Factors Influencing Drug Injection History among Prisoners: A Comparison between Classification and Regression Trees and Logistic Regression Analysis

Background: Due to the importance of medical studies, researchers of this field should be familiar with various types of statistical analyses to select the most appropriate method based on the characteristics of their data sets. Classification and regression trees (CARTs) can be as complementary to regression models. We compared the performance of a logistic regression model and a CART in predicting drug injection among prisoners. Methods: Data of 2720 Iranian prisoners was studied to determine the factors influencing drug injection. The collected data was divided into two groups of training and testing. A logistic regression model and a CART were applied on training data. The performance of the two models was then evaluated on testing data. Findings: The regression model and the CART had 8 and 4 significant variables, respectively. Overall, heroin use, history of imprisonment, age at first drug use, and marital status were important factors in determining the history of drug injection. Subjects without the history of heroin use or heroin users with short-term imprisonment were at lower risk of drug injection. Among heroin addicts with long-term imprisonment, individuals with higher age at first drug use and married subjects were at lower risk of drug injection. Although the logistic regression model was more sensitive than the CART, the two models had the same levels of specificity and classification accuracy. Conclusion: In this study, both sensitivity and specificity were important. While the logistic regression model had better performance, the graphical presentation of the CART simplifies the interpretation of the results. In general, a combination of different analytical methods is recommended to explore the effects of variables. Keywords: Classification and regression trees, Logistic regression model, History of drug injection, Drug abus

A Comparison between APGAR Scores and Birth Weight in Infants of Addicted and Non-Addicted Mothers

Background: Addiction in pregnant women causes complications such as abortion, asphyxia and cerebral and physical problems. APGAR score assesses vital signs and birth weight and represents the physical and brain growth of newborns. In this study, the effects of opium addiction in mothers on birth weight and APGAR scores of neonates were discussed.Methods: This study analytic, descriptive study was conducted on 49 pregnant women addicted to oral consumption of opium (0.5-0.8 grams daily) and 49 non-addicted women who referred to Afzalipour Hospital associated with Kerman University of Medical Sciences. Information including various personal characteristics, history of addiction and drug consumption, and the possibility of taking other drugs was collected by a researcher and recorded confidentially in a checklist. Birth weight and APGAR score t first, fifth and tenth minutes were also recorded. Statistical analysis was performed using Pearson correlation test, independent t-test, and repeated measure to evaluate the APGAR scores and other characteristics of the two groups of infants.Findings: Average birth weight of infants with addicted mothers was 2255 grams which had a significant difference with infants born by non-addicted mothers (P < 0.0001). Average APGAR scores at the first minute were 7.6 ± 1.1 and 8.6 ± 1.1 among infants from addicted and non-addicted mothers, respectively. Average APGAR scores over time (at minutes 1, 5 and 10) had a significant difference (P < 0.0001) where an ascending trend was seen. This difference was significant in both groups (P = 0.003).Conclusion: Drug addiction in mothers decreases the APGAR score and birth weight of infants.Keywords: APGAR score, Addicted mother, Birth weight, Opiate

Influence of Pattern of Missing Data on Performance of Imputation Methods: An Example Using National Data on Drug Injection in Prisons

Background : 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. Methods : 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. Results : 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. Conclusion : 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 dat

The Frequency of Alcohol Use in Iranian Urban Population: The Results of a National Network Scale Up Survey

Background: In Islamic countries alcohol consumption is considered as against religious values. Therefore, estimation of frequency of alcohol consumptions using direct methods is prone to different biases. In this study, we indirectly estimated the frequency of alcohol use in Iran, in network of a representative sample using network scale up (NSU) method. Methods: In a national survey, about 400 participants aged above 18 at each province, around 12 000 in total, were recruited. In a gender-match face to face interview, respondents were asked about the number of those who used alcohol (even one episode) in previous year in their active social network, classified by age and gender. The results were corrected for the level of visibility of alcohol consumption. Results: The relative frequency of alcohol use at least once in previous year, among general population aged above 15, was estimated at 2.31% (95% CI: 2.12%, 2.53%). The relative frequency among males was about 8 times higher than females (4.13% versus 0.56%). The relative frequency among those aged 18 to 30 was 3 times higher than those aged above 30 (3.97% versus 1.36%). The relative frequency among male aged 18 to 30 was about 7%. Conclusion: It seems that the NSU is a feasible method to monitor the relative frequency of alcohol use in Iran, and possibly in countries with similar culture. Alcohol use was lower than non-Muslim countries, however, its relative frequency, in particular in young males, was noticeable

National population size estimation of illicit drug users through the network scale-up method in 2013 in Iran

Background: For a better understanding of the current situation of drug use in Iran, we utilized the network scale-up approach to estimate the prevalence of illicit drug use in the entire country. Methods: We implemented a self-administered, street-based questionnaire to 7535 passersby from the general public over 18 years of age by street based random walk quota sampling (based on gender, age and socio-economic status) from 31 provinces in Iran. The sample size in each province was approximately 400, ranging from 200 to 1000. In each province 75% of sample was recruited from the capital and the remaining 25% was recruited from one of the large cities of that province through stratified sampling. The questionnaire comprised questions on demographic information as well as questions to measure the total network size of participants as well as the network size in each of seven drug use groups including Opium, Shire (combination of Opium residue and pure opium), Crystal Methamphetamine, heroin/crack (which in Iranian context is a cocaine-free drug that mostly contains heroin, codeine, morphine and caffeine with or without other drugs), Hashish, Methamphetamine/LSD/ ecstasy, and injecting drugs. The estimated size for each group was adjusted for transmission and barrier ratios. Results: The most common type of illicit drug used was opium with the prevalence of 1500 per 100,000 population followed by shire (660), crystal methamphetamine (590), hashish (470), heroin/crack (350), methamphetamine, LSD and ecstasy (300) and injecting drugs (280). All types of substances were more common among men than women. The use of opium, shire and injecting drugs was more common in individuals over 30 whereas the use of stimulants and hashish was largest among individuals between 18 and 30 years of age. Conclusion: It seems that younger individuals and women are more desired to use new synthetic drugs such as crystal methamphetamine. Extending the preventive programs especially in youth as like as scaling up harm reduction services would be the main priorities in prevention and control of substance use in Iran. Because of poor service coverage and high stigma in women, more targeted programs in this affected population are needed

Influence of Pattern of Missing Data on Performance of Imputation Methods: An Example from National Data on Drug Injection in Prisons

Background: 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. Methods: 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. Results: 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. Conclusion: 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

Comparison of conventional risk factors in middle-aged versus elderly diabetic and nondiabetic patients with myocardial infarction: prediction with decision-analytic model

BACKGROUND: We sought to predict occurrence of myocardial infarction (MI) by means of a classification and regression tree (CART) model by conventional risk factors in middle-aged versus elderly (age ⩾65years) diabetic and nondiabetic patients from the Modares Heart Study. METHOD: A total of 469 patients were randomly selected and categorized into two groups according to clinical diabetes status. Group I consisted of 238 diabetic patients and group II consisted of 231 nondiabetic patients. Our population was MI positive. The outcome investigated was diabetes mellitus. We used a decision-analytic model to predict the diagnosis of patients with suspected MI. RESULTS: We constructed 4 predictive patterns using 12 input variables and 1 output variable in terms of their sensitivity, specificity and risk. The differences among patterns were due to inclusion of predictor variables. The CART model suggested different variables of hypertension, mean cell volume, fasting blood sugar, cholesterol, triglyceride and uric acid concentration based on middle-aged and elderly patients at high risk for MI. Levels of biochemical measurements identified as best risk cutoff points. In evaluating the precision of different patterns, sensitivity and specificity were 47.9-84.0% and 56.3-93.0%, respectively. CONCLUSIONS: The CART model is capable of symbolizing interpretable clinical data for confirming and better prediction of MI occurrence in clinic or in hospital. Therefore, predictor variables in pattern could affect the outcome based on age group variable. Hyperglycemia, hypertension, hyperlipidemia and hyperuricemia were serious predictors for occurrence of MI in diabetics

Association of triage time Shock Index, Modified Shock Index, and Age Shock Index with mortality in Emergency Severity Index level 2 patients

BACKGROUND: Shock Index (SI) is considered to be a predictor of mortality in many medical and trauma settings. Many studies have shown its superiority to conventional vital sign measurements in mortality prediction. OBJECTIVES: The objectives were to compare mortality and intensive care unit admission prediction of triage time SI, Modified SI (MSI), and Age SI with each other and with triage time blood pressure in Emergency Severity Index (ESI) level 2 patients. METHODS: A retrospective medical record review was performed in the internal medicine emergency department of a general hospital in Kerman, Iran. Triage time vital signs were used to calculate the indices. Multivarible regression analysis was used to create the final model. RESULTS: A total of 1285 patients triaged to ESI level 2 were enrolled in the study. In the multivariate analysis, SI, MSI, and Age SI were found to be the only variables independently associated with mortality, whereas none of them were associated with intensive care unit admission. Sensitivity, specificity, and area under curve in the receiver operating characteristic curve for the model including SI, MSI, and Age SI were 60.8%, 65.4%, and 0.675, respectively. Sensitivity, specificity, and area under curve did not change significantly by excluding SI, MSI, or Age SI from the final model. CONCLUSION: In nontrauma adult patients, triage time SI, MSI, and Age SI are superior to blood pressure for mortality prediction in ESI level 2. They can be used alone or in combination with similar results, but their low sensitivity and specificity make them usable only as an adjunct for this purpose. Copyright © 2015 Elsevier Inc. All rights reserved

The effect of cognitive behavioral counseling on anxiety and worry level of women with intermediate risk during first trimester screening for down syndrome: a randomized controlled trial: a randomized controlled trial

Abstract Background Anxiety related to prenatal screening programs negatively affects maternal and child health. Objective The study aimed to determine the effect of Cognitive Behavioral Counseling on the anxiety and worry levels of women with intermediate risk during first-trimester screening for Down Syndrome. Methods The study was a randomized controlled trial conducted on 52 pregnant women with intermediate risk (1: 51 − 1:1500) during first-trimester screening for Down Syndrome and without additional structural anomalies that referred to three cities of Zanjan province in 2021. The eligible women were randomly assigned to intervention and control groups, with a block size of four. The intervention group received CBC in four sessions of 120 min two times a week by phone. Data were collected using Vandenberg Anxiety Questionnaire, and Cambridge Worry Questionnaire in three phases baseline, after the intervention, and 6 weeks follow-ups. Data were analyzed using independent t-test, chi-square, and repeated measures ANOVA at a 95% confidence level. (P < 0.05). Results In the counselling group, the mean (SD) of a total score of anxiety before the intervention was 67.11 (20.68) which decreased to 32.50 (13.58) in six weeks after the intervention. Furthermore, the mean (SD) of a total score of worry before the intervention was 56.19 (16.76) which decreased to 32.96 (8.89) six weeks after the intervention. Based on the repeated measures ANOVA test, the mean total score of anxiety and worry were statistically significant 6 weeks after the intervention compared with the control group(p < 0.001). Conclusion Based on the study results, CBC can reduce the anxiety and worry levels of women with intermediate risk during first trimester screening for Down Syndrome. Trial registration The study was registered at the Iranian Registry of Clinical Trials website under the code IRCT20160608028352N8, ( https://en.irct.ir/trial/49998 ). The first trial registration date was (29/08/2020)