Estimating the Size and Age-gender Distribution of Women’s Active Social Networks

Document Type : Original Article(s)

Authors

1 PhD Student, Modeling in Health Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran

2 Associate Professor, Modeling in Health Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran

3 Professor, HIV/STI Surveillance Research Center, WHO Collaborating Center for HIV Surveillance, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran

4 MSc Student, Department of Nursing and Midwifery, School of Nursing and Midwifery, Kerman University of Medical Sciences, Kerman, Iran

5 Resident, Physiology Research Center AND Department of Obstetrics and Gynaecology, School of Medicine, Kerman University of Medical Sciences, Kerman, Iran

6 Associate Professor, HIV/STI Surveillance Research Center, WHO Collaborating Center for HIV Surveillance, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran

Abstract

Background: Network Scale-up (NSU) method is an indirect method for the estimation of hidden behaviors. In NSU, respondents are asked about the number of members they know from a subpopulation of interest (e.g., injecting drug user) and assume that the prevalence of risky behavior in the networks of a random sample of respondents is similar to that of the population. However, first, we need to identify the total number of people each respondent knows [the social network size (C)]; Moreover, certain risky behaviors happen in particular age and gender groups. Our aim was to determine the size and age-gender distribution of female networks.Methods: This cross-sectional study was conducted in the city of Kerman, Iran. A total sample of 1275 women was recruited using multistage sampling. In this study, 25 first names were selected as reference groups. Participants were asked how many people they know with the selected names. The respondent’s answers were categorized into eight separate age-gender subgroups and C was estimated for each subgroup.Findings: The results of this study showed that, on average, each Kermanian woman knows about 234 people and about two-thirds of them are female (82 males and 152 females); moreover, participants were more likely to communicate with their peers. The majority of males (88%) known by Kermanian women were in young and middle age groups; in contrast the female young and middle age groups, who are at reproductive age, form only 45% of the female part of their networks.Conclusion: We have seen that the age-gender distribution of the networks is not the same as that of the general population. Our figures can be applied in NSU studies focusing on risky behaviors of particular age and gender groups.

Keywords

References

  1. 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.
  2. 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.
  3. Paniotto V, Petrenko T, Kupriyanov V, Pakhok O. Estimating the size of populations with high risk for HIV using the network scale-up method [Analytical Report]. Kiev, Ukraine: Kiev International Institute of Sociology; 2009.
  4. 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.
  5. McCormick TH, Salganik MJ, Zheng T. How many people do you know?: Efficiently estimating personal network size. J Am Stat Assoc 2010; 105(489): 59-70.
  6. Bao L, Raftery AE, Reddy A. Estimating the size of populations at high risk of HIV in Bangladesh using a Bayesian hierarchical model [Working Paper no. 103]. Seattle, WA: Center for Statistics and the Social Sciences, University of Washington; 2010.
  7. 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.
  8. 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 2016; 0(0): 1-4.
  9. UNAIDS Reference Group. Estimation of the size of high risk groups and HIV prevalence in high risk groups in concentrated epidemics (Technical Report and Recommendations). Proceedings of the Meeting of the UNAIDS Reference Group on Estimates, Modelling and Projections; 2008 Dec 9-10; Amsterdam, the Netherlands.
  10. 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-ii15.
  11. UNAIDS/WHO Working Group. Guidelines on estimating the size of populations most at risk to HIV. Geneva, Switzerland: Co-published by World Health Organization and UNAIDS; 2010.
  12. UNAIDS. Network scale-up: a promising method for national estimates of the sizes of populations at higher risk. UNAIDS Quarterly Update on HIV Epidemiolog/2Q [Online]. [cited 2010]; Available from: URL: http://files.unaids.org/en/media/unaids/contentassets/documents/epidemiology/2010/JC2032_20102_epialert_en.pdf
  13. UNAIDS. The US Office of the Global AIDS Coordinator. Consultation on Network scale-up and other size estimation methods from general population surveys [Online]. [cited 2012 May]; Available from: URL: http://www.epidem.org/sites/default/files/reports/Consultation%20on%20network%20scale-up.pdf
  14. Rastegari A, Haji-Maghsoudi S, Haghdoost A, Shatti M, Tarjoman T, Baneshi MR. The estimation of active social network size of the Iranian population. Glob J Health Sci 2013; 5(4): 217-27.
  15. Snidero S, Morra B, Corradetti R, Gregori D. Estimating the Number of Foreign Bodies Injuries in Children with the Scale-up Method. Proceedings of the ASA Joint Statistical Meetings; 2008 Aug 7-11; Minneapolis, USA.
  16. Shati M, Haghdoost A, Majdzadeh R, Mohammad K, Mortazavi S. Social network size estimation and determinants in tehran province residents. Iran J Public Health 2014; 43(8): 1079-90.
  17. Wang J, Yang Y, Zhao W, Su H, Zhao Y, Chen Y, et al. Application of Network Scale Up Method in the Estimation of Population Size for Men Who Have Sex with Men in Shanghai, China. PLoS One 2015; 10(11): e0143118.
  18. Killworth PD, Johnsen EC, Mccarty C, Shelley GA, Bernard HR. A social network approach to estimating seroprevalence in the United States. Social Networks 1998; 2: 23-50.
  19. Habecker P, Dombrowski K, Khan B. Improving the network scale-up estimator: Incorporating means of sums, recursive back estimation, and sampling weights. PLoS One 2015; 10(12): e0143406.
  20. Mccarty C, Killworth PD, Bernard HR, Johnsen EC, Shelley GA. Comparing two methods for estimating network size. Hum Orga 2001; 60(1): 28-39.
  21. 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.
  22. 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.
  23. Rastegari A, Baneshi MR, Haji-Maghsoudi S, Nakhaee N, Eslami M, Malekafzali H, et al. Estimating the annual incidence of abortions in Iran applying a network scale-up approach. Iran Red Crescent Med J 2014; 16(10): e15765.
  24. Shokoohi M, Baneshi MR, Haghdoost AA. Estimation of the active network size of Kermanian males. Addict Health 2010; 2(3-4): 81-8.
Addiction & Health
Volume 8, Issue 3 - Serial Number 3
18
October 2016
Pages 170-178

Files

Share

How to cite

Statistics