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By continuing to use our site, you agree to our use of cookies. Suivre fusacparis sur Twitter. A dazzling, stylish biography of a fabled Parisian photographer, adventurer, and pioneer. Of all of the legendary figures who thrived in midth-century Paris—a cohort that includes Victor Hugo, Baudelaire, Gustave Courbet, and Alexandre Dumas—Nadar was perhaps the most innovative, the most restless, the most modern.

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French readers will lift up their eyebrows and see their own language from a new perspective. Get Updates Subscribe to our e-mail newsletter to receive updates. Share This Post Tweet. Video: Passer une annonce. The main goal of the present analysis was to assess the relationship between metrics of fluke infection, as measured using abattoir surveillance data, with herd level characteristics and geographically referenced environmental and meteorological predictors.

The analysis demonstrates the importance of good surveillance and databasing systems and how these can be utilised for research and policy development. Despite the potential for low animal level sensitivity of abattoir surveillance, aggregating at the herd level allows for useful predictions from a readily available data source [ 15 ]. This adds value and understanding to these datasets [ 13 ].

Predictions from this work can directly inform further analyses on the potential effects of concurrent infections on the management of other diseases in cattle herds e. These data included animal-level animal identifier, sex, age, bTB status and fluke status , herd-level herd identifier, herd type, turnover , and geographic variables region and farm coordinates. More details of the variables used are presented in Additional file 1 : Tables S1—S6.

All animals were included, with the exception of animals that did not go through an abattoir e. The apparent prevalence was assessed initially using descriptive statistics at animal, within-herd and across-herd levels. The relationship between the number of animals sampled and the estimated prevalence at the herd level was also investigated. Due to the high levels of fluke exposure across herds see results , the apparent within-herd prevalence of infection was modelled in relation to risk factors.

Prevalence was measured on a scale of 0 to 1, by calculating the proportion of animals within a given year that exhibited evidence of fluke infection. There is a trade-off between increasing the minimum number of animals sampled per herd to increase the robustness of within-herd prevalence estimates, and the number of herds included in a dataset. In the present data set, the mean number of samples per herd was Separate models were developed for each year of the study, , , and , respectively.

Summary statistics of the independent variables are described in the Additional file 1 : Tables S1—S6. These variables were selected based on availability and literature searches e. The majority of herds slaughtered a proportion of their herd size in a given year i. However, some herds had a greater number of animals slaughtered than the recorded herd size i.

This arose due to high turnover in certain herds, and the fact that herd size was estimated from one static annual recording the annual bovine tuberculosis test at a given point during the year. Therefore, turnover captured both the buying in and slaughtering of cattle high turnover herds have to repopulate their herds to maintain stock. The coordinates for each farm point location of herd as registered within APHIS within the dataset were used to spatially associate the location of each cattle herd during its last ordinary herd before being sent to slaughter. Official data was gathered from the UK Meteorological Office www.

We also gathered Vapour Pressure data, however the long-term mean monthly values available for the region only extended from — The mean temperature derived from the minimum and maximum data and climatic variance standard deviation around the mean of each long-term monthly variable was included. For each month, the long-term monthly mean was extracted, and then averaged these values and calculated how much variation there was in terms of the standard deviation SD around the mean.

Land cover variables based on CORINE and the UK land cover map [ 27 ] were also used as a cofactor, as landscape composition has been shown to affect fluke prevalence in cattle e. Within herd prevalence was modelled in two ways. This outcome was modelled using a multivariable logit model, with independent variables presented in Additional file 1 : Tables S1—S6. The sensitivity probability that a positively classified farm is truly positive , specificity probability that a negatively classified farm is truly negative , and the percentage of farms that were correctly classified, using a standard cut-point of 0.

Assessment of whether there was any spatial autocorrelation in the residuals of the final models was assessed by plotting a semi-variogram using the package geoR [ 28 ] within the R statistical environment [ 29 ]. Observed and predicted within-herd liver fluke prevalence in Northern Ireland based on meat inspection data. Panels a - c depicts the distribution of herds with high or low within-herd infection prevalence for —, respectively. Panels d - f represents predicted probability of a herd being in a high infection category from — from a logistic model, respectively.

The benefit of this modelling approach is that all the data can be used as opposed to the binary approach used above , and the expected outcome fraction positive per year can be modelled as a continuous variable while bounding the outcome between O and 1 i. The models were developed using Generalised Linear Model GLM modelling with the proportion infected as the outcome using a logit link function and robust standard errors and compared the results with those of the fractional response regression. We compared the predictions from these models against the observed proportion infected, but binning the predicted proportions in categories groups of 0.

During model building, all independent variables were tested for association with the outcome variable using univariable models either logistic regressions or GLMs. The functional form of the relationship between the outcome variable and continuous predictors was assessed using LOWESS smoothed curves. Where necessary, independent variables were suitably transformed [ 34 ]. Throughout, model building was undertaken using backwards, forwards and stepwise sequential model building [ 34 ].

Correlations between independent variables were assessed, and Variance Inflation Factor VIF was used during model building to assess if multi-collinearity may have been a problem using OLS models. The best models were those with the lowest AIC value. Of these, , The percentage of animals with fluke varied across years, with higher apparent prevalence in This difference between and other years was statistically significant vs OR: 0.

Animal level variation in post-mortem fluke infection status across years — in Northern Ireland. The mean within-herd percentage positive overall was The within-herd percentage varied across years; varying from The mean percentage of animals within-herds that exhibited evidence of liver fluke infection.

The herd-level fluke infection status was assessed for each herd-year and for each herd respectively. In total, there were 39, herd-years within the dataset, of which 25, herd-years This varied over time, between Aggregating across years, there were 18, herds represented in the dataset, with 12, herds having evidence of fluke infection within their herd over the study period However, there was a significant sampling effect found, with a relationship between the number of animals sampled, and the probability of being an exposed herd see next section.

Herd level variation in fluke infection status, with herds categorised as exposed with at least one infected animal culled in a given year or non-exposed without any culled animals with evidence of fluke infection at post-mortem across years — in Northern Ireland. There was a significant relationship between the numbers of animals sampled culled within an abattoir per herd across years —, and the probability of that herd having a positive exposed status log no. Herd-level percentage positive increased from The relationship between apparent prevalence and sampling was steep Fig.

The overall status of herds in NI relative to the number of samples taken per herd over the period — Note, the cut-point where there were no fluke free herds was animals sampled. The relationship between the probability of a herd having evidence of fluke infection and the number of animals slaughtered per herd in Northern Ireland — Similar patterns were found when status was assessed on individual years. The yearly status of herds in NI relative to the number of samples taken per herd for the years —, respectively. Note, the cut-point where there were no fluke free herds was animals sampled in , in , and in This resulted in 7, total herds, 3, The discriminatory ability of the model was good AUC: 0.

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Apparent sensitivity was There was no evidence of spatial autocorrelation within the residuals from the final model. There was a large difference in the risk amongst abattoirs; for example, the odds ratio for abattoir 1 relative to abattoir 0 was Dairy herds had the highest risk of being in the high severity category across herd types OR: 1. Beef fattening herds had significantly lower risk of being classified as a high severity herd relative to beef breeding OR: 0.

Herds within the regions of Derry, Armagh, Larne County Antrim and Omagh County Tyrone had the lowest odds of being high severity herds; whereas herds in Ballymena County Antrim and Enniskillen County Fermanagh had the highest probability of being high severity herds. Outcomes from three multivariable logit models relating the risk of cattle herd exhibiting high severity fluke infection for the years , and , respectively. Significance Sig. The distribution and status of farms making up this dataset is presented in Fig.

Predictions from the logit model are presented in Fig. These areas correspond to marginal or unproductive lands upland, and areas with bog and urban areas Fig. The final multivariable logit model contained 2, herds, with a pseudo-R 2 of 0. The discriminatory ability of the model was very good AUC: 0. Herds predominately using abattoirs 1 OR: 9. Dairy herds again were the highest risk amongst herd types OR: 2.

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The final multivariable logit model contained 2, herds and had a pseudo-R 2 of 0. The model exhibited very good discriminatory ability with an AUC of 0. Using a probability cut-off of 0. Residuals from the final model did not exhibit spatial autocorrelation Additional file 1 : Figures S1 and S2. Dairy herds had the highest risk of exhibiting high severity fluke infection relative to beef or other herd types. Armagh south-east region had the lowest risk, while Ballymena north-east region had the highest risk of being in the severe fluke category.

The risk of severe fluke infection decreased with the increasing variation in rainfall, minimum temperature and humidity. Both the GLM and the fractional response regression models produced very similar results. Consequently, the GLM models are presented here only. The ability of the model to capture the relative within-herd prevalence was assessed by plotting the predicted prevalence against the observed prevalence. The calibration of the models was generally good, with some evidence of the models marginally overestimating within-herd prevalence at low infection levels, and underestimating the mean within-herd prevalence at the high infection levels see Additional file 1 : Figures S11—S Generalised linear models GLM relating the proportion of animals with evidence of fluke infection at post-mortem within-herds for the years , and respectively.

The GLM was fitted with a logit link function to ensure predictions from the model would be constrained between 0 and 1 i. Across models, abattoir, region and herd type were significant predictors of within-herd apparent prevalence. Abattoir 1 was consistently associated with the highest proportion of animals with evidence of fluke infection across years OR: 1. Abattoir 4 was consistently associated with the lowest proportion of animals with evidence of fluke infection across years OR: 0.

Dairy herds exhibited a higher proportion of animals with evidence of fluke infection relative to other herd types OR: 1. Herd size was negatively associated with increasing within-herd prevalence only during OR: 0. Across years, different climatic variables were associated with within-herd prevalence. In and , within-herd prevalence was positively associated with increasing long-term variation in humidity OR: 1. In , fluke within-herd prevalence was also associated with decreasing humidity OR: 0.

In , there was an association with the long-term minimum temperature and within-herd fluke prevalence OR: 0. Our study has demonstrated that there are high levels of liver fluke exposure at animal and herd levels in Northern Ireland. However, the levels of fluke infection reported at abattoir are lower than previous post-mortem studies undertaken in the Republic of Ireland [ 12 ] and Northern Ireland [ 36 ]. Murphy et al. This reflects the fact that routine surveillance can have low sensitivity to detect infection. A study by Rapsch et al.

In Switzerland, abattoir surveys suggested that the prevalence of fluke infection in cattle was between 8.

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However, accounting for test sensitivity, the true prevalence in a study of 1, animals was It should be noted, however, estimates of the sensitivity of routine meat inspection in Northern Ireland is currently unknown. Importantly, the levels of infection reported here are considerably higher than reports from meat inspection data from Denmark presented in Olsen et al. These international differences are possibly the result of a true difference in the exposure to liver fluke between the countries [ 4 , 42 ].

The island of Ireland is possibly the best suited region within Europe for liver fluke to thrive, both in terms of climate and habitat suitability, as highlighted by recent international modelling efforts [ 4 , 43 , 44 ]. Furthermore, climate projections suggest the weather on the island of Ireland will be more favourable for fluke in the future [ 3 , 4 ]. Warmer, wetter winters will ensure survival of fluke metacercariae and their intermediate host within the Irish landscape, increasing risk of transmission.

Furthermore, the advent of flukicidal resistance is an emerging issue having a negative impact on the control of fluke infection [ 5 , 6 ]. However, we have shown that there was a strong sampling effect with this estimate Fig. These studies indicate that the vast majority of herds in Ireland have been exposed to liver fluke infection which could be as a result of exposure on farm, or buying in infected animals in contrast to other European countries [ 4 , 41 , 43 ].

In the Netherlands, in a study of 2, farms, Over the study period, there was no clear increasing or decreasing trend in apparent prevalence of liver fluke infection. Instead, metrics of infection at animal, within-herd, and between herd levels, suggested that had significantly lower infection levels than either or A contemporary study of fluke infection levels in sheep in Ireland suggested also that there was not significant inter-annual variation in fluke prevalence over a similar period [ 44 ].

There was significant variation in risk depending on the region DVO area in which the farm resided. Across models, there was greater likelihood of herds being higher risk if they were found in Ballymena or Coleraine, relative to other DVOs. After model building, we did not find evidence of spatial autocorrelation amongst model residuals, in comparison with randomised variograms. This suggests that most of the residual spatial information was captured by the fixed effects in the model. There were a number of fixed effects that were spatially explicit, that may have helped to control for spatial variation, including the REGION fixed effects.

Across models, we consistently found that there was higher risk of fluke infection in dairy herds than in non-dairy herd types. Dupuy et al. The authors found that liver fluke infection was associated with beef cattle. However, liver fluke infection also clustered with female cows and older cows, both of which are characteristic of dairy cattle herds. In Denmark, Olsen et al. The actual mechanism as to why dairy herds tended to have higher within-herd prevalence is yet to be determined, but could be related to the frequency, timing and type of anthelmintics used, the age profile of the herds, differences in habitats or the grazing management amongst herd types.

One of the most significant factors affecting the variation in fluke prevalence in the present study was the main abattoir used by the farmer over the study period. The average proportion of animals disclosed as fluke positive could be twice as high in one abattoir relative to another abattoir in a given year. Similar variation between slaughterhouses has been reported from Denmark [ 15 ]. There, herds associated with the highest disclosing abattoir were 3. In the present study, across all models, abattoir 1 was associated with herds with high prevalence, while abattoir 4 had the lowest proportion positive.

However, the relative rank of other abattoirs varied somewhat across years and model types. The variation in abattoir is related to the performance of meat inspection and the region in which the abattoir was based. While generally animals would be processed in abattoirs close to the herd, there was variation in this pattern across Northern Ireland. When looking at the relationship between abattoir and region, there were some weak correlations found, however Variance Inflation Factor used in OLS models was low.

A correlation matrix between region and abattoir across the study period suggested that there were some farmers from each DVO sending animals to each of the respective abattoirs. This is maybe linked to the variation in prices achieved across the market. Previous research from Ireland has found significant variation in the disclosure by meat inspection of bovine tuberculosis lesions from slaughter houses [ 49 , 50 ]. Frankena et al. A follow-up survey from — suggested that the variation across factories reduced between surveys a seven-fold difference to a five-fold difference in disclosure and the mean proportion disclosed increased [ 50 ], indicating that there was scope for improvements in standardisation and the ability to increase detection probabilities [ 51 ].

Other issues may relate to variation in the actual recording data quality of fluke infection between abattoirs. The large variation between abattoirs cannot easily be explained currently, and may be an important future research topic in Northern Ireland. Across three of the models, there was a significant negative association between increasing minimum temperature and liver fluke prevalence.

This would indicate that areas that were cooler than average may have increased risk of having higher within-herd prevalence, which is in keeping with larger scale studies [ 43 ]. However, in one model, there was a positive association between mean maximum temperature and within-herd fluke prevalence. Such variation in association between weather variables and metrics of fluke prevalence has been reported previously in a study in England and Wales [ 26 ]. Surprisingly, variation in rainfall was retained as a predictor in only two models in the current study.

In those models, there was an increased risk of higher prevalence with increasing rainfall days, and a decreased risk with the long-term variation in rainfall days, respectively. A recent study comparing fluke infection in sheep between Ireland, Switzerland and Italy found that longer-term weather variables temperature and rainfall explained between country variations in fluke prevalence, but were not associated with year-to-year variation within country [ 44 ]. Instead, shorter-term weather variation in mean temperature, rainfall and seasonality was important in explaining the variation from year to year within country [ 44 ].

McCann et al. However, there was only a marginal benefit to using a 5-year mean with contemporaneous weather, than using a single time point alone. This concurs with work undertaken Ducheyne et al. The authors reported that weather variables were very important predictors in their models; however the vegetation variables were not important. For example, they found the improved grassland was a positive predictor of fluke infection in four of their models. In Switzerland, soil conditions and forest cover was also used to create indicative map of fluke risk, based on the potential exposure of herds to the intermediate host Galba truncatula [ 37 ].

Other authors have highlighted how the lack of detail of land cover maps may reduce the accuracy of generating risk maps for liver fluke [ 53 ]; indeed high resolution mapping may help to elucidate the risk to individual farms and their landscape composition. The farm locations were only measured using point locations of the farmstead.

This has obvious accuracy issues see [ 54 ] for a discussion on positional error in epidemiological studies , as farms may expand out across a landscape and is often associated with a number of farm parcels. However, research has shown that in large scale epidemiological investigations, the farm location can perform well as a practical indicator of farm location [ 55 ]. Overall, centroids performed best in terms of identifying with the largest area of the farm; however the authors judged the farmstead location was the overall best practical point location.

The authors highlight the benefits of using the farmstead as it can often be close to the focal point for housed animals over winter, and a centre of activities such as calf rearing and milking. The long-term weather datasets may reflect climate variation across Northern Ireland, rather than shorter term weather variation that may drive fluke prevalence.

When using post-mortem meat inspection data, we are uncertain when during the life of the animal were they infected with fluke. The mean age of animals was 3.