016-01-19
Volume
11
Issue
1
Pages
e0145839
ISSN
1932-6203 (ISSN-L)
DOI
10.1371/journal.pone.0145839
PMC
PMC4718673
OpenAlex
W2284802218
Cited by
242
References
68
Citations by year
2026: 18 · 2025: 21 · 2024: 15 · 2023: 22 · 2022: 22 · 2021: 27 · 2020: 38 · 2019: 17 · 2018: 26 · 2017: 25 · 2016: 7
FWCI
31.50
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS ONE (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Explore Bristol Research (acceptedVersion, cc-by)
  • Zurich Open Repository and Archive (University of Zurich) (acceptedVersion, cc-by-nc)
  • Ghent University Academic Bibliography (Ghent University) (submittedVersion, other-oa)
  • Bristol Research (University of Bristol) (submittedVersion, cc-by)
  • DIAL (Catholic University of Leuven) (submittedVersion, other-oa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
  • Figshare (submittedVersion, cc-by)
  • ANU Open Research (Australian National University) (submittedVersion)
  • Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) (submittedVersion, other-oa)
  • Rivm Repository (Netherlands National Institute for Public Health and the Environment) (submittedVersion)
  • Zurich Open Repository and Archive (University of Zurich) (submittedVersion, cc-by-nc)
  • Zurich Open Repository and Archive (University of Zurich)
Funders
  • World Health Organization
  • European Food Safety Authority
  • Natural Environment Research Council (NE/J017450/1)
Topics
  • Life Sciences › Agricultural and Biological Sciences › Food Science › Salmonella and Campylobacter epidemiology
  • Life Sciences › Agricultural and Biological Sciences › Food Science › Food Safety and Hygiene
  • Social Sciences › Sociology and Political Science › Delphi Technique in Research
Keywords
food poisoning, global burden of disease, transmission routes, diarrheal disease, infectious disease transmission, lead exposure, structured expert elicitation, reservoir hosts, human-to-human transmission, waterborne transmission, non-typhoidal Salmonella, Toxoplasma gondii, Shigella, norovirus, Salmonella typhi, Vibrio cholerae, expert judgment uncertainty, social network sampling
MeSH terms
Food Microbiology, Foodborne Diseases, Humans, World Health Organization, Food Parasitology

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