Volume 11, Number 7—July 2005
Perspective
Attributing Illness to Food
Table
Approach | Primary advantages | Primary limitations | Refs |
---|---|---|---|
Denmark Salmonella Accounts | Microbial subtyping provides direct link between public health endpoint and animal | Difficult to expand to other pathogens; requires distinctive subtypes across reservoirs | 5,6 |
High reporting of illnesses (social health care) | Focus on animals ignores nonanimal sources | ||
National, temporal coverage for both illnesses and animal/product monitoring | Focus on reservoirs, not food products at point of consumption | ||
UK outbreak data | Large dataset: national, temporal coverage | May not correlate with sporadic case data | 8 |
Results correlate with local epidemiologic findings | Not all pathogens well represented | ||
Dependence on general practitioners | |||
US outbreak data | National and temporal coverage | May not correlate with sporadic case data | 11,12 |
Large common dataset | Geographic and temporal inconsistencies (local reporting) and biases towards certain foods | ||
Straightforward, uses existing data | Not all pathogens well represented | ||
Outbreaks and outbreak cases can be aggregated into food categories | |||
Case-control studies | Population-based studies | Survey format has recall bias and other limits | 17,20–24 |
Captures risk factors not included in most surveillance data (travel, food preparation questions) | Long exposure windows (problems with common exposures) | ||
Can implicate risks missed by laboratory testing | Durable immunity in population can impede associating exposures with illnesses | ||
No laboratory verification | |||
Microbial subtyping | Subtyping of illnesses and foods can provide direct link between public health endpoint and source of infection | For animal sourcing, subtypes must be distinctive across species (see Danish Salmonella Accounts) | 25–28,5,6 |
Can be used to identify specific foods (outbreak investigations) or animal reservoirs (source tracking by species) | Utility may be limited to certain pathogens | ||
Many different techniques, growing fast | Resource intensive; requires human surveillance, extensive monitoring of food and animals, plus laboratory testing, data storage, analysis | ||
Risk assessments | Can estimate cases not captured by surveillance methods (not limited by underreporting or biases in epidemiologic methods) | Predictive; cannot be verified | 29–33 |
Uses consumption and contamination data ignored by surveillance-based approaches | Large uncertainties in dose-response models and exposure estimates | ||
Resource- and time-intensive (each pathogen-food combination requires its own exhaustive study) | |||
Food monitoring data | Captures upstream contamination (avoids environmental and cross-contamination after purchase) | Not usable for food attribution unless made compatible (through subtyping or other means) with public health data | 34–36 |
Expert elicitation/judgment | Useful when data are sparse or conflicting | Respondents can be similarly biased | 15,37–39 |
Formal methods increase utility | Requires some level of consensus for reasonable error bounds | ||
Based on perception, not data |
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1In addition to the authors, the presenters and attendees who constitute the Food Attribution Working Group are: Fred Angulo (CDC), Robert Buchanan (FDA), H. Gregg Claycamp (FDA), Caroline Smith DeWaal, Center for Science in the Public Interest (CSPI), Jorge Santo Domingo (EPA), Katherine Field (Oregon State University), David Goldman (USDA), Matthew Moore (CDC), Sarah O'Brien (Communicable Disease Surveillance Centre, England), Efrain Ribot (CDC), Stephen Sundlof (FDA), and Catherine Woteki (Iowa State University).
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