Surveillance of Food and Waterborne Pathogens in North-East India: Protocol for a Laboratory-Based Sentinel Surveillance Study
Bibliographic record
Abstract
BACKGROUND: Food safety is a global concern, which is often underestimated owing to challenges in investigating foodborne diseases. These challenges arise from the increased globalization of the food trade, advancements in agricultural practices, and shifts in environmental factors. In North-East India, common diarrheal outbreaks from fermented foods pose significant health risks. Despite these challenges, systematic data on foodborne pathogens is lacking in India, highlighting a crucial gap in understanding food safety issues. OBJECTIVE: The aim of this research protocol is to establish an integrated surveillance system to identify enteric pathogens circulating within humans, food animals, and the environment through a health approach in North-East India, and to conduct outbreak investigations. METHODS: The Indian Council of Medical Research (ICMR) initiated a surveillance study across all 8 North-East states in India, employing a centralized digital database for data collation. The project aims to enhance the infrastructure for microbial culture, antibiotic sensitivity testing, and molecular epidemiological studies. The study involves laboratory-based surveillance of foodborne pathogens in market foods, hospitalized diarrheal patients, poultry and animal farms, slaughterhouses, butcher shops, and diarrheal outbreaks. A standardized case report form ensures consistent data collection of age, sex, signs, symptoms, and admission dates for diarrheal cases. Stool and rectal swabs will undergo testing for pathogen identification and antimicrobial resistance. Similarly, samples of market foods, food animals, and the environment will be collected. Outbreaks confirmed by the Integrated Disease Surveillance Project (IDSP) will be thoroughly investigated following standardized guidelines. RESULTS: In phase I, 5 surveillance centers were established across 4 states (ie, Assam [Dibrugarh and Guwahati], Tripura, Sikkim, and Arunachal Pradesh) in 2020. Following an interim phase I data assessment and the successful establishment of a streamlined system for data procurement, investigation, recording, and analysis, along with the implementation of regular training and monitoring programs, phase II expansion was initiated in 2023-24. This includes the addition of 7 more centers (including 3 veterinary centers) in the remaining 4 states (ie, Manipur, Meghalaya, Mizoram, and Nagaland), eventually covering the entire North-Eastern Region of India. CONCLUSIONS: Food and waterborne diseases are a constant public health problem in many countries. Key challenges to the enhancement of food safety policy include the paucity of systematic data and awareness. With this background, ICMR's initiative is the first systematic surveillance study in the country to adopt a single health approach. Data obtained from this project will help to understand the risk of acquiring food and waterborne pathogens, their transmission pathways, and antimicrobial resistance patterns. The scientific evidence generated through this project will be helpful in formulating and strengthening food safety policy and in initiating government programs to protect the health of the nation. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/56469.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.040 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".