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6. Effects of counselling and spiritual care program on anxiety in patients with chronic diseases: A systematic review and meta-analysis

Author

Salari, Nader, Kazeminia, Mohsen, Abdi, Alireza, Abdolmaleki, Amir, Abdoli, Nasrin, Mohammadi, Masoud, and Shohaimi, Shamarina

Subjects
Pastoral counseling -- Influence -- Psychological aspects -- Health aspects, Chronic diseases -- Complications and side effects -- Psychological aspects, Psychotherapy -- Religious aspects, Anxiety -- Care and treatment -- Demographic aspects -- Risk factors, Psychology and mental health
Abstract

One of the essential non-pharmacological treatments for patients with chronic diseases are psychosocial supports and counselling services such as spiritual psychotherapy. Although several preliminary studies represent the effects of counselling and spiritual psychotherapy on anxiety in patients with chronic diseases, there are various contradictions among the findings. The present meta-analysis aimed to determine the effects of counselling and spiritual care programs on anxiety in patients with chronic diseases. For data collection, various databases were searched regarding no time limitation until May 2021, including SID, MagIran, Embase, ProQuest, Scopus, PubMed, and Web of Science (WoS) using Google Scholar search engine. Inclusion criteria were original research articles, interventional studies, and exclusion criteria were irrelevant assessments, systematic review and meta-analysis studies, case reports, letters to the editor, and duplicate papers. I.sup.2 test was used to calculate heterogeneity of studies, and the Egger's test was applied to examine the publication bias. According to the inclusion criteria, 26 articles were selected among 612 initial papers with a sample size of 1040 individuals. The mean anxiety scores before and after the interventions were 33.02 ± 3.77 and 25.27 ± 3.66, respectively. The significant (P < 0.05) mean difference before and after the interventions was 1.74 ± 0.23. Counselling and psychotherapy can reduce anxiety in patients with chronic diseases., Author(s): Nader Salari [sup.1] [sup.2] , Mohsen Kazeminia [sup.3] , Alireza Abdi [sup.4] , Amir Abdolmaleki [sup.5] , Nasrin Abdoli [sup.6] , Masoud Mohammadi [sup.4] , Shamarina Shohaimi [sup.7] Author [...]

Published
2023
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10. Population and fertility by age and sex for 195 countries and territories, 1950–2017: a systematic analysis for the Global Burden of Disease Study 2017

Author

Murray, Christopher JL, Callender, Charlton SKH, Kulikoff, Xie Rachel, Srinivasan, Vinay, Abate, Degu, Abate, Kalkidan Hassen, Abay, Solomon M, Abbasi, Nooshin, Abbastabar, Hedayat, Abdela, Jemal, Abdelalim, Ahmed, Abdel-Rahman, Omar, Abdi, Alireza, Abdoli, Nasrin, Abdollahpour, Ibrahim, Abdulkader, Rizwan Suliankatchi, Abebe, Haftom Temesgen, Abebe, Molla, Abebe, Zegeye, Abebo, Teshome Abuka, Abejie, Ayenew Negesse, Aboyans, Victor, Abraha, Haftom Niguse, Abreu, Daisy Maria Xavier, Abrham, Aklilu Roba, Abu-Raddad, Laith Jamal, Abu-Rmeileh, Niveen ME, Accrombessi, Manfred Mario Kokou, Acharya, Pawan, Adamu, Abdu A, Adebayo, Oladimeji M, Adedeji, Isaac Akinkunmi, Adekanmbi, Victor, Adetokunboh, Olatunji O, Adhena, Beyene Meressa, Adhikari, Tara Ballav, Adib, Mina G, Adou, Arsène Kouablan, Adsuar, Jose C, Afarideh, Mohsen, Afshin, Ashkan, Agarwal, Gina, Agesa, Kareha M, Aghayan, Sargis Aghasi, Agrawal, Sutapa, Ahmadi, Alireza, Ahmadi, Mehdi, Ahmed, Muktar Beshir, Ahmed, Sayem, Aichour, Amani Nidhal, Aichour, Ibtihel, Aichour, Miloud Taki Eddine, Akanda, Ali S, Akbari, Mohammad Esmaeil, Akibu, Mohammed, Akinyemi, Rufus Olusola, Akinyemiju, Tomi, Akseer, Nadia, Alahdab, Fares, Al-Aly, Ziyad, Alam, Khurshid, Alebel, Animut, Aleman, Alicia V, Alene, Kefyalew Addis, Al-Eyadhy, Ayman, Ali, Raghib, Alijanzadeh, Mehran, Alizadeh-Navaei, Reza, Aljunid, Syed Mohamed, Alkerwi, Ala'a, Alla, François, Allebeck, Peter, Almasi, Ali, Alonso, Jordi, Al-Raddadi, Rajaa M, Alsharif, Ubai, Altirkawi, Khalid, Alvis-Guzman, Nelson, Amare, Azmeraw T, Ammar, Walid, Anber, Nahla Hamed, Andrei, Catalina Liliana, Androudi, Sofia, Animut, Megbaru Debalkie, Ansari, Hossein, Ansha, Mustafa Geleto, Antonio, Carl Abelardo T, Appiah, Seth Christopher Yaw, Aremu, Olatunde, Areri, Habtamu Abera, Arian, Nicholas, Ärnlöv, Johan, Artaman, Al, Aryal, Krishna K, Asayesh, Hamid, Asfaw, Ephrem Tsegay, Asgedom, Solomon Weldegebreal, Assadi, Reza, Atey, Tesfay Mehari Mehari, and Atique, Suleman

Subjects
Health Sciences, Behavioral and Social Science, Contraception/Reproduction, Basic Behavioral and Social Science, Aging, Aetiology, 2.4 Surveillance and distribution, Reproductive health and childbirth, Good Health and Well Being, Adolescent, Adult, Aged, Birth Rate, Child, Child, Preschool, Female, Global Burden of Disease, Global Health, Humans, Infant, Infant, Newborn, Male, Maternal Age, Middle Aged, Mortality, Population Density, Population Growth, Young Adult, GBD 2017 Population and Fertility Collaborators, Medical and Health Sciences, General & Internal Medicine, Biomedical and clinical sciences, Health sciences
Abstract

BackgroundPopulation estimates underpin demographic and epidemiological research and are used to track progress on numerous international indicators of health and development. To date, internationally available estimates of population and fertility, although useful, have not been produced with transparent and replicable methods and do not use standardised estimates of mortality. We present single-calendar year and single-year of age estimates of fertility and population by sex with standardised and replicable methods.MethodsWe estimated population in 195 locations by single year of age and single calendar year from 1950 to 2017 with standardised and replicable methods. We based the estimates on the demographic balancing equation, with inputs of fertility, mortality, population, and migration data. Fertility data came from 7817 location-years of vital registration data, 429 surveys reporting complete birth histories, and 977 surveys and censuses reporting summary birth histories. We estimated age-specific fertility rates (ASFRs; the annual number of livebirths to women of a specified age group per 1000 women in that age group) by use of spatiotemporal Gaussian process regression and used the ASFRs to estimate total fertility rates (TFRs; the average number of children a woman would bear if she survived through the end of the reproductive age span [age 10-54 years] and experienced at each age a particular set of ASFRs observed in the year of interest). Because of sparse data, fertility at ages 10-14 years and 50-54 years was estimated from data on fertility in women aged 15-19 years and 45-49 years, through use of linear regression. Age-specific mortality data came from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 estimates. Data on population came from 1257 censuses and 761 population registry location-years and were adjusted for underenumeration and age misreporting with standard demographic methods. Migration was estimated with the GBD Bayesian demographic balancing model, after incorporating information about refugee migration into the model prior. Final population estimates used the cohort-component method of population projection, with inputs of fertility, mortality, and migration data. Population uncertainty was estimated by use of out-of-sample predictive validity testing. With these data, we estimated the trends in population by age and sex and in fertility by age between 1950 and 2017 in 195 countries and territories.FindingsFrom 1950 to 2017, TFRs decreased by 49·4% (95% uncertainty interval [UI] 46·4-52·0). The TFR decreased from 4·7 livebirths (4·5-4·9) to 2·4 livebirths (2·2-2·5), and the ASFR of mothers aged 10-19 years decreased from 37 livebirths (34-40) to 22 livebirths (19-24) per 1000 women. Despite reductions in the TFR, the global population has been increasing by an average of 83·8 million people per year since 1985. The global population increased by 197·2% (193·3-200·8) since 1950, from 2·6 billion (2·5-2·6) to 7·6 billion (7·4-7·9) people in 2017; much of this increase was in the proportion of the global population in south Asia and sub-Saharan Africa. The global annual rate of population growth increased between 1950 and 1964, when it peaked at 2·0%; this rate then remained nearly constant until 1970 and then decreased to 1·1% in 2017. Population growth rates in the southeast Asia, east Asia, and Oceania GBD super-region decreased from 2·5% in 1963 to 0·7% in 2017, whereas in sub-Saharan Africa, population growth rates were almost at the highest reported levels ever in 2017, when they were at 2·7%. The global average age increased from 26·6 years in 1950 to 32·1 years in 2017, and the proportion of the population that is of working age (age 15-64 years) increased from 59·9% to 65·3%. At the national level, the TFR decreased in all countries and territories between 1950 and 2017; in 2017, TFRs ranged from a low of 1·0 livebirths (95% UI 0·9-1·2) in Cyprus to a high of 7·1 livebirths (6·8-7·4) in Niger. The TFR under age 25 years (TFU25; number of livebirths expected by age 25 years for a hypothetical woman who survived the age group and was exposed to current ASFRs) in 2017 ranged from 0·08 livebirths (0·07-0·09) in South Korea to 2·4 livebirths (2·2-2·6) in Niger, and the TFR over age 30 years (TFO30; number of livebirths expected for a hypothetical woman ageing from 30 to 54 years who survived the age group and was exposed to current ASFRs) ranged from a low of 0·3 livebirths (0·3-0·4) in Puerto Rico to a high of 3·1 livebirths (3·0-3·2) in Niger. TFO30 was higher than TFU25 in 145 countries and territories in 2017. 33 countries had a negative population growth rate from 2010 to 2017, most of which were located in central, eastern, and western Europe, whereas population growth rates of more than 2·0% were seen in 33 of 46 countries in sub-Saharan Africa. In 2017, less than 65% of the national population was of working age in 12 of 34 high-income countries, and less than 50% of the national population was of working age in Mali, Chad, and Niger.InterpretationPopulation trends create demographic dividends and headwinds (ie, economic benefits and detriments) that affect national economies and determine national planning needs. Although TFRs are decreasing, the global population continues to grow as mortality declines, with diverse patterns at the national level and across age groups. To our knowledge, this is the first study to provide transparent and replicable estimates of population and fertility, which can be used to inform decision making and to monitor progress.FundingBill & Melinda Gates Foundation.

Published
2018

18. Nomophobia (no mobile phone phobia) and psychological health issues among young adult students

Author

Abdoli, Nasrin, Sadeghi Bahmani, Dena, Salari, Nader, Khodamoradi, Mehdi, Farnia, Vahid, Jahangiri, Somayeh, Beatrix Brühl, Annette, Dürsteler, Kenneth M., Stanga, Zeno, Brand, Serge, Abdoli, Nasrin, Sadeghi Bahmani, Dena, Salari, Nader, Khodamoradi, Mehdi, Farnia, Vahid, Jahangiri, Somayeh, Beatrix Brühl, Annette, Dürsteler, Kenneth M., Stanga, Zeno, and Brand, Serge

Abstract

Background: Smart phone use has become a part of people’s everyday life. However, when the lack of using the smart phone to establish and maintain electronic communication is related to psychological distress, such a behavior may be considered a modern-age phobia, or nomophobia (no mobile phone phobia). The aims of the present study were to investigate among a sample of young adults the associations between scores for nomophobia and symptoms of depression, anxiety, stress, insomnia, and obsessive–compulsive disorders. Methods: A total of 537 students (mean age: 25.52 years; 42.3% females) participated in the study. They completed a booklet of self-rating questionnaires covering sociodemographic information and symptoms of nomophobia, depression, anxiety, stress, insomnia, and obsessive–compulsive disorders. Results: Higher scores for nomophobia were associated with higher scores for depression, anxiety, and stress, but not with scores for insomnia and obsessive–compulsive disorders. The regression model confirmed that symptoms of anxiety predicted nomophobia. Conclusions: The present results support the assumption that nomophobia appears to be a mood disturbance related to stronger associations with symptoms of anxiety and, to a lesser extent, with symptoms of depression and stress. By contrast, nomophobia appeared to be unrelated to insomnia and symptoms of obsessive–compulsive disorders.

Published
2023

23. Risk-Taking Behavior among Suicide Attempters

Author

Abdoli, Nasrin, primary, Salari, Nader, additional, Farnia, Vahid, additional, Khodamoradi, Mehdi, additional, Jahangiri, Somayeh, additional, Mohammadi, Masoud, additional, Brühl, Annette, additional, Sadeghi-Bahmani, Dena, additional, and Brand, Serge, additional

Published
2022
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26. Sources of Sleep Disturbances and Psychological Strain for Hospital Staff Working during the COVID-19 Pandemic

Author

Abdoli, Nasrin, primary, Farnia, Vahid, additional, Jahangiri, Somayeh, additional, Radmehr, Farnaz, additional, Alikhani, Mostafa, additional, Abdoli, Pegah, additional, Davarinejad, Omran, additional, Dürsteler, Kenneth M., additional, Brühl, Annette Beatrix, additional, Sadeghi-Bahmani, Dena, additional, and Brand, Serge, additional

Published
2021
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27. Sources of Sleep Disturbances and Psychological Strain for Hospital Staff Working during the COVID-19 Pandemic

Author

Abdoli, Nasrin, Farnia, Vahid, Jahangiri, Somayeh, Radmehr, Farnaz, Alikhani, Mostafa; https://orcid.org/0000-0001-6848-3027, Abdoli, Pegah, Davarinejad, Omran, Dürsteler, Kenneth M, Brühl, Annette Beatrix; https://orcid.org/0000-0003-4704-4986, Sadeghi-Bahmani, Dena; https://orcid.org/0000-0002-1301-5522, Brand, Serge; https://orcid.org/0000-0003-2175-2765, Abdoli, Nasrin, Farnia, Vahid, Jahangiri, Somayeh, Radmehr, Farnaz, Alikhani, Mostafa; https://orcid.org/0000-0001-6848-3027, Abdoli, Pegah, Davarinejad, Omran, Dürsteler, Kenneth M, Brühl, Annette Beatrix; https://orcid.org/0000-0003-4704-4986, Sadeghi-Bahmani, Dena; https://orcid.org/0000-0002-1301-5522, and Brand, Serge; https://orcid.org/0000-0003-2175-2765

Abstract

Hospital staff members reported increased stress-related workload when caring for inpatients with COVID-19 (“frontline hospital staff members”). Here, we tested if depression, anxiety, and stress were associated with poor sleep and lower general health, and if social support mediated these associations. Furthermore, we compared current insomnia scores and general health scores with normative data. A total of 321 full-time frontline hospital staff members (mean age: 36.86; 58% females) took part in the study during the COVID-19 pandemic. They completed a series of questionnaires covering demographic and work-related information, symptoms of depression, anxiety, stress, social support, self-efficacy, and symptoms of insomnia and general health. Higher symptoms of depression, anxiety, and stress were associated with higher symptoms of insomnia and lower general health. Higher scores of depression, anxiety, and stress directly predicted higher insomnia scores and lower general health scores, while the indirect effect of social support was modest. Compared to normative data, full-time frontline hospital staff members had a 3.14 higher chance to complain about insomnia and a significantly lower general health. Symptoms of insomnia and general health were unrelated to age, job experience, educational level, and gender. Given this background, it appears that the working context had a lower impact on individuals’ well-being compared to individual characteristics.

Published
2021

28. Validation and Psychometric Properties of the Persian Version of the 21-Item Game Addiction Scale With a Sample of Adolescents and Young Adults

Author

Abdoli, Nasrin, Farnia, Vahid, Alikhani, Mostafa, Sadeghi-Bahmani, Dena, Dürsteler, Kenneth M, Esmaeili, Maryam, Brühl, Annette, Brand, Serge, Abdoli, Nasrin, Farnia, Vahid, Alikhani, Mostafa, Sadeghi-Bahmani, Dena, Dürsteler, Kenneth M, Esmaeili, Maryam, Brühl, Annette, and Brand, Serge

Abstract

Background: Excessive gaming has become a psychological health issue for both gamers and their social environments. This observation holds true for western but also non-western countries such as Iran. The aim of the present study was to translate and validate a Persian version of the Game Addiction Scale 21 (GAS 21) using a sample of adolescents and adults.Methods: A total of 412 participants (mean age: 22.16 years; 55.3% females) took part in the study. They completed questionnaires covering sociodemographic information and gaming-related information, as well as the Persian version of the GAS 21, the GAS 7, the Internet Addiction Test, and the Generalized Self-Efficacy Scale.Results: Of the initial 21 items of the Persian version of the GAS 21, five proved redundant and were eliminated. Factors analyses yielded four factors explaining 66.35% of the variance: 1. Withdrawal; 2. Feelings of guilt and addiction; 3. Mood modification; 4. Issues of time budget. Cronbach's alphas were satisfactory (alphas > 0.87). To validate the results, scores on the translated version were compared with the well-established Young Internet Addiction test. Factors correlated positively (rs between 0.21 and 0.31) with overall score on this latter test but, against expectations, positively with the generalized self-efficacy scale.Conclusions: A Persian version of the Game Addiction Scale-21 proved to be a useful tool for assessing the risk of game addiction behavior. Further, the solution with 16 items loading on four factors appears respond to the ecological need of parsimony.

Published
2021

31. Game Addiction Scale--Persian Version

Author

Abdoli, Nasrin, primary, Farnia, Vahid, additional, Alikhani, Mostafa, additional, Sadeghi-Bahmani, Dena, additional, Dürsteler, Kenneth M., additional, Esmaeili, Maryam, additional, Brühl, Annette, additional, and Brand, Serge, additional

Published
2021
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36. Population and fertility by age and sex for 195 countries and territories, 1950-2017: a systematic analysis for the Global Burden of Disease Study 2017

Author

Murray, Christopher J L, Callender, Charlton S K H, Kulikoff, Xie Rachel, Srinivasan, Vinay, Abate, Degu, Abate, Kalkidan Hassen, Abay, Solomon M, Abbasi, Nooshin, Abbastabar, Hedayat, Abdela, Jemal, Abdelalim, Ahmed, Abdel-Rahman, Omar, Abdi, Alireza, Abdoli, Nasrin, Abdollahpour, Ibrahim, Abdulkader, Rizwan Suliankatchi, Abebe, Haftom Temesgen, Abebe, Molla, Abebe, Zegeye, Abebo, Teshome Abuka, Abejie, Ayenew Negesse, Aboyans, Victor, Abraha, Haftom Niguse, Abreu, Daisy Maria Xavier, Abrham, Aklilu Roba, Abu-Raddad, Laith Jamal, Abu-Rmeileh, Niveen M E, Accrombessi, Manfred Mario Kokou, Acharya, Pawan, Adamu, Abdu A, Adebayo, Oladimeji M, Adedeji, Isaac Akinkunmi, Adekanmbi, Victor, Adetokunboh, Olatunji O, Adhena, Beyene Meressa, Adhikari, Tara Ballav, Adib, Mina G, Adou, Arsène Kouablan, Adsuar, Jose C, Afarideh, Mohsen, Afshin, Ashkan, Agarwal, Gina, Agesa, Kareha M, Aghayan, Sargis Aghasi, Agrawal, Sutapa, Ahmadi, Alireza, Ahmadi, Mehdi, Ahmed, Muktar Beshir, Ahmed, Sayem, Aichour, Amani Nidhal, Aichour, Ibtihel, Aichour, Miloud Taki Eddine, Akanda, Ali S, Akbari, Mohammad Esmaeil, Akibu, Mohammed, Akinyemi, Rufus Olusola, Akinyemiju, Tomi, Akseer, Nadia, Alahdab, Fares, Al-Aly, Ziyad, Alam, Khurshid, Alebel, Animut, Aleman, Alicia V, Alene, Kefyalew Addis, Al-Eyadhy, Ayman, Ali, Raghib, Alijanzadeh, Mehran, Alizadeh-Navaei, Reza, Aljunid, Syed Mohamed, Alkerwi, Ala'a, Alla, François, Allebeck, Peter, Almasi, Ali, Alonso, Jordi, Al-Raddadi, Rajaa M, Alsharif, Ubai, Altirkawi, Khalid, Alvis-Guzman, Nelson, Amare, Azmeraw T, Ammar, Walid, Anber, Nahla Hamed, Andrei, Catalina Liliana, Androudi, Sofia, Animut, Megbaru Debalkie, Ansari, Hossein, Ansha, Mustafa Geleto, Antonio, Carl Abelardo T, Appiah, Seth Christopher Yaw, Aremu, Olatunde, Areri, Habtamu Abera, Arian, Nicholas, Ärnlöv, Johan, Artaman, Al, Aryal, Krishna K, Asayesh, Hamid, Asfaw, Ephrem Tsegay, Asgedom, Solomon Weldegebreal, Assadi, Reza, Atey, Tesfay Mehari Mehari, Atique, Suleman, Atteraya, Madhu Sudhan, Ausloos, Marcel, Avokpaho, Euripide F G A, Awasthi, Ashish, Ayala Quintanilla, Beatriz Paulina, Ayele, Yohanes, Ayer, Rakesh, Ayuk, Tambe B, Azzopardi, Peter S, Babalola, Tesleem Kayode, Babazadeh, Arefeh, Badali, Hamid, Badawi, Alaa, Bali, Ayele Geleto, Banach, Maciej, Barker-Collo, Suzanne Lyn, Bärnighausen, Till Winfried, Barrero, Lope H, Basaleem, Huda, Bassat, Quique, Basu, Arindam, Baune, Bernhard T, Baynes, Habtamu Wondifraw, Beghi, Ettore, Behzadifar, Masoud, Behzadifar, Meysam, Bekele, Bayu Begashaw, Belachew, Abate Bekele, Belay, Aregawi Gebreyesus, Belay, Ezra, Belay, Saba Abraham, Belay, Yihalem Abebe, Bell, Michelle L, Bello, Aminu K, Bennett, Derrick A, Bensenor, Isabela M, Bergeron, Gilles, Berhane, Adugnaw, Berman, Adam E, Bernabe, Eduardo, Bernstein, Robert S, Bertolacci, Gregory J, Beuran, Mircea, Bhattarai, Suraj, Bhaumik, Soumyadeep, Bhutta, Zulfiqar A, Biadgo, Belete, Bijani, Ali, Bikbov, Boris, Bililign, Nigus, Bin Sayeed, Muhammad Shahdaat, Birlik, Sait Mentes, Birungi, Charles, Biswas, Tuhin, Bizuneh, Hailemichael, Bleyer, Archie, Basara, Berrak Bora, Bosetti, Cristina, Boufous, Soufiane, Brady, Oliver J, Bragazzi, Nicola Luigi, Brainin, Michael, Brazinova, Alexandra, Breitborde, Nicholas J K, Brenner, Hermann, Brewer, Jerry D, Briant, Paul Svitil, Britton, Gabrielle, Burstein, Roy, Busse, Reinhard, Butt, Zahid A, Cahuana-Hurtado, Lucero, Campos-Nonato, Ismael R, Campuzano Rincon, Julio Cesar, Cano, Jorge, Car, Mate, Cárdenas, Rosario, Carrero, Juan J, Carvalho, Félix, Castañeda-Orjuela, Carlos A, Castillo Rivas, Jacqueline, Castro, Franz, Catalá-López, Ferrán, Çavlin, Alanur, Cerin, Ester, Chalek, Julian, Chang, Hsing-Yi, Chang, Jung-Chen, Chattopadhyay, Aparajita, Chaturvedi, Pankaj, Chiang, Peggy Pei-Chia, Chin, Ken Lee, Chisumpa, Vesper Hichilombwe, Chitheer, Abdulaal, Choi, Jee-Young J, Chowdhury, Rajiv, Christopher, Devasahayam J, Cicuttini, Flavia M, Ciobanu, Liliana G, Cirillo, Massimo, Claro, Rafael M, Collado-Mateo, Daniel, Constantin, Maria-Magdalena, Conti, Sara, Cooper, Cyrus, Cooper, Leslie Trumbull, Cornaby, Leslie, Cortesi, Paolo Angelo, Cortinovis, Monica, Costa, Megan, Cromwell, Elizabeth, Crowe, Christopher Stephen, Cukelj, Petra, Cunningham, Matthew, Daba, Alemneh Kabeta, Dachew, Berihun Assefa, Dandona, Lalit, Dandona, Rakhi, Dargan, Paul I, Daryani, Ahmad, Das Gupta, Rajat, Das Neves, José, Dasa, Tamirat Tesfaye, Dash, Aditya Prasad, Weaver, Nicole Davis, Davitoiu, Dragos Virgil, Davletov, Kairat, De Leo, Diego, De Neve, Jan-Walter, Degefa, Meaza Girma, Degenhardt, Louisa, Degfie, Tizta Tilahun, Deiparine, Selina, Demoz, Gebre Teklemariam, Demtsu, Balem, Denova-Gutiérrez, Edgar, Deribe, Kebede, Dervenis, Nikolaos, Des Jarlais, Don C, Dessie, Getenet Ayalew, Dharmaratne, Samath D, Dhimal, Meghnath, Dicker, Daniel, Ding, Eric L, Dinsa, Girmaye Deye, Djalalinia, Shirin, Do, Huyen Phuc, Dokova, Klara, Doku, David Teye, Dolan, Kate A, Doyle, Kerrie E, Driscoll, Tim R, Dubey, Manisha, Dubljanin, Eleonora, Duken, Eyasu Ejeta, Duraes, Andre R, Ebrahimpour, Soheil, Edvardsson, David, El Bcheraoui, Charbel, El-Khatib, Ziad, Elyazar, Iqbal Rf, Enayati, Ahmadali, Endries, Aman Yesuf, Ermakov, Sergey Petrovich, Eshrati, Babak, Eskandarieh, Sharareh, Esmaeili, Reza, Esteghamati, Alireza, Esteghamati, Sadaf, Estep, Kara, Fakhim, Hamed, Farag, Tamer, Faramarzi, Mahbobeh, Fareed, Mohammad, Farinha, Carla Sofia E Sá, Faro, Andre, Farvid, Maryam S, Farzadfar, Farshad, Farzaei, Mohammad Hosein, Fay, Kairsten A, Fazeli, Mir Sohail, Feigin, Valery L, Feigl, Andrea B, Feizy, Fariba, Fenny, Ama P, Fentahun, Netsanet, Fereshtehnejad, Seyed-Mohammad, Fernandes, Eduarda, Feyissa, Garumma Tolu, Filip, Irina, Finegold, Samuel, Fischer, Florian, Flor, Luisa Sorio, Foigt, Nataliya A, Foreman, Kyle J, Fornari, Carla, Fürst, Thomas, Fukumoto, Takeshi, Fuller, John E, Fullman, Nancy, Gakidou, Emmanuela, Gallus, Silvano, Gamkrelidze, Amiran, Ganji, Morsaleh, Gankpe, Fortune Gbetoho, Garcia, Gregory M, Garcia-Gordillo, Miguel Á, Gebre, Abadi Kahsu, Gebre, Teshome, Gebregergs, Gebremedhin Berhe, Gebrehiwot, Tsegaye Tewelde, Gebremedhin, Amanuel Tesfay, Gelano, Tilayie Feto, Gelaw, Yalemzewod Assefa, Geleijnse, Johanna M, Genova-Maleras, Ricard, Gething, Peter, Gezae, Kebede Embaye, Ghadami, Mohammad Rasoul, Ghadimi, Reza, Ghadiri, Keyghobad, Ghasemi Falavarjani, Khalil, Ghasemi-Kasman, Maryam, Ghiasvand, Hesam, Ghimire, Mamata, Ghoshal, Aloke Gopal, Gill, Paramjit Singh, Gill, Tiffany K, Giussani, Giorgia, Gnedovskaya, Elena V, Goli, Srinivas, Gomez, Ricardo Santiago, Gómez-Dantés, Hector, Gona, Philimon N, Goodridge, Amador, Gopalani, Sameer Vali, Goulart, Alessandra C, Goulart, Bárbara Niegia Garcia, Grada, Ayman, Grosso, Giuseppe, Gugnani, Harish Chander C, Guo, Jingwen, Guo, Yuming, Gupta, Prakash C, Gupta, Rahul, Gupta, Rajeev, Gupta, Tanush, Haagsma, Juanita A, Hachinski, Vladimir, Hafezi-Nejad, Nima, Hagos, Tekleberhan B, Hailegiyorgis, Tewodros Tesfa, Hailu, 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Subjects
Adult, Male, Population Density, GBD, population, fertility, Adolescent, Medicine (all), Infant, Newborn, Infant, 11 Medical And Health Sciences, Middle Aged, Global Health, Global Burden of Disease, Young Adult, Child, Preschool, General & Internal Medicine, Female, Human medicine, Mortality, Child, Population Growth, Birth Rate, Aged, Human, Maternal Age
Abstract

Background Population estimates underpin demographic and epidemiological research and are used to track progress on numerous international indicators of health and development. To date, internationally available estimates of population and fertility, although useful, have not been produced with transparent and replicable methods and do not use standardised estimates of mortality. We present single-calendar year and single-year of age estimates of fertility and population by sex with standardised and replicable methods. Methods We estimated population in 195 locations by single year of age and single calendar year from 1950 to 2017 with standardised and replicable methods. We based the estimates on the demographic balancing equation, with inputs of fertility, mortality, population, and migration data. Fertility data came from 7817 location-years of vital registration data, 429 surveys reporting complete birth histories, and 977 surveys and censuses reporting summary birth histories. We estimated age-specific fertility rates (ASFRs; the annual number of livebirths to women of a specified age group per 1000 women in that age group) by use of spatiotemporal Gaussian process regression and used the ASFRs to estimate total fertility rates (TFRs; the average number of children a woman would bear if she survived through the end of the reproductive age span [age 10-54 years] and experienced at each age a particular set of ASFRs observed in the year of interest). Because of sparse data, fertility at ages 10-14 years and 50-54 years was estimated from data on fertility in women aged 15-19 years and 45-49 years, through use of linear regression. Age-specific mortality data came from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 estimates. Data on population came from 1257 censuses and 761 population registry location-years and were adjusted for underenumeration and age misreporting with standard demographic methods. Migration was estimated with the GBD Bayesian demographic balancing model, after incorporating information about refugee migration into the model prior. Final population estimates used the cohort-cotnponent method of population projection, with inputs of fertility, mortality, and migration data. Population uncertainty was estimated by use of out-of-sample predictive validity testing. With these data, we estimated the trends in population by age and sex and in fertility by age between 1950 and 2017 in 195 countries and territories. Findings From 1950 to 2017, TFRs decreased by 49.4% (95% uncertainty interval [UI] 46.4-52.0). The TFR decreased from 4.7 livebirths (4.5-4.9) to 2.4 livebirths (2.2-2.5), and the ASFR of mothers aged 10-19 years decreased from 37 livebirths (34-40) to 22 livebirths (19-24) per 1000 women. Despite reductions in the TFR, the global population has been increasing by an average of 83.8 million people per year since 1985. The global population increased by 197-2% (193.3-200.8) since 1950, from 2.6 billion (2.5-2.6) to 7.6 billion (7.4-7.9) people in 2017; much of this increase was in the proportion of the global population in south Asia and sub-Saharan Africa. The global annual rate of population growth increased between 1950 and 1964, when it peaked at 2.0%; this rate then remained nearly constant until 1970 and then decreased to 1.1% in 2017. Population growth rates in the southeast Asia, east Asia, and Oceania GBD super-region decreased from 2.5% in 1963 to O7% in 2017, whereas in sub-Saharan Africa, population growth rates were almost at the highest reported levels ever in 2017, when they were at 2.7%. The global average age increased from 26.6 years in 1950 to 32.1 years in 2017, and the proportion of the population that is of working age (age 15-64 years) increased from 59.9% to 65.3%. At the national level, the TFR decreased in all countries and territories between 1950 and 2017; in 2017, TFRs ranged from a low of 1.0 livebirths (95% UI 0. 9-1.2) in Cyprus to a high of 7.1 livebirths (6.8-7.4) in Niger. The TFR under age 25 years (TFU25; number of livebirths expected by age 25 years for a hypothetical woman who survived the age group and was exposed to current ASFRs) in 2017 ranged from 0.08 livebirths (0.07-0.09) in South Korea to 2.4 livebirths (2.2-2.6) in Niger, and the TFR over age 30 years (I F030; number of livebirths expected for a hypothetical woman ageing from 30 to 54 years who survived the age group and was exposed to current ASFRs) ranged from a low of 0.3 livebirths (0.3-0-4) in Puerto Rico to a high of 3.1 livebirths (3.0-3.2) in Niger. TF030 was higher than TFU25 in 145 countries and territories in 2017.33 countries had a negative population growth rate from 2010 to 2017, most of which were located in central, eastern, and western Europe, whereas population growth rates of more than 2.0% were seen in 33 of 46 countries in sub-Saharan Africa. In 2017, less than 65% of the national population was of working age in 12 of 34 high-income countries, and less than 50% of the national population was of working age in Mali, Chad, and Niger. Interpretation Population trends create demographic dividends and headwinds (ie, economic benefits and detriments) that affect national economies and determine national planning needs. Although TFRs are decreasing, the global population continues to grow as mortality declines, with diverse patterns at the national level and across age groups. To our knowledge, this is the first study to provide transparent and replicable estimates of population and fertility, which can be used to inform decision making and to monitor progress. Copyright (C) 2018 The Author(s). Published by Elsevier Ltd.

Published
2018

37. The effect of self-compassion training on craving and self-efficacy in female patients with methamphetamine dependence: a one-year follow-up.

Author

Abdoli, Nasrin, Farnia, Vahid, Radmehr, Farnaz, Alikhani, Mostafa, Moradinazar, Mehdi, Khodamoradi, Mehdi, Salemi, Safora, Rezaei, Mansour, and Davarinejad, Omran

Subjects
PREVENTION of drug addiction, CONFIDENCE intervals, MULTIPLE regression analysis, DESIRE, METHAMPHETAMINE, SELF-efficacy, RANDOMIZED controlled trials, PRE-tests & post-tests, RESEARCH funding, QUESTIONNAIRES, DESCRIPTIVE statistics, STATISTICAL sampling, DATA analysis software, ODDS ratio, DRUG abusers, WOMEN'S health
Abstract

Background: The use of methamphetamine has had arising trend among Iranian women in recent years. The present study was conducted to assess the effect of self-compassion training on craving and self-efficacy in female patients with methamphetamine users and the rate of relapse over a one-year follow-up in 2017–2018 in the city of Kermanshah, Iran. Methods: This study was a randomized, controlled, clinical trial. A total of 40 female patients diagnosed with methamphetamine dependency were randomly assigned either to self-compassion training and control groups. Both groups were assessed before and one month after the educational intervention using craving and self-efficacy questionnaires. The groups were followed-up in terms of methamphetamine use relapse one year after the intervention. Results: After the intervention, the mean craving score decreased (51.82) while the mean self-efficacy score increased (28.82) in the trial group. In the one-year follow-up, ten participants (27.7%) from the trial group and 28 (75.7%) from the control group relapsed into methamphetamine users. Conclusions: Given the present findings regarding the effect of self-compassion training on reduced craving and increased self-efficacy in female patients with methamphetamine dependence, the researchers recommend that self-compassion skills training be applied in drug rehabilitation centers and hospital wards to complement conventional therapies. [ABSTRACT FROM AUTHOR]

Published
2021
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38. Validation of the Persian version of the AWAER questionnaire (preventive warning of relapse – revised form) in methamphetamine users.

Author

Farnia, Vahid, Abdoli, Nasrin, Radmehr, Farnaz, Alikhani, Mostafa, Moradinazar, Mehdi, Rezaei, Mansour, Behrooz, Behrooz, and Davarinejad, Omron

Subjects
CLUSTER sampling, RESEARCH, REHABILITATION centers, STATISTICAL reliability, RESEARCH evaluation, RESEARCH methodology evaluation, RESEARCH methodology, CROSS-sectional method, METHAMPHETAMINE, QUESTIONNAIRES, FACTOR analysis, MENTAL depression, DESCRIPTIVE statistics, SCALE analysis (Psychology), CHI-squared test, RESEARCH funding, STATISTICAL sampling, STATISTICAL correlation, ANXIETY, DATA analysis software, EVALUATION
Abstract

Background: The present study was conducted to validate the Persian version of the AWAER questionnaire (Preventive Warning of Relapse – Revised Form) in methamphetamine users. Method: The statistical population consisted of all the methamphetamine users referring to the addiction treatment centers of Kermanshah, Iran, and 388 people were selected by two-step random cluster sampling. Result: The Cronbach's alpha coefficient for the AWARE questionnaire was 0.95, indicating the desired validity of the Persian form. Exploratory factor analysis with varimax rotation indicated three factors, namely depression, anxiety, and relapse, with a total variance explanation of 63.3%. Also, the correlation coefficient obtained between the extracted factors with each other and the entire items indicated a desirable structural validity. Given the elimination of three items, the Persian version of the questionnaire was finally obtained with 25 items and three factors. Test–retest reliability was 0.79 with a one-week interval. The correlation coefficient between the AWARE questionnaire and AWQ-V2 scores was obtained as 0.526 for determining the convergent validity (P <.01). Conclusion: The Persian version of the AWARE questionnaire in methamphetamine users' community has a favorable validity and reliability and can therefore be used to assess the symptoms of advance warning of relapse in Persian-speaking methamphetamine users, such as those in Iran. [ABSTRACT FROM AUTHOR]

Published
2021
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39. Among substance-abusing traffic offenders, poor sleep and poor general health predict lower driving skills but not slower reaction times

Author

Abdoli, Nasrin, Sadeghi Bahmani, Dena, Farnia, Vahid, Alikhani, Mostafa, Golshani, Sanobar, Holsboer-Trachsler, Edith, and Brand, Serge

Subjects
general health, reaction time, Psychology Research and Behavior Management, aggression, driving behavior, sleep, Original Research
Abstract

Nasrin Abdoli,1 Dena Sadeghi Bahmani,1–4 Vahid Farnia,1 Mostafa Alikhani,1 Sanobar Golshani,1 Edith Holsboer-Trachsler,2 Serge Brand1–5 1Substance Abuse Prevention Research Center, Kermanshah University of Medical Sciences, Kermanshah, Iran; 2Center for Affective, Stress and Sleep Disorders, Psychiatric Clinics, University of Basel, Basel, Switzerland; 3Kermanshah University of Medical Sciences, Sleep Disorders Research Center, Kermanshah, Iran; 4Isfahan Neurosciences Research Center, Alzahra Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran; 5Division of Sport Science, and Psychosocial Health, Department of Sport, Exercise and Health, University of Basel, Basel, Switzerland Background: Traffic accidents are a significant health issue in Iran. Explanations for such accidents have included single consideration of the role of poor sleep and negative psychological trait and state variables. In this study, we examined whether and to what extent sleep, general health, and aggression can concomitantly predict driving behavior. Methods: A total of 360 male traffic offenders (driving under substance use; mean age: 31years) participated in this study. They completed the questionnaires covering sociodemographic, sleep-related, and behavior-related variables. In addition, their visual and acoustic reaction times were objectively tested. Results: Poor sleep, poor general health, and higher aggression scores were associated with self-rated poor driving behavior. Poor sleep was directly associated with poor driving behavior and indirectly via poor general health and aggression. In contrast, visual and acoustic reaction times were unrelated to sleep, general health, aggression, or self-rated driving behavior. Conclusion: To our knowledge, this is the first study in Iran to assess concomitantly poor sleep, poor general health, and higher aggression scores as independent predictors of poor driving behavior among a larger sample of substance-abusing traffic offenders. Furthermore, visual and acoustic reaction times were unrelated to sleep, general health, aggression, and driving behavior. Finally, importantly, poor sleep predicted both directly and indirectly poor driving behavior. Keywords: driving behavior, sleep, aggression, general health, reaction time

Published
2018

40. TFRC and ACTB as the best reference genes to quantify Urokinase Plasminogen Activator in breast cancer

Author

Abdoli Nasrin, Esmaeili Rezvan, and Majidzadeh-A Keivan

Subjects
Medicine, Biology (General), QH301-705.5, Science (General), Q1-390
Abstract

Abstract Background Biomedical researchers have long looked for ways to diagnose and treat cancer patients at the early stages through biomarkers. Although conventional techniques are routinely applied in the detection of biomarkers, attitudes towards using Real-Time PCR techniques in detection of many biomarkers are increasing. Normalization of quantitative Real-Time PCR is necessary to validate non-biological alteration occurring during the steps of RNA quantification. Selection of variably expressed housekeeping genes (HKs) will affect the validity of the data. The aim of the present study was to identify uniformly expressed housekeeping genes in order to use in the breast cancer gene expression studies. Urokinase Plasminogen Activator was used as a gene of interest. Findings The expression of six HKs (TFRC, GUSB, GAPDH, ACTB, HPRT1 and RPLP0) was investigated using geNorm and NormFinder softwares in forty breast tumor, four normal and eight adjacent tissues. RPLP0 and GAPDH revealed maximum M value, while TFRC demonstrated lowest M value. Conclusions In the present study the most and the least stable genes were TFRC and RPLP0 respectively. TFRC and ACTB were verified as the best combination of two genes for breast cancer quantification. The result of this study shows that in each gene expression analysis HKs selection should be done based on experiment conditions.

Published
2011
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42. Population and fertility by age and sex for 195 countries and territories, 1950–2017: a systematic analysis for the Global Burden of Disease Study 2017

Author

Murray, C, Callender, C, Kulikoff, X, Srinivasan, V, Abate, D, Abate, K, Abay, S, Abbasi, N, Abbastabar, H, Abdela, J, Abdelalim, A, Abdel-Rahman, O, Abdi, A, Abdoli, N, Abdollahpour, I, Abdulkader, R, Abebe, H, Abebe, M, Abebe, Z, Abebo, T, Abejie, A, Aboyans, V, Abraha, H, Abreu, D, Abrham, A, Abu-Raddad, L, Abu-Rmeileh, N, Accrombessi, M, Acharya, P, Adamu, A, Adebayo, O, Adedeji, I, Adekanmbi, V, Adetokunboh, O, Adhena, B, Adhikari, T, Adib, M, Adou, A, Adsuar, J, Afarideh, M, Afshin, A, Agarwal, G, Agesa, K, Aghayan, S, Agrawal, S, Ahmadi, A, Ahmadi, M, Ahmed, M, Ahmed, S, Aichour, A, Aichour, I, Aichour, M, Akanda, A, Akbari, M, Akibu, M, Akinyemi, R, Akinyemiju, T, Akseer, N, Alahdab, F, Al-Aly, Z, Alam, K, Alebel, A, Aleman, A, Alene, K, Al-Eyadhy, A, Ali, R, Alijanzadeh, M, Alizadeh-Navaei, R, Aljunid, S, Alkerwi, A, Alla, F, Allebeck, P, Almasi, A, Alonso, J, Al-Raddadi, R, Alsharif, U, Altirkawi, K, Alvis-Guzman, N, Amare, A, Ammar, W, Anber, N, Andrei, C, Androudi, S, Animut, M, Ansari, H, Ansha, M, Antonio, C, Appiah, S, Aremu, O, Areri, H, Arian, N, Ärnlöv, J, Artaman, A, Aryal, K, Asayesh, H, Asfaw, E, Asgedom, S, Assadi, R, Atey, T, Atique, S, Atteraya, M, Ausloos, M, Avokpaho, E, Awasthi, A, Ayala Quintanilla, B, Ayele, Y, Ayer, R, Ayuk, T, Azzopardi, P, Babalola, T, Babazadeh, A, Badali, H, Badawi, A, Bali, A, Banach, M, Barker-Collo, S, Bärnighausen, T, Barrero, L, Basaleem, H, Bassat, Q, Basu, A, Baune, B, Baynes, H, Beghi, E, Behzadifar, M, Bekele, B, Belachew, A, Belay, A, Belay, E, Belay, S, Belay, Y, Bell, M, Bello, A, Bennett, D, Bensenor, I, Bergeron, G, Berhane, A, Berman, A, Bernabe, E, Bernstein, R, Bertolacci, G, Beuran, M, Bhattarai, S, Bhaumik, S, Bhutta, Z, Biadgo, B, Bijani, A, Bikbov, B, Bililign, N, Bin Sayeed, M, Birlik, S, Birungi, C, Biswas, T, Bizuneh, H, Bleyer, A, Basara, B, Bosetti, C, Boufous, S, Brady, O, Bragazzi, N, Brainin, M, Brazinova, A, Breitborde, N, Brenner, H, Brewer, J, Briant, P, Britton, G, Burstein, R, Busse, R, Butt, Z, Cahuana-Hurtado, L, Campos-Nonato, I, Campuzano Rincon, J, Cano, J, Car, M, Cárdenas, R, Carrero, J, Carvalho, F, Castañeda-Orjuela, C, Castillo Rivas, J, Castro, F, Catalá-López, F, Çavlin, A, Cerin, E, Chalek, J, Chang, H, Chang, J, Chattopadhyay, A, Chaturvedi, P, Chiang, P, Chin, K, Chisumpa, V, Chitheer, A, Choi, J, Chowdhury, R, Christopher, D, Cicuttini, F, Ciobanu, L, Cirillo, M, Claro, R, Collado-Mateo, D, Constantin, M, Conti, S, Cooper, C, Cooper, L, Cornaby, L, Cortesi, P, Cortinovis, M, Costa, M, Cromwell, E, Crowe, C, Cukelj, P, Cunningham, M, Daba, A, Dachew, B, Dandona, L, Dandona, R, Dargan, P, Daryani, A, Das Gupta, R, Das Neves, J, Dasa, T, Dash, A, Weaver, N, Davitoiu, D, Davletov, K, De Leo, D, De Neve, J, Degefa, M, Degenhardt, L, Degfie, T, Deiparine, S, Demoz, G, Demtsu, B, Denova-Gutiérrez, E, Deribe, K, Dervenis, N, Des Jarlais, D, Dessie, G, Dharmaratne, S, Dhimal, M, Dicker, D, Ding, E, Dinsa, G, Djalalinia, S, Do, H, Dokova, K, Doku, D, Dolan, K, Doyle, K, Driscoll, T, Dubey, M, Dubljanin, E, Duken, E, Duraes, A, Ebrahimpour, S, Edvardsson, D, El Bcheraoui, C, El-Khatib, Z, Elyazar, I, Enayati, A, Endries, A, Ermakov, S, Eshrati, B, Eskandarieh, S, Esmaeili, R, Esteghamati, A, Esteghamati, S, Estep, K, Fakhim, H, Farag, T, Faramarzi, M, Fareed, M, Farinha, C, Faro, A, Farvid, M, Farzadfar, F, Farzaei, M, Fay, K, Fazeli, M, Feigin, V, Feigl, A, Feizy, F, Fenny, A, Fentahun, N, Fereshtehnejad, S, Fernandes, E, Feyissa, G, Filip, I, Finegold, S, Fischer, F, Flor, L, Foigt, N, Foreman, K, Fornari, C, Fürst, T, Fukumoto, T, Fuller, J, Fullman, N, Gakidou, E, Gallus, S, Gamkrelidze, A, Ganji, M, Gankpe, F, Garcia, G, Garcia-Gordillo, M, Gebre, A, Gebre, T, Gebregergs, G, Gebrehiwot, T, Gebremedhin, A, Gelano, T, Gelaw, Y, Geleijnse, J, Genova-Maleras, R, Gething, P, Gezae, K, Ghadami, M, Ghadimi, R, Ghadiri, K, Ghasemi Falavarjani, K, Ghasemi-Kasman, M, Ghiasvand, H, Ghimire, M, Ghoshal, A, Gill, P, Gill, T, Giussani, G, Gnedovskaya, E, Goli, S, Gomez, R, Gómez-Dantés, H, Gona, P, Goodridge, A, Gopalani, S, Goulart, A, Goulart, B, Grada, A, Grosso, G, Gugnani, H, Guo, J, Guo, Y, Gupta, P, Gupta, R, Gupta, T, Haagsma, J, Hachinski, V, Hafezi-Nejad, N, Hagos, T, Hailegiyorgis, T, Hailu, G, Haj-Mirzaian, A, Hamadeh, R, Hamidi, S, Handal, A, Hankey, G, Hao, Y, Harb, H, Haririan, H, Haro, J, Hasan, M, Hassankhani, H, Hassen, H, Havmoeller, R, Hay, S, He, Y, Hedayatizadeh-Omran, A, Hegazy, M, Heibati, B, Heidari, B, Hendrie, D, Henok, A, Henry, N, Herteliu, C, Heydarpour, F, Hibstu, D, Hole, M, Homaie Rad, E, Hoogar, P, Hosgood, H, Hosseini, S, Hosseini Chavoshi, M, Hosseinzadeh, M, Hostiuc, M, Hostiuc, S, Hsairi, M, Hsiao, T, Hu, G, Huang, J, Iburg, K, Igumbor, E, Ikeda, C, Ilesanmi, O, Iqbal, U, Irenso, A, Irvani, S, Isehunwa, O, Islam, S, Jahangiry, L, Jahanmehr, N, Jain, S, Jakovljevic, M, Jalu, M, James, S, Jassal, S, Javanbakht, M, Jayatilleke, A, Jeemon, P, Jha, R, Jha, V, Ji, J, Jonas, J, Jozwiak, J, Jungari, S, Jürisson, M, Kabir, Z, Kadel, R, Kahsay, A, Kalani, R, Kapil, U, Karami, M, Matin, B, Karch, A, Karema, C, Karimi, S, Kasaeian, A, Kassa, D, Kassa, G, Kassa, T, Kassa, Z, Kassebaum, N, Kastor, A, Katikireddi, S, Kaul, A, Kawakami, N, Karyani, A, Kebede, S, Keiyoro, P, Kemp, G, Kengne, A, Keren, A, Kereselidze, M, Khader, Y, Khafaie, M, Khajavi, A, Khalid, N, Khalil, I, Khan, E, Khan, M, Khang, Y, Khanna, T, Khater, M, Khatony, A, Khazaeipour, Z, Khazaie, H, Khoja, A, Khosravi, A, Khosravi, M, Kibret, G, Kidanemariam, Z, Kiirithio, D, Kilgore, P, Kim, D, Kim, J, Kim, Y, Kimokoti, R, Kinfu, Y, Kinra, S, Kisa, A, Kivimäki, M, Kochhar, S, Kokubo, Y, Kolola, T, Kopec, J, Kosek, M, Kosen, S, Koul, P, Koyanagi, A, Krishan, K, Krishnaswami, S, Krohn, K, Defo, B, Bicer, B, Kumar, G, Kumar, M, Kumar, P, Kumsa, F, Kutz, M, Lad, S, Lafranconi, A, Lal, D, Lalloo, R, Lam, H, Lami, F, Lang, J, Lanksy, S, Lansingh, V, Laryea, D, Lassi, Z, Latifi, A, Laxmaiah, A, Lazarus, J, Lee, J, Lee, P, Leigh, J, Leshargie, C, Leta, S, Levi, M, Li, S, Li, X, Li, Y, Liang, J, Liang, X, Liben, M, Lim, L, Limenih, M, Linn, S, Liu, S, Lorkowski, S, Lotufo, P, Lozano, R, Lunevicius, R, Mabika, C, Macarayan, E, Mackay, M, Madotto, F, Mahmood, T, Mahotra, N, Majdan, M, Majdzadeh, R, Majeed, A, Malekzadeh, R, Malik, M, Mamun, A, Manamo, W, Manda, A, Mangalam, S, Mansournia, M, Mantovani, L, Mapoma, C, Marami, D, Maravilla, J, Marcenes, W, Marina, S, Martins-Melo, F, März, W, Marzan, M, Mashamba-Thompson, T, Masiye, F, Mason-Jones, A, Massenburg, B, Mathur, M, Maulik, P, Mazidi, M, Mcgrath, J, Mehata, S, Mehendale, S, Mehndiratta, M, Mehrotra, R, Mehrzadi, S, Mehta, K, Mehta, V, Mekonnen, T, Meles, H, Meles, K, Melese, A, Melku, M, Memiah, P, Memish, Z, Mendoza, W, Mengesha, M, Mengistu, D, Mengistu, G, Mensah, G, Mereta, S, Meretoja, A, Meretoja, T, Mestrovic, T, Mezgebe, H, Miangotar, Y, Miazgowski, B, Miazgowski, T, Miller, T, Miller-Petrie, M, Mini, G, Mirabi, P, Mirica, A, Mirrakhimov, E, Misganaw, A, Moazen, B, Mohammad, K, Mohammadi, M, Mohammadifard, N, Mohammadi-Khanaposhtani, M, Mohammed, M, Mohammed, S, Mokdad, A, Mola, G, Molokhia, M, Monasta, L, Montañez, J, Moradi, G, Moradi, M, Moradi-Lakeh, M, Moradinazar, M, Moraga, P, Morgado-Da-Costa, J, Mori, R, Morrison, S, Mosapour, A, Moschos, M, Mousavi, S, Muche, A, Muchie, K, Mueller, U, Mukhopadhyay, S, Muller, K, Murphy, T, Murthy, G, Musa, J, Musa, K, Mustafa, G, Muthupandian, S, Nachega, J, Nagel, G, Naghavi, M, Naheed, A, Nahvijou, A, Naik, G, Naik, P, Najafi, F, Naldi, L, Nangia, V, Nansseu, J, Nascimento, B, Nawaz, H, Ncama, B, Neamati, N, Negoi, I, Negoi, R, Neupane, S, Newton, C, Ngalesoni, F, Ngunjiri, J, Nguyen, G, Nguyen, L, Nguyen, T, Ningrum, D, Nirayo, Y, Nisar, M, Nixon, M, Nomura, S, Noroozi, M, Noubiap, J, Nouri, H, Shiadeh, M, Nowroozi, M, Nyandwi, A, Nyasulu, P, Odell, C, Ofori-Asenso, R, Ogah, O, Ogbo, F, Oh, I, Okoro, A, Oladimeji, O, Olagunju, A, Olagunju, T, Olivares, P, Olusanya, B, 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S., Musa, Jonah, Musa, Kamarul Imran, Mustafa, Ghulam, Muthupandian, Saravanan, Nachega, Jean B, Nagel, Gabriele, Naghavi, Mohsen, Naheed, Aliya, Nahvijou, Azin, Naik, Gurudatta, Naik, Paulami, Najafi, Farid, Naldi, Luigi, Nangia, Vinay, Nansseu, Jobert Richie, Nascimento, Bruno Ramos, Nawaz, Haseeb, Ncama, Busisiwe P, Neamati, Nahid, Negoi, Ionut, Negoi, Ruxandra Irina, Neupane, Subas, Newton, Charles Richard James, Ngalesoni, Frida N, Ngunjiri, Josephine W, Nguyen, Grant, Nguyen, Long Hoang, Nguyen, Trang Huyen, Ningrum, Dina Nur Anggraini, Nirayo, Yirga Legesse, Nisar, Muhammad Imran, Nixon, Molly R, Nomura, Shuhei, Noroozi, Mehdi, Noubiap, Jean Jacques, Nouri, Hamid Reza, Shiadeh, Malihe Nourollahpour, Nowroozi, Mohammad Reza, Nyandwi, Alypio, Nyasulu, Peter S, Odell, Christopher M, Ofori-Asenso, Richard, Ogah, Okechukwu Samuel, Ogbo, Felix Akpojene, Oh, In-Hwan, Okoro, Anselm, Oladimeji, Olanrewaju, Olagunju, Andrew T, Olagunju, Tinuke O, Olivares, Pedro R, Olusanya, Bolajoko Olubukunola, Olusanya, Jacob Olusegun, Ong, Sok King, Ortiz, Alberto, Osgood-Zimmerman, Aaron, Ota, Erika, Otieno, Brenda Achieng, Otstavnov, Stanislav S, Owolabi, Mayowa Ojo, Oyekale, Abayomi Samuel, P A, Mahesh, Pakhale, Smita, Pakhare, Abhijit P, Pana, Adrian, Panda, Basant Kumar, Panda-Jonas, Songhomitra, Pandey, Achyut Raj, Park, Eun-Kee, Parsian, Hadi, Patel, Shanti, Patil, Snehal T, Patle, Ajay, Patton, George C, Paturi, Vishnupriya Rao, Paudel, Deepak, Pedroso, Marcel Moraes, Peprah, Emmanuel K, Pereira, David M, Perico, Norberto, Pesudovs, Konrad, Petri, William A, Petzold, Max, Pierce, Maxwell, Pigott, David M, Pillay, Julian David, Pirsaheb, Meghdad, Polanczyk, Guilherme V, Postma, Maarten J, Pourmalek, Farshad, Pourshams, Akram, Poustchi, Hossein, Prakash, Swayam, Prasad, Narayan, Purcell, Caroline A, Purwar, Manorama B, Qorbani, Mostafa, Quansah, Reginald, Radfar, Amir, Rafay, Anwar, Rafiei, Alireza, Rahim, Fakher, Rahimi-Movaghar, Afarin, Rahimi-Movaghar, Vafa, Rahman, Mahfuzar, Rahman, Md Shafiur, Rahman, Mohammad Hifz Ur, Rahman, Muhammad Aziz, Rahman, Sajjad Ur, Rai, Rajesh Kumar, Rajati, Fatemeh, Rajsic, Sasa, Ram, Usha, Ranabhat, Chhabi Lal, Ranjan, Prabhat, Rawaf, David Laith, Rawaf, Salman, Ray, Sarah E, Razo-García, Christian, Reiner, Robert C, Reis, Cesar, Remuzzi, Giuseppe, Renzaho, Andre M N, Resnikoff, Serge, Rezaei, Satar, Rezaeian, Shahab, Rezai, Mohammad Sadegh, Riahi, Seyed Mohammad, Rios-Blancas, Maria Jesus, Roba, Kedir Teji, Roberts, Nicholas L S, Roever, Leonardo, Ronfani, Luca, Roshandel, Gholamreza, Rostami, Ali, Rubagotti, Enrico, Ruhago, George Mugambage, Sabde, Yogesh Damodar, Sachdev, Perminder S, Saddik, Basema, Saeedi Moghaddam, Sahar, Safari, Hosein, Safari, Yahya, Safari-Faramani, Roya, Safdarian, Mahdi, Safi, Sare, Safiri, Saeid, Sagar, Rajesh, Sahebkar, Amirhossein, Sahraian, Mohammad Ali, Sajadi, Haniye Sadat, Salahshoor, Mohamadreza, Salam, Nasir, Salama, Joseph S, Salamati, Payman, Saldanha, Raphael De Freitas, Saleem, Zikria, Salimi, Yahya, Salimzadeh, Hamideh, Salomon, Joshua A, Salvi, Sundeep Santosh, Salz, Inbal, Sambala, Evanson Zondani, Samy, Abdallah M, Sanabria, Juan, Sanchez-Niño, Maria Dolores, Santos, Itamar S, Santric Milicevic, Milena M, Sao Jose, Bruno Piassi, Sardana, Mayank, Sarker, Abdur Razzaque, Sarmiento-Suárez, Rodrigo, Saroshe, Satish, Sarrafzadegan, Nizal, Sartorius, Benn, Sarvi, Shahabeddin, Sathian, Brijesh, Satpathy, Maheswar, Sawant, Arundhati R, Sawhney, Monika, Saxena, Sonia, Schaeffner, Elke, Schelonka, Kathryn, Schneider, Ione J C, Schwebel, David C, Schwendicke, Falk, Seedat, Soraya, Sekerija, Mario, Sepanlou, Sadaf G, Serván-Mori, Edson, Shabaninejad, Hosein, Shackelford, Katya Anne, Shafieesabet, Azadeh, Shaheen, Amira A, Shaikh, Masood Ali, Shakir, Raad A, Shams-Beyranvand, Mehran, Shamsi, Mohammadbagher, Shamsizadeh, Morteza, Sharafi, Heidar, Sharafi, Kiomars, Sharif, Mehdi, Sharif-Alhoseini, Mahdi, Sharma, Jayendra, Sharma, Rajesh, She, Jun, Sheikh, Aziz, Shi, Peilin, Shibuya, Kenji, Shigematsu, Mika, Shiri, Rahman, Shirkoohi, Reza, Shiue, Ivy, Shokraneh, Farhad, Shukla, Sharvari Rahul, Si, Si, Siabani, Soraya, Sibai, Abla Mehio, Siddiqi, Tariq J, Sigfusdottir, Inga Dora, Sigurvinsdottir, Rannveig, Silpakit, Naris, Silva, Diego Augusto Santos, Silva, João Pedro, Silveira, Dayane Gabriele Alves, Singam, Narayana Sarma Venkata, Singh, Jasvinder A, Singh, Narinder Pal, Singh, Virendra, Sinha, Dhirendra Narain, Sliwa, Karen, Soares Filho, Adauto Martins, Sobaih, Badr Hasan, Sobhani, Soheila, Soofi, Moslem, Soriano, Joan B, Soyiri, Ireneous N, Sreeramareddy, Chandrashekhar T, Starodubov, Vladimir I, Steiner, Caitlyn, Stewart, Leo G, Stokes, Mark A, Strong, Mark, Subart, Michelle L, Sufiyan, Mu'awiyyah Babale, Sulo, Gerhard, Sunguya, Bruno F, Sur, Patrick John, Sutradhar, Ipsita, Sykes, Bryan L, Sylaja, P. N., Sylte, Dillon O, Szoeke, Cassandra E I, Tabarés-Seisdedos, Rafael, Tabb, Karen M, Tadakamadla, Santosh Kumar, Tandon, Nikhil, Tassew, Aberash Abay, Tassew, Segen Gebremeskel, Taveira, Nuno, Tawye, Nega Yimer, Tehrani-Banihashemi, Arash, Tekalign, Tigist Gashaw, Tekle, Merhawi Gebremedhin, Temsah, Mohamad-Hani, Terkawi, Abdullah Sulieman, Teshale, Manaye Yihune, Tessema, Belay, Teweldemedhin, Mebrahtu, Thakur, Jarnail Singh, Thankappan, Kavumpurathu Raman, Thirunavukkarasu, Sathish, Thomas, Nihal, Thomson, Alan J, Tilahun, Binyam, To, Quyen G, Tonelli, Marcello, Topor-Madry, Roman, Torre, Anna E, Tortajada-Girbés, Miguel, Tovani-Palone, Marcos Roberto, Toyoshima, Hideaki, Tran, Bach Xuan, Tran, Khanh Bao, Tripathy, Srikanth Prasad, Truelsen, Thomas Clement, Truong, Nu Thi, Tsadik, Afewerki Gebremeskel, Tsegay, Amanuel, Tsilimparis, Nikolaos, Tudor Car, Lorainne, Ukwaja, Kingsley N, Ullah, Irfan, Usman, Muhammad Shariq, Uthman, Olalekan A, Uzun, Selen Begüm, Vaduganathan, Muthiah, Vaezi, Afsane, Vaidya, Gaurang, Valdez, Pascual R, Varavikova, Elena, Varughese, Santosh, Vasankari, Tommi Juhani, Vasconcelos, Ana Maria Nogales, Venketasubramanian, Narayanaswamy, Villafaina, Santos, Violante, Francesco S, Vladimirov, Sergey Konstantinovitch, Vlassov, Vasily, Vollset, Stein Emil, Vos, Theo, Vosoughi, Kia, Vujcic, Isidora S, Wagnew, Fasil Shiferaw, Waheed, Yasir, Walson, Judd L, Wang, Yanping, Wang, Yuan-Pang, Weiderpass, Elisabete, Weintraub, Robert G, Weldegwergs, Kidu Gidey, Werdecker, Andrea, Westerman, Ronny, Whiteford, Harvey, Widecka, Justyna, Widecka, Katarzyna, Wijeratne, Tissa, Winkler, Andrea Sylvia, Wiysonge, Charles Shey, Wolfe, Charles D A, Wu, Shouling, Wyper, Grant M A, Xu, Gelin, Yamada, Tomohide, Yano, Yuichiro, Yaseri, Mehdi, Yasin, Yasin Jemal, Ye, Pengpeng, Yentür, Gökalp Kadri, Yeshaneh, Alex, Yimer, Ebrahim M, Yip, Paul, Yisma, Engida, Yonemoto, Naohiro, Yoon, Seok-Jun, Yotebieng, Marcel, Younis, Mustafa Z, Yousefifard, Mahmoud, Yu, Chuanhua, Zadnik, Vesna, Zaidi, Zoubida, Zaman, Sojib Bin, Zamani, Mohammad, Zare, Zohreh, Zeleke, Mulugeta Molla, Zenebe, Zerihun Menlkalew, Zerfu, Taddese Alemu, Zhang, Xueying, Zhao, Xiu-Ju, Zhou, Maigeng, Zhu, Jun, Zimsen, Stephanie R M, Zodpey, Sanjay, Zoeckler, Leo, Lopez, Alan D, and Lim, Stephen S

Abstract

Background: Population estimates underpin demographic and epidemiological research and are used to track progress on numerous international indicators of health and development. To date, internationally available estimates of population and fertility, although useful, have not been produced with transparent and replicable methods and do not use standardised estimates of mortality. We present single-calendar year and single-year of age estimates of fertility and population by sex with standardised and replicable methods. Methods: We estimated population in 195 locations by single year of age and single calendar year from 1950 to 2017 with standardised and replicable methods. We based the estimates on the demographic balancing equation, with inputs of fertility, mortality, population, and migration data. Fertility data came from 7817 location-years of vital registration data, 429 surveys reporting complete birth histories, and 977 surveys and censuses reporting summary birth histories. We estimated age-specific fertility rates (ASFRs; the annual number of livebirths to women of a specified age group per 1000 women in that age group) by use of spatiotemporal Gaussian process regression and used the ASFRs to estimate total fertility rates (TFRs; the average number of children a woman would bear if she survived through the end of the reproductive age span [age 10–54 years] and experienced at each age a particular set of ASFRs observed in the year of interest). Because of sparse data, fertility at ages 10–14 years and 50–54 years was estimated from data on fertility in women aged 15–19 years and 45–49 years, through use of linear regression. Age-specific mortality data came from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 estimates. Data on population came from 1257 censuses and 761 population registry location-years and were adjusted for underenumeration and age misreporting with standard demographic methods. Migration was estimated with th

Published
2018

43. Among substance-abusing traffic offenders, poor sleep and poor general health predict lower driving skills but not slower reaction times

Author

Abdoli,Nasrin, Sadeghi Bahmani,Dena, Farnia,Vahid, Alikhani,Mostafa, Golshani,Sanobar, Holsboer-Trachsler,Edith, Brand,Serge, Abdoli,Nasrin, Sadeghi Bahmani,Dena, Farnia,Vahid, Alikhani,Mostafa, Golshani,Sanobar, Holsboer-Trachsler,Edith, and Brand,Serge

Abstract

Nasrin Abdoli,1 Dena Sadeghi Bahmani,1–4 Vahid Farnia,1 Mostafa Alikhani,1 Sanobar Golshani,1 Edith Holsboer-Trachsler,2 Serge Brand1–5 1Substance Abuse Prevention Research Center, Kermanshah University of Medical Sciences, Kermanshah, Iran; 2Center for Affective, Stress and Sleep Disorders, Psychiatric Clinics, University of Basel, Basel, Switzerland; 3Kermanshah University of Medical Sciences, Sleep Disorders Research Center, Kermanshah, Iran; 4Isfahan Neurosciences Research Center, Alzahra Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran; 5Division of Sport Science, and Psychosocial Health, Department of Sport, Exercise and Health, University of Basel, Basel, Switzerland Background: Traffic accidents are a significant health issue in Iran. Explanations for such accidents have included single consideration of the role of poor sleep and negative psychological trait and state variables. In this study, we examined whether and to what extent sleep, general health, and aggression can concomitantly predict driving behavior. Methods: A total of 360 male traffic offenders (driving under substance use; mean age: 31 years) participated in this study. They completed the questionnaires covering sociodemographic, sleep-related, and behavior-related variables. In addition, their visual and acoustic reaction times were objectively tested. Results: Poor sleep, poor general health, and higher aggression scores were associated with self-rated poor driving behavior. Poor sleep was directly associated with poor driving behavior and indirectly via poor general health and aggression. In contrast, visual and acoustic reaction times were unrelated to sleep, general health, aggression, or self-rated driving behavior. Conclusion: To our knowledge, this is the first study in Iran to assess concomitantly poor sleep, poor general health, and higher aggression scores as independent predictors of poor driving behavior among a larger sample o

Published
2018

44. Mini mental state examination (MMSE) in substance users and non substance users: A comparison study.

Author

Shakeri, Jalal, Farnia, Vahid, Golshani, Sanobar, Rahami, Bahareh, Salemi, Safora, Hookari, Sara, Alikhani, Mostafa, and Abdoli, Nasrin

Subjects
ANALYSIS of variance, CHI-squared test, FISHER exact test, NEUROPSYCHOLOGICAL tests, METHAMPHETAMINE, MULTIVARIATE analysis, OPIUM, QUESTIONNAIRES, RESEARCH funding, STATISTICAL sampling, DRUG abusers, CROSS-sectional method, DATA analysis software, DESCRIPTIVE statistics
Abstract

Background: Due to its progressive nature, substance use impacts the entire aspects of life, and can result in adverse repercussions on mental status. Accordingly, the current article seeks to compare the mental status of substance abusers with that of normal individuals.Methods: The study is a cross-sectional research. The study population is comprised of 50 methamphetamine users, 52 opium users, and 100 normal individuals referred to Farabi Psychiatric Hospital in Kermanshah, 2018, in Iran. The subjects were selected by convenience sampling method and matched in terms of demographic characteristics. Data collection was performed using Mental State Examination (MMSE) and demographic questionnaire.Results: The results showed that mean scores of orientation, attention, and mental status were significantly different between the normal and substance abusers (P <.05). However, the above-mentioned difference was not significant between methamphetamine abusers and opioid abusers (P >.05). In addition, the mean score of the language tests was statistically significant only among the normal and methamphetamine abusers (P <.05). Conclusions: Based on the obtained results, it can be concluded that the opium and methamphetamine users showed poorer performance in MMSE than the normal group. [ABSTRACT FROM AUTHOR]

Published
2021
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48. Efficacy of the Marlatt cognitive-behavioral model on decreasing relapse and craving in women with methamphetamine dependence: A clinical trial.

Author

Abdoli, Nasrin, Farnia, Vahid, Salemi, Safora, Tatari, Faezeh, Juibari, Touraj Ahmadi, Alikhani, Mostafa, and Basanj, Behrad

Subjects
PREVENTION of drug addiction, COGNITIVE therapy, DESIRE, RESEARCH methodology, METHAMPHETAMINE, QUESTIONNAIRES, STATISTICAL sampling, WOMEN'S health, DISEASE relapse, RANDOMIZED controlled trials, TREATMENT effectiveness, PRE-tests & post-tests, CONTROL groups
Abstract

Methamphetamine use is now a global concern, which warrants a thorough assessment of the effectiveness of its different treatment modalities. To evaluate the efficacy of the Marlatt cognitive-behavioral model on decreasing the rate of relapse and craving in women with methamphetamine dependence, we conducted the present quasi-experimental study with a pretest-posttest and control group design in which 80 women with methamphetamine dependence who referred to Farabi Hospital of Kermanshah were chosen through convenience sampling and randomly allocated to two groups of intervention and control. Before and after the intervention, the members of both groups completed the Craving Belief Questionnaire (by Clark and Beck) and the Desire for Drug Questionnaire (by Franklin et al.). The results showed that in the intervention group, the Marlatt cognitive-behavioral treatment led to decreased relapse and craving. Cognitive-behavioral therapy can, therefore, be a treatment option to diminish relapse and craving in individuals with methamphetamine dependence. [ABSTRACT FROM AUTHOR]

Published
2019
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49. Poor mental health status and aggression are associated with poor driving behavior among male traffic offenders

Author

Abdoli,Nasrin, Farnia,Vahid, Delavar,Ali, Esmaeili,Alirez, Dortaj,Fariborz, Farrokhi,Noorali, Karami,Majid, Shakeri,Jalal, Holsboer-Trachsler,Edith, Brand,Serge, Abdoli,Nasrin, Farnia,Vahid, Delavar,Ali, Esmaeili,Alirez, Dortaj,Fariborz, Farrokhi,Noorali, Karami,Majid, Shakeri,Jalal, Holsboer-Trachsler,Edith, and Brand,Serge

Abstract

Nasrin Abdoli,1,2 Vahid Farnia,3 Ali Delavar,4 Alirez Esmaeili,5 Fariborz Dortaj,4 Noorali Farrokhi,4 Majid Karami,6 Jalal Shakeri,3 Edith Holsboer-Trachsler,7 Serge Brand7,8 1International University of Imam Reza, Mashhad, 2Kermanshah University of Medical Sciences, Kermanshah, 3Substance Abuse Prevention Research Center, Psychiatry Department, Kermanshah University of Medical Sciences, Kermanshah, 4Allameh Tabataba’i University, Tehran, 5Police University, Tehran, 6Baharestan Research Center, Kermanshah Transportation Terminal, Kermanshah, Iran, 7Center for Affective, Stress and Sleep Disorders, Psychiatric Clinics of the University of Basel, Basel, 8Department of Sport and Health Science, Sport Science Section, University of Basel, Basel, Switzerland Background: In Iran, traffic accidents and deaths from traffic accidents are among the highest in the world, and generally driver behavior rather than either technical failures or environmental conditions are responsible for traffic accidents. In the present study, we explored the extent to which aggressive traits, health status, and sociodemographic variables explain driving behavior among Iranian male traffic offenders. Method: A total of 443 male driving offenders (mean age: M =31.40 years, standard deviation =9.56) from Kermanshah (Iran) took part in the study. Participants completed a questionnaire booklet covering sociodemographic variables, traits of aggression, health status, and driving behavior. Results: Poor health status, such as symptoms of depression, anxiety, insomnia, and social dysfunction, and also higher levels of trait aggression explained poor driving behavior. Multiple regressions indicated that poor health status, but not aggression, independently predicted poor driving behavior. Conclusion: Results suggest that health status concerns are associated with poor driving behavior. Prevention and intervention might therefore focus on drivers reporting poor mental health status. Ke

Published
2015

50. Mental health status, aggression, and poor driving distinguish traffic offenders from non-offenders but health status predicts driving behavior in both groups

Author

Abdoli,Nasrin, Farnia,Vahid, Delavar,Ali, Dortaj,Fariborz, Esmaeili,Alireza, Farrokhi,Noorali, Karami,Majid, Shakeri,Jalal, Holsboer-Trachsler,Edith, Brand,Serge, Abdoli,Nasrin, Farnia,Vahid, Delavar,Ali, Dortaj,Fariborz, Esmaeili,Alireza, Farrokhi,Noorali, Karami,Majid, Shakeri,Jalal, Holsboer-Trachsler,Edith, and Brand,Serge

Abstract

Nasrin Abdoli,1,2 Vahid Farnia,3 Ali Delavar,4 Fariborz Dortaj,4 Alireza Esmaeili,5  Noorali Farrokhi,4 Majid Karami,6 Jalal Shakeri,3 Edith Holsboer-Trachsler,7 Serge Brand7,81International University of Imam Reza, Mashhad, Iran; 2Kermanshah University of Medical Sciences, Kermanshah, Iran; 3Substance Abuse Prevention Research Center, Psychiatry Department, Kermanshah University of Medical Sciences, Kermanshah, Iran; 4Allameh Tabataba’i University, Tehran, Iran; 5Police University, Tehran, Iran; 6Baharestan Research Center, Kermanshah Transportation Terminal, Kermanshah, Iran; 7Center for Affective, Stress and Sleep Disorders, Psychiatric Clinics of the University of Basel, Basel, Switzerland; 8Department of Sport and Health Science, Sport Science Section, University of Basel, Basel, SwitzerlandBackground: In Iran, traffic accidents and deaths from traffic accidents are among the highest in the world, and generally, driver behavior rather than technical failures or environmental conditions are responsible for traffic accidents. In a previous study, we showed that among young Iranian male traffic offenders, poor mental health status, along with aggression, predicted poor driving behavior. The aims of the present study were twofold, to determine whether this pattern could be replicated among non-traffic offenders, and to compare the mental health status, aggression, and driving behavior of male traffic offenders and non-offenders.Methods: A total of 850 male drivers (mean age =34.25 years, standard deviation =10.44) from Kermanshah (Iran) took part in the study. Of these, 443 were offenders (52.1%) and 407 (47.9%) were non-offenders with lowest driving penalty scores applying for attaining an international driving license. Participants completed a questionnaire booklet covering socio-demographic variables, traits of aggression, health status, and driving behavior.Results: Compared to non-offenders, offenders reported higher aggression, poorer menta

Published
2015

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