Volume 10, Issue 4 (Autumn 2021)                   JOHE 2021, 10(4): 239-248 | Back to browse issues page


XML Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Peykani S, Gheirati E, Rezaeian M, Vazirinejad R, Ahmadinia H. Population Size Estimation of Students with High-Risk Behaviors Using the Network Scale-up Method in Rafsanjan University of Medical Sciences, Iran (2017). JOHE. 2021; 10 (4) :239-248
URL: http://johe.rums.ac.ir/article-1-443-en.html
1- MSc in Epidemiology, Dept. of Epidemiology and Biostatistics, School of Medicine, Rafsanjan University of Medical Sciences, Rafsanjan, Iran.
2- MSc in Epidemiology Dept. of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
3- Professor, Dept. of Epidemiology and Biostatistics, Occupational Environmental Research Center, School of Medicine, Rafsanjan University of Medical Sciences, Rafsanjan, Iran.
4- Professor, Dept. of Epidemiology and Biostatistics, Social Determinants of Health Research Center, Rafsanjan University of Medical Sciences, Rafsanjan, Iran.
5- Assistant Prof., Dept. of Epidemiology and Biostatistics, Occupational Environmental Research Center, School of Medicine, Rafsanjan University of Medical Sciences, Rafsanjan, Iran. , H.ahmadinia@gmail.com
Abstract:   (473 Views)

Background: To manage a problem, knowing the size of the population associated is of great significance. In this study, direct and indirect (network scale-up [NSU]) methods were used to estimate the population size of students of the university of medical sciences with high-risk behaviors in Rafsanjan, Iran.
Materials & Methods: In this cross-sectional study, using stratified random sampling, 440 students were selected and interviewed from the target group by a standard questionnaire, with three social network size estimation, NSU, and direct methods. The frequency approach of the NSU method was used to estimate the size of groups with high-risk behaviors. Correction coefficients were applied to adjust common errors in this method.
Results: Using the maximum likelihood method, the means of social network size (C) for male and female students were 25.71 and 24.45, respectively. Using the NSU method, the prevalence rates of alcohol drinking, extra-marital sexual relationship, and opium use were 26.57%, 15.28%, and 9.69% among male students and 3.13%, 2.89%, and 1.3% among female students, respectively. Using the direct method, the prevalence rates of alcohol drinking, extra-marital sexual relationship, and opium use were 23.2%, 14.3%, and 6.25% among male students and 2.1%, 2.8%, and 0.34% among female students, respectively.
Conclusions: Our results showed high-risk behaviors to have a relatively high prevalence among students of Rafsanjan University of Medical Sciences (RUMS). These behaviors were more prevalent among males than females. Thus, it seems necessary to plan preventative measures against drug abuse in academic departments.

Article number: 5
Full-Text [PDF 545 kb]   (100 Downloads)    
Short Report: Original Article | Subject: Occupational Health
Received: 2021/07/7 | Accepted: 2021/12/29 | ePublished: 2022/02/26

References
1. 1. Atashnafas E, Ghorbani R, Tabatabaee M, Abdoos H, Abbas Poor S, Mahmoudian AR. Prevalent High Risk Behaviors and Important Family Factors from the Point of View of Adolescents: A Qualitative Research. J Fam Stud 2014; 10(2):217-33.
2. 2. Hosseni F. An Analysis Survey of Effective Factors on Addiction of Narcotic Drug in Order to Present Preventive Solutions. J Boushehr Police Sci 2012; 3(9):1-28. [Article]
3. 3. Hillebrand J, Monterio M. Substance Use and Its Toll on Society. Common Health 2001; 9(1):9-13.
4. 4. World Health Organization. Iran (Islami Republic of), Alcohol Consumption: Levels and Patterns. Geneva, Switzerland: World Health Organization; 2018. [Article]
5. 5. Bagheri Lankarani K, Afshari R. Alcohol consumption in Iran. Lancet. 2014; 384(9958):1927-8. [DOI]
6. 6. Sarrami H, Ghorbami M, Taghavi M. The Survey Two Decades of Prevalence Studies among Iran University Students. Res Addict 2013; 7(27):9-36. [Article]
7. 7. Tajdari A, Zakariayi MA. Indirect Methods of Estimating Drug User Population Size. J Social Proble Iran 2011; 1(4):111-29.
8. 8. Haghdoost AA, Pourkhandani A, Motaghipisheh S, Farhoudi B, Fahimifar N, Sadeghirad B. Knowledge and Attitude Concerning HIV/AIDS among Iranian Population: A Systematic Review and Meta-Analysis. Epidemiol 2011; 6(4):8-20. [Article]
9. 9. Azin S. An overview on the 2008 UNAIDS Report on the 2008 UNAIDS Report on the Global AIDS Epidemic. Epidemiol 2010; 6(2):56-9. [Article]
10. 10. Vakilian K, Mousavi SA, Keramat A. Estimation of sexual behavior in the 18-to-24-years-old Iranian youth based on a crosswise model study. BMC Res Notes 2014; 7:28. [DOI] [PMID] [PMCID]
11. 11. Siyam S. Drug abuse prevalence between male students of different universities in Rasht in 2005. Zahedan J Res Med Sci 2007; 8(4):e94869. [Article]
12. 12. Salganik MJ, Fazito D, Bertoni N, Abdo AH, Mello MB, Bastos FI. Assessing network scale-up estimates for groups most at risk of HIV/AIDS: evidence from a multiple-method study of heavy drug users in Curitiba, Brazil. Am J Epidemiol 2011; 174(10):1190-6. [DOI] [PMID] [PMCID]
13. 13. Walker S, Cosden M. Reliability of college student self-reported drinking behavior. J Subst Abuse Treat 2007; 33(4):405-9. [DOI] [PMID]
14. 14. Cooper ML. Alcohol use and risky sexual behavior among college students and youth: evaluating the evidence. J Stud Alcohol Suppl 2002; (14):101-17. [DOI] [PMID]
15. 15. Deressa W, Azazh A. Substance use and its predictors among undergraduate medical students of Addis Ababa University in Ethiopia. BMC Public Health 2011; 11:660. [DOI] [PMID] [PMCID]
16. 16. Babaei Heydarabadi A, Ramezankhani A, Barekati H, Vejdani M, Shariatinejad K, Panahi R, et al. Prevalence of Substance Abuse among Dormitory Students of Shahid Beheshti University of Medical Sciences, Tehran, Iran. Int J High Risk Behav Addict 2015; 4(2):e22350 [DOI] [PMID] [PMCID]
17. 17. Sheikhzadeh K, Baneshi MR, Afshari M, Haghdoost AA. Comparing direct, network scale-up, and proxy respondent methods in estimating risky behaviors among collegians. J Subst Use 2016; 21(1):9-13. [DOI]
18. 18. Maghsoudi A, Baneshi MR, Neydavoodi M, Haghdoost A. Network scale-up correction factors for population size estimation of people who inject drugs and female sex workers in Iran. PloS One 2014; 9(11):e110917. [DOI] [PMID] [PMCID]
19. 19. Shokouhi M, Mohebbi E, Rastegari A, Hajimaghsoudi S, Haghdoost AA, Baneshi MR. The Introduction of Network Scale-up Method: An Indirect Method to Estimate the Hard-to-Reach Populations. Epidemiol 2014;10(1):81-92. [Article]
20. 20. Kadushin C, Killworth PD, Bernard HR, Beveridge AA. Scale-Up Methods as Applied to Estimates of Heroin Use. J Drug Issues 2006; 36(2):417-40. [DOI]
21. 21. Bernard HR, Hallett T, Iovita A, Johnsen EC, Lyerla R, McCarty C, et al. Counting hard-to-count populations: the network scale-up method for public health. Sex Transm Infect 2010; 86(Suppl 2):ii11-5. [DOI] [PMID] [PMCID]
22. 22. Shokoohi M, Baneshi MR, Haghdoost AA. Estimation of the active network size of Kermanian males. Addict Health 2010; 2(3-4):81-8. [PMID] [PMCID]
23. 23. Ezoe S, Morooka T, Noda T, Sabin ML, Koike S. Population size estimation of men who have sex with men through the network scale-up method in Japan. PloS One 2012; 7(1):e31184. [DOI] [PMID] [PMCID]
24. 24. Shokoohi M, Baneshi MR, Haghdoost AA. Size Estimation of Groups at High Risk of HIV/AIDS Using Network Scale Up in Kerman, Iran. Int J Prev Med 2012; 3(7):471-6. [DOI] [PMID] [PMCID]
25. 25. Dehghani K, Zare A, Dehghani H, Sedghi H, Poormovahed Z. Drug Abuse Prevalence and Risk Factors in Students of Shaheed Sadoughi University of Medical Sciences, Yazd. J Shahid Sadoughi Uni Med Sci 2010; 18(3):164-9. [Article]
26. 26. Pisani E. Estimating the size of populations at risk for HIV: Issues and Methods. North Carolina, United States: Family Health International; 2003.
27. 27. Heydari ST, Izedi S, Sarikhani Y, Kalani N, Akbary A, Miri A, et al. The Prevalence of Substance Use and Associated Risk Factors among University Students in the City of Jahrom, Southern Iran. Int J High Risk Behav Addict 2015; 4(2):e22381. [DOI] [PMID] [PMCID]
28. 28. Hedayati-Moghaddam MR, Eftekharzadeh-Mashhadi I, Fathimoghadam F, Pourafzali SJ. Sexual and Reproductive Behaviors among Undergraduate University Students in Mashhad, a City in Northeast of Iran. J Reprod Infertil 2015; 16(1):43-8. [DOI] [PMID] [PMCID]
29. 29. Randolph ME, Torres H, Gore-Felton C, Lloyd B, McGarvey EL. Alcohol use and sexual risk behavior among college students: understanding gender and ethnic differences. Am J Drug Alcohol Abuse 2009; 35(2):80-4. [DOI] [PMID] [PMCID]
30. 30. Kazemzadeh Y, Shokoohi M, Baneshi MR, Haghdoost AA. The Frequency of High-Risk Behaviors among Iranian College Students Using Indirect Methods: Network Scale-Up and Crosswise Model. Int J High Risk Behav Addict 2016; 5(3):e25130. [DOI] [PMID] [PMCID]
31. 31. Maghsoudi A, Jalali M, Neydavoodi M, Rastad H, Hatami I, Dehghan A. Estimating the prevalence of high-risk behaviors using network scale-up method in university students of Larestan in 2014. J Subst Use 2017; 22(2):145-8. [DOI]
32. 32. Guo W, Bao S, Lin W, Wu G, Zhang W, Hladik W, et al. Estimating the size of HIV key affected populations in Chongqing, China, using the network scale-up method. PloS One 2013; 8(8):e71796. [DOI] [PMID] [PMCID]
33. 33. Barbosa CDS, Stefanello CR, Dias MB, Beskow MH, Hirdes É, Krüger EH, et al. Risk Factors for Illicit Drug Disorders during Adolescence: An Analysis According To Substance of Preference. J Subst Abuse Alcohol 2017;5(2):1057. [Article]
34. 34. Mirzazadeh A, Shokoohi M, Navadeh S, Danesh A, Jain J, Sedaghat A, et al. Underreporting in HIV-related high-risk behaviors: comparing the results of multiple data collection methods in a behavioral survey of prisoners in Iran. Prison J 2018; 98(2):213-28. [DOI] [PMID] [PMCID]

Add your comments about this article : Your username or Email:
CAPTCHA

Send email to the article author


Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

© 2022 CC BY-NC 4.0 | Journal of Occupational Health and Epidemiology

Designed & Developed by : Yektaweb