Ali Khalooei; Mohammadreza Mashayekhi-Dowlatabad; Mohammad Reza Rajabalipour; Abedin Iranpour
Volume 8, Issue 4 , Autumn 2016, , Pages 227-234
Abstract
Background: Prisoner’s addiction is one of the major problems in many countries which imposes very high medical costs and social harm to communities. This study investigated the pattern of substance use and related factors in male prisoners in one of the prisons in southeastern Iran.Methods: This cross-sectional ...
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Background: Prisoner’s addiction is one of the major problems in many countries which imposes very high medical costs and social harm to communities. This study investigated the pattern of substance use and related factors in male prisoners in one of the prisons in southeastern Iran.Methods: This cross-sectional study was carried out in 2016. The study population was inmates of a prison in southeast Iran. Sampling was carried out randomly according to the list of prisoners. Data were collected using a form and were analyzed with statistics software SPSS.Findings: More than four-fifths (75.3%) of the subjects consumed at least one substance (alcohol, tobacco and other drugs), 74.4% were smoking, 73.2% used a narcotic substance, and about one-fifth (19.3%) reported drinking alcohol. With a frequency of 62.0%, opium was the most frequently utilized narcotic substance. Poppy juice (31.6%), cannabis (29.8%), crystal (16.9%) and tramadol (16.9%) were the next frequent substances used. A percentage of 41.5% subjects reported using two or more drugs. A percentage of 80.7% subjects reported substance use among their friends, 39.2% by siblings and 37.2% by father. Regression analysis showed predictor variables of substance use were education, substance use by prisoner before being imprisoned, substance use by father, friends and siblings.Conclusion: This study showed a remarkable prevalence of substance use in prisons, which was more than general population. Therefore, it is necessary to consider alternative penalties of imprisonment due to the factors associated with substance use. Screening of people at high risk for substance use should be considered on admission to prison, and primary prevention measures should be focused on them.
Saiedeh Haji-Maghsoudi; Ali Akbar Haghdoost; Mohammad Reza Baneshi
Volume 6, 1-2 , Winter 2014, , Pages 36-44
Abstract
Background: Prisoners, compared to the general population, are at greater risk of infection. Drug injection is the main route of HIV transmission, in particular in Iran. What would be of interest is to determine variables that govern drug injection among prisoners. However, one of the issues that challenge ...
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Background: Prisoners, compared to the general population, are at greater risk of infection. Drug injection is the main route of HIV transmission, in particular in Iran. What would be of interest is to determine variables that govern drug injection among prisoners. However, one of the issues that challenge model building is incomplete national data sets. In this paper, we addressed the process of model development when missing data exist. Methods: Complete data on 2720 prisoners was available. A logistic regression model was fitted and served as gold standard. We then randomly omitted 20%, and 50% of data. Missing date were imputed 10 times, applying multiple imputation by chained equations (MICE). Rubin’s rule (RR) was applied to select candidate variables and to combine the results across imputed data sets. In S1, S2, and S3 methods, variables retained significant in one, five, and ten imputed data sets and were candidate for the multifactorial model. Two weighting approaches were also applied. Findings: Age of onset of drug use, recent use of drug before imprisonment, being single, and length of imprisonment were significantly associated with drug injection among prisoners. All variable selection schemes were able to detect significance of these variables. Conclusion: We have seen that the performances of easier variable selection methods were comparable with RR. This indicates that the screening step can be used to select candidate variables for the multifactorial model.
