Salmonella source attribution based on microbial subtyping: Does including data on food consumption matter?
Introduction
Over 100,000 human cases of salmonellosis are notified each year in the 27 European Union (EU) Member States (~ 502 million population) (EFSA, 2011), yet it is widely accepted that notified cases are subject to considerable underreporting. By using disease risks of returning Swedish travelers for the period 2005–2009 anchored to a Dutch population-based study on gastroenteritis, Havelaar et al. (2013) have estimated that more than 6.2 million human cases of salmonellosis occur annually in the 27 EU Member States. This poses salmonellosis as the second most important cause of human bacterial gastroenteritis, after campylobacteriosis.
Contaminated food is by far the primary source of human salmonellosis, with 86–95% of cases estimated to be foodborne (Majowicz et al., 2010). To ascertain the main reservoirs of salmonellosis and to assess the impact of food safety interventions, source attribution based on microbial subtyping is being performed in many countries within (David et al., 2013a, David et al., 2013b, Hald et al., 2004, Hald et al., 2007, Mughini-Gras et al., 2014a, Mughini-Gras et al., 2014b, Pires et al., 2011, Pires and Hald, 2010, van Pelt et al., 1999, Wahlström et al., 2011) and outside (Guo et al., 2011, Mullner et al., 2009a) the EU. The microbial subtyping approach, based on the comparison of frequency distributions of pathogen subtypes isolated from humans with those isolated from putative sources of infection (Barco et al., 2013, Pires et al., 2009), has received considerable attention since the development of the Hald model for Salmonella source attribution in Denmark (Hald et al., 2004). The Hald model uses a Bayesian approach and has been developed separately from the frequentist Dutch model (van Pelt et al., 1999) to attribute stochastically human cases to putative sources of infection while accounting for differences in Salmonella subtypes and sources to cause human infection (Hald et al., 2004). To further improve identifiability and to handle uncertainty in data of poorer quality, a modified Hald model has also been proposed (Mullner et al., 2009a). More recently, a modified Dutch model has been developed and its attributions do not seem to differ from those of the modified Hald model (Mughini-Gras et al., 2014b).
The amount of food consumed or available for consumption within a country (expressed as either total tons of food consumed or average food intake per capita) is often included in Salmonella source attribution models to account for differences in the overall exposure to the different sources. For this purpose a variety of food consumption data have been used, including data from national food (agricultural) supply statistics (Guo et al., 2011, Hald et al., 2004, Hald et al., 2007, Mughini-Gras et al., 2014a, Mughini-Gras et al., 2014b, Pires et al., 2011, Pires and Hald, 2010) and individual dietary surveys (David et al., 2013a, David et al., 2013b). Food supply statistics are usually readily available for many years in most countries. However, even when these data are adjusted for losses and wastage of inedible portions during food processing, distribution, storing and preparation (at either retail or household level), their use requires the assumption that all food available for consumption is effectively consumed (Guo et al., 2011). Conversely, individual dietary surveys are costly and labor-intensive, but they may provide more accurate data on the actual food intake and even on the practices of preparation. Yet, individual dietary surveys are hampered by a plethora of limitations linked to misreporting (mainly recall and prevarication biases), non-coverage and/or non-response of certain groups of the population, as well as seasonality and trends in food consumption patterns (Hallström and Börjesson, 2013). Therefore, to be functional to source attribution, these surveys need to be performed on a regular basis or at least be updated by combining their figures with food supply data. This was done by David et al. (2013b) for attributing salmonellosis cases that occurred in France in 2005 using individual food consumption data obtained from a French survey conducted in 1999, when the BSE crisis in Europe had almost reached its highest levels and had already had serious repercussions in the food market.
An aspect that is often overlooked when using food consumption data in source attribution analyses is that not all foods have the same probability of serving as vehicles for salmonellas, as some foods are clearly more likely to be consumed raw or undercooked than others (Mughini-Gras et al., 2014a, Mughini-Gras et al., 2014b). Such foods are relatively more likely to cause infection directly, and possibly also indirectly through cross-contamination (Hald et al., 2004). The Hald-type models are supposed to account for this by incorporating the so-called source-dependent parameter (aj, see Section 2.3.2), which is an uninformative multiplication factor fitted by the model to arrive to the most likely solution given the observed data (Hald et al., 2004, Mullner et al., 2009a). It has been suggested that values taken by this parameter are somehow biologically interpretable, with high values for sources such as beef being compatible with its higher likelihood of being consumed raw or just slightly cooked (Hald et al., 2004). However, evidence for this notion is rather inconsistent, as other model runs may depict situations that are not directly interpretable with insufficient cooking of certain foods (Pires and Hald, 2010), as the aj parameter also accounts for other factors (Section 2.3.2).
Using Salmonella data from the Netherlands during 2001–2004, this study aims at elucidating whether and how the inclusion of data on food consumption in the (modified) Dutch and Hald models influences their attributions. Next, we propose the incorporation of an additional parameter in the modified Dutch model that weights the amount of food consumed by its likelihood to be consumed raw or undercooked by the population, thereby allowing food consumption data in this source attribution model to reflect more closely the actual chance for a given food to serve as a vehicle for Salmonella.
Section snippets
Data on human salmonellosis cases
A dataset consisting of 3735 sero/phage typed Salmonella isolates from human cases that occurred in the Netherlands between January 2002 and December 2003 was obtained from the EU and national reference laboratory for Salmonella at the Dutch National Institute for Public Health and the Environment (RIVM). These salmonellosis cases were identified by the Dutch Regional Public Health Laboratories through passive surveillance of people with gastroenteritis. Serotyping and further phage typing of S.
Attributions without food consumption weights and without modeled pathogen prevalence
Attribution estimates from the oDM and mHM with no food consumption weights or modeled prevalence pij are reported in panel A of Fig. 1, Fig. 2, respectively. Layers/eggs were the primary reservoir, accounting for 43.5% (95% CI: 41.5–45.5%) and 55.6% (48.6–61.1%) of cases in these oDM and mHM, respectively. Pigs were the second most important reservoir (oDM: 29.3%, 28.1–30.5%; mHM: 38.2%, 31.5–43.7%), followed by cattle (oDM: 14.6%, 13.2–16.2%; mHM: 3.7%, 0.4–10.3%) and broilers (oDM: 12.6%,
Discussion
We assessed the effect of including and excluding data on food consumption in two commonly used Salmonella source attribution models based on microbial subtyping. We also proposed the incorporation of an additional parameter in the mDM to weight the amount of food consumed by its likelihood to be consumed raw or undercooked by the population.
We found that the incorporation of the standard food consumption weight mj, together with the modeled pathogen prevalence, caused a drastic change in the
Conclusions
Going back to the original title question, does including data on food consumption matter? The answer depends on the attribution model in question. The incorporation of the amount of food consumed appears to bias the attribution estimates of the mDM, but not those of the mHM. Yet the incorporation of the additional raw/undercooked food consumption weight into the mDM may compensate for this bias and effectively adjust the attribution estimates. Conversely, the mHM works properly regardless of
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