Investigation of HIV/AIDS prevalence and associated risk factors among female sex workers from 2010 to 2017: a meta-analysis study

Figures & data

Figure 1 Prisma flow diagram illustrating selection of articles.

Note: 14 articles were omitted due to the inaccessibility of their full text.

Table 1 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^Citation30	2010	Sudan	321	28	0.9	66.3
Mahfoud et al^Citation17	2010	Lebanon	135		0	39
Todd et al^Citation31	2010	Afghanistan	520	23.3	0.19	38.2	6.9
Strathdee et al^Citation32	2011	Mexico	620	33	5.3	30	33
Braunstein et al^Citation33	2011	Rwanda	397	24	3.5	18
Vandepitte et al^Citation16	2011	Uganda	482		38	33.9
Xu et al^Citation34	2011	China	1,642		1.8
Ramesh et al^Citation35	2012	India	2,042	<30	2.7
Vuylsteke et al^Citation24	2012	Coˆ te d’Ivoire	1,110		26.6	26.4
Goldenberg et al^Citation36	2014	Canada	508		11.22	21.24	1.6
Schwartz et al^Citation37	2015	Coˆte d’Ivoire	466		0.97	19
Deuba et al^Citation38	2016	Nepal	610		1	78	94.8
Afzal et al^Citation25	2017	South Africa	97	36	32.9
Pando et al^Citation39	2013	Argentina	1,255	33.5	2	17.6
Qyra et al^Citation40	2011	Albania	90	28	1.08	35
Argento et al^Citation41	2017	Canada	455	36	2.69		2.93
Corneli et al^Citation42	2016	Kenya	172	29	18	33.9
Decker et al^Citation43	2012	Russia	147	17–40	4.8	78.9
Decker et al^Citation44	2016	Cameroon	1,817	≥18	5.1	59.2
Fan et al^Citation45	2015	China	622	>16	1.1	66.2	2.1
Forbi et al^Citation26	2011	Nigeria	900	18–35	37.2	46.7
Couture et al^Citation46	2011	Cambodia	160	<29	23	85.7
Le et al^Citation47	2015	Vietnam	5,298	≥18	1.76
Dias et al^Citation48	2015	Portugal	853	≥18	7.4		18.1
Magnani et al^Citation49	2010	Indonesia	5,947	28	10.5
Medhi et al^Citation50	2012	India	426	≥18	11.6		36.7
Wirtz et al^Citation51	2015	Russia	754		6.7		11
Silitonga et al^Citation52	2011	Indonesia	3,086		1.4
Wang et al^Citation53	2011	China	751		4.9		8.8
Wayal et al^Citation54	2011	India	326		26
Kang et al^Citation18	2013	China	3,326		0
Platt et al^Citation55	2011	UK	268		1.1
Chen et al^Citation56	2012	China	5,322	>16	0.54
Zhu et al^Citation57	2017	China	589	>16	2.74
Lu et al^Citation58	2017	Tibet China	2,000		5.81
Okafor et al^Citation59	2017	Nigeria	1,050	15–24	15.5
Kakchapati et al^Citation60	2016	Nepal	2,093		1.8

Table 2 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.

Figure 2 The 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 3 The 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 4 The 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 5 Begg’s funnel plot for publication bias.

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