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

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

Prev Vet Med

University of Lyon, French Agency for Food, Environmental and Occupational Health & Safety (ANSES), Laboratory of Lyon, 31 Avenue Tony Garnier, 69364, Lyon Cedex 07, France. Electronic address:

Published: February 2022

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.

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Source
http://dx.doi.org/10.1016/j.prevetmed.2021.105549DOI Listing

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