Risk factor, parameter or model feature affected by the uncertainty	One sentence description of the cause of uncertainty affecting this risk factor, parameter or model feature (one row per cause of uncertainty)	One sentence description of how this source of uncertainty might lead to incorrect ranking of control options, or why that might be possible	Which types of model or study does this uncertainty affect? (e.g. PAFs, C model, etc.)
Risk factors for broiler infection with Campylobacter spp.	Small numbers of broiler farms are a cause of uncertainty in many studies	Sources of Campylobacter on broiler farms are many and varied and may depend on the farms studied. Thus, the findings of a given study, which often includes small numbers of farms, may not be applicable on other farms and the ranking will reflect the choice of farms in published studies and might be different if the observations were made using a larger number of farms	PAFs
Risk factors for broiler infection with Campylobacter spp.	The difference in key climate, broiler husbandry practices etc. in different countries is a cause of uncertainty	Many studies on the risk factors for Campylobacter in broilers are undertaken in a single country or a few countries and the data generated and conclusions may not be applicable to other countries in the EU, where, for example, the climate and broiler farm practices may be different	PAFs – representativeness of all of the EU is uncertain
Risk factors for broiler infection with Campylobacter spp.	Sample size is a cause of uncertainty	Sample size may also be an issue with many studies using 1 g or 10 g samples, which may or may not be representative of the flock or broiler house. For example, a 10 g sample of broiler faeces or 10 cloacal swabs from a flock of 30,000 may or may not be representative of the flock as a whole The result may be false‐negative flocks	PAFs
Risk factors for broiler infection with Campylobacter spp.	Seasonal effects are a cause of uncertainty	Seasonality greatly affects Campylobacter survival in the environment, sources and dissemination routes and data from studies that do not include a seasonal consideration may not be applicable throughout the year	PAFs
Risk factors for broiler infection with Campylobacter spp.	Assumptions about the direction of Campylobacter spread is a cause on uncertainty	Contrary to the conclusion in some papers, the detection of similar Campylobacter genotypes in farm animals adjacent to the broiler house before they appear in the birds should not be indicative of horizontal transmission form these animals to the birds. It is equally possible that these animals were infected by Campylobacter from previous broiler flocks and/or the levels in the birds were below the level of detection if tested at the same time as the farm animals	PAFs
Risk factor and control options	No/little adjustment for confounding or interaction of all other factors	Measured effects or risks may actually be caused by other factors, unless these are deliberately adjusted for – either by exclusion (laboratory trials) or statistically (epidemiological studies)	All‐except for epistudies and carefully designed experimental studies
Control options for preventing broiler infection with Campylobacter spp.	Unrepresentative laboratory studies are a cause of uncertainty	Laboratory studies may not accurately reflect the conditions encountered in the broiler house and there is considerable uncertainty in laboratory data with respect to its application on commercial broiler farms	The model including the impact of interventions on Campylobacter count reduction?
Control options for preventing broiler infection with Campylobacter spp.	Unrepresentative field trials are a cause of uncertainty	Field trials may not reliably predict the impact of specific interventions under real‐world conditions where there may be up to 30,000 birds in a shed with a stocking density of 20 birds per m2	The model including the impact of interventions on Campylobacter count reduction?
Control options for preventing broiler infection with Campylobacter spp.	Human behaviour is a cause of uncertainty	In biosecurity studies, the participants are often volunteers who are aware they are being assessed and may behave differently to their normal practices	Minor for PAFs as variability – incl. Compliance is included and thus accounted for in epi‐studies
Control options for preventing broiler infection with Campylobacter spp.	Broiler farm selection is a cause of uncertainty	Many studies are undertaken on a relatively low number of broiler farms that may not be representative of broiler farms in general. Moreover, their selection is often agreed with their contract processor, who is more likely to suggest farms that have a good Campylobacter performance record	?
Control options for preventing broiler infection with Campylobacter spp.	The specificity of bacteriophage is a cause of uncertainty	The high specificity of phage in terms of the Campylobacter strains they can infect introduces great uncertainty in phage research especially when extrapolating to a real‐world situation where there may be multiple Campylobacter strains within a given bird or flock	?
Control options for preventing broiler infection with Campylobacter spp.	The age and lineage of the test birds is a source of uncertainty	When evaluating probiotic bacteria as a feed additive, a range of factors including the age and lineage of the birds and the mode of administration greatly influence the outcome of the experiments. Moreover, if the birds are exposed to Campylobacter first, the probiotic strain is considerably less likely to have a positive effect	?
Control options for preventing broiler infection with Campylobacter spp.	Lack of reproducibility between trials Experimental level, not tested on field	In one trial, the product can have a very significant effect in terms of reduction but when repeating the same conditions in a second trial, no significant effect can be obtained. This loss of significance between two trials is often (but not the only reason) the variability of Campylobacter counts between (control) animals	C‐model
Risk factors for broiler infection with Campylobacter spp. Control options for preventing broiler infection with Campylobacter spp.	The ability of Campylobacter to enter the viable but not culturable (VBNC) state is a cause of uncertainty	Campylobacter can enter a viable but not culturable (VBNC) state which may greatly affect the data obtained, especially in studies designed to reduce or eliminate these pathogens. An alternative is to use PCR‐based methods, but the presence of Campylobacter DNA does not indicate the presence of viable cells	?
C‐model: Use of caecal concentration	It is not necessarily the caeca that contaminate the carcass, it may also be leaking faeces, which has a lower concentration	This increases the variability in the data and the uncertainty about the regression line and the uncertainty of the ranking. (In general: The ranking between measures within the C‐model is not altered, only the ranking compared to the P‐model measures)	C‐model
C‐smodel: Use of caecal concentration	It need not be the caecal count that is used to measure the impact of the intervention	The regression line is not representative for the evaluated intervention if no caecal counts are used, it increases the overall uncertainty of the ranking	C‐model
C‐model: In reality, the relation between caecal concentration and carcass concentration is probably not linear	If the slope at high caecal concentrations is larger than the slope at low values (i.e. there is not really a linear relation, it is j‐shaped), the regression line may overestimate the effect of the intervention at low concentrations	The effect of the intervention may be slightly overestimated if the regression is based on high concentrations	C‐model
C‐model: Censored data on caeca and/or skins	Concentration data usually suffer from the presence of data below (or above) a limit of quantification, so‐called censored data. The way these are traditionally dealt with (taking a fixed value, the lower limit or so; or simply omitting the data) may have high large impact on the regression line obtained. Methods for regression with censored data may help (Tobit regression), but there is no established method to deal with regression and censored data in both the dependent and independent variable	This impacts the uncertainty of the slope obtained	C‐model
C‐model: assumption that τ = 1: the contamination of the meat is always 1 log less than the skin	Lack of data led to an expert estimate of the Campylobacter WG in 2010	This impacts the effect estimate (in terms of relative risk reduction), it is included in the model	C‐model
C‐model: CPM used (with assumptions on cross contamination) is representative for all prepared chicken meals	Consumer food preparation is highly variable, not under control and hard to measure or influence	Increases the overall uncertainty; this is studied by comparing different CPMs	C‐model
C‐model: DR model used: actual dose response for Campylobacter on chicken is highly uncertain	The DR relations are based on one or a few strains; not from chicken meat; are for healthy adults or primates that are not immune	Increases the overall uncertainty; this is studied by comparing different DRs	C‐model
C‐model: use of 2008 baseline data on skin samples	These are old data, but collected all over Europe in a harmonised way. In the meantime, action may have been taken to reduce these concentrations	Not really sure how this impacts the results; we may overestimate the (relative) risks. The high incidence of human infection suggests that Campylobacter prevalence at farm and slaughter at has not improved since 2008	C‐model
C‐model: implementation effect of control options	The variation between log reductions as observed in the (raw) data is interpreted both as variability (the st. dev in the data) and the uncertainty (the s.e.m.), whilst it actually is a combination of both	Both variability and uncertainty are overestimated, which leads to underestimation of the effect and overestimation of the uncertainty. The impact of this phenomenon can be assessed by considering the scenarios where either the variability or uncertainty are omitted, in the sensitivity analysis	C‐model
Risk factors (all) for PAF calculation	PAFs are calculated from adjusted Relative Risks, different studies adjust for different confounders in the final multivariate model	Impact on the estimates of the multivariate model and thus on the size of PAFs	PAF
Risk factors (all) for PAF calculation	Few studies considered the effect of including non‐significant (but potentially confounding) factors in the final multivariate model	Impact on the estimates of the multivariate model and thus on the size of PAFs	PAF
Risk factors (all) for PAF calculation	Heterogeneity in the biological samples that are collected on farm/slaughter (Pool of different number of caeca samples, cloacal swabs, sock samples) and analysed to define a flock as ‘positive’. Possible differences in sensitivity between methods	Probably little impact under the assumption that within‐flock‐prevalence is typically considered to by high in infected flocks	PAF
All risk factors	Underreporting of non‐significant effects	If no significant effect is found for a risk factor or control option, it may not be published. This means the ‘mean published’ effect size of a potential control option may be larger than it actually is	All
Control measure: ‘Avoid presence of standing water in drinkers’	Two of the studies were convenience samples of farms and may not represent all of Germany. Furthermore, a convenience sample may have less variability than a representative sample	Introduces more uncertainty about the effect in all of EU and the altered variance may have over‐ or underestimated the PAF	PAF
Effective hygiene barrier at broiler house entrance	One of the studies was only representative of 60% of French broilers and the Spanish study was only carried out in Andalusia	This introduces more uncertainty about the control effect of all of EU and the biased variability may over‐ or underestimate the PAF	PAF
Effective rodent control	Two of the studies were carried out in regions (Brittany and Andalusia) and are unlikely to be nationally representative	This introduces uncertainty about the control effect of all of EU and the biased variability may over‐ or underestimate the PAF	PAF
No animals in close proximity	This control estimate was derived from a broad range of risk factors	This adds uncertainty around the precision of the PAF estimate	PAF
Have few and permanent staff			PAF
Additives to drinking water	One of the studies was carried out using a convenience sample and another was from Brittany	This introduces uncertainty about the control effect in all of EU and a biased variability may over‐ or underestimate the PAF	PAF