Implementation of One Health surveillance systems: Opportunities and challenges - lessons learned from the OH-EpiCap application.

As the complexity of health systems has increased over time, there is an urgent need for developing multi-sectoral and multi-disciplinary collaborations within the domain of One Health (OH). Despite the efforts to promote collaboration in health surveillance and overcome professional silos, implementing OH surveillance systems in practice remains challenging for multiple reasons. In this study, we describe the lessons learned from the evaluation of OH surveillance using OH-EpiCap (an online evaluation tool for One Health epidemiological surveillance capacities and capabilities), the challenges identified with the implementation of OH surveillance, and the main barriers that contribute to its sub-optimal functioning, as well as possible solutions to address them. We conducted eleven case studies targeting the multi-sectoral surveillance systems for antimicrobial resistance in Portugal and France, Salmonella in France, Germany, and the Netherlands, Listeria in The Netherlands, Finland and Norway, Campylobacter in Norway and Sweden, and psittacosis in Denmark. These evaluations facilitated the identification of common strengths and weaknesses, focusing on the organization and functioning of existing collaborations and their impacts on the surveillance system. Lack of operational and shared leadership, adherence to FAIR data principles, sharing of techniques, and harmonized indicators led to poor organization and sub-optimal functioning of OH surveillance systems. In the majority of studied systems, the effectiveness, operational costs, behavioral changes, and population health outcomes brought by the OH surveillance over traditional surveillance (i.e. compartmentalized into sectors) have not been evaluated. To this end, the establishment of a formal governance body with representatives from each sector could assist in overcoming long-standing barriers. Moreover, demonstrating the impacts of OH-ness of surveillance may facilitate the implementation of OH surveillance systems.

Epidemic intelligence activities among national public and animal health agencies: a European cross-sectional study.

Epidemic Intelligence (EI) encompasses all activities related to early identification, verification, analysis, assessment, and investigation of health threats. It integrates an indicator-based (IBS) component using systematically collected surveillance data, and an event-based component (EBS), using non-official, non-verified, non-structured data from multiple sources. We described current EI practices in Europe by conducting a survey of national Public Health (PH) and Animal Health (AH) agencies. We included generic questions on the structure, mandate and scope of the institute, on the existence and coordination of EI activities, followed by a section where respondents provided a description of EI activities for three diseases out of seven disease models. Out of 81 gatekeeper agencies from 41 countries contacted, 34 agencies (42%) from 26 (63%) different countries responded, out of which, 32 conducted EI activities. Less than half (15/32; 47%) had teams dedicated to EI activities and 56% (18/34) had Standard Operating Procedures (SOPs) in place. On a national level, a combination of IBS and EBS was the most common data source. Most respondents monitored the epidemiological situation in bordering countries, the rest of Europe and the world. EI systems were heterogeneous across countries and diseases. National IBS activities strongly relied on mandatory laboratory-based surveillance systems. The collection, analysis and interpretation of IBS information was performed manually for most disease models. Depending on the disease, some respondents did not have any EBS activity. Most respondents conducted signal assessment manually through expert review. Cross-sectoral collaboration was heterogeneous. More than half of the responding institutes collaborated on various levels (data sharing, communication, etc.) with neighbouring countries and/or international structures, across most disease models. Our findings emphasise a notable engagement in EI activities across PH and AH institutes of Europe, but opportunities exist for better integration, standardisation, and automatization of these efforts. A strong reliance on traditional IBS and laboratory-based surveillance systems, emphasises the key role of in-country laboratories networks. EI activities may benefit particularly from investments in cross-border collaboration, the development of methods that can automatise signal assessment in both IBS and EBS data, as well as further investments in the collection of EBS data beyond scientific literature and mainstream media.

OH-EpiCap: a semi-quantitative tool for the evaluation of One Health epidemiological surveillance capacities and capabilities.

Although international health agencies encourage the development of One Health (OH) surveillance, many systems remain mostly compartmentalized, with limited collaborations among sectors and disciplines. In the framework of the OH European Joint Programme "MATRIX" project, a generic evaluation tool called OH-EpiCap has been developed to enable individual institutes/governments to characterize, assess and monitor their own OH epidemiological surveillance capacities and capabilities. The tool is organized around three dimensions: organization, operational activities, and impact of the OH surveillance system; each dimension is then divided into four targets, each including four indicators. A semi-quantitative questionnaire enables the scoring of each indicator, with four levels according to the degree of satisfaction in the studied OH surveillance system. The evaluation is conducted by a panel of surveillance representatives (during a half-day workshop or with a back-and-forth process to reach a consensus). An R Shiny-based web application facilitates implementation of the evaluation and visualization of the results, and includes a benchmarking option. The tool was piloted on several foodborne hazards (i.e., Salmonella, Campylobacter, Listeria), emerging threats (e.g., antimicrobial resistance) and other zoonotic hazards (psittacosis) in multiple European countries in 2022. These case studies showed that the OH-EpiCap tool supports the tracing of strengths and weaknesses in epidemiological capacities and the identification of concrete and direct actions to improve collaborative activities at all steps of surveillance. It appears complementary to the existing EU-LabCap tool, designed to assess the capacity and capability of European microbiology laboratories. In addition, it provides opportunity to reinforce trust between surveillance stakeholders from across the system and to build a good foundation for a professional network for further collaboration.

Mapping food surveillance chains through different sectors.

European countries are investing in strengthening disease surveillance from a One Health (OH) perspective. During the MATRIX project, in the context of the One Health European Joint Programme, existing surveillance chains across the sectors of animal health, food safety, and public health have been investigated through questionnaires. Provided information has then been selected to be displayed in a single slide using an implemented mapping template. Two real-life scenarios are presented as case studies: the surveillance activities in place in France for Salmonella in the pork meat food chain, and in Norway for Listeria monocytogenes in the dairy food chain. The results collected through the questionnaires and the lessons learnt during the mapping process are reported, to share the advantages and drawbacks of the methodology. Moreover, the presented template could be adjusted and applied to different contexts. Mapping the components of existing disease surveillance systems is a fundamental step in understanding the relationships between its components, and subsequently facilitating their collaboration and integration under a OH approach.

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.

Puumala Virus Variants Circulating in Forests of Ardennes, France: Ten Years of Genetic Evolution.

In Europe, Puumala virus (PUUV) transmitted by the bank vole (Myodes glareolus) is the causative agent of nephropathia epidemica (NE), a mild form of haemorrhagic fever with renal syndrome. In France, very little is known about the spatial and temporal variability of the virus circulating within bank vole populations. The present study involved monitoring of bank vole population dynamics and PUUV microdiversity over a ten-year period (2000-2009) in two forests of the Ardennes region: Elan and Croix-Scaille. Ardennes region is characterised by different environmental conditions associated with different NE epidemiology. Bank vole density and population parameters were estimated using the capture/marking/recapture method, and blood samples were collected to monitor the overall seroprevalence of PUUV in rodent populations. Phylogenetic analyses of fifty-five sequences were performed to illustrate the genetic diversity of PUUV variants between forests. The pattern of the two forests differed clearly. In the Elan forest, the rodent survival was higher, and this limited turn-over resulted in a lower seroprevalence and diversity of PUUV sequences than in the Croix-Scaille forest. Uncovering the links between host dynamics and virus microevolution is improving our understanding of PUUV distribution in rodents and the NE risk.

Sensitivity of bovine tuberculosis surveillance through intradermal tests in cattle in France: An evaluation of different scenarios.

The current situation regarding bovine tuberculosis (bTB) in Europe is spatially heterogeneous, with stagnating or increasing trends in bTB prevalence in many European regions, underlying the challenge in controlling this disease. In France, in spite of the implementation of two control programs in 2010-2012 to eradicate the disease and maintain the bTB-free status, bTB prevalence has continued to increase, underlying the need to reinforce and adapt surveillance measures. The goal of this study was to evaluate the effectiveness of bTB surveillance in high-risk areas in metropolitan France, with an emphasis on the criteria to select herds and animals within herds in the context of programmed surveillance and movement testing. The fraction of bTB-infected herds detected by the surveillance was quantified using a stochastic scenario tree modelling approach, with input parameter values based on surveillance and cattle traceability data and literature. The detection fraction was assessed for the current surveillance system and for alternative scenarios. The model predicted that the median detection fraction of infected herds by the current programmed surveillance in high-risk areas, which consists in annual testing of herds with a minimum age of testing of 24 months, was 71.5 % (interquartile interval: 47.4-89.4). The results showed a significant gain of the detection fraction with a decrease from 24 to 12 months old (83.5 % [60.6-95.9]) or to six weeks old (91.3 % [71.6-99.0]). Regarding pre-movement surveillance, tests are currently mandatory for bovines that originate from a previously infected herd or from a herd epidemiologically linked to a bTB-infected herd. The median detection fraction predicted by the model for this surveillance scenario was 1.2 % [0.7-1.8]. For the alternative scenario, where surveillance would be extended to all herds in high-risk areas, the model predicted a significant increase of the detection fraction to 26.5 % [18.1-37.9]. The results were sensitive to the following input values: the number of infected bovines within herds and, to a lower extent, the comparative intradermal tuberculin test sensitivity for both models, and surveillance coverage for the model on pre-movement surveillance. Our study underlines several complementary ways to improve the detection of infected herds, which is critical for implementing control measures and epidemiological investigations as early as possible. These necessary changes in surveillance must be accompanied by a global reflexion on surveillance financing.

Modeling Culicoides abundance in mainland France: implications for surveillance.

BACKGROUND: Biting midges of the genus Culicoides Latreille (Diptera: Ceratopogonidae) are involved in the transmission of several viruses affecting humans and livestock, particularly bluetongue (BTV). Over the last decade, Culicoides surveillance has been conducted discontinuously and at various temporal and spatial scales in mainland France following the BTV epizootics in 2008-2009 and its reemergence and continuous circulation since 2015. The ability to predict seasonal dynamics and spatial abundance of Culicoides spp. is a key element in identifying periods and areas at high risk of transmission in order to strengthen surveillance for early detection and to establish seasonally disease-free zones. The objective of this study was to model the abundance of Culicoides spp. using surveillance data. METHODS: A mixed-effect Poisson model, adjusted for overdispersion and taking into account temperature data at each trap location, was used to model the weekly relative abundance of Culicoides spp. over a year in 24 vector zones, based on surveillance data collected during 2009-2012. Vector zones are the spatial units used for Culicoides surveillance since 2016 in mainland France. RESULTS: The curves of the predicted annual abundance of Culicoides spp. in vector zones showed three different shapes: unimodal, bimodal or plateau, reflecting the temporal variability of the observed counts between zones. For each vector zone, the model enabled to identify periods of vector activity ranging from 25 to 51 weeks. CONCLUSIONS: Although the data were collected for surveillance purposes, our modeling approach integrating vector data with daily temperatures, which are known to be major drivers of Culicoides spp. activity, provided areas-specific predictions of Culicoides spp. abundance. Our findings provide decisions makers with essential information to identify risk periods in each vector zone and guide the allocation of resources for surveillance and control. Knowledge of Culicoides spp. dynamics is also of primary importance for modeling the risk of establishment and spread of midge-borne diseases in mainland 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.

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.

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.

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.

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.

Quantitative and qualitative assessment of the bovine abortion surveillance system in France.

Bovine abortion is the main clinical sign of bovine brucellosis, a disease of which France has been declared officially free since 2005. To ensure the early detection of any brucellosis outbreak, event-driven surveillance relies on the mandatory notification of bovine abortions and the brucellosis testing of aborting cows. However, the under-reporting of abortions appears frequent. Our objectives were to assess the aptitude of the bovine abortion surveillance system to detect each and every bovine abortion and to identify factors influencing the system's effectiveness. We evaluated five attributes defined by the U.S. Centers for Disease Control with a method suited to each attribute: (1) data quality was studied quantitatively and qualitatively, as this factor considerably influences data analysis and results; (2) sensitivity and representativeness were estimated using a unilist capture-recapture approach to quantify the surveillance system's effectiveness; (3) acceptability and simplicity were studied through qualitative interviews of actors in the field, given that the surveillance system relies heavily on abortion notifications by farmers and veterinarians. Our analysis showed that (1) data quality was generally satisfactory even though some errors might be due to actors' lack of awareness of the need to collect accurate data; (2) from 2006 to 2011, the mean annual sensitivity - i.e. the proportion of farmers who reported at least one abortion out of all those who detected such events - was around 34%, but was significantly higher in dairy than beef cattle herds (highlighting a lack of representativeness); (3) overall, the system's low sensitivity was related to its low acceptability and lack of simplicity. This study showed that, in contrast to policy-makers, most farmers and veterinarians perceived the risk of a brucellosis outbreak as negligible. They did not consider sporadic abortions as a suspected case of brucellosis and usually reported abortions only to identify their cause rather than to reject brucellosis. The system proved too complex, especially for beef cattle farmers, as they may fail to detect aborting cows at pasture or have difficulties catching them for sampling. By investigating critical attributes, our evaluation highlighted the surveillance system's strengths and needed improvements. We believe our comprehensive approach can be used to assess other event-driven surveillance systems. In addition, some of our recommendations on increasing the effectiveness of event-driven brucellosis surveillance may be useful in improving the notification rate for suspected cases of other exotic diseases.

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.

Why do farmers and veterinarians not report all bovine abortions, as requested by the clinical brucellosis surveillance system in France?

BACKGROUND: Since 2005, France has been officially free of brucellosis, an infectious disease that causes abortion in cattle and can be transmitted from cattle to humans. Recent animal and human cases have drawn attention to the need to prevent infection of humans and animals from any primary outbreaks. In order to detect any new outbreaks as soon as possible, a clinical surveillance system requires farmers and veterinarians to report each abortion and to test the aborting cow for brucellosis. However, under-reporting limits the sensitivity of this system. Our objective was to identify the barriers and motivations influencing field actors in their decision to report or not to report bovine abortions. We used a qualitative approach with semi-structured interviews of 12 cattle farmers and their eight veterinarians. RESULTS: Our analysis showed that four main themes influence the decision-making process of farmers and veterinarians: 1) the perceived risk of brucellosis and other abortive diseases; 2) the definition of a suspected case of brucellosis and other abortive diseases adopted by field actors, which is less sensitive than the mandatory definition; 3) the cost-benefit analysis conducted by actors, taking into account regulatory and health aspects, economic and financial losses, technical and practical factors; 4) the level of cooperation within the socio-technical network. We discussed how early detection may be improved by revising the definition of abortion, extending the time frame for notification and generalising the differential diagnosis of the causes of abortion. CONCLUSIONS: In contrast to quantitative approaches, qualitative studies can identify the factors (including unknown factors) influencing the decision-making process of field actors and reveal why they take those factors into consideration. Our qualitative study sheds light on the factors underlying the poor sensitivity of clinical brucellosis surveillance system for cattle in France, and suggests that early detection may be improved by considering actors' perceptions. We believe our findings may provide further insight into ways of improving other clinical surveillance systems and thus reduce the risk of disease.

Assessment of the impact of the 2003 and 2006 heat waves on cattle mortality in France.

OBJECTIVES: While several studies have highlighted and quantified human mortality during the major heat waves that struck Western Europe in 2003 and 2006, the impact on farm animals has been overlooked. The aim of this study was to assess the effect of these two events on cattle mortality in France, one of the most severely impacted countries. METHODS: Poisson regressions were used to model the national baseline for cattle mortality between 2004 and 2005 and predict the weekly number of expected deaths in 2003 and 2006 for the whole cattle population and by subpopulation based on age and type of production. Observed and estimated values were compared to identify and quantify excess mortality. The same approach was used at a departmental scale (a French department being an administrative and territorial division) to assess the spatio-temporal evolution of the mortality pattern. RESULTS: Overall, the models estimated relative excess mortality of 24% [95% confidence interval: 22-25%] for the two-week heat wave of 2003, and 12% [11-14%] for the three-week heat wave of 2006. In 2003, most cattle subpopulations were impacted during the heat wave and some in the following weeks too. In 2006, cattle subpopulations were impacted for a limited time only, with no excess mortality at the beginning or after the heat wave. No marked differences in cattle mortality were found among the different subpopulations by age and type of production. The implications of these results for risk prevention are discussed.

Transmission of chronic wasting disease in Wisconsin white-tailed deer: implications for disease spread and management.

Few studies have evaluated the rate of infection or mode of transmission for wildlife diseases, and the implications of alternative management strategies. We used hunter harvest data from 2002 to 2013 to investigate chronic wasting disease (CWD) infection rate and transmission modes, and address how alternative management approaches affect disease dynamics in a Wisconsin white-tailed deer population. Uncertainty regarding demographic impacts of CWD on cervid populations, human and domestic animal health concerns, and potential economic consequences underscore the need for strategies to control CWD distribution and prevalence. Using maximum-likelihood methods to evaluate alternative multi-state deterministic models of CWD transmission, harvest data strongly supports a frequency-dependent transmission structure with sex-specific infection rates that are two times higher in males than females. As transmissible spongiform encephalopathies are an important and difficult-to-study class of diseases with major economic and ecological implications, our work supports the hypothesis of frequency-dependent transmission in wild deer at a broad spatial scale and indicates that effective harvest management can be implemented to control CWD prevalence. Specifically, we show that harvest focused on the greater-affected sex (males) can result in stable population dynamics and control of CWD within the next 50 years, given the constraints of the model. We also provide a quantitative estimate of geographic disease spread in southern Wisconsin, validating qualitative assessments that CWD spreads relatively slowly. Given increased discovery and distribution of CWD throughout North America, insights from our study are valuable to management agencies and to the general public concerned about the impacts of CWD on white-tailed deer populations.

Assessing the mandatory bovine abortion notification system in France using unilist capture-recapture approach.

The mandatory bovine abortion notification system in France aims to detect as soon as possible any resurgence of bovine brucellosis. However, under-reporting seems to be a major limitation of this system. We used a unilist capture-recapture approach to assess the sensitivity, i.e. the proportion of farmers who reported at least one abortion among those who detected such events, and representativeness of the system during 2006-2011. We implemented a zero-inflated Poisson model to estimate the proportion of farmers who detected at least one abortion, and among them, the proportion of farmers not reporting. We also applied a hurdle model to evaluate the effect of factors influencing the notification process. We found that the overall surveillance sensitivity was about 34%, and was higher in beef than dairy cattle farms. The observed increase in the proportion of notifying farmers from 2007 to 2009 resulted from an increase in the surveillance sensitivity in 2007/2008 and an increase in the proportion of farmers who detected at least one abortion in 2008/2009. These patterns suggest a raise in farmers' awareness in 2007/2008 when the Bluetongue Virus (BTV) was detected in France, followed by an increase in the number of abortions in 2008/2009 as BTV spread across the country. Our study indicated a lack of sensitivity of the mandatory bovine abortion notification system, raising concerns about the ability to detect brucellosis outbreaks early. With the increasing need to survey the zoonotic Rift Valley Fever and Q fever diseases that may also cause bovine abortions, our approach is of primary interest for animal health stakeholders to develop information programs to increase abortion notifications. Our framework combining hurdle and ZIP models may also be applied to estimate the completeness of other clinical surveillance systems.