we compared monthly listeriosis data from england and wales with temperature records from 1989 through 2007 to determine the influence of various potential predictors on the number of listeriosis cases .
uk health protection agency ( hpa ) data listing total monthly cases of human listeriosis in england and wales during 19902007 ( 4,5 ) are aggregate .
all age categories and regions were included and were collated by the hpa centre for infections from voluntary reporting by microbiology laboratories and from referrals of cultures .
these publicly available data were also validated by the hpa , and in our analysis we used revised figures based on that validation .
our analysis covered the period from 1990 , when active surveillance of listeriosis began , through 2007 .
undated cases that could not be assigned to a particular month were excluded from analysis .
we used the uk met office mean monthly area temperature time series for 19892007 and 30-year means averaged during 19611990 ( 6 ) .
exploratory linear regression analyses suggested a positive correlation between the number of listeriosis cases and the monthly mean uk ambient temperature , as well as suggesting a change in this relationship after 2000 ( p = 0.001 ; figure 1 , panel a ) .
however , residual variability was not constant , and the monthly counts are likely to be overdispersed due to clustering of cases ( 3 ) .
the data were fitted again by using a negative binomial generalized linear model with a logarithmic link function , a common model for time series of foodborne illness cases ( 7,8 ) . to separate seasonality of listeriosis rates from dependence on temperature , we considered the 30-year mean monthly temperatures from 19611990 , as well as monthly temperature anomalies ( observed mean temperature minus 30-year mean ) . to determine whether temperatures could have a delayed effect on listeriosis incidence
, we also included mean and anomaly temperature variables lagged by 1 or more months . to allow for 2 break points
at which the incidence of listeriosis may have suddenly changed , dummy variables were used to represent periods before , between , and after the months in which these increases might have occurred . a best - fit model was selected according to the corrected akaike information criterion ( aic ) ( 9 ) by using stepwise regression at all combinations of 2 break point months from january 1996 through december 2007 . to examine changing effects on incidence ,
interactions between break point indicators and other variables were considered , even if the main effects were not yet in the model .
main effects were subsequently added to the final model if any interaction terms were included .
a ) listeriosis cases compared with mean observed monthly uk temperatures , 19902000 ( triangles ) and 20012007 ( circles ) . shown
are an increased overall incidence in 20012007 ( dashed line ) versus 19902000 ( solid line ) and a significant change in the linear dependence of incidence on temperature ( p = 0.001 ) .
b ) best - fit pair of break points and other pairs of break points with support , according to the corrected akaike information criterion ( aic ) .
the 2 break - point months are varied to find the lowest value ( black square ) .
pairs of break points with good support relative to the best model ( corrected aic within 4 of the best fit ; dark gray squares ) or moderate to weak support ( corrected aic within 10 of the best fit ; light gray squares ) are also shown .
pairs of break points with little or no support ( corrected aic > 10 greater than the best fit ; white area ) include those models for which only 1 break point exists ( squares along the dashed line ) .
the best - fit model included 2 break points ( tables 1 , 2 ; figure 1 , panel b ; figure 2 ) .
the overall rate of cases increased by 40.83% in april 2001 ( adjusted p = 0.001 , 95% confidence interval [ ci ] 17.29%68.93% ) ; a further increase of 47.76% occurred in march 2003 ( adjusted p<0.001 , 95% ci 27.92%71.13% ) . according to the corrected aic , there is statistical evidence of changes in the incidence of listeriosis in a range of months around these best - fit values , and weak support for a change in early 2005 , apparently due to low numbers of cases in the first months of that year and the following winter ( figure 1 , panel b ) .
most of the seasonality in the number of cases was accounted for by the 30-year mean monthly temperatures .
each extra degree celsius of mean monthly temperature corresponded to a 2.42% increase in cases in the current month ( adjusted p = 0.044 , 95% ci , 0.55%4.33% ) and a 4.09% increase in the following month ( adjusted p<0.001 , 95% ci 2.19%6.03% ) .
the 1-month lag reflects delays between food production and consumption plus the known long incubation of listeria infections .
a relationship also appears to exist between the rate of listeriosis and the 1-month lagged temperature anomaly starting in april 2001 , which corresponded to an additional 5.71% of listeriosis cases per degree celsius ( 95% ci 2.10%14.15% ) when compared with the main effect of temperature anomaly in 19902007 .
this increase was not significant ( adjusted p = 0.312 ) , although the power of this test is only 15% at an adjusted significance level of 0.05 ( post hoc power analysis performed by computer simulation of the fitted model in the r statistical computing environment ) .
the model also includes a slight negative trend in cases over time ( 0.13% per month , 95% ci 0.26%0.01% ) , but evidence for this trend is very weak ( adjusted p = 0.188 ) . *
p values are for 2-sided tests of significance of the difference of each coefficient from zero ; adjusted p values are according to holm s method ( 10 ) .
percentage changes in the main text can be derived from these coefficients by taking exponentials , subtracting 1 , and multiplying by 100 .
the monthly number of listeriosis cases ( circles , lower plot ) is strongly seasonal , with a close relationship to the monthly mean temperature ( solid line , upper plot ) .
overall listeriosis incidence per month underwent 2 sudden increases , at break points around april 2001 and march 2003 .
our fitted statistical model ( dashed line , lower plot ) provides a close fit to the observed data ; the seasonally - adjusted fitted model ( solid line , lower plot ) shows the large magnitudes of the jumps in the rate of cases at the 2 break points .
the changes in the incidence of listeriosis appear to have been quite sudden ( figure 2 ) .
causative explanations based on gradual demographic or behavioral changes have previously been ruled out ( 3 ) , and those based on dynamic processes seem unlikely because no evidence exists for epidemiologic feedback between the source of infections and clinical cases .
one possibility is that the increases are due to contamination of a small number of food products , as has been suggested as an explanation for an upsurge in listeriosis rates in the late 1980s .
however , a restricted range of strains was responsible for most of the additional cases at that time ( 12 ) , and no evidence exists of such a pattern since 2001 ( 3 ) .
an alternative possibility is that the phenomenon is a consequence of changes in government policy or business practices that have had more widespread effects on food processing , distribution , or preparation .
we found notable coincidences between the dates of the increases in listeriosis infection rates and the dates of events associated with the 2001 foot and mouth disease ( fmd ) outbreak in the united kingdom .
the april 2001 increase in listeriosis rates occurred shortly after the outbreak of fmd in february 2001 , allowing for a delay similar to the incubation period of listeria infections . the march 2003 increase in listeriosis rates occurred in the same month as the relaxation of movement restrictions on livestock instituted after the 2001 fmd outbreak ( 13 )
the diverse and widespread consequences of the 2001 fmd outbreak are well - documented ( 14,15 ) , and it seems plausible that such major disturbances to agricultural production could be the ultimate cause of the large increases in listeriosis rates that have been observed .
the coincidence of these events raises the possibility that the change in listeriosis rates would be an unrecognized outcome of the 2001 uk fmd crisis , although we caution that our analysis of the of these cases does not demonstrate whether a causative link exists .
listeriosis incidence in england and wales has increased notably since the beginning of 2001 , with 2 separate , sudden increases recorded in april 2001 and march 2003 .
gillespie et al . argue that blame for these increases can not be ascribed to any of a variety of specific factors ( 3 ) . instead
, more widespread changes affecting food production , processing , or consumption could be the root of the problem .
incidence of this serious disease has risen substantially in england and wales , and an understanding of why will be important for management of listeriosis as a public health issue .
 the monthly incidence of listeriosis infections in england and wales had 2 sudden increases during april 2001 ( 41% ) and march 2003 ( 48% ) . 
 although no causative association is demonstrated , these increases correspond to key dates relating to the onset and aftermath of the 2001 foot and mouth disease outbreak in the united kingdom . 
The Study
Conclusions