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> Disorders > Injury or Poisoning > Foodborne Disease

Foodborne Disease

Foodborne Disease refers to illnesses caused by the consumption of contaminated food or beverages.
These diseases can be caused by a variety of pathogenic microorganisms, as well as chemical or natural toxins present in the food.
Symtoms may include nausea, vomiting, diarrhea, abdominal pain, and in severe cases, life-threatening complications.
Proper food handling, storage, and preparation are key to preventing foodborne diseases.
Early recognition and prompt treatment are essential for managing these conditions and reducing the risk of serious health consequences.
Researchers in this field work to identify causative agents, develop effective interventions, and improve public health measures to enhance food safety and minimize the burden of foodborne illnesses worldwide.

Most cited protocols related to «Foodborne Disease»

1
Estimating Foodborne Illness Burden
Adequate data for preparing national estimates were available for 31 pathogens. We estimated the number of foodborne illnesses, hospitalizations, and deaths caused by these 31 domestically acquired pathogens by using data shown in Table A1 and Technical Appendix 1.
Data were mostly from 2000–2008, and all estimates were based on the US population in 2006 (299 million persons). Estimates were derived from statistical models with many inputs, each with some measure of uncertainty (5 (link)). To reflect this uncertainty, we used probability distributions to describe a range of plausible values for all model inputs. We expressed model outputs as probability distributions summarized by a mean point estimate with 90% credible intervals (CrIs). We used 2 types of modeling approaches for different types of data: 1) models that began with counts of laboratory-confirmed illnesses and were adjusted for undercounts (because of underreporting and underdiagnosis) and thus scaled up to the estimated number of illnesses and 2) models that began with a US population and used incidence data to scale down to the estimated number of illnesses (Table 1). The modeling approaches used and parameters of these probability distributions are detailed in Technical Appendix 2 and Technical Appendix 3; the proportions cited are modal values.
Scallan E., Hoekstra R.M., Angulo F.J., Tauxe R.V., Widdowson M.A., Roy S.L., Jones J.L, & Griffin P.M. (2011). Foodborne Illness Acquired in the United States—Major Pathogens. Emerging Infectious Diseases, 17(1), 7-15.
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Publication 2011
Foodborne Disease Hospitalization pathogenesis
2
Assessing Food Safety Knowledge, Attitude, and Practices
A structured questionnaire was adopted from previous published research articles in order to meet the objective of this study [17-19 (link)]. The language of the questionnaire was translated to the local language (Amharic) in which all the participants can communicate. After pre-testing the questionnaire at a neighbour town (Harar) of the study area with 20 meat handlers the last version was prepared.
The questionnaire structured into four distinct parts including demographic information such as respondents’ sex, age, years of experience, responsibility/duty, income, employment status, having health certificate and attending food safety training. The second section of the questionnaire is about food safety knowledge. Questions on knowledge referred to their personal hygiene, cross-contamination, causes and symptoms of food borne diseases, and time temperature control. It contains 22 close-ended questions and each question has three optional answers (“Yes”, “No” and “I do not know”). The response was analyzed as categorical variables (right or wrong answer). A score of one was given to right answer and zero to the wrong and I do not know answer. A scale ranging between 0 and 22 which representing the total number of questions on food safety knowledge. Meat handlers that got overall score ≤ 14 points were considered to have “unsatisfactory” and those scored ≥ 15 points (≥ 68 % accuracy) “satisfactory” knowledge of food safety.
The third part of the questionnaire was about food safety attitude of meat handlers. It comprises 20 questions about hand washing, cross contamination, food handling, storage etc. In this section, the respondents’ answers were “agree”, “disagree”, and “don’t know”. The response was analyzed as categorical variables (right or wrong answer). A score of one was given to right answer and zero to the wrong and I do not know answer. Each correct answer was given one point whereas incorrect answer including the answer I do not know was awarded zero point. For evaluation, food-handlers that answered 14 or more questions correctly were measured to have “good” attitude whereas respondents answer 13 or less questions correctly were measured to have “poor” attitude.
The last section dealt with food hygiene practices. The question comprises the issues of personal hygiene, hand washing practices, practices against food borne diseases and cross contamination. This section had 20 questions with two possible responses: “yes”, and “no”. Each correct practice reported scored one (1) point. For evaluation, a score ≥ 70% that means food-handlers practiced 14 or more out of 20 hygienic practices which are listed in the questions was considered as having “good” food hygienic practice [20 (link)].
TEGEGNE H.A, & PHYO H.W. (2017). Food safety knowledge, attitude and practices of meat handler in abattoir and retail meat shops of Jigjiga Town, Ethiopia. Journal of Preventive Medicine and Hygiene, 58(4), E320-E327.
Publication 2017
Food Foodborne Disease Meat
3
Estimating Unspecified Foodborne Gastroenteritis
We defined unspecified agents as agents that cause acute gastroenteritis but that were not included in our estimate of foodborne illness caused by 31 major known pathogens (1 (link)). They include known agents with insufficient data for estimating agent-specific episodes of illness; known agents not yet recognized as causing foodborne illness; microbes, chemicals, or other substances known to be in food but for which pathogenicity is unproven; and agents not yet described. To estimate the extent of foodborne illness caused by unspecified agents, we estimated the number of acute gastroenteritis illnesses, hospitalizations, and deaths and subtracted the estimated number of acute gastroenteritis illnesses, hospitalizations, and deaths caused by 24 major known pathogens that typically or often cause diarrhea or vomiting (Figure 1). We refer to them as the 24 known gastroenteritis pathogens, although for a few, diarrhea or vomiting was not the main clinical sign. Estimates of illness were not made for unspecified agents that do not typically result in acute gastroenteritis.
We used data from the 24 known gastroenteritis pathogens to estimate the proportion of unspecified agents that were acquired in the United States (hereafter referred to as domestically acquired) and transmitted in food. Most of our data were from 2000 through 2007, and all estimates were based on the US population in 2006 (299 million persons) (9 ). To account for uncertainty, we used probability distributions to describe a range of plausible values for all model inputs. The modeling approach used and parameters of these probability distributions are detailed in the Technical Appendix. Our model outputs are in the form of probability distributions summarized by a mean point estimate with 90% credible intervals (CrIs).
Scallan E., Griffin P.M., Angulo F.J., Tauxe R.V, & Hoekstra R.M. (2011). Foodborne Illness Acquired in the United States—Unspecified Agents. Emerging Infectious Diseases, 17(1), 16-22.
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Publication 2011
Diarrhea Food Foodborne Disease Gastroenteritis Hospitalization pathogenesis Pathogenicity
4
Estimating Foodborne Illness Burden
The approach used by the U.S. CDC (Scallan et al., 2011a (link), b (link)) and the Canadian estimates of foodborne illness provided the basis for this work (Thomas et al., 2013 (link)). Hospitalizations and deaths associated with the same 30 pathogens used in the Canadian estimates of foodborne illness were calculated as well as those associated with unspecified agents (Thomas et al., 2013 (link)).
Thomas M.K., Murray R., Flockhart L., Pintar K., Fazil A., Nesbitt A., Marshall B., Tataryn J, & Pollari F. (2015). Estimates of Foodborne Illness–Related Hospitalizations and Deaths in Canada for 30 Specified Pathogens and Unspecified Agents. Foodborne Pathogens and Disease, 12(10), 820-827.
Publication 2015
Foodborne Disease Hospitalization Pathogenicity
5
IFSAC Foodborne Disease Outbreak Analysis
We conducted descriptive analyses to summarize reported foodborne disease outbreaks using the IFSAC categorization scheme. For analytical purposes, we combined the categories of Multiple-Ingredient Foods, Multiple Foods Reported, Unclassifiable, and No Food Entered into an aggregate Un-assignable food category to group all outbreaks that could not be assigned to a single food category. We used SAS software (version 9.3; SAS Institute, Inc., Cary, NC) to generate summary tables depicting the number and percentage of outbreaks assignable to each hierarchical level of the IFSAC categorization scheme, stratified by 24 aggregate food categories containing a sufficient number of outbreaks to facilitate appropriate outbreak summary analyses. Hereafter, we refer to these 24 categories as the current analytical food categories (CAFCs).
Richardson L.C., Bazaco M.C., Parker C.C., Dewey-Mattia D., Golden N., Jones K., Klontz K., Travis C., Kufel J.Z, & Cole D. (2017). An Updated Scheme for Categorizing Foods Implicated in Foodborne Disease Outbreaks: A Tri-Agency Collaboration. Foodborne pathogens and disease, 14(12), 701-710.
Publication 2017
Food Foodborne Disease Food Ingredients

Most recents protocols related to «Foodborne Disease»

1
Quantifying HAV Risk in Fermented Clams
In an Excel spreadsheet, a simulation model was developed using the HAV level of contamination in fermented clams, predictive models, probabilistic distributions for temperature and time during transportation and storage, probabilistic distribution of consumption amount, consumption ratio, and dose–response model. This simulation model was used for the simulation using @RISK with 10,000 iterations to calculate the risk of HAV foodborne illnesses caused by fermented clam ingestion per person per day. Furthermore, variables with a major impact on risk were identified.
Yoo Y., Sung M., Hwang J., Yeo D., Zhao Z., Choi C, & Yoon Y. (2023). Quantitative Risk Assessment of Hepatitis a Virus Infection Arising from the Consumption of Fermented Clams in South Korea. Foods, 12(4), 796.
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Publication 2023
Clams Foodborne Disease
2
Worldwide Prevalence of Staphylococcal Enterotoxins
The search strategies (Table 2) were designed considering the topic and the particularities of the information desired to answer the research question: What is the worldwide prevalence of staphylococcal enterotoxins in food contaminated by Staphylococcus spp.?
The terms used were defined based on information from PECOS, which would convey the criteria in formulating a search strategy. Terms related to population (staphylococcal enterotoxins) and exposure (contaminated food, food contamination, staphylococcal food poisoning, disease outbreak, foodborne disease, food poisoning, Staphylococcus spp.) will be used. The operators ‘AND’, ‘OR’ and ‘AND NOT’ will also be used in the construction of the search strategy which will be adapted to meet the specifications of the search syntax of each database.
The following databases will be used: Medline (OVID), GALE, Science Direct, CAB Direct (CABI), and Google Scholar. In addition, manual search will be done in records’ reference lists, directory of theses and dissertations, and countries’ health agencies. Gray literature will be included. Studies from the last 20 years (2002–2022) will be included. There will be no language restriction.
Freitas J.K., de Assis C.F., de Oliveira T.R., Maia C.M., de Sousa B.J., de Medeiros G.C., Seabra L.M, & Chaves Damasceno K.S. (2023). Prevalence of staphylococcal toxin in food contaminated by Staphylococcus spp.: Protocol for a systematic review with meta-analysis. PLOS ONE, 18(2), e0282111.
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Publication 2023
Disease Outbreaks Food Foodborne Disease Food Contamination Scabies Staphylococcal Enterotoxins Staphylococcus
3
Foodborne Disease Surveillance in Zhejiang
The Zhejiang Provincial CDC (ZJCDC) has been collecting FBD- relevant data through the China National Foodborne Diseases Surveillance Network (NFDSN) since 2012. One hundred and one hospitals were asked to detect Salmonella pathogens and their corresponding subtypes for all suspected foodborne disease cases, and reported illnesses through NFDSN since 2012. In this study, cases reported by 101 hospitals in Zhejiang Province between 2012 and 2021 were included. Epidemiologists from the health departments first conducted an investigation to ascertain the full extent of the foodborne illness, and the information collected for each case includes the reporting region, date of occurrence, setting, etiology, food categories, number of illnesses/hospitalizations, and other details. Unknown etiology refers to foodborne disease cases in which the confirmed etiology has not been identified. Settings were classified into eight categories. Food items were identified as sources of disease through epidemiological or laboratory methods and were classified into 14 categories. Food that could not be determined was classified as “Unknown.” The GIS map data of Zhejiang Province was downloaded from the national basic geographic information center of China (http://bzdt.ch.mnr.gov.cn/).
He Y., Wang J., Zhang R., Chen L., Zhang H., Qi X, & Chen J. (2023). Epidemiology of foodborne diseases caused by Salmonella in Zhejiang Province, China, between 2010 and 2021. Frontiers in Public Health, 11, 1127925.
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Publication 2023
Epidemiologists Food Foodborne Disease Food Poisoning Hospitalization pathogenesis Salmonella
4
Salmonella Isolation and Identification Protocol
Fresh stool specimens or anal swabs were collected from cases. The best specimens were a fecal specimen, anal swab was used only when the patient had no stool specimen. Specimens collected were tested as soon as possible. Specimens placed in the culture-Blair medium were tested within 24 h of refrigeration. Fresh fecal samples were placed in clean, dry containers without soap or disinfectant residue, and sent for examination within 8 h of refrigeration.
Isolation and identification of Salmonella were performed as described in the Operation Procedure for Salmonella Inspection in the Foodborne Disease Surveillance Work Manual of the National Center for Food Safety Risk Assessment. In brief, the above specimens were placed in SBG augmenting solution and cultured at 36°C for 18 to 24 h. Furthermore, after gently shaking the expanding liquid tube we applied 1 ring line to the Salmonella chromogenic medium or XLD AGAR plate and incubated it at 36 ± 1°C for 18 to 24 h. We picked three to five suspected colonies, inoculated in TSI AGAR, lysine decarboxylase, and nutrient AGAR plates, at 36 ± 1°C for 18 to 24 h. A single colony was scraped from a nutrient AGAR plate for systematic biochemical identification. Either of biochemical identification kit or automatic microbial biochemical identification system can be selected for identification.
The Salmonella serovar was identified with specific O and H antiserum samples according to the Kauffmann–White scheme as described in the instructions provided by the manufacturer of the antiserum samples (Statens Serum Institute, SSI).
He Y., Wang J., Zhang R., Chen L., Zhang H., Qi X, & Chen J. (2023). Epidemiology of foodborne diseases caused by Salmonella in Zhejiang Province, China, between 2010 and 2021. Frontiers in Public Health, 11, 1127925.
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Publication 2023
Agar Anus azo rubin S Feces Foodborne Disease Health Risk Assessment Immune Sera isolation LINE-1 Elements lysine decarboxylase Nutrients Patients Salmonella Serum
5
Quantifying Salmonella Transmission in Poultry
The prevalence and concentration of Salmonella in chicken and duck from farms to slaughterhouse and to home consumption were applied into the QMRA model. The model consisted of two modules; (A) risk factor analysis in the production stages and (B) risk analysis by cooking methods were both considered when evaluating the risk of Salmonella exposure (Figure 1). In module (A), the prevalence and contamination of Salmonella in farms and slaughterhouses, which are production stages, were investigated, and major risk factors were presented. In module (B), the risk of Salmonella in the process of transport, storage, and display of the products to supermarkets and traditional markets in the distribution stage, and consumption after cooking at home by consumers was determined by @Risk (Palisade Corp., Ithaca, NY, USA). Using 10,000 random repetitions, the possibility of Salmonella-related foodborne disease from consuming chicken and duck was estimated.
Oh H., Yoon Y., Yoon J.W., Oh S.W., Lee S, & Lee H. (2023). Salmonella Risk Assessment in Poultry Meat from Farm to Consumer in Korea. Foods, 12(3), 649.
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Publication 2023
Chickens Ducks Foodborne Disease Salmonella

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More about "Foodborne Disease"

Foodborne illnesses, foodborne infections, foodborne poisoning, foodborne outbreaks, enteric diseases, gastrointestinal infections, food contamination, food safety, microbial pathogens, bacterial foodborne diseases, viral foodborne diseases, parasitic foodborne diseases, chemical foodborne toxins, food handling, food preparation, food storage, food hygiene, public health, disease surveillance, epidemiology, diagnosis, treatment, prevention, intervention strategies, SAS 9.4, Nextera XT DNA Library Preparation Kit, MiSeq platform, DNeasy Blood and Tissue Kit, API 20E typing system, AccuPrep DNA extraction kit, Phosphate-buffered saline, QIAamp DNA Mini Kit.
Foodborne diseases are a significant global health issue, causing millions of illnesses and thousands of deaths annually.
These illnesses are typically caused by the consumption of contaminated food or beverages, which can be contaminated with a variety of pathogenic microorganisms, such as bacteria, viruses, and parasites, as well as chemical toxins.
Symptoms of foodborne diseases can range from mild gastrointestinal discomfort, such as nausea, vomiting, and diarrhea, to more severe, life-threatening complications.
Early recognition and prompt treatment are crucial for managing these conditions and reducing the risk of serious health consequences.
Researchers in this field utilize a variety of analytical techniques, including SAS 9.4, Nextera XT DNA Library Preparation Kit, MiSeq platform, DNeasy Blood and Tissue Kit, API 20E typing system, AccuPrep DNA extraction kit, Phosphate-buffered saline, and QIAamp DNA Mini Kit, to identify causative agents, develop effective interventions, and improve public health measures to enhance food safety and minimize the burden of foodborne illnesses worldwide.