Ranking the Disease Burden of 14 Pathogens in Food Sources in the United States Using Attribution Data from Outbreak Investigations and Expert Elicitation

被引:346
|
作者
Batz, Michael B. [1 ]
Hoffmann, Sandra [2 ,3 ]
Morris, J. Glenn, Jr. [1 ]
机构
[1] Univ Florida, Emerging Pathogens Inst, Gainesville, FL 32610 USA
[2] Resources Future Inc, Washington, DC 20036 USA
[3] Econ Res Serv, USDA, Washington, DC 20036 USA
基金
美国食品与农业研究所;
关键词
LISTERIA-MONOCYTOGENES; RISK-FACTORS; FOODBORNE ILLNESS; NEW-ZEALAND; INFECTIONS; CONSUMPTION; ENGLAND; WALES;
D O I
10.4315/0362-028X.JFP-11-418
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
Understanding the relative public health impact of major microbiological hazards across the food supply is critical for a risk-based national food safety system. This study was conducted to estimate the U.S. health burden of 14 major pathogens in 12 broad categories of food and to then rank the resulting 168 pathogen-food combinations. These pathogens examined were Campylobacter, Clostridium perfringens, Escherichia coli O157:H7, Listeria monocytogenes, norovirus, Salmonella enterica, Toxoplasma gondii, and all other FoodNet pathogens. The health burden associated with each pathogen was measured using new estimates of the cost of illness and loss of quality-adjusted life years (QALYs) from acute and chronic illness and mortality. A new method for attributing illness to foods was developed that relies on both outbreak data and expert elicitation. This method assumes that empirical data are generally preferable to expert judgment; thus, outbreak data were used for attribution except where evidence suggests that these data are considered not representative of food attribution. Based on evaluation of outbreak data, expert elicitation, and published scientific literature, outbreak-based attribution estimates for Campylobacter, Toxoplasma, Cryptosporidium, and Yersinia were determined not representative; therefore, expert-based attribution were included for these four pathogens. Sensitivity analyses were conducted to assess the effect of attribution data assumptions on rankings. Disease burden was concentrated among a relatively small number of pathogen-food combinations. The top 10 pairs were responsible for losses of over $8 billion and 36,000 QALYs, or more than 50% of the total across all pairs. Across all 14 pathogens, poultry, pork, produce, and complex foods were responsible for nearly 60% of the total cost of illness and loss of QALYs.
引用
收藏
页码:1278 / 1291
页数:14
相关论文
共 28 条
  • [1] Recency-Weighted Statistical Modeling Approach to Attribute Illnesses Caused by 4 Pathogens to Food Sources Using Outbreak Data, United States
    Batz, Michael B.
    Richardson, LaTonia C.
    Bazaco, Michael C.
    Parker, Cary Chen
    Chirtel, Stuart J.
    Cole, Dana
    Golden, Neal J.
    Griffin, Patricia M.
    Gu, Weidong
    Schmitt, Susan K.
    Wolpert, Beverly J.
    Kufel, Joanna S. Zablotsky
    Hoekstra, R. Michael
    [J]. EMERGING INFECTIOUS DISEASES, 2021, 27 (01) : 214 - 222
  • [2] Attribution of Foodborne Illnesses, Hospitalizations, and Deaths to Food Commodities by using Outbreak Data, United States, 1998-2008
    Painter, John A.
    Hoekstra, Robert M.
    Ayers, Tracy
    Tauxe, Robert V.
    Braden, Christopher R.
    Angulo, Frederick J.
    Griffin, Patricia M.
    [J]. EMERGING INFECTIOUS DISEASES, 2013, 19 (03) : 407 - 415
  • [3] Attribution of Illnesses Transmitted by Food and Water to Comprehensive Transmission Pathways Using Structured Expert Judgment, United States
    Beshearse, Elizabeth
    Bruce, Beau B.
    Nane, Gabriela F.
    Cooke, Roger M.
    Aspinall, Willy
    Hald, Tine
    Crim, Stacy M.
    Griffin, Patricia M.
    Fullerton, Kathleen E.
    Collier, Sarah A.
    Benedict, Katharine M.
    Beach, Michael J.
    Hall, Aron J.
    Havelaar, Arie H.
    [J]. EMERGING INFECTIOUS DISEASES, 2021, 27 (01) : 182 - 195
  • [4] Attributing human foodborne illness to food sources and water in Latin America and the Caribbean using data from outbreak investigations
    Pires, Sara M.
    Vieira, Antonio R.
    Perez, Enrique
    Wong, Danilo Lo Fo
    Hald, Tine
    [J]. INTERNATIONAL JOURNAL OF FOOD MICROBIOLOGY, 2012, 152 (03) : 129 - 138
  • [5] Estimating the burden of liver disease in the United States using administrative claims data
    Di Bisceglie, Adrian M.
    Gavard, Jeffrey A.
    Stirnemann, Paula M.
    Xiao, Huiling
    Burroughs, Thomas E.
    Schnitzler, Mark A.
    Takemoto, Steven K.
    [J]. GASTROENTEROLOGY, 2006, 130 (04) : A812 - A812
  • [6] Foodborne disease outbreaks in flour and flour-based food products from microbial pathogens in the United States, and their health economic burden
    Rahman, Rubait
    Scharff, Robert L.
    Wu, Felicia
    [J]. RISK ANALYSIS, 2023, 43 (12) : 2519 - 2526
  • [7] Evaluating the burden of peripheral neuropathy in multiple myeloma using two real-world data sources from the United States
    Archambault, Alexi
    Makaratzi, Martha
    Lorenc, Karen Rodriguez
    Kroog, Glenn
    [J]. PHARMACOEPIDEMIOLOGY AND DRUG SAFETY, 2022, 31 : 320 - 320
  • [8] Estimating Influenza Disease Burden from Population-Based Surveillance Data in the United States
    Reed, Carrie
    Chaves, Sandra S.
    Kirley, Pam Daily
    Emerson, Ruth
    Aragon, Deborah
    Hancock, Emily B.
    Butler, Lisa
    Baumbach, Joan
    Hollick, Gary
    Bennett, Nancy M.
    Laidler, Matthew R.
    Thomas, Ann
    Meltzer, Martin I.
    Finelli, Lyn
    [J]. PLOS ONE, 2015, 10 (03):
  • [9] The Growing Burden of Disability Related to Chronic Liver Disease in the United States: Data From the Global Burden of Disease Study 2007-2017
    Paik, James M.
    Golabi, Pegah
    Younossi, Youssef
    Saleh, Nazaneen
    Nhyira, Annan
    Younossi, Zobair M.
    [J]. HEPATOLOGY COMMUNICATIONS, 2021, 5 (05) : 749 - 759
  • [10] ESTIMATE OF UNITED STATES ANTIHYPERTENSIVE MEDICATION COSTS USING DATA FROM THREE PUBLICLY AVAILABLE SOURCES
    Tajeu, Gabriel
    Ruiz-Negron, Natalia
    King, Jordan
    Nelson, Richard
    Moran, Andrew
    Bellows, Brandon
    [J]. MEDICAL DECISION MAKING, 2020, 40 (01) : E282 - E283