Estimates of the global and regional burden of foodborne parasites as determined by the World Health Organization
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1 Estimates of the global and regional burden of foodborne parasites as determined by the World Health Organization Brecht Devleesschauwer IAFWP International Symposium Kuala Lumpur, Malaysia, 06/09/2017 Rue Juliette Wytsmanstraat Brussels Belgium T brecht.devleesschauwer@wiv-isp.be
2 Foodborne Disease Burden Epidemiology Reference Group (WHO/FERG)
3 Why estimate global burden of foodborne disease? Full extent of foodborne disease (FBD) unknown FBD outbreaks are highly visible, but true burden remains invisible FBD not a risk factor in studies on global burden of disease Very many (potentially) foodborne hazards not possible to monitor and control all of them at the same time and level need for prioritization, risk ranking
4 Challenges in estimating the global burden of FBD FBD are complex: numerous hazards, numerous health outcomes, effects on different time scales Food is not the only transmission pathway of many food-related hazards Limited data availability, particularly in developing countries Different methods for estimating disease burden due to different hazards
5 Burden of disease How to define disease impact or importance? Disease have an impact on multiple aspects Burden of disease = quantification of any of these aspects This requires specific metrics
6 Burden of disease Which disease is most important? Number of cases, number of deaths Severity of case: duration, reduction quality of life Severity of death: residual life expectancy Summary Measures of Population Health
7 Disability-Adjusted Life Years 1 DALY = 1 healthy life year lost Summary measure of population health Morbidity + mortality Disease occurrence + disease severity DALY = YLD + YLL YLD = Years Lived with Disability = Number of incident cases Duration Disability Weight YLL = Standard Expected Years of Life Lost = Number of deaths Residual Life Expectancy
8 Disability-Adjusted Life Years DW = 40 DALYs = 10 YLDs 30 1 = 30 YLLs Age DALY = YLD + YLL YLD = Years Lived with Disability = N D DW YLL = Years of Life Lost = M RLE
9 Disease models Disease model, outcome tree Schematic representation of health states acute, chronic stages complications (sequelae, long-term health outcomes) death + multiple severity levels
10 Disease models Toxoplasma gondii, congenital
11 Global Burden of FBD WHO/FERG methodology Systematic reviews disease epidemiology, disease model Limited data statistical imputation models Complexity Disability-Adjusted Life Years incidence perspective, hazard based reference year 2010 disability weights: WHO/GHE residual life expectancy: WHO/GHE standard (92) Multiple transmission pathways source attribution (exposure, food) using structured expert elicitation
12 Global Burden of FBD WHO/FERG scope 31 microbiological and chemical hazards 3 intestinal protozoa: Cryptosporidium spp., Entamoeba histolytica, Giardia spp. 1 invasive protozoon: Toxoplasma gondii 2 nematodes: Ascaris spp., Trichinella spp. 3 cestodes: Echinococcus granulosus, Echinococcus multilocularis, Taenia solium 5 trematodes: Clonorchis sinensis, Fasciola spp., intestinal flukes, Opisthorchis spp., Paragonimus spp.
13 Global Burden of FBP 400 million illnesses (~20%) 90 million foodborne illnesses (~15%) 100,000 deaths (~10%) 50,000 foodborne deaths (~15%) 12 million DALYs (~15%) 7 million foodborne DALYs (~20%)
14 Global Burden of FBP Cases ( 000) Deaths DALYs ( 000) Giardia spp. (28 236) Taenia solium (28 114) Taenia solium (2 788) Entamoeba histolytica (28 024) E. multilocularis (7 771) Paragonimus spp. (1 049) Ascaris spp. (12 281) Clonorchis sinensis (5 770) Toxoplasma gondii (829) Toxoplasma gondii (10 280) Cryptosporidium spp. (3 759) Ascaris spp. (605) Cryptosporidium spp. (8 585) Opisthorchis spp. (1 498) Clonorchis sinensis (523) Taenia solium (370) Entamoeba histolytica (1 470) E. multilocularis (312) Paragonimus spp. (139) Ascaris spp. (1 008) Cryptosporidium spp. (296) E. granulosus (43) Toxoplasma gondii (684) Opisthorchis spp. (188) Clonorchis sinensis (32) E. granulosus (482) Intestinal flukes (155) Intestinal flukes (19) Paragonimus spp. (250) Entamoeba histolytica (139) Opisthorchis spp. (16) Trichinella spp. (4) Fasciola spp. (90) Fasciola spp. (11) Fasciola spp. (0) E. granulosus (40) E. multilocularis (8) Giardia spp. (0) Giardia spp. (26) Trichinella spp. (4) Intestinal flukes (0) Trichinella spp. (0.6)
15 Global Burden of FBP Cases ( 000) Deaths DALYs ( 000) Giardia spp. (28 236) Taenia solium (28 114) Taenia solium (2 788) Entamoeba histolytica (28 024) E. multilocularis (7 771) Paragonimus spp. (1 049) Ascaris spp. (12 281) Clonorchis sinensis (5 770) Toxoplasma gondii (829) Toxoplasma gondii (10 280) Cryptosporidium spp. (3 759) Ascaris spp. (605) Cryptosporidium spp. (8 585) Opisthorchis spp. (1 498) Clonorchis sinensis (523) Taenia solium (370) Entamoeba histolytica (1 470) E. multilocularis (312) Paragonimus spp. (139) Ascaris spp. (1 008) Cryptosporidium spp. (296) E. granulosus (43) Toxoplasma gondii (684) Opisthorchis spp. (188) Clonorchis sinensis (32) E. granulosus (482) Intestinal flukes (155) Intestinal flukes (19) Paragonimus spp. (250) Entamoeba histolytica (139) Opisthorchis spp. (16) Trichinella spp. (4) Fasciola spp. (90) Fasciola spp. (11) Fasciola spp. (0) E. granulosus (40) E. multilocularis (8) Giardia spp. (0) Giardia spp. (26) Trichinella spp. (4) Intestinal flukes (0) Trichinella spp. (0.6)
16 Global Burden of FBP Cases ( 000) Deaths DALYs ( 000) Giardia spp. (28 236) Taenia solium (28 114) Taenia solium (2 788) Entamoeba histolytica (28 024) E. multilocularis (7 771) Paragonimus spp. (1 049) Ascaris spp. (12 281) Clonorchis sinensis (5 770) Toxoplasma gondii (829) Toxoplasma gondii (10 280) Cryptosporidium spp. (3 759) Ascaris spp. (605) Cryptosporidium spp. (8 585) Opisthorchis spp. (1 498) Clonorchis sinensis (523) Taenia solium (370) Entamoeba histolytica (1 470) E. multilocularis (312) Paragonimus spp. (139) Ascaris spp. (1 008) Cryptosporidium spp. (296) E. granulosus (43) Toxoplasma gondii (684) Opisthorchis spp. (188) Clonorchis sinensis (32) E. granulosus (482) Intestinal flukes (155) Intestinal flukes (19) Paragonimus spp. (250) Entamoeba histolytica (139) Opisthorchis spp. (16) Trichinella spp. (4) Fasciola spp. (90) Fasciola spp. (11) Fasciola spp. (0) E. granulosus (40) E. multilocularis (8) Giardia spp. (0) Giardia spp. (26) Trichinella spp. (4) Intestinal flukes (0) Trichinella spp. (0.6)
17 Regional Burden of FBP Africa Americas Eastern Mediterranean Europe Southeast Asia Western Pacific
18 Where to go from here? Limitations due to data availability & quality Not all foodborne parasites included Anisakis, Trypanosoma cruzi, Taenia saginata, Not all outcomes considered toxoplasmosis and schizophrenia? Data gaps particularly in low-income countries where burden is highest!
19 Number of data gaps by country From good estimates to better data!
20 Where to go from here? DALYs only tell one part of the story Measures problems, not solutions potential for health gain, unmet needs does not replace cost-effectiveness studies! Health impact is just one of many aspects economic impact rare, but severe stakeholder interests general awareness MCDA??
21 Conclusion WHO/FERG launched the most comprehensive estimates of the global burden of FBP to date These address the lack of data to support food safety policy making Despite data gaps and other limitations, the results demonstrate a considerable burden Priority hazards differ between regions Control methods do exist for many hazards, and are linked to economic development and effective food safety systems Hazards of global significance need novel control methods
22 Learn more WHO website foodborne-diseases/ferg/en/ PLOS collection Interactive tool s&path=/who_hq_reports/g36/prod/e XT/FoodborneDiseaseBurden EURO-FBP Cost Action
23 Thank you
24 Estimates of the global and regional burden of foodborne parasites as determined by the World Health Organization Brecht Devleesschauwer IAFWP International Symposium Kuala Lumpur, Malaysia, 06/09/2017 Rue Juliette Wytsmanstraat Brussels Belgium T brecht.devleesschauwer@wiv-isp.be
25 FERG sub-regions AMR A EUR A EUR B EUR C AFR D EMR B EMR D SEAR D WPR B AMR D AMR B AFR E SEAR B WPR A
26 Imputation model Provide reasonable value + uncertainty range Bayesian random effects log-normal regression model Only when data from different subregions available If not: no imputation, no global estimates Model description log(θ ij ) ~ Normal(μ i, σ 2 w ) 2 μ i ~ Normal μ 0, σ b θ ij = incidence in country j belonging to region i μ i = regional mean; μ 0 = global mean 2 2 σ w = within-region variance; σ b = between-region variance
27 Imputation model green countries: no imputation orange countries: LN μ i, σ w 2 random country within concerned subregion UI describes variability within subregions red countries: LN(μ 0, σ b 2 + σ w 2 ) random country within a random subregion UI describes variability between and within subregions
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