Toxoplasma gondii
publication ID |
https://doi.org/ 10.1016/j.ijppaw.2020.05.006 |
persistent identifier |
https://treatment.plazi.org/id/03AC2D4F-FFFB-FFC5-3E2B-FACCFDC95EFA |
treatment provided by |
Felipe |
scientific name |
Toxoplasma gondii |
status |
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3.3. Risk factors associated with Toxoplasma gondii qPCR positivity
Logistic regression was performed using the qPCR dataset only due to its greater sample size. Weight, as a proxy for age, was modelled as a continuous and a categorical variable. When weight was included as a categorical variable, the interaction term between weight and season could not be modelled due to numerical issues associated with sample size. Akaike's information criterion was compared between three possible models for the total effect of weight on T. gondii qPCR positivity Weight (categorical) = categorical term, four categories based on data quantiles.
Weight 2 = continuous quadratic term centred on 4 kg, of the form (weight - 4 kg) 2.
( Table 4). Model 3, which included weight as a continuous variable and the interaction term between weight and season, had the lowest AIC and was chosen as the final model. Outputs from Model 3 are presented in Table 5. Outputs from individual models constructed to estimate the total effect of each risk factor considered, as guided by the putative causal diagram, are presented in Supplementary materials ( Table S1). To aid in interpretation of the interaction term, the outputs from the multivariable logistic regression model for the total effect of weight were plotted as predicted probabilities of qPCR positivity in cats versus weight in kilograms, for each season ( Fig. 3 View Fig ).
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