Critical Orientation in the Jungle of Currently Available Methods and Types of Data for Source Attribution of Foodborne Diseases

Abstract

With increased interest in source attribution of foodborne pathogens, there is a need to sort and assess the applicability of currently available methods. Herewith we reviewed the most frequently applied methods for source attribution of foodborne diseases, discussing their main strengths and weaknesses to be considered when choosing the most appropriate methods based on the type, quality, and quantity of data available, the research questions to be addressed, and the (epidemiological and microbiological) characteristics of the pathogens in question. A variety of source attribution approaches have been applied in recent years. These methods can be defined as top-down, bottom-up, or combined. Top-down approaches assign the human cases back to their sources of infection based on epidemiological (e.g., outbreak data analysis, case-control/cohort studies, etc.), microbiological (i.e., microbial subtyping), or combined (e.g., the so-called 'source-assigned case-control study' design) methods. Methods based on microbial subtyping are further differentiable according to the modeling framework adopted as frequency-matching (e.g., the Dutch and Danish models) or population genetics (e.g., Asymmetric Island Models and STRUCTURE) models, relying on the modeling of either phenotyping or genotyping data of pathogen strains from human cases and putative sources. Conversely, bottom-up approaches like comparative exposure assessment start from the level of contamination (prevalence and concentration) of a given pathogen in each source, and then go upwards in the transmission chain incorporating factors related to human exposure to these sources and dose-response relationships. Other approaches are intervention studies, including 'natural experiments,' and expert elicitations. A number of methodological challenges concerning all these approaches are discussed. In absence of an universally agreed upon 'gold' standard, i.e., a single method that satisfies all situations and needs for all pathogens, combining different approaches or applying them in a comparative fashion seems to be a promising way forward.

Keywords: epidemiological studies; expert knowledge elicitation; foodborne pathogen; frequency-matching models; population genetics models; quantitative risk assessment; source attribution; typing methods.

FIGURE 1

General overview of the different source attribution approaches for foodborne diseases.

FIGURE 2

Example of attribution of human cases (720 cases) of a given foodborne disease to three potential sources based on four microbial subtypes. (A) The attribution takes into account only the prevalence of all subtypes in each source, and the exposure to each source is then assumed to be constant. (B) The attribution takes into account both the prevalence and the exposure to each source.

FIGURE 3

Illustration of the approach for source attribution of the STRUCTURE model. Table: allelic profile of 12 strains from three sources (source 1 in red, source 2 in green, source 3 in blue) and four human strains. Bar chart: membership coefficients of the four human strains for the three sources. Each vertical bar represents a strain to be assigned. The relative lengths of the color bars for a strain are proportional to the membership coefficients.

FIGURE 4

Illustration of the approach for source attribution of the asymmetric island model. Pie charts: migration rate (segments with colors different from the source name) and mutation (black segments) for each of the three sources according to the allelic frequencies of the sources shown in Figure 3. Bar chart: attribution probabilities of the four human strains for the three sources (source 1 in red, source 2 in green and source 3 in blue) estimated by the asymmetric island model according to the allelic profiles presented in Figure 3. Each vertical bar represents a strain. The relative lengths of the color bars for a strain are proportional to their attribution probability.

FIGURE 5

Preferential choice of source attribution methods based on public health issues. ^(a)Ranking and/or quantifying the relative importance.