Abstract
Identification of factors associated with an outcome can be challenging when the number of explanatory variables is large in relation to the number of observations. Multiple model triangulation, where results from several model types are combined, improves the likelihood of identifying true predictor variables. The aim of this study was to use triangulation to identify covariates likely to be truly associated with the prevalence of lameness in sheep flocks in Great Britain.Data were collected using a questionnaire sent to 3200 sheep farmers in Great Britain in 2018. The useable response rate was 14.1 %. The geometric mean prevalence of lameness was 1.4 % (95 % CI 1.2−1.7) for ewes, and 0.6 % (95 % CI 0.5−0.9) for lambs, however, approximately 60 % flocks had >2% prevalence of lameness in ewes.Four model types were investigated, two generalised linear models (negative binomial and quasi-Poisson) built using stepwise selection, and two elastic net models (Poisson and Gaussian distributions) refined with selection stability estimation.Triangulated covariates were those selected in three or all four models – 10 for ewes and 12 for lambs. Higher prevalence of lameness in ewes was associated with 5−100% feet bleeding during routine foot trimming compared with not foot trimming, footbathing the flock to treat severe footrot (SFR) and always using formalin in footbaths, both compared with not footbathing, using FootVax™ for <1 year compared with not using FootVax™, and never quarantining new or returning sheep to the farm for >3 weeks compared with always. Lower prevalence of lameness in ewes was associated with vaccinating with FootVax™ for >5 years compared with not vaccinating, peat soil compared with no peat soil, and having no lame ewes to treat.Higher prevalence of lameness in lambs was associated with 5−100% feet bleeding during routine foot trimming, always foot trimming ewes with SFR, not knowingly selecting replacement ewes from ewes that were never lame compared with always, replacement sheep purchased and homebred compared with only homebred, treating lambs >3 days after recognition of lameness compared with 0-3 days and footbathing the flock to treat interdigital dermatitis compared with not footbathing at all. Lower prevalence of lameness in lambs was associated with peat soil, flocks in Scotland versus England, an altitude of >230−500 m compared with ≤230 m, never using antibiotic injection to treat lambs with SFR compared with always, and having no lame lambs to treat.We conclude triangulation identified reliable management practices for farmers to implement to minimise lameness in sheep.
Highlights
Epidemiological research includes identification of factors associated with known health conditions, which can be challenging when analysing ‘wide’ data such as questionnaires when the number of explanatory variables is typically large in relation to the number of observations.Abbreviations: SFR, severe footrot; ID, interdigital dermatitis. * Corresponding author.Different model structures, analytic workflows, and variable selection techniques can give rise to different covariate selection because of the method used (Botvinik-Nezer et al, 2020; Lima et al, 2020a, 2021; Tercerio, 2003), raising questions for users on how to choose a model ling workflow and to improve the reproducibility of results
Footrot initially presents as an interdigital dermatitis (ID) that can progress to severe footrot (SFR) when the hoof horn separates from the living dermis
The elastic net is designed to implement a balance between ridge regression and the least absolute shrinkage and selection operator (LASSO) penalties (Friedman et al, 2010)
Summary
Epidemiological research includes identification of factors associated with known health conditions, which can be challenging when analysing ‘wide’ data such as questionnaires when the number of explanatory variables is typically large in relation to the number of observations.Different model structures, analytic workflows, and variable selection techniques can give rise to different covariate selection because of the method used (Botvinik-Nezer et al, 2020; Lima et al, 2020a, 2021; Tercerio, 2003), raising questions for users on how to choose a model ling workflow and to improve the reproducibility of results. Several studies have reported statistical associations between the prevalence of lameness in sheep flocks and management practices using retrospective postal and online questionnaires, typically requesting an estimate of the average proportion of lame sheep in the flock, flock size and management practices over a time period (Angell et al, 2014; Best et al, 2020; Dickins et al, 2016; Kaler and Green, 2009; Prosser et al, 2019; Reeves et al, 2019; Wassink et al, 2004; Winter et al, 2015). There are many management practices associated with prevalence of lameness including recognition of lame sheep, intention to treat lame sheep, time to treatment of lame sheep, type of treatment, vaccination, footbathing, foot trimming and poor flock bio security (Best et al, 2020; Dickins et al, 2016; Kaler and Green, 2009; Prosser et al, 2019; Wassink et al, 2003, Wassink et al, 2004; Winter et al, 2015)
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