Addictive Behavior

To assess problematic social media use, the Bergen Social Media Addiction Scale (BSMAS; [11 ]) was used. The 6-item scale was adapted from the previously validated Bergen Facebook Addiction Scale (BFAS; [10 (link)]). The original scale specifically assessed problematic Facebook use during the last year. The scale incorporated the theoretical framework of the addiction components of the biopsychosocial model [31 ]. The BFAS was developed by selecting the items with the highest possible factor loadings for each component (i.e., salience, mood modification, tolerance, withdrawal symptoms, conflict, and relapse) from an item-pool of 18 initial items. In the present study, the Bergen Social Media Addiction Scale (BSMAS) which is based on rephrasing of the BFAS, was to assess social media use in general over the past 12 months. The scale was translated to Hungarian and then back-translated by independent translators. The back-translation was then compared with the original scale and adjustments were made as necessary. The items are answered on a 5-point scale (“never” to “always”). The Cronbach’s alpha of the translated BSMAS was .85 in the present sample.

The 3 month post-treatment follow-up telephone interview asked about the use of alcohol or drugs during the last four weeks. Patients indicated how often they had used alcohol/drugs during this period, with the following response options: “less than once a week,” “approximately weekly,” “2–4 times a week,” “daily or almost daily”. We defined relapse as return to regular use [15 (link)], thus those who reported using alcohol or drugs 2–4 times or more per week were categorized as having a relapse. The interview also enquired about any contact (yes/no) with outpatient SUD treatment services; and/or a community mental health and addiction health care provider; and/or readmission to SUD inpatient treatment. A small number of patients who reported readmission to SUD treatment was included in the relapse group (see also [34 (link)].

The sample comprised 611 patients who were included in the prospective cohort study, of whom 289 patients (47.3%) had at least one co-occurring psychiatric diagnosis (F20-F99).
In total, 426 of the patients participated in the follow-up interview 3 months after discharge from treatment (70%), of whom 206 (48.4%) were patients with COD. The follow-up response rate was similar for patients with COD (71.3%) and those without COD (68.3%). Among patients with COD, those who did not respond were more likely younger (OR = 2.54, p = 0.002), with lower education level (OR = 1.77, p = 0.035), and less likely to have an alcohol use disorder (OR = 0.588, p = 0.053). Among patients without COD, those who were lost for follow-up appeared more likely younger (OR = 2.157, p = 0.002), and without a permanent housing situation (OR = 1.694, p = 0.042). About half of those who were reached at follow-up (n = 227) reported they had been in contact with SUD outpatient treatment services during the last month. Slightly fewer patients (n = 194) reported contact with a community health provider. The probability of contact with outpatient SUD services was somewhat higher for patients with COD (58.3%) than for patients without COD (48.6%) (p = 0.047). There was no difference between the groups regarding any contact with community mental health and addiction services.

The present population based cross-sectional study, used the national database on people diagnosed with HIV from 1986 to 2016 (11 (link)). The data is managed by the Iranian Ministry of Health and Medical Education (MOHME) covering all 31 provinces. In Iran, MOHME integrated the HIV/AIDS program into a broad and coherent structure of the national health care system. The routine initial HIV diagnostic tests include ELISA and Western Blot. The confirmed HIV cases are reported to the regional health centers and then to the AIDS coordination department in MOHME. As the result, the national database takes several years to be completed as after collecting data from all provinces the data goes under huge mining and cleaning procedures to make it ready for any particular research use.
After being registered with the system, every HIV-positive individual is to receive standard treatments and gets routine medical follow-up at least twice a year in local HIV centers. All individuals' data is recorded in an unified online registration system under MOHME after being checked and cleaned by the local registry centers (11 (link)). For each HIV case, at the time of diagnosis, data on demographic characteristics and HIV associated behaviors is obtained via an interview conducted by trained and experienced health staffs in all counties. The collected information includes age, gender (female or male), level of education (elementary, high school, or above), marital status (married, single), occupation (employed or unemployed/housewife), year of HIV diagnosis, history of addiction (yes, no), and major HIV related behaviors. The predefined major behaviors, include history of drug injection (yes, no), out of marriage sexual contact (yes, no), and other conditions (i.e., mother to child transmission, blood transfusion, having sex with the same sex, occupational exposure, and no reported related behavior).
We used the annual number of new cases to define the trend of risky behaviors during the study period. As each individual could report more than one HIV related behavior, we used a logistic regression model for each of the risk factors separately to define their associated factors. To handle missing data, we used multiple imputation via applying Chained Equations (MICE) method before running multivariate logistic regression analysis. Analysis was conducted in SPSS, version 22 and STATA, version 14.0 (Stata Corporation, College Station, TX).