Mehdi Noroozi1, Alireza Noroozi2, Hamid Sharifi3, Gholamreza Ghaedamini Harouni4, Brandon D L Marshall5, Hesam Ghisvand1, Mostafa Qorbani6, Bahram Armoon7,8,9. 1. Social Determinants of Health Research Center, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran. 2. Department of Neuroscience and Addiction, School of Advanced Technologies in Medicine, University of Medical Sciences, Tehran, Iran. 3. HIV/STI Surveillance Research Center, and WHO Collaborating Center for HIV Surveillance, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran. 4. Social Welfare Management Research Center, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran. 5. Department of Epidemiology, Brown University School of Public Health, Providence, RI, USA. 6. Non-Communicable Diseases Research Center, Alborz University of Medical Sciences, Karaj, Iran. 7. Social Determinants of Health Research Center, Saveh University of Medical Sciences, Saveh, Iran. Bahramarmun@gmail.com. 8. School of Nursing and Midwifery, Saveh University of Medical Sciences, Saveh, Iran. Bahramarmun@gmail.com. 9. , Tehran-Saveh freeway, Kaveh Industrial Estate company, Saveh, 3914334911, Iran. Bahramarmun@gmail.com.
Abstract
BACKGROUND: Many studies have found significant differences in HIV risk at the community and socioeconomic levels. However, few have considered variations in needle and syringe program (Jin et al., Oral Dis. 1;22(7):609-19) coverage and other community characteristics on HIV risk behaviors among people who inject drugs (PWIDs). Our objective was to study the relationship between individual factors and city-level characteristics (such as the city's coverage of harm reduction programs) on HIV risk behavior among PWID residing in two cities in Iran. METHODS: The study was conducted from March to August 2016 in Tehran and Kermanshah provinces. One thousand PWID were recruited by a convenience sampling recruitment at local NSP Drop-in Centers (DIC) and through "snowball sampling" (i.e., using peers to refer participants to the study). We first examined associations between individual-level variables and HIV risk behaviors in bivariate analysis using the chi-square or Fisher's exact tests, as appropriate. Next, multi-level models were constructed to determine the amount of variability in HIV risk behavior that could be accounted for by individual- and community-level characteristics. Variables with p value < 0.2 were included in the multiple logistic regression model. RESULTS: The results of the multilevel modeling showed that 32% of the variability in HIV risk behaviors among PWID could be explained by factors that differed between the two cities. When individual factors including higher HIV knowledge, access to NSP, higher HIV risk perception, and methamphetamine use were all included in the final model, 22% of the variability in HIV risk behaviors could be explained to city-level variables. CONCLUSION: Findings suggest that expanding the accessibility (i.e., hours and venues) and community-level coverage of NSP services by establishing programs where PWID congregate might reduce HIV risk behavior among PWID.
BACKGROUND: Many studies have found significant differences in HIV risk at the community and socioeconomic levels. However, few have considered variations in needle and syringe program (Jin et al., Oral Dis. 1;22(7):609-19) coverage and other community characteristics on HIV risk behaviors among people who inject drugs (PWIDs). Our objective was to study the relationship between individual factors and city-level characteristics (such as the city's coverage of harm reduction programs) on HIV risk behavior among PWID residing in two cities in Iran. METHODS: The study was conducted from March to August 2016 in Tehran and Kermanshah provinces. One thousand PWID were recruited by a convenience sampling recruitment at local NSP Drop-in Centers (DIC) and through "snowball sampling" (i.e., using peers to refer participants to the study). We first examined associations between individual-level variables and HIV risk behaviors in bivariate analysis using the chi-square or Fisher's exact tests, as appropriate. Next, multi-level models were constructed to determine the amount of variability in HIV risk behavior that could be accounted for by individual- and community-level characteristics. Variables with p value < 0.2 were included in the multiple logistic regression model. RESULTS: The results of the multilevel modeling showed that 32% of the variability in HIV risk behaviors among PWID could be explained by factors that differed between the two cities. When individual factors including higher HIV knowledge, access to NSP, higher HIV risk perception, and methamphetamine use were all included in the final model, 22% of the variability in HIV risk behaviors could be explained to city-level variables. CONCLUSION: Findings suggest that expanding the accessibility (i.e., hours and venues) and community-level coverage of NSP services by establishing programs where PWID congregate might reduce HIV risk behavior among PWID.
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