Identifying the Factors Affecting Methamphetamine Use: A Scoping Review.

Background: One of the critical factors affecting chronic diseases is the use of drugs, especially industrial substances, such as methamphetamine. Methamphetamine use is increasingly common among the younger members of society. Methamphetamine is not only physically and mentally destructive, but also has a significant impact on the families of abusers and society, and imposes a financial burden on society. The present study aims to identify the factors affecting methamphetamine use in a scoping review. Methods: Different keywords of methamphetamine were selected in the Mesh database and were searched in valid English databases from January 1, 2008, to April 5, 2022. Inclusion and exclusion criteria in this study were languages, reported findings, time range, and type of article. This study was designed by scoping review method developed by Askey Malley'O. Results: The total articles that were finally analyzed in this article were 42 including 12 English articles and 30 Persian articles. Among these articles, the most important factors affecting Methamphetamine are individual, social, and family factors, which have received the most cited. Conclusion: The majority of research highlights the importance of individual factors, society, and family factors, respectively, while formulating policies for prevention, treatment, and rehabilitation must be considered. It is suggested that structural path analysis be determined by prioritizing the identified factors and the weights of these components.

Population Size Estimation of People Who Use Illicit Drugs and Alcohol in Iran (2015-2016).

BACKGROUND: Estimating the number of people using illicit drugs and alcohol is necessary for informing health policy and programming. However, it is often challenging to reliably estimate the size of these marginalized populations through direct methods. In this study, we estimated the population size of these groups using the indirect Network Scale-Up (NSU) method in Iran from 2015 to 2016. METHODS: Using a self-administered questionnaire, we asked 15 124 individuals (54% men) about the number of people they know who used different types of drugs at least once in the past 12 months. Prevalence estimates were reported per 100 000 population. The uncertainty level (UL) was calculated using the bootstrap method. RESULTS: The average age of the respondents was 33 years old, and 35.1% of them were unmarried. The most common drugs and their prevalence were as follows: opium (2534 [95% UL: 2467-2598]), hashish (849 [95% UL: 811-886]), stimulants (methamphetamine, ecstasy pills, cocaine, and Ritalin) (842 [95% UL: 802-879]), heroin/crack (578 [95% UL: 550-607]), and drug injection (459 [95% UL: 438-484]). Additionally, we estimated the prevalence of alcohol use as 2797 (95% UL: 2731-2861). On average, substance use was 5.23 times more prevalent among men than women. Opium use was more prevalent among individuals aged >50 years old. Moreover, alcohol use was more prevalent among participants between 18 and 30 years old (5164 per 100 000 population). CONCLUSION: Although opium continues to be the most prevalent illicit drug in Iran, the patterns of illicit drug use are heterogeneous among different age groups, genders, and provinces. Age-gender specific and culturally appropriate interventions are warranted to meet the needs of people in different subgroups.

Review of Size Estimation Methods.

Understanding and measuring the burden of HIV faces several remaining challenges around the globe. HIV prevention, care, and treatment efforts, including advocacy for populations at risk of HIV, the design and implementation of national guidelines, monitoring the coverage of HIV programs and evaluating their impact, are not feasible without relatively precise estimates of the impact and magnitude of HIV among different subpopulations.

Methods to Estimate the Average Social Network Size.

One of the requirements to calculate the size of a KP by the NSU method is to know the average social network size of the general population accurately (shown by C).

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.

Shock index, modified shock index, and age shock index for prediction of mortality in Emergency Severity Index level 3.

OBJECTIVES: To elucidate the predictive capability of shock index (SI), modified SI (MSI), and age SI for mortality in patients assigned to Emergency Severity Index (ESI) level 3 patients. METHODS: This was a retrospective medical record review performed in an academic internal medicine emergency department in Kerman, Iran. All patients older than 14 years triaged to ESI level 3 were enrolled in the study. Triage time vital signs were used to calculate SI, MSI, and age SI. The primary outcome was in-hospital mortality. RESULTS: A total number of 3375 patients were enrolled in the study, in which 84 (2.5%) died during hospital stay. In the adjusted multivariate analysis, age SI, systolic blood pressure (SBP), and sex were independently associated with mortality, with P values (odds ratio [95% confidence interval]) of <.001 (1.03 [1.01-1.04]), .003 (0.97 [0.96-0.99]), and .04 (1.61 [1.01-2.59]), respectively. Receiver operating characteristic curve showed an area under curve of 0.717 for the 3-variable final model and an area under curve of 0.678 for age SI in mortality prediction. CONCLUSIONS: In ESI level 3 patients, age SI and SBP showed to be better than SI or MSI in predicting mortality. However, because their predictive capability was modest, age SI or SBP should be considered adjuncts to sort actions in favor of patients with higher risk for mortality.

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.

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.

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.

Estimating the Annual Incidence of Abortions in Iran Applying a Network Scale-up Approach.

BACKGROUND: Abortions are of major public health concern in developing countries. In settings in which abortion is highly prohibited, the direct interview is not a reliable method to estimate the abortion rate. The indirect estimation methods to measure the rate of abortion might overcome this dilemma; They are practical methods to estimate the size of the hidden group who do not agree to participate in a direct interview. OBJECTIVES: The aim of this study was to explore the practicality of an indirect method for estimating the abortion rate , Known as Network Scale-up, and to provide an estimate about the episode of abortion with and without medical indications (AWMI+ and AWMI-) in Iran. MATERIALS AND METHODS: This cross-sectional study was conducted in 31 provinces of Iran in 2012. A random sample between 200 and 1000 was selected in each province by the multistage sampling method that 75% of the data were collected from the capital and 25% from one main city. We selected samples from urban people more than 18 years old (12960) and we asked them about the number of abortion in women they knew who had experienced the medical and non-medical abortions in the past year. A range for the transparency factor was estimated based on the expert opinion. RESULTS: The range of the transparency factors for AWMI+ and AWOMI- were 0.43-0.75 and 0.2-0.34, respectively. Regarding the AWMI+, our minimum and maximum estimations (per 1000 pregnancies) were 70.54 and 116.9, respectively. The corresponding figures for AWMI- were 93.18, and 148.7. CONCLUSIONS: The frequency rates for AWMI+ and AWMI- were relatively high. Therefore, the system has to address to this hidden problem using the appropriate preventive policies.

The estimation of active social network size of the Iranian population.

OBJECTIVES: The size of active network (C) of Iranian population is a very important parameter to estimate the size of unknown population using Network Scale Up (NSU) technique. However, there is little information about this parameter not only in Iran but also in other countries in Middle East region. Based on these needs, the aim of this paper is to estimate C for the Iranian population. METHODS: Based on available national statistics, 23 reference groups, with known population sizes were selected. Using multistage sampling method, 7454 individuals were recruited randomly around the country. We asked from our samples how many people they knew from each of the reference groups. Using NSU formulae, we maximized the goodness of fit of our estimation about the size of the reference groups by fitting the best C. However, the final C was set by excluding some of the reference groups with no added information; these inappropriate groups were selected by two techniques; regression, and ratio based approaches. RESULTS: Applying regression and ratio based approaches the estimated C was 308 and 380 respectively. The Pearson correlation coefficient between the real and estimated size of reference groups (based on our C) in both methods was above 0.95. However, results of ratio based had better performance. We saw that the network of males, singles, younger age groups, and those with higher education was larger than those in other groups. CONCLUSION: It seems that C in Iran is higher than that in developed countries, possibly because of its social structure. Because of cultural and social similarities in Middle East courtiers, C in other countries also might be higher than that in developed countries.

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.

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 data.

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.