| Literature DB >> 31191036 |
Owrang Eilami1, Ali Nazari2, Majid Dousti3, Fatemeh Sayehmiri4, Maryam Ghasemi5.
Abstract
Objectives: Female sex workers (FSW) are highly at risk of HIV, and can potentially transmit the human immunodeficiency virus (HIV) in different societies. Study design: The aims of the present study were to investigate the prevalence of HIV/AIDS and associated risk factors among FSW between 2010 and 2017 using a systematic literature review and meta-analysis approach.Entities:
Keywords: HIV/AIDS; female sex workers; immunodeficiency; prevalence; risk factors
Year: 2019 PMID: 31191036 PMCID: PMC6529623 DOI: 10.2147/HIV.S196085
Source DB: PubMed Journal: HIV AIDS (Auckl) ISSN: 1179-1373
The initial data on the prevalence of AIDS based on risk factors in the articles entered in the meta-analysis
| Authors | Year | Prevalence in BD | Prev. in HBV | Prev.in HCV | Prev in female | Prev in male | Prev. in prisoners | Prev. in Thalassemia | Prev. in Hemophilia | Total prevalence | Sexual high risk | Injection drug users (IDUs) | Men who have sex with men (MSM) | FSWs | <25 years | 26–30 years | 31–35 years | 36–40 years | >40 years |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Scheibe | 2015 | 18 | 13 | 14 | 34 | 2.1 | |||||||||||||
| Emeka-Nwabunnia | 2014 | 4.31 | 2.91 | 3.69 | 46 | 53 | |||||||||||||
| Girardi | 2011 | 4 | 10.3 | 15.2 | 15.2 | ||||||||||||||
| Forbi | 2011 | 37.2 | 37.2 | ||||||||||||||||
| Behzanpour | 2012 | 75.5 | 62.8 | 88.4 | |||||||||||||||
| Moradi | 2012 | 4.2 | 4.2 | 0 | 3.8 | 15 | 5.6 | 0 | |||||||||||
| Haghdoost | 2011 | 29.1 | 69.3 | 15.95 | 11.04 | 9.04 | |||||||||||||
| Mmbaga | 2013 | 6.3 | 7.7 | 7 | |||||||||||||||
| Mmbaga | 2013 | 4.7 | 6.8 | 5.7 | |||||||||||||||
| Shahbazi | 2014 | 2.7 | 2.7 | ||||||||||||||||
| Ghafouri | 2010 | 0 | 0 | ||||||||||||||||
| Kolivand | 2010 | 0.05 | |||||||||||||||||
| Bani Aghil | 2010 | 0.0015 | 0.0015 | ||||||||||||||||
| Acar | 2010 | 0.008 | |||||||||||||||||
| Feng | 2012 | 13.54 | 13.54 | 13.1 | |||||||||||||||
| Puglia | 2015 | 87.7 | 52.8 | ||||||||||||||||
| Carnicer-Pont | 2015 | 0.14 | |||||||||||||||||
| Grinberg | 2015 | 4.6 | 12.1 | 7.62 | |||||||||||||||
| Haghgoo | 2015 | 11.4 | 59.3 | ||||||||||||||||
| Chipeta | 13.3 | 10.2 | 11.8 | 12.4 | 5.9 | 12.6 | 19.2 | 17.7 | 14.4 | ||||||||||
| Wang | 2015 | 2.9 | |||||||||||||||||
| Ghasemian | 2011 | 18.2 | 18.2 | ||||||||||||||||
| Sorouri Zanjani | 2013 | 0 | 0 | ||||||||||||||||
| Maokhtarifar | 2014 | 0 | 0.9 | ||||||||||||||||
| Forbi | 2012 | 5.4 | 37.2 | 12.2 | |||||||||||||||
| Fayemiwo | 2014 | 25.6 | 25.6 | 6.2 | 35.6 | 18.8 | 3.1 | 1.6 | |||||||||||
| Li | 2014 | 2.1 | 2.1 | ||||||||||||||||
| Couture | 2010 | 23.13 | 23.13 | 13.3 | 28.9 | ||||||||||||||
| Page | 2013 | 9.2 | 9.2 | ||||||||||||||||
| Priddy | 2011 | 5.6 | 5.6 | ||||||||||||||||
| Javadi | 2014 | 0 | 21.4 | 1.1 | 1.1 | ||||||||||||||
| Yeganeh | 2015 | 0.6 | |||||||||||||||||
| Javadzadeh Shahshahani | 1.4- | 1.4 | |||||||||||||||||
| Javadzadeh Shahshahani | 2015 | 0 | 0 | ||||||||||||||||
| De Castro | 2010 | 3.6 | 6.5 | 4.8 | 4.3 | 24.8 | 2.6 | 6.4 | 6.4 | 6.4 | 4.8 | ||||||||
| Nokhodian | 2012 | 0 | 0 | 0 | 0 | ||||||||||||||
| Karimy | 2013 | 8 | 31 | 33 | |||||||||||||||
| Kheirandish | 2010 | 24.4 | 25.4 | 24.4 | 30.4 | 10.8 | 24.7 | 24.7 | 27.1 | 36.4 | |||||||||
| Nasirian | 2012 | 35 | 56 | 10.9 | 8.3 | ||||||||||||||
| Kupek | 2014 | 2.75 | 0.9 | 1.85 | 1.46 | 1.22 | 1.72 | 1.45 | 1.43 | ||||||||||
| Dargahi | 1.05 | 1.05 | 0.003 | ||||||||||||||||
| Navadeh | 2013 | 1.9 | 2.1 | 2.1 | 2.1 | 2.7 | 8.1 | 3.7 | 2.3 | 2.3 | 1.8 | 1.8 | 1.8 | ||||||
| Egger | 0.29 | 0.29 | |||||||||||||||||
| Karabulut | 2015 | 0 | 0 | ||||||||||||||||
| Nikkhooy | 2012 | 14.72 | 14.72 | ||||||||||||||||
| Mittal | 2013 | 0.31 | 0.31 | ||||||||||||||||
| Ilami | 2010 | 4.6 | 0.5 | 2.4 | 2.7 | 0 | 0 | 12.5 | 9.9 | 3.2 | 3.2 | ||||||||
| Kim | 2012 | 3 | 3 | ||||||||||||||||
| Wang | 2014 | 0.04 | 0.05 | 0.038 | 0.073 | 0.073 | 0.042 | 0.26 | |||||||||||
| Jain | 2012 | 0.33 | 0.35 | 0.31 | 0.31 | 0.31 | 0.35 | ||||||||||||
| Zou | 2012 | 0.0097 | 0.0014 | 0.004 | |||||||||||||||
| Zhao Hua | 2013 | 0.02 | 0.02 | 0.02 | 0.01 | 0.01 | 0.02 | 0.02 | 0.03 | ||||||||||
| Malleki | 2014 | 2.5 | |||||||||||||||||
| Nwokeukwu | 2014 | 0.3 | 0 | 0.3 | |||||||||||||||
| Hosseini | 2010 | 26.4 | 24.4 | 31.8 | 25.7 | 8.6 | 24.4 | 24.4 | 26.9 | 36.6 | |||||||||
| Khattak | 0.007 | 0.007 | |||||||||||||||||
| Khan | 2011 | 0 | |||||||||||||||||
| Khedmat | 2009 | 0.2 | 0.2 | ||||||||||||||||
| Karimi | 2001 | 0.36 | |||||||||||||||||
| Dayan | 2013 | 0.0004 | |||||||||||||||||
| Noubiap | 2013 | 4.1 | 3.1 | 4.1 | 0 | 4.2 | 3.5 | 3.5 | 9.9 | ||||||||||
| Nagalo | 2011 | 2.21 | 2 | 2.28 | 1.95 | 2.38 | 1.18 | 1.18 | 3.7 | ||||||||||
| Mohamadali | 2014 | 5.4 | |||||||||||||||||
| Fadhil Rahman | 2011 | 0 | |||||||||||||||||
| Rahimi Movaghar | 2010 | 15.3 | 25.4 | 10.5 | 10.7 | 10.7 |
Abbreviations: BD, blood donor; FSW, female sex workers; HBV, Hepatitis B Viruses; HCV, Hepatitis C Viruses; IUDs, Injection drug users; MSM, men who have sex with men; Prev, Prevalence.
Demographic characteristics of studies involved in meta-analysis
| Authors | Year of publication | Country | Sample size | Age (years) | HIV prevalence in FSWs (%) | Condom use (%) | Used illicit drugs (%) |
|---|---|---|---|---|---|---|---|
| Abdelrahim | 2010 | Sudan | 321 | 28 | 0.9 | 66.3 | |
| Mahfoud et al | 2010 | Lebanon | 135 | 0 | 39 | ||
| Todd et al | 2010 | Afghanistan | 520 | 23.3 | 0.19 | 38.2 | 6.9 |
| Strathdee et al | 2011 | Mexico | 620 | 33 | 5.3 | 30 | 33 |
| Braunstein et al | 2011 | Rwanda | 397 | 24 | 3.5 | 18 | |
| Vandepitte et al | 2011 | Uganda | 482 | 38 | 33.9 | ||
| Xu et al | 2011 | China | 1,642 | 1.8 | |||
| Ramesh et al | 2012 | India | 2,042 | <30 | 2.7 | ||
| Vuylsteke et al | 2012 | Coˆ te d’Ivoire | 1,110 | 26.6 | 26.4 | ||
| Goldenberg et al | 2014 | Canada | 508 | 11.22 | 21.24 | 1.6 | |
| Schwartz et al | 2015 | Coˆte d’Ivoire | 466 | 0.97 | 19 | ||
| Deuba et al | 2016 | Nepal | 610 | 1 | 78 | 94.8 | |
| Afzal et al | 2017 | South Africa | 97 | 36 | 32.9 | ||
| Pando et al | 2013 | Argentina | 1,255 | 33.5 | 2 | 17.6 | |
| Qyra et al | 2011 | Albania | 90 | 28 | 1.08 | 35 | |
| Argento et al | 2017 | Canada | 455 | 36 | 2.69 | 2.93 | |
| Corneli et al | 2016 | Kenya | 172 | 29 | 18 | 33.9 | |
| Decker et al | 2012 | Russia | 147 | 17–40 | 4.8 | 78.9 | |
| Decker et al | 2016 | Cameroon | 1,817 | ≥18 | 5.1 | 59.2 | |
| Fan et al | 2015 | China | 622 | >16 | 1.1 | 66.2 | 2.1 |
| Forbi et al | 2011 | Nigeria | 900 | 18–35 | 37.2 | 46.7 | |
| Couture et al | 2011 | Cambodia | 160 | <29 | 23 | 85.7 | |
| Le et al | 2015 | Vietnam | 5,298 | ≥18 | 1.76 | ||
| Dias et al | 2015 | Portugal | 853 | ≥18 | 7.4 | 18.1 | |
| Magnani et al | 2010 | Indonesia | 5,947 | 28 | 10.5 | ||
| Medhi et al | 2012 | India | 426 | ≥18 | 11.6 | 36.7 | |
| Wirtz et al | 2015 | Russia | 754 | 6.7 | 11 | ||
| Silitonga et al | 2011 | Indonesia | 3,086 | 1.4 | |||
| Wang et al | 2011 | China | 751 | 4.9 | 8.8 | ||
| Wayal et al | 2011 | India | 326 | 26 | |||
| Kang et al | 2013 | China | 3,326 | 0 | |||
| Platt et al | 2011 | UK | 268 | 1.1 | |||
| Chen et al | 2012 | China | 5,322 | >16 | 0.54 | ||
| Zhu et al | 2017 | China | 589 | >16 | 2.74 | ||
| Lu et al | 2017 | Tibet China | 2,000 | 5.81 | |||
| Okafor et al | 2017 | Nigeria | 1,050 | 15–24 | 15.5 | ||
| Kakchapati et al | 2016 | Nepal | 2,093 | 1.8 |
Figure 1Prisma flow diagram illustrating selection of articles.
Note: 14 articles were omitted due to the inaccessibility of their full text.
Figure 2The prevalence of HIV among sex workers based on the random effects model. The midpoint of each section estimates the prevalence and length of the line, the 95% confidence interval in each study, and the diamond shows the prevalence of HIV among sex workers in this study.
Figure 3The prevalence of condom use in sex with clients by sex workers based on the random effects model. The midpoint of every line estimates the prevalence in each study, and the diamond shows the overall prevalence for all study.
Figure 4The prevalence of injecting drug use in sex workers based on the random effects model. The midpoint of each section of the line estimated prevalence in each study, and the diamond indicates the prevalence of injecting drug use for all studies.
Figure 5Begg’s funnel plot for publication bias.