جستجوی مقالات مرتبط با کلیدواژه "drug injection" در نشریات گروه "پزشکی"

Injection of drugs is one of the most serious  health problems among Iranian living with HIV/AIDS. The injection of drugs, accounting for the transmission of more than two-thirds of HIV infections. HIV remains a major concern around the world and is expected to be the third leading cause of mortality worldwide. Thus, the aim of this study was to explore the predictors of injection drug use in the elderly patients living with HIV/AIDS.

This was a cross-sectional study executed in Tehran City, Iran, in 2018. A total of 160 individuals aged 60 years and older with HIV from different counseling centers were selected by convenience sampling. Data were collected using questionnaires including a positive state of mind, coping, social support, and a checklist of demographical variables. Bivariate analysis and multiple logistic regression using SPSS software version 21.0 were employed to determine factors associated with drug injection. The statistical tests were performed with a significance level of 5% (p≥0.05).

The study participants’ mean(SD) age was 65.6(±6.6) years. In total, 33 people (20.6%) of the samples reported injection drug use. The frequency of injection drug use was greater among men (AOR: 2.28, 95% CI 2.2-22.8; p=0.010), those reporting a monthly income of ≥30000000 Rials (AOR: 31.56, 95% CI 2.95-338; p=0.004), subjects with past experience of drug use (AOR: 7.11, 95% CI 2.18-23.2; p=0.001), those with ≥2 years past from their HIV diagnosis (AOR: 4.04, 95% CI 1.12-14.58; p=0.033), and those living with more than two people in one residential place (Household size AOR: 5.9, 95% CI 1.64-21.24; p=0.007).

It seems that the design and implementation of harm reduction programs among the elderly with HIV/AIDS who inject drugs are essential and should be considered as an agenda of policymakers and health professionals.

Transmitting blood-borne diseases is alarming in places with high prevalence of people who inject drugs. This study aimed to determine the prevalence of drug injection and its related predictors among prisoners with a history of tattooing in Iran. Study design: Cross-sectional study. By using a census sampling, 5493 prisoners with a history of tattooing of 11988 prisoners participated for hepatitis B and C bio-behavioral surveillance surveys (BSS) in prisons of Iran, during 2015-2016 from 55 prisons in 19 provinces were assessed. The data for the BSS were collected using face-to-face checklist-based interviews. Weighted prevalence and the association between variables and history of drug injection were determined using Chi-square test and adjusted odds ratio (AOR) was estimated through multivariate logistic regression test using survey package. The mean age of participants was 33.9 ±8.3 yr. Most of them were male (96.4%) and had a history of drug use (85.4%). The prevalence of drug injection among drug users was 20.2%, of which 33.9% had a history of shared injection. The prevalence of drug injection among prisoners with a history of tattooing is associated with male gender (P=0.047), age ≥35 yr (P<0.001), being single (P=0.002), being divorced/widow (P=0.039), and a history of imprisonment (P<0.001). The prevalence of drug injection increases in the presence of other high-risk behaviors. It is necessary to initiate harm reduction programs and preventive interventions in groups with multiple high-risk behaviors.

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. 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. 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. 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.

Policy makers need models to be able to detect groups at high risk of HIV infection. Incomplete records and dirty data are frequently seen in national data sets. Presence of missing data challenges the practice of model development. Several studies suggested that performance of imputation methods is acceptable when missing rate is moderate. One of the issues which was of less concern, to be addressed here, is the role of the pattern of missing data. We used information of 2720 prisoners. Results derived from fitting regression model to whole data were served as gold standard. Missing data were then generated so that 10%, 20% and 50% of data were lost. In scenario 1, we generated missing values, at above rates, in one variable which was significant in gold model (age). In scenario 2, a small proportion of each of independent variable was dropped out. Four imputation methods, under different Event Per Variable (EPV) values, were compared in terms of selection of important variables and parameter estimation. In scenario 2, bias in estimates was low and performances of all methods for handing missing data were similar. All methods at all missing rates were able to detect significance of age. In scenario 1, biases in estimations were increased, in particular at 50% missing rate. Here at EPVs of 10 and 5, imputation methods failed to capture effect of age. In scenario 2, all imputation methods at all missing rates, were able to detect age as being significant. This was not the case in scenario 1. Our results showed that performance of imputation methods depends on the pattern of missing data.