Four variables related to difficulties in access to healthcare (distance, appointment, waiting time, and cost) were analysed using binomial logistic regression to identify socio- and demographic predictors of inequity.
Finally, univariate and multivariate analyses were conducted using binomial logistic regression, to identify the individual contribution of each of these variables (need level, diagnosis of psychotic disorder, sex, ethnicity, access to family physician and diagnosis of substance dependence) to 30-year CVD risk level category (low or increased risk).
Univariate and multivariate log binomial regression analyses to identify factors associated with willingness to accept free HIV testing are shown in Table 3.
We used a negative binomial regression model to identify factors associated with the numbers of infected chickens (offset by the total number of chickens tested), and a similar model for the number of human cases identified.
We used binomial regression to calculate RRs adjusted for possible confounders.
After testing different models, we used negative binomial regression to adjust rural and urban cases.
We used logistic binomial regression to estimate relative risks (RR) and control potential confounders.
Logistic regression was used, instead of binomial regression, to avoid convergence issues when controlling for many confounding variables.
We used logistic regression and zero truncated negative binomial regression to model the zero and count processes, respectively.
The association of patient- and service-related characteristics with length of stay was analysed using zero-truncated negative binomial regressions to account for the identified overdispersion in count data.
