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Using latent class analysis to identify money boys at highest risk of HIV infection.
Public Health 2019; 177:57-65PH

Abstract

OBJECTIVES

Limited research has been conducted to investigate the characteristics of money boys (MBs) in China. This study was aimed to identify the subgroups of MBs based on sexual behaviors, Net-based venue sex-seeking, and substance abuse.

STUDY DESIGN

Cross-sectional study.

METHODS

Convenience sampling was used to recruit MBs from December 2014 to June 2015 in Tianjin, China. Face-to-face interviews were conducted for 330 MBs, and trained interviewers collected data.

RESULTS

The laboratory-confirmed human immunodeficiency virus (HIV)-positive rate was 11.52% among 330 MBs. Four classes were identified through latent class analysis (LCA) method: 'relatively safe behavior' group, 'higher sexual risk' group, 'multiple sexual-partners' group, and 'unprotected sex and substance abuse' group, and there is a significant difference based on the HIV status. Significant differences were found in original residence, monthly income, duration in sex trade, employment, history of sexually transmitted infection (STI), HIV testing, knowledge of free antiviral treatment policy, and awareness of free AIDS testing between the four latent classes (P < 0.05). MBs who used Net-based venues to seek sexual partners; who have inconsistent condom use, substance abuse, a longer duration in sex trade, multiple sexual clients, and multiple anal sex; and who were full-time employed had the highest risk of HIV infection.

CONCLUSIONS

The utility of LCA to identify subgroups based on risky behaviors attributes to formulating targeted intervention strategy.

Authors+Show Affiliations

Section of STD & AIDS Control and Prevention, Tianjin Center for Disease Control and Prevention, Tianjin, China.Department of Epidemiology and Health Statistics, Tianjin Medical University, Heping District, Tianjin, China.Section of STD & AIDS Control and Prevention, Tianjin Center for Disease Control and Prevention, Tianjin, China.Section of STD & AIDS Control and Prevention, Tianjin Nankai District Center for Disease Control and Prevention, Tianjin, China.Department of Epidemiology and Health Statistics, Tianjin Medical University, Heping District, Tianjin, China.Department of Epidemiology and Health Statistics, Tianjin Medical University, Heping District, Tianjin, China.Department of Epidemiology and Health Statistics, Tianjin Medical University, Heping District, Tianjin, China.Department of Medical English and Health Communication, Tianjin Medical University, Tianjin, China.National Center for AIDS/STD Control and Prevention, Beijing, China.GAP Program Office of U.S CDC, Atlanta, USA.Section of STD & AIDS Control and Prevention, Tianjin HongQiao District Center for Disease Prevention and Control, Tianjin, China.Tianjin Shen-Lan Public Health Counseling Service Center, Tianjin, China.Department of Epidemiology and Health Statistics, Tianjin Medical University, Heping District, Tianjin, China. Electronic address: cuizhuang@tmu.edu.cn.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

31536863

Citation

Yu, M-H, et al. "Using Latent Class Analysis to Identify Money Boys at Highest Risk of HIV Infection." Public Health, vol. 177, 2019, pp. 57-65.
Yu MH, Guo CM, Gong H, et al. Using latent class analysis to identify money boys at highest risk of HIV infection. Public Health. 2019;177:57-65.
Yu, M. H., Guo, C. M., Gong, H., Li, Y., Li, C. P., Liu, Y., ... Cui, Z. (2019). Using latent class analysis to identify money boys at highest risk of HIV infection. Public Health, 177, pp. 57-65. doi:10.1016/j.puhe.2019.07.020.
Yu MH, et al. Using Latent Class Analysis to Identify Money Boys at Highest Risk of HIV Infection. Public Health. 2019 Sep 16;177:57-65. PubMed PMID: 31536863.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Using latent class analysis to identify money boys at highest risk of HIV infection. AU - Yu,M-H, AU - Guo,C-M, AU - Gong,H, AU - Li,Y, AU - Li,C-P, AU - Liu,Y, AU - Guo,M, AU - Zhao,Y-Q, AU - Xu,J, AU - Li,Z, AU - Gao,Y-J, AU - Yang,J, AU - Cui,Z, Y1 - 2019/09/16/ PY - 2019/01/22/received PY - 2019/07/04/revised PY - 2019/07/20/accepted PY - 2019/9/20/pubmed PY - 2019/9/20/medline PY - 2019/9/20/entrez KW - High-risk sexual behavior KW - LCA KW - Money boys KW - Sexual behavior KW - Sexual partner-seeking SP - 57 EP - 65 JF - Public health JO - Public Health VL - 177 N2 - OBJECTIVES: Limited research has been conducted to investigate the characteristics of money boys (MBs) in China. This study was aimed to identify the subgroups of MBs based on sexual behaviors, Net-based venue sex-seeking, and substance abuse. STUDY DESIGN: Cross-sectional study. METHODS: Convenience sampling was used to recruit MBs from December 2014 to June 2015 in Tianjin, China. Face-to-face interviews were conducted for 330 MBs, and trained interviewers collected data. RESULTS: The laboratory-confirmed human immunodeficiency virus (HIV)-positive rate was 11.52% among 330 MBs. Four classes were identified through latent class analysis (LCA) method: 'relatively safe behavior' group, 'higher sexual risk' group, 'multiple sexual-partners' group, and 'unprotected sex and substance abuse' group, and there is a significant difference based on the HIV status. Significant differences were found in original residence, monthly income, duration in sex trade, employment, history of sexually transmitted infection (STI), HIV testing, knowledge of free antiviral treatment policy, and awareness of free AIDS testing between the four latent classes (P < 0.05). MBs who used Net-based venues to seek sexual partners; who have inconsistent condom use, substance abuse, a longer duration in sex trade, multiple sexual clients, and multiple anal sex; and who were full-time employed had the highest risk of HIV infection. CONCLUSIONS: The utility of LCA to identify subgroups based on risky behaviors attributes to formulating targeted intervention strategy. SN - 1476-5616 UR - https://www.unboundmedicine.com/medline/citation/31536863/Using_latent_class_analysis_to_identify_money_boys_at_highest_risk_of_HIV_infection L2 - https://linkinghub.elsevier.com/retrieve/pii/S0033-3506(19)30247-1 DB - PRIME DP - Unbound Medicine ER -