We therefore created a model with variables for prison (prison X or Y), DM status, and the interaction term for age and race/ethnicity.
Each of 19 independent variables predicting a prison sentence is significant.
The dependent variable (present contact with prison health services or not) was dichotomous, and logistic regression analysis was performed.
We collected data on i) socio-demographic and penitentiary variables: age, gender, race, and the year of their first stay in prison; ii) variables related to the use of alcohol and/or drugs: lifetime history of alcohol abuse, lifetime and recent (previous year) intravenous drug use (IDU); and iii) clinical variables: degree of hepatic fibrosis (FIB), HIV infection, HCV RNA level, and HCV genotype.
Demographic variables included in the models were gender, indigenous (ie, Aboriginal or Torres Strait Islander) status and age at entry to prison; this latter variable increased as necessary with subsequent incarcerations of an individual.
His strategy for changing their behavior was to throw a new variable into the cost-benefit equation: prison.
As regards the individual variables, adolescents who declared having a prison inmate in the Family and resided in homes with a larger number of persons, showed a higher number of decayed teeth.
Furthermore, each prison's capacity of prisoners per full-time equivalent health service worker, the size of the prison and the type of prison (open, closed or mixed) were included as independent variables at the health service level.
We first analysed the variance attributable to differences between prison health services, without any explanatory variables included and adjusted for the independent variables mentioned above (Table 3).
First, the use of change in state imprisonment rates as the mediating variable in the model did not allow the discrimination of prison admissions from prison releases.
