Selected Publications

The series of events leading to the decision to cull a cow is complex, involving both individual-level and herd-level factors. While the decision is guided by financial returns, it is also influenced by social and psychological factors. Research studies on the motivational and behavioural aspects of farmers' decision utility are sparse, and nonexistent regarding culling expectations and its decision process. Our goal was to identify shared criteria on culling decisions held by dairy producers and farm advisers, with the help of the Q-methodology. Forty-one dairy producers and 42 advisers (17 veterinarians, 13 feed mill advisers, and 12 dairy herd improvement (DHI) advisers) undertook a Q-sort with 40 statements that represented a range of views about cow and herd health, production performance, management issues, and material factors that might impact their culling decision-making process. The sorts were analysed by-person using factor analysis and oblimin rotation. A single view on culling could be identified among dairy producers that can be extended to dairy farm advisers, who showed two variations of the same well-structured, uni-dimensional decision-making process. Udder health, milk production performance, and milk quota management were the key criteria for the culling decision. Farm management parameters (debts, amortization, employees, milking parlour capacity, herd size) did not play any role in the decision process. Three key differences were, however, identified between producers and the two types of advisers. One group of advisers followed the recommendations from mathematical models, where pregnancy is a major determinant of a cow's value. They assessed the cow in a more abstract way than did the other participants, still taking into account udder health and milk production, but adding economic considerations, like the availability of financial incentives and an evaluation of the post-partum health of the cow. Dairy producers were also more concerned about producing healthy and safe milk, which might reflect a different value given to dairy farming than by advisers. Very different degrees of importance were given to animal welfare by the three groups, which could represent different views on the attributed relationships between dairy farmers and their animals. Our findings suggest that dairy producers and their advisers hold a general common view regarding culling decision-making. However there are significant differences between producers and advisers, and among advisers. Understanding and managing these differences is important for assisting the change management processes required to increase farm profitability, and call for further investigation.
In Preventive Veterinary Medicine.

The relationship between cows' health, reproductive performance or disorders and their longevity is well demonstrated in the literature. However these associations at the cow level might not hold true at the herd level, and herd-level variables can modify cow-level outcomes independently of the cows' characteristics. The interaction between cow-level and herd-level variables is a relevant issue for understanding the culling of dairy cows. However it requires the appropriate group-level variables to assess any contextual effect. Based on 10 years of health and production data, the objectives of this paper are: (a) to quantify the culling rates of dairy herds in Québec; (b) to determine the profiles of the herds based on herd-level factors, such as demographics, reproduction, production and health indicators, and whether these profiles can be related to herd culling rates; and (c) to determine potential contextual variables affecting the culling risk of individual cows. A retrospective longitudinal study was conducted on data from dairy herds in Québec, Canada, by extracting health information events from the dairy herd health management software used by most Québec producers and their veterinarians. Data were extracted for all lactations taking place between January 1st, 2001 and December 31st, 2010. A total of 432,733 lactations from 156,409 cows out of 763 herds were available for analysis. Thirty herd-level variables were aggregated for each herd and years of follow-up, and their relationship was investigated by Multiple Factor Analysis (MFA). The overall yearly culling rate was 32%, with a 95% confidence interval (CI) of [31.6,32.5]. The dairy sale rate by 60 days in milk (DIM) was 3.2% [2.8,3.6]. The yearly culling rate within 60 DIM was 8.2% [7.9,8.4]. The explained variance for each axis from the MFA was very low: 14.8% for the first axis and 13.1% for the second. From the MFA results, we conclude there is no relationship between the groups of herd-level indicators, demonstrating the heterogeneity among herds for their demographics, reproduction and production performance, and health status. However, the profiles of herds could be determined according to specific domains independently. The relationships between culling rates and specific herd-level variables were limited to livestock sales, proportion of first lactation cows, herd size, proportion of calvings occurring in the fall, longer calving intervals and reduced 21-day pregnancy rates, increased days to first service, average age at first calving, and reduced milk fever incidence. The indicators found could be considered as contextual variables in multilevel model-building strategies to investigate cow culling risk.
In Preventive Veterinary Medicine.

Health disorders, such as milk fever, displaced abomasum, or retained placenta, as well as poor reproductive performance, are known risk factors for culling in dairy cows. Clinical mastitis (CM) is one of the most influential culling risk factors. However the culling decision could be based either on the disease status or on the current milk yield, milk production being a significant confounder when modelling dairy cow culling risk. But milk yield (and somatic cell count) are time-varying confounders, which are also affected by prior CM and therefore lie on the causal pathway between the exposure of interest, CM, and the outcome, culling. Including these time-varying confounders could result in biased estimates. A marginal structural model (MSM) is a statistical technique allowing estimation of the causal effect of a time-varying exposure in the presence of time-varying covariates without conditioning on these covariates. The objective of this paper is to estimate the causal effect on culling of CM occurring between calving and 120 days in milk, using MSM to control for such time-varying confounders affected by previous exposure. A retrospective longitudinal study was conducted on data from dairy herds in the Province of Québec, Canada, by extracting health information events from the dairy herd health management software used by most Québec dairy producers and their veterinarians. The data were extracted for all lactations starting between January 1 and December 31, 2010. A total of 3,952 heifers and 8,724 cows from 261 herds met the inclusion criteria and were used in the analysis. The estimated CM causal hazard ratios were 1.96 [1.57--2.45] and 1.47 [1.28--1.69] for heifers and cows, respectively, and as long as causal assumptions hold. Our findings confirm that CM was a risk factor for culling, but with a reduced effect compared to previous studies, which did not properly control for the presence of time-dependent confounders such as milk yield and somatic cell count. Cows experienced a lower risk for CM, with milk production having more influence on culling risk in cows than heifers.
In Preventive Veterinary Medicine.

Several health disorders, such as milk fever, displaced abomasum, and mastitis, as well as impaired reproductive performance, are known risk factors for the removal of affected cows from a dairy herd. While cow-level risk factors are well documented in the literature, herd-level associations have been less frequently investigated. The objective of this study was to investigate the effect of cow- and herd-level determinants on variations in culling risk in Québec dairy herds: whether herd influences a cow's culling risk. For this, we assessed the influence of herd membership on cow culling risk according to displaced abomasum, milk fever, and retained placenta. A retrospective longitudinal study was conducted on data from dairy herds in the Province of Québec, Canada, by extracting health information events from the dairy herd health management software used by most Québec dairy producers and their veterinarians. Data were extracted for all lactations starting between January 1st and December 31st, 2010. Using multilevel logistic regression, we analysed a total of 10,529 cows from 201 herds that met the inclusion criteria. Milk fever and displaced abomasum were demonstrated to increase the cow culling risk. A minor general herd effect was found for the culling risk (i.e. an intra-class correlation of 1.0% and median odds ratio [MOR] of 1.20). The proportion of first lactation cows was responsible for this significant, but weak herd effect on individual cow culling risk, after taking into account the cow-level factors. On the other hand, the herd's average milk production was a protective factor. The planning and management of forthcoming replacement animals has to be taken into consideration when assessing cow culling risks and herd culling rates.
In Preventive Veterinary Medicine.

The regulatory limit in Canada for bulk tank somatic cell count (BTSCC) was recently lowered from 500,000 to 400,000 cells/mL. Herd indices based on changes in cow somatic cell count over 2 consecutive months (e.g., proportion of healthy or chronically infected cows, cows cured, and new intramammary infection rate) could be used as predictors for BTSCC violations. The objective of this study was to develop a predictive model for exceeding the limit of 400,000 cells/mL in the next month using these herd indices. Dairy Herd Improvement (DHI) data were used from 924 dairy herds in Québec, Canada. Test-day BTSCC was estimated by dividing the sum of all cows' DHI test-day somatic cell count times DHI test-day milk production by the total volume of milk produced by the herd on that test-day. In total, 986 of 8,681 (11.4%) estimated BTSCC exceeded 400,000 cells/mL. The final predictive model included 6 variables: mean herd somatic cell score at the current test-month, proportion of cows >500,000 cells/mL at the current test-month, proportion of healthy cows during lactation at the current test-month, proportion of chronically infected cows at the current test-month, average days in milk at the current test-month, and annual mean daily milk production. The optimized sensitivity and specificity of the model were 76 and 74%, respectively. The positive predictive value and negative predictive value were 25 and 95%, respectively. This low positive predictive value and high negative predictive value demonstrated that the model was less accurate at predicting herds that would violate the estimated BTSCC threshold but very accurate at identifying herds that would not. In addition, the area under the curve for the receiver operating characteristic curve was 0.82, suggesting that the model had excellent discrimination between test-months that did and did not exceed 400,000 cells/mL. An internal validation was completed using a bootstrapped resampling-based estimation method and confirmed that the final model provided a validated estimate of predictive accuracy. This model could be used to monitor and advise clients on impending risks of exceeding the BTSCC limit.
In Journal of Dairy Science.

Bacteriological culture (BC) is the traditional method for intramammary infection diagnosis but lacks sensitivity and is time consuming. Multiplex real-time PCR (mr-PCR) enables testing the presence of several bacteria and reduces diagnosis time. Our objective was to estimate bacterial species-specific sensitivity (Se) and specificity of both BC and mr-PCR tests for detecting bacteria in milk samples from clinical mastitis cases and from apparently normal quarters, using a Bayesian latent class model. Milk samples from 1,014 clinical mastitis cases and 1,495 samples from apparently normal quarters were analyzed by BC and mr-PCR. Two positive culture definitions were used: ≥1 cfu/0.01 mL and ≥10 cfu/0.01 mL of the specified bacteria. The mr-PCR was designed to simultaneously detect Staphylococcus aureus, Streptococcus uberis, Escherichia coli, and Streptococcus agalactiae. The priors used in our Bayesian model were weakly informative, with BC priors using the best available error data. Results were compared with those obtained using uniform priors for mr-PCR to test robustness. Weak and uniform priors gave about the same posterior distributions except for Strep. uberis from normal quarters and Strep. agalactiae. Multiplex real-time PCR Se on milk from clinical mastitis were lower than mr-PCR Se on milk from normal quarters. Multiplex real-time PCR Se was higher than BC on milk from normal quarters. Multiplex real-time PCR Se was generally lower than BC on milk from clinical mastitis and it varied by clinical severity. The estimate specificities of detection for all pathogens were ≥99%, regardless of sample type. The effect of milk sample preservation before testing was evaluated and may have been a factor that affected our observed results. A significant association was observed between sample age and mr-PCR results leading to reduced detection of E. coli and Strep. agalactiae in nonclinical samples. Differences in sample age between conduct of BC and of mr-PCR did not concur with any apparent differences between Se estimates of the 2 tests. Further work should be done to extend these results to other PCR-based tests for detecting bacterial species in milk samples, for which presented results could be used as prior parameter distributions. Limits of sample handling and storage and the potential existence of substances in clinical case samples that may interfere with PCR reactions also are worth further investigation.
In Journal of Dairy Science.

Recent Publications

More Publications

  • Culling from the actors' perspectives—Decision-making criteria for culling in Québec dairy herds enrolled in a veterinary preventive medicine program

    Details Link (valid until November 26, 2017)

  • Culling from the herd's perspective—Exploring herd-level management factors and culling rates in Québec dairy herds

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  • Marginal structural Cox model to estimate the causal effect of clinical mastitis on Québec dairy cow culling risk

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  • Contextual herd factors associated with cow culling risk in Québec dairy herds: A multilevel analysis

    Details Link

  • Prediction of bulk tank somatic cell count violations based on monthly individual cow somatic cell count data

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  • Genotypic and phenotypic characterization of Staphylococcus aureus causing persistent and nonpersistent subclinical bovine intramammary infections during lactation or the dry period

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  • Zinc as an agent for the prevention of biofilm formation by pathogenic bacteria

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  • Characterization of the ability of coagulase-negative staphylococci isolated from the milk of Canadian farms to form biofilms

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  • Bayesian estimation of the diagnostic accuracy of a multiplex real-time PCR assay and bacteriological culture for 4 common bovine intramammary pathogens

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  • Examining the effect of intramammary infections with minor mastitis pathogens on the acquisition of new intramammary infections with major mastitis pathogens - A systematic review and meta-analysis

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Bob Muenchen has a series of articles on the Popularity of Data Science Software. He found that SPSS is the most used software, followed by R, SAS, Stata, GraphPad Prism, and MATLAB, by looking at scholarly articles in Google Scholar. He presents his methodology here. While he’s showing popularity (or market share) of several softwares for data science, statistical analysis, machine learning, artificial intelligence, predictive analytics, business analytics, and business intelligence, I was always wondering what the results would be like for my specific field, epidemiology.

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Bug fixes were brought to episensr R package, regarding the use of distributions and computations of odds and risk ratios in probsens.conf function for the probabilistic sensitivity analysis for unmeasured confounder. Improvement on the use of distributions was also brought to other probsens series of functions. Let’s run the example from Modern Epidemiology by Rothman, Greenland & Lash, on page 365-366. This example is taken from a paper by Greenland et al.

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My R package episensr can now also be used through a new Shiny application, episensr_shiny, to more easily assess the effect of biases on epidemiological results. Not all functions and options are available yet, only selection bias and misclassification of exposure or outcome can be specified. But direct consequences of modifying the bias parameters by moving the sliders can be checked on the 2-by-2 tables and measures of association. This is therefore still in “beta” but more will come.

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I was curious to see if I could identify some patterns of collaboration and research topics in cow health research made in Canada. For this, I’m trying the R package bibliometrix. This package allows quantitative research in scientometrics and bibliometrics by providing different routines for importing bibliographic data from Scopus and ISI Web of Knowledge databases, and performing various bibliometric analyses. The Bibliometrix website provides a good tutorial that I will mainly follow, with the sole objective to satisfy my curiosity and have fun!

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A small update for my episensr R package is now available on CRAN. The update focus on misclassification. First, covariate misclassification is now available, via the function misclassification_cov. For example, the paper by Berry et al. looked if misclassification of the confounding variable in vitro fertilization (IVF), a confounder, resulted wrongly on an association between increase folic acid and having twins. IVF increases the risk of twins, and women undergoing IVF might be more likely to take folic acid supplements, i.

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