Flow of information for bovine health surveillance: Two socio-economic models demonstrating the impact of the organisational profiles of local actors.

Passive surveillance is based on spontaneous reporting to veterinary authorities of disease suspicions by farmers and other stakeholders in animal production. Stakeholders are considered "actors" in sociology of organisations research. In veterinary public health, passive surveillance is considered to be the most effective method to detect disease outbreaks and to generate epidemiological information for decision-making on surveillance and control strategies. Nevertheless, under-reporting of cases is an inherent problem, reducing the ability of the system to rapidly detect infected animals. Previous studies have shown, for example, that passive surveillance for bovine brucellosis in France, through compulsory reporting of all bovine abortions, has limited sensitivity, with variability in reporting rates despite similar cattle farming profiles. Based on this observation and on sociological literature in health surveillance, we hypothesised that oversight organisational factors in different areas influence health actor contributions to passive surveillance. Therefore, to improve the efficiency of surveillance systems, we need to understand the organisational levers (supporting factors) and organisational drags (hindering factors) on the production and dissemination of health information. We conducted semi-structured interviews with the surveillance actors in two administrative geographic divisions in France (Departments A and B) with similar cattle farming profiles but contrasting abortion reporting rates (low and high, respectively). We assumed that these rates were related to health actor organisation in each administrative division. We mapped actor relationships and looked for behavioural recurrences and differences between the two departments. This analysis led to two socio-economic models explaining the configurations observed: pro-curative in Department A, and pro-preventive in Department B. These models showed a link between the level of competition endured by veterinarians on the sale of veterinary medicinal products and the overall contribution of the actors to health surveillance. The pro-preventive model had a higher contribution to surveillance than the pro-curative model. Importantly, the nature of the information produced in this configuration of actors corresponded to the needs of surveillance, providing collective and early information that circulated more readily between actors. We highlighted three characteristics that help to identify the configuration of a system of actors: 1) the pressure of competition exerted on veterinarian activities; 2) the dominant business model and form of organisation of veterinary clinics; and 3) the frequency of interactions between the main surveillance actors outside of crises. The first two characteristics affect the local contribution to data reporting for surveillance, and the third affects network responsiveness in a health crisis.

Serological evidence of Rift Valley fever in domestic ruminants in Tunisia underlines the need for effective surveillance.

Background: Rift Valley fever (RVF) is an infectious zoonotic disease infecting, mainly, domestic ruminants and causing significant economic and public health problems. RVF is a vector-borne disease transmitted by mosquitoes. Aim: In this work, we tried to seek any RVF virus circulation in Tunisia. Methods: Thus, we investigated 1,723 sera from different parts of Tunisia, collected in 2009 and 2013-2015 from sheep, goats, cattle, and dromedaries. All sera were assessed using enzyme-linked immunosorbent assay techniques. Results: Eighty-seven sera were detected positive and 11 doubtful. All of them were investigated by the virus-neutralization technique (VNT), which confirmed the positivity of three sera. Conclusion: This is the first case of RVF seropositive confirmed by the VNT in Tunisian ruminants. Such a result was expected considering the climate, entomology, and geographic location of the country. Further investigations must enhance our findings to understand the RVF epidemiologic situation better and implement risk-based surveillance programs and effective control strategies.

Virological and epidemiological patterns of swine influenza A virus infections in France: Cumulative data from the RESAVIP surveillance network, 2011-2018.

Swine influenza A viruses (swIAVs) cause acute respiratory syndromes in pigs and may also infect humans. Following the 2009 pandemic, a network was established in France to reinforce swIAV monitoring. This study reports virological and epidemiological data accumulated through passive surveillance conducted during 1,825 herd visits from 2011 to 2018. Among them, 887 (48.6 %) tested swIAV-positive. The proportion of positive cases remained stable year-on-year and year-round. The European avian-like swine H1N1 (H1avN1) virus was the most frequently identified (69.6 %), and was widespread across the country. The European human-like reassortant swine H1N2 (H1huN2) virus accounted for 22.1 % and was only identified in the north-western quarter and recently in the far north. The 2009 pandemic H1N1 (H1N1pdm) virus (3.6 %) was detected throughout the country, without settling in areas of higher pig densities. Its proportion increased in winter, during the seasonal epidemics in humans. The European human-like reassortant swine H3N2 as well as H1avN2 viruses were identified sporadically. In up to 30 % of swIAV-positive cases, pigs exhibited clinical signs of high intensity, regardless of the viral subtype and vaccination program. The recurrent pattern of the disease, i.e., an endemic infection at the herd level, was reported in 41% of cases and mainly affected post-weaning piglets (OR = 5.11 [3.36-7.76]). Interestingly, the study also revealed a significant association between the recurrent pattern and sow vaccination (OR = 1.96 [1.37-2.80]). Although restricted to the studied pig population, these results bring new knowledge about swIAV dynamics and infection patterns in pig herds in France.

Impact of Imperfect Disease Detection on the Identification of Risk Factors in Veterinary Epidemiology.

Risk factors are key epidemiological concepts that are used to explain disease distributions. Identifying disease risk factors is generally done by comparing the characteristics of diseased and non-diseased populations. However, imperfect disease detectability generates disease observations that do not necessarily represent accurately the true disease situation. In this study, we conducted an extensive simulation exercise to emphasize the impact of imperfect disease detection on the outcomes of logistic models when case reports are aggregated at a larger scale (e.g., diseased animals aggregated at farm level). We used a probabilistic framework to simulate both the disease distribution in herds and imperfect detectability of the infected animals in these herds. These simulations show that, under logistic models, true herd-level risk factors are generally correctly identified but their associated odds ratio are heavily underestimated as soon as the sensitivity of the detection is less than one. If the detectability of infected animals is not only imperfect but also heterogeneous between herds, the variables associated with the detection heterogeneity are likely to be incorrectly identified as risk factors. This probability of type I error increases with increasing heterogeneity of the detectability, and with decreasing sensitivity. Finally, the simulations highlighted that, when count data is available (e.g., number of infected animals in herds), they should not be reduced to a presence/absence dataset at the herd level (e.g., presence or not of at least one infected animal) but rather modeled directly using zero-inflated count models which are shown to be much less sensitive to imperfect detectability issues. In light of these simulations, we revisited the analysis of the French bovine abortion surveillance data, which has already been shown to be characterized by imperfect and heterogeneous abortion detectability. As expected, we found substantial differences between the quantitative outputs of the logistic model and those of the zero-inflated Poisson model. We conclude by strongly recommending that efforts should be made to account for, or at the very least discuss, imperfect disease detectability when assessing associations between putative risk factors and observed disease distributions, and advocate the use of zero-inflated count models if count data is available.

Swab cloths as a tool for revealing environmental contamination by Q fever in ruminant farms.

Q fever is a zoonotic abortive disease of ruminants mostly transmitted by inhalation of aerosols contaminated by Coxiella burnetii. Clusters of cases or even epidemics regularly occur in humans but, to date, there is no consensus about the best way to carry out outbreak investigations in order to identify potential farms at risk. Although environmental samples might be useful during such investigations, there are few baseline data on the presence of C. burnetii in the environment of ruminant farms. We thus investigated dust samples from cattle, sheep and goat farm buildings in order to (a) estimate C. burnetii detection frequency and bacterial loads in the environment, and (b) determine whether this environmental contamination is associated with series of abortions attributed to Q fever. We considered 113 herds with a recent abortive episode potentially related (n = 60) or not (n = 53) to C. burnetii. Dust was sampled using a swab cloth and tested by a quantitative PCR method targeting the IS1111 gene. Coxiella burnetii DNA was detected on 9 of 50 cattle farms, 13 of 19 goat farms and 30 of 40 sheep farms. On 16 cloths, bacterial loads were higher than 108 genome equivalents, levels as high as in infectious materials such as placentas and aborted foetuses. Overall, the probability of detecting C. burnetii DNA was higher on small ruminant farms than cattle farms, in herds suspected of Q fever and in large herds. We conclude that swab cloths are a putative indicator of contamination of ruminant farms by C. burnetii.

Designing a Syndromic Bovine Mortality Surveillance System: Lessons Learned From the 1-Year Test of the French OMAR Alert Tool.

Between May 2018 and 2019, a syndromic bovine mortality surveillance system (OMAR) was tested in 10 volunteer French départements (French intermediate-level administrative unit) to assess its performance in real conditions, as well as the human and financial resources needed to ensure normal functioning. The system is based on the automated weekly analysis of the number of cattle deaths reported by renderers in the Fallen Stock Data Interchange Database established in January 2011. In our system, every Thursday, the number of deaths is grouped by ISO week and small surveillance areas and then analyzed using traditional time-series analysis steps (cleaning, prediction, signal detection). For each of the five detection algorithms implemented (i.e., the exponentially weighted moving average chart, cumulative sum chart, Shewhart chart, Holt-Winters, and historical limits algorithms), seven detection limits are applied, giving a signal score from 1 (low excess mortality) to 7 (high excess mortality). The severity of excess mortality (alarm) is then classified into four categories, from very low to very high, by combining the signal scores, the relative excess mortality, and the persistence of the signal(s) over the previous 4 weeks. Detailed and interactive weekly reports and a short online questionnaire help pilot départements and the OMAR central coordination cell assess the performance of the system. During the 1-year test, the system showed highly variable sensitivity among départements. This variability was partly due not only to the demographic distribution of cattle (very few signals in low-density areas) but also to the renderer's delay in reporting to the Fallen Stock Data Interchange Database (on average, only 40% of the number of real deaths had been transmitted within week, with huge variations among départements). As a result, in the pilot départements, very few alarms required on-farm investigation and excess mortality often involved a small number of farms already known to have health or welfare problems. Despite its perfectibility, the system nevertheless proved useful in the daily work of animal health professionals for collective and individual surveillance. The test is still ongoing for a second year in nine départements to evaluate the effectiveness of the improvements agreed upon at the final meeting.

Impact of heat and cold waves on female cattle mortality beyond the effect of extreme temperatures.

Although heat and cold temperatures are known to have an impact on cattle mortality, no study has evidenced and quantified the influence of the prolonged exposure to extreme temperatures beyond the single effect of daily temperatures. We defined a heat (or cold) wave by a continuous variable indicating the number of successive days with temperatures above (or below) a given threshold. For heat wave, the threshold was set to the 95th or 99th percentile of the mean daily temperature distribution and for cold wave to the 1st or 5th percentile. We collected female cattle mortality data by type of production and age classes between 2001 and 2015 for 100 iso-hygro-thermal areas in France. We used time-series analyses to estimate the area-specific heat wave- and cold wave-mortality relationships. Then, we applied meta-analyses to pool area-specific effects at the country level for each definition of heat and cold wave. For each type of production and age classes, our models predicted symmetrical relationships between temperature and mortality, with a temperature range of minimum mortality located approximately between 15 and 20 °CTHI in most categories. Outside that range, relative risks between 1.3 and 2.5 were estimated for extreme cold temperatures and relative risks between 1.1 and 1.5 were estimated for extreme hot temperatures depending on age categories and production type. Our results indicated that a prolonged exposure to high (or low) temperatures caused a significant increase on mortality (up to 40% during heat waves and 23% for cold waves, depending on type of production and age classes), in addition to the effect of extreme temperature alone. This additional mortality risk increased along with the duration and intensity of the exposure. Our results suggest that not discriminating the effect of the prolonged exposure to extreme temperature, may overestimate the effect of temperature alone on mortality.

Why do veterinarians ask for antimicrobial susceptibility testing? A qualitative study exploring determinants and evaluating the impact of antibiotic reduction policy.

For public health reasons, increasing attention has focused on more rational use of antimicrobials in farm animals. Guidance concerning the prescription of antibiotics and antimicrobial susceptibility testing (antibiograms in this case) are beneficial tools to help control the development of antimicrobial resistance. Nevertheless, even though there are already several qualitative studies analysing the determinants of antimicrobial prescription and use in veterinary medicine, little is known about decision-making concerning the use of antibiograms. The aim of this study was to provide a better understanding of veterinarians' motivations and role-players' influence concerning the choice of whether to ask for an antibiogram in the bovine, porcine, poultry and equine sectors in France. We concurrently evaluated the impact of a new French decree (2016) requiring an antibiogram before certain critically important antimicrobial agents can be used in veterinary medicine. Semi-structured interviews with veterinarians were conducted in France. Thematic analysis was used to analyse transcripts. In all, we surveyed 66 veterinarians. Use of antibiograms in veterinary medicine was multifactorial - 46 factors grouped into 11 categories were identified - and differed between animal sectors: use was almost systematic in poultry, frequent in pigs and rare in both the bovine and equine sectors. The decree has not increased the use of antibiograms but has induced a change in prescriptions due to field constraints and the time needed to obtain the results of antibiograms. Respondents see the decree as an aid in promoting responsible and rational use of antibiotics, fostering the use of alternatives. Our findings provide the basis of veterinarians' position regarding antibiogram use and antimicrobial resistance, pointing out levers to facilitate the use of antibiograms in veterinary medicine (for example communication on the benefits of this test and external financial support). Furthermore, the evaluation of the impact of the decree aimed at reducing the use of critically important antibiotic highlights key factors for a successful change in regulations, such as advance planning, precise and adapted communication, and demonstration of the measure's legitimacy. These results will be useful in guiding representative veterinary bodies and regulatory authorities during their decision-making, communication, and policy and regulation choices to combat antimicrobial resistance.

Antimicrobial resistance in bacteria isolated from mastitis in dairy cattle in France, 2006-2016.

In dairy cattle, mastitis is the most frequent bacterial disease, and the routine use of antibiotics for treatment and prevention can drive antimicrobial resistance (AMR). The aim of our study was to estimate the levels of AMR of the 3 main bacteria isolated from dairy cattle with mastitis in France (Streptococcus uberis, Escherichia coli, and coagulase-positive staphylococci) and to investigate their changes over time. Data collected between 2006 and 2016 by the French surveillance network for AMR in pathogenic bacteria of animal origin (called RESAPATH) were analyzed. The proportions of mono- and multidrug resistance were calculated and the trends were investigated using nonlinear analyses applied to time series. Over the whole period, the lowest proportions of resistance in S. uberis isolates were observed for oxacillin (2.2%) and gentamicin (2.4%) and most resistance levels were below 20%. The trends in resistance showed some significant variation, mainly for S. uberis, but without a common pattern across the various antibiotics examined. For only 2 combinations of bacteria-antibiotic the trend in resistance showed a continuous increase from 2006 to 2016: tetracycline resistance in S. uberis isolates and third-generation cephalosporin resistance in E. coli isolates. In E. coli, the highest proportions of resistance were observed for amoxicillin (28.1%) and tetracycline (23.1%). Resistance to third-generation cephalosporins in E. coli from dairy cattle was almost nil in 2006, but reached 2.4% in December 2016. This increase is particularly concerning because these antibiotics constitute one of the latest therapeutic alternatives to fight severe infectious diseases in humans. Except for penicillin (33.9%), the proportions of resistance in coagulase-positive staphylococci were below 11% during the whole study period. Multidrug resistance (isolates with acquired resistance to at least one antibiotic in 3 or more antibiotic classes) ranged from 2.4% for coagulase-positive staphylococci to 9.9% for S. uberis. These findings can serve as guidelines for practitioners in the choice of the most appropriate antibiotic according to the prevailing epidemiological context. Ultimately, our results contribute to risk assessment of AMR and provide a baseline for setting up and evaluating control measures and designing strategies to limit AMR.

Evaluation of the cost-effectiveness of bovine brucellosis surveillance in a disease-free country using stochastic scenario tree modelling.

Surveillance systems of exotic infectious diseases aim to ensure transparency about the country-specific animal disease situation (i.e. demonstrate disease freedom) and to identify any introductions. In a context of decreasing resources, evaluation of surveillance efficiency is essential to help stakeholders make relevant decisions about prioritization of measures and funding allocation. This study evaluated the efficiency (sensitivity related to cost) of the French bovine brucellosis surveillance system using stochastic scenario tree models. Cattle herds were categorized into three risk groups based on the annual number of purchases, given that trading is considered as the main route of brucellosis introduction in cattle herds. The sensitivity in detecting the disease and the costs of the current surveillance system, which includes clinical (abortion) surveillance, programmed serological testing and introduction controls, were compared to those of 19 alternative surveillance scenarios. Surveillance costs included veterinary fees and laboratory analyses. The sensitivity over a year of the current surveillance system was predicted to be 91±7% at a design prevalence of 0.01% for a total cost of 14.9±1.8 million €. Several alternative surveillance scenarios, based on clinical surveillance and random or risk-based serological screening in a sample (20%) of the population, were predicted to be at least as sensitive but for a lower cost. Such changes would reduce whole surveillance costs by 20 to 61% annually, and the costs for farmers only would be decreased from about 12.0 million € presently to 5.3-9.0 million € (i.e. 25-56% decrease). Besides, fostering the evolution of the surveillance system in one of these directions would be in agreement with the European regulations and farmers perceptions on brucellosis risk and surveillance.

A tool to support the identification of suspect cases of exotic diseases in cattle.

Maintaining vigilance with regard to the introduction of exotic diseases is a challenge, particularly because these diseases are numerous, some are not well known, and they are not immediately suspected by people in day-to-day practice, specifically veterinary practitioners. The objective of this article is to present a tool to support the identification of suspect cases of exotic diseases in cattle, based on a Bayesian approach. A list of 22 exotic diseases in mainland France was selected mainly on the basis of their potential consequences if introduced, and the ability to detect them on a clinical basis. In response of a set of epidemio-clinical criteria observed in the field this tool provides a list of exotic diseases by descending order of likelihood. The tool's performance was assessed by simulation. When simulating epidemio-clinical observations of each of the 22 diseases included in the tool with some uncertainty, the right disease was ranked in the first place between 83.8% and 100% of the times, and always in the five most likely diseases. Even when some noise was introduced in the epidemio-clinical observations simulated by addition of criteria non-characteristic of the simulated diseases, the right disease was always in the five most likely diseases. This tool could be usefully included in a global approach aiming to improve vigilance against exotic diseases.

Assessment of fallen equine data in France and their usefulness for epidemiological investigations.

Quantitative information about equine mortality is relatively scarce, yet it could be of great value for epidemiology purposes. Several European projects based on the exploitation of data from rendering plants have been developed to improve livestock surveillance. Similar data are available for equines in France but have never been studied to date. The objective of this research was to evaluate the potential of the French Ministry of Agriculture's Fallen Stock Data Interchange (FSDI) database to provide quantitative mortality information on the French equine population. The quality of FSDI equine data from 2011 to 2014 was assessed using complementary data registered in the French equine census database, SIRE. Despite a perfectible quality, the FSDI database proved to be a valuable source for studying the basal patterns of mortality over time in the French equine population as illustrated by the spatial representation of the number of deaths. However, improvements in the FSDI database are needed, in particular regarding the registration of animal identification numbers, in order to detail equine mortality for epidemiology purposes.

Iso-population partition: An innovative epidemiological approach to mapping and analyzing spatially aggregated data.

In epidemiology, data are often aggregated using administrative boundaries or regular spatial lattices. Iso-population partitioning methods allow the aggregation of small units for which population data are available into larger units that are contiguous, as compact as possible, and have a similar population size. The objective of this paper was to study the influence of three spatial data aggregation approaches on data visualization and data analysis: iso-populated units (IPUs), administrative units, and iso-geometric units. This study was conducted using results and simulations from the brucellosis clinical surveillance system for dairy cattle in France. Our findings indicate that using spatial partitioning methods for generating IPUs enhances the ability to interpret the spatial distribution of epidemiological indicators under study. In addition, it provides information on population density and improves the consistency of the power of statistical tests across units. By defining the target population size per spatial unit, IPUs can be used to control the statistical power of a study. Finally, by adding criteria based on environmental factors to generate spatial units, they can be used to control the variation of exposure to these factors within the units.

Simulation-Based Evaluation of the Performances of an Algorithm for Detecting Abnormal Disease-Related Features in Cattle Mortality Records.

We performed a simulation study to evaluate the performances of an anomaly detection algorithm considered in the frame of an automated surveillance system of cattle mortality. The method consisted in a combination of temporal regression and spatial cluster detection which allows identifying, for a given week, clusters of spatial units showing an excess of deaths in comparison with their own historical fluctuations. First, we simulated 1,000 outbreaks of a disease causing extra deaths in the French cattle population (about 200,000 herds and 20 million cattle) according to a model mimicking the spreading patterns of an infectious disease and injected these disease-related extra deaths in an authentic mortality dataset, spanning from January 2005 to January 2010. Second, we applied our algorithm on each of the 1,000 semi-synthetic datasets to identify clusters of spatial units showing an excess of deaths considering their own historical fluctuations. Third, we verified if the clusters identified by the algorithm did contain simulated extra deaths in order to evaluate the ability of the algorithm to identify unusual mortality clusters caused by an outbreak. Among the 1,000 simulations, the median duration of simulated outbreaks was 8 weeks, with a median number of 5,627 simulated deaths and 441 infected herds. Within the 12-week trial period, 73% of the simulated outbreaks were detected, with a median timeliness of 1 week, and a mean of 1.4 weeks. The proportion of outbreak weeks flagged by an alarm was 61% (i.e. sensitivity) whereas one in three alarms was a true alarm (i.e. positive predictive value). The performances of the detection algorithm were evaluated for alternative combination of epidemiologic parameters. The results of our study confirmed that in certain conditions automated algorithms could help identifying abnormal cattle mortality increases possibly related to unidentified health events.

An optimal cut-off point for the calving interval may be used as an indicator of bovine abortions.

The bovine abortion surveillance system in France aims to detect as early as possible any resurgence of bovine brucellosis, a disease of which the country has been declared free since 2005. It relies on the mandatory notification and testing of each aborting cow, but under-reporting is high. This research uses a new and simple approach which considers the calving interval (CI) as a "diagnostic test" to determine optimal cut-off point c and estimate diagnostic performance of the CI to identify aborting cows, and herds with multiple abortions (i.e. three or more aborting cows per calving season). The period between two artificial inseminations (AI) was considered as a "gold standard". During the 2006-2010 calving seasons, the mean optimal CI cut-off point for identifying aborting cows was 691 days for dairy cows and 703 days for beef cows. Depending on the calving season, production type and scale at which c was computed (individual or herd), the average sensitivity of the CI varied from 42.6% to 64.4%; its average specificity from 96.7% to 99.7%; its average positive predictive value from 27.6% to 65.4%; and its average negative predictive value from 98.7% to 99.8%. When applied to the French bovine population as a whole, this indicator identified 2-3% of cows suspected to have aborted, and 10-15% of herds suspected of multiple abortions. The optimal cut-off point and CI performance were consistent over calving seasons. By applying an optimal CI cut-off point to the cattle demographics database, it becomes possible to identify herds with multiple abortions, carry out retrospective investigations to find the cause of these abortions and monitor a posteriori compliance of farmers with their obligation to report abortions for brucellosis surveillance needs. Therefore, the CI could be used as an indicator of abortions to help improve the current mandatory notification surveillance system.

Respective influence of veterinarians and local institutional stakeholders on the event-driven surveillance system for bovine brucellosis in France.

BACKGROUND: The event-driven surveillance system for bovine brucellosis implemented in France aims to ensure the early detection of cases of bovine brucellosis, a disease of which the country has been declared free since 2005. It consists of mandatory notification of bovine abortions by farmers and veterinarians. However, as underlined by a previous qualitative study, several factors influence the decision-making process of actors in the field. This process is particularly influenced by the level of cooperation between institutional stakeholders in their département (a French département being an administrative and territorial unit), veterinarians and farmers. In this context, the objectives of this study were 1) to quantify the respective influence of veterinarians and all local institutional stakeholders on the proportion of notifying farmers and identify which actors have most influence on farmers' decisions; 2) to analyse whether the influence of veterinarians is correlated with that of local institutional stakeholders. RESULTS: In addition to factors relating to the farm itself (production type and herd size), the proportion of notifying farmers was influenced by the number of veterinarians per practice and the veterinary practice's membership of a technical association. This proportion was also influenced by unknown factors relating to the veterinary practice and, to a lesser extent, the département in which the farm was located. There was no correlation between variability in the proportion of notifying farmers among veterinary practices per département and the effect of the département itself. CONCLUSION: To our knowledge, this is the first study to quantify the influence of veterinarians and local institutional stakeholders on the notification process for a mandatory disease. In addition to carrying out regulatory interventions, veterinarians play a major role in encouraging farmers to participate in the surveillance systems. The results of this study, combined with a previous qualitative study, shed light on the need to consolidate the involvement of veterinarians and local stakeholders in the organisation of surveillance by national institutional bodies.

Quantifying the influence of ambient temperature on dairy and beef cattle mortality in France from a time-series analysis.

In the context of climate change, the frequency and severity of extreme weather events are expected to increase in temperate regions, and potentially have a severe impact on farmed cattle through production losses or deaths. In this study, we used distributed lag non-linear models to describe and quantify the relationship between a temperature-humidity index (THI) and cattle mortality in 12 areas in France. THI incorporates the effects of both temperature and relative humidity and was already used to quantify the degree of heat stress on dairy cattle because it does reflect physical stress deriving from extreme conditions better than air temperature alone. Relationships between daily THI and mortality were modeled separately for dairy and beef cattle during the 2003-2006 period. Our general approach was to first determine the shape of the THI-mortality relationship in each area by modeling THI with natural cubic splines. We then modeled each relationship assuming a three-piecewise linear function, to estimate the critical cold and heat THI thresholds, for each area, delimiting the thermoneutral zone (i.e. where the risk of death is at its minimum), and the cold and heat effects below and above these thresholds, respectively. Area-specific estimates of the cold or heat effects were then combined in a hierarchical Bayesian model to compute the pooled effects of THI increase or decrease on dairy and beef cattle mortality. A U-shaped relationship, indicating a mortality increase below the cold threshold and above the heat threshold was found in most of the study areas for dairy and beef cattle. The pooled estimate of the mortality risk associated with a 1°C decrease in THI below the cold threshold was 5.0% for dairy cattle [95% posterior interval: 4.4, 5.5] and 4.4% for beef cattle [2.0, 6.5]. The pooled mortality risk associated with a 1°C increase above the hot threshold was estimated to be 5.6% [5.0, 6.2] for dairy and 4.6% [0.9, 8.7] for beef cattle. Knowing the thermoneutral zone and temperature effects outside this zone is of primary interest for farmers because it can help determine when to implement appropriate preventive and mitigation measures.

Devising an indicator to detect mid-term abortions in dairy cattle: a first step towards syndromic surveillance of abortive diseases.

Bovine abortion surveillance is essential for human and animal health because it plays an important role in the early warning of several diseases. Due to the limited sensitivity of traditional surveillance systems, there is a growing interest for the development of syndromic surveillance. Our objective was to assess whether, routinely collected, artificial insemination (AI) data could be used, as part of a syndromic surveillance system, to devise an indicator of mid-term abortions in dairy cattle herds in France. A mid-term abortion incidence rate (MAIR) was computed as the ratio of the number of mid-term abortions to the number of female-weeks at risk. A mid-term abortion was defined as a return-to-service (i.e., a new AI) taking place 90 to 180 days after the previous AI. Weekly variations in the MAIR in heifers and parous cows were modeled with a time-dependent Poisson model at the département level (French administrative division) during the period of 2004 to 2010. The usefulness of monitoring this indicator to detect a disease-related increase in mid-term abortions was evaluated using data from the 2007-2008 episode of bluetongue serotype 8 (BT8) in France. An increase in the MAIR was identified in heifers and parous cows in 47% (n = 24) and 71% (n = 39) of the departements. On average, the weekly MAIR among heifers increased by 3.8% (min-max: 0.02-57.9%) when the mean number of BT8 cases that occurred in the previous 8 to 13 weeks increased by one. The weekly MAIR among parous cows increased by 1.4% (0.01-8.5%) when the mean number of BT8 cases occurring in the previous 6 to 12 weeks increased by one. These results underline the potential of the MAIR to identify an increase in mid-term abortions and suggest that it is a good candidate for the implementation of a syndromic surveillance system for bovine abortions.