2.3. Data analysis
2.3.1. Prediction of vaccination compliance against LSD in 2019 by
previous vaccination:
We examined the influence of the following factors on LSD vaccination in
2019 among 566 farmers: previous vaccination (divided into four
categories: 1. No previous vaccination. 2. Vaccination only in 2017. 3.
Vaccination only in 2018. 4. Vaccination in both 2017 and 2018.), farm
type (family vs. cooperative vs. school), geographic area (North, Centre
and South of Israel) and previous occurrence of the disease in the herd
(yes/no). Univariable analysis was performed by using the Chi-square
test. Multivariable analysis was performed by fitting a logistic
regression model to the 2019 vaccination data. Variables were included
in the model in a forward stepwise process with a p-value of 0.05 in the
univariable analysis as a cut-off for inclusion and a
p-value> 0.05 in the multivariable analysis as a cut-off
value for variable exclusion.
2.3.2. Analysis of attitude, subjective norms, and perceived
behavioural control among farmers:
We used the intersection data (Figure 1 ) for determining the
association of the three constructs with actual vaccination among
farmers who showed high intention to vaccinate. Thirty eight of the 56
dairy farmers intended to vaccinate their herd (scores 4-5)
(Figure 1 ). Vaccination records for the period preceding
questionnaire filling were available for half of these 38 (future
behaviour). For the other half, we had data on recent vaccination
behaviour (up to one year prior to questionnaire filling). Within this
group, we compared the three constructs’ direct and indirect
measurements between those who did and did not vaccinate.
For determining the association of the three constructs with intention
among the 90 farmers (Figure 1, Dataset 2 ), we compared A, N
and PBC between farmers with negative and positive intention to
vaccinate. We performed univariable analysis for each construct’s direct
and indirect measurement using T tests. The intention was analysed as a
dichotomic variable, scores 1-2-3 were marked as a negative intention
and scores 4-5 as a positive intention. The same was performed for each
belief. Similarly, we determined the direct effect of the background
variables (farm and behavioural) on the intention to vaccinate. In that
univariable analysis we used Fisher/Chi-square tests.
2.3.3. Effect of distance in time from LSD epidemic on the
intention to vaccinate and the three constructs’ direct and indirect
measurements:
Ideally, we would have distributed the questionnaires each year and
followed the change in these factors. However, the questionnaire study
began only in 2018, and data on vaccination intention in preceding years
was not available. In 2019, during the questionnaire study, another LSD
epidemic took place in Israel. This epidemic enabled us to compare
questionnaires filled in by farmers before the epidemic and after the
epidemic. Fifty-seven questionnaires were answered before this epidemic
while 33 questionnaires were answered during and after its occurrence
(Figure 1 ). These 33 questionnaires represent a status
resembling the situation after the epidemic of 2012-2013, when
vaccination became voluntary. The questionnaires which were answered
before the epidemic in 2019 (the ”before” group) represent the situation
long after the epidemic in 2013 (Figure 2 ). The comparison
between the before and during/after the epidemic in 2019, therefore,
represent different time distances from an LSD epidemic. Comparing the
intention to vaccinate and the three constructs’ direct and indirect
measurements between the two groups was performed by using t tests after
ensuring that the data were normally distributed.
Intention to vaccinate was modelled using the data of 90 RAA
questionnaires. A general linear model of intetion to vaccinate was
fitted to the farmers’ beliefs, the location of the herd and the time
from the last epidemic (”before” or ”after”) as the explanatory
variables. The model was fitted using a stepwize process with a p-value
of 0.05 in the multivariable analysis as a cut-off value for variable
exclusion.
All statistical analysis was performed using R version 3.6.0 (R Core
Team, 2019), the ”dplyr” , ”ltm” , ”Hmisc” ,”olsrr” and ”lme4” packages (Bates et al., 2015; Harrell
Jr et al., 2021; Hebbali, 2020; Rizopoulos, 2006 & Wickham et al.,
2020).