Estimates of the number of female sex workers in different

Estimates of the number of female sex workers in different regions of the world J Vandepitte, R Lyerla, G Dallabetta, F Crabbe´, M Alary, A Buve´ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . See end of article for authors’ affiliations . . . . . . . . . . . . . . . . . . . . . . . Correspondence to: Dr J Vandepitte, STD/HIV Research and Intervention Unit, Institute of Tropical Medicine, Nationalestraat 155, 2000 Antwerp, Belgium; jvdpitte@itg.be Accepted for publication 18 April 2006 . . . . . . . . . . . . . . . . . . . . . . . Sex Transm Infect 2006;82(Suppl III):iii18–iii25. doi: 10.1136/sti.2006.020081 Objectives: To collect estimated numbers of female sex workers (FSW) and present proportions of FSW in the female population (FSW prevalence) in different regions of the world. Methods: Subnational and national estimated numbers of FSW reported in published and unpublished literature, as well as from field investigators involved in research or interventions targeted at FSW, were collected. The proportion of FSW in the adult female population was calculated. Subnational estimates were extrapolated to national estimates if appropriate. Population surveys were scanned for proportions of adult women having sex in exchange for money or goods. Results: In sub-Saharan Africa, the FSW prevalence in the capitals ranged between 0.7% and 4.3% and in other urban areas between 0.4% and 4.3%. Population surveys from this same region yielded even higher proportions of women involved in transactional sex. The national FSW prevalence in Asia ranged between 0.2% and 2.6%; in the ex-Russian Federation between 0.1% and 1.5%; in East Europe between 0.4% and 1.4%; in West Europe between 0.1% and 1.4%; and in Latin America between 0.2% and 7.4%. Estimates from rural areas were only available from one country. Conclusions: Although it is well known and accepted that FSW are a highly vulnerable group in the scope of the HIV epidemic, most countries in the world do not know the size of this population group. The estimates of the prevalence of FSW presented in this paper show how important this hard-to-reach population group is in all parts of the world. I n many countries, the HIV epidemic is concentrated in subgroups of the population whose behaviour exposes them to a high risk of acquiring HIV infection. These subgroups include injecting drug users, men who have sex with men, and commercial sex workers—female and male. Numerous studies have documented significantly higher rates of HIV infection in women involved in sex work, when compared to women in the general population.1 Surveillance of HIV infection among female sex workers (FSW) is needed, not only for monitoring the HIV epidemic but also to provide data needed for the planning, implementation, and evalua- tion of prevention and care programmes targeted at sex workers. In addition, in some countries with low level or concentrated epidemics, prevention efforts targeting high risk populations such as commercial sex workers have been effective in changing the dynamics of the epidemic. Conducting surveillance and developing appropriate inter- ventions require an understanding of the different types of sex work and the size of this population group. However, FSW are a marginal and stigmatised, hard-to-reach sub- population: sex work is illegal in most countries and many women prefer not to disclose their sex work activities. Sex work has many faces with considerable differences between populations, in the way sex work is organised, in levels of visibility, and in risk. Sex work has been defined as the provision of sexual services in exchange for money, goods, or other benefits. Most sex work has a strong economic basis with motivations ranging from survival, debt alleviation, drug dependency, coercion, or a desire for wealth. Sex work is usually classified as ‘‘direct’’ (open, formal) or ‘‘indirect’’ (hidden, clandestine, informal). Direct FSW are typically women who do define themselves as sex workers and earn their living by selling sex. Indirect FSW are women for whom sex work is not the first source of income. They may work as waitresses, hairdressers, tailors, massage girls, street vendors, or beer promotion girls and supplement their income by selling sex on a regular basis or occasionally. They do not consider themselves as sex workers and often work outside of known venues for sex work. Therefore they are even more difficult to reach than women known as direct sex workers.2–10 As a consequence, the absolute size of the FSW population remains largely unknown. In many countries data are available on HIV prevalence among sex workers. However in estimating national HIV prevalence in countries with concentrated epidemics, the lack of information about the size of high risk populations is the limiting factor in producing reasonable estimates of the national HIV prevalence. This paper attempts to provide some guidance about the size of FSW populations for different regions of the world. We reviewed estimated population sizes of FSW from different parts of the world. FSW were defined as women having sex in exchange for money or goods. From these estimates, the proportion of women in the population who engage in sex work was calculated. The validity of the obtained estimates as well as their usefulness for HIV surveillance is discussed. METHODS Information sources Different sources of information were used for estimates on the size of the FSW populations. Firstly, we performed a MEDLINE search for the 10 year period 1995–2005, using the search terms ‘‘female sex workers’’, ‘‘sex workers’’, ‘‘sex work’’, ‘‘prostitutes’’, ‘‘prostitution’’, and ‘‘vulnerable women’’. Additional sources were identified through refer- ences from relevant publications. Secondly, we looked for relevant information in HIV/AIDS/STI surveillance reports from national or regional World Health Organization (WHO)/ Joint United Nations Programme on HIV/AIDS (UNAIDS) offices, which were obtained via internet or personal Abbreviations: DHS, Demographic and Health Survey; EUROPAP, European Network for HIV-STD Prevention in Prostitution; FHI, Family Health International; FSW, female sex workers; UNAIDS, Joint United Nations Programme on HIV/AIDS; UNDP, United Nations Development Programme; WHO, World Health Organization. iii18 www.stijournal.com contacts. Thirdly, we looked for published and unpublished reports on the websites of international agencies involved in interventions or research projects on HIV/AIDS/STI, especially those targeting FSW. Lastly, a lot of information was obtained from leading investigators in the field, who provided us with unpublished data from their projects and documents of workshops or meetings conducted in their region. The estimates of the numbers of FSW obtained from the above sources were contrasted with estimates of the numbers of women having sex for money or goods, as obtained from the Reproductive Health and Sexual Behaviour surveys (CDC, Measure); Behavioural Surveillance Surveys (FHI); Multiple Indicator Cluster Surveys (MICS, UNICEF) and Demographic and Health Surveys (DHS Measure). Selection criteria Only those studies and reports were retained that met the following criteria: N A definition of FSW was available corresponding to the definition previously mentioned—that is, women selling sex in exchange for money or goods. N The methods to estimate the numbers of FSW were described. If the methods were not described, estimates were still included if they were reported by a reliable source, such as the United Nations Development Programme (UNDP), WHO, or the European Network for HIV-STD Prevention in Prostitution. N Only women >15 years were included. N The geographic area was defined. Calculation of FSW prevalence We defined the FSW prevalence as the proportion of FSW in the adult female population (15–49 years): % FSW in area A = Estimated number of FSW in area A × 100 Number of adult women living in area A The number of adult women was calculated as follows: Total population number6% women6% women (15–49 years). The population number, the proportion of women and the proportion of women 15–49 years were taken from the Population Estimates and Projections database of the UN Population Division (http://www.un.org/esa/population/ unpop.htm) or, in some cases from national census reports. Subnational versus national estimates For a number of countries national estimates were found in reports. For other countries the estimates were subnational, meaning that they were for a limited geographical area in the country only. These estimates were mainly obtained from research or intervention projects that attempted to assess the number of FSW in their intervention area. National estimates were then obtained by extrapolating the subnational esti- mates. National estimates are presented separately for urban areas, rural areas, and the entire country. RESULTS Subnational estimates of numbers of female sex workers Table 1 presents by country and region in the world: (1) the estimated population size of FSW in subnational areas, (2) the methods used for the estimation, and (3) the FSW prevalence calculated as the percentage of adult women in the area who are FSW. Most studies used mapping and census of FSW to estimate the number of FSW. This is an elaborate but straightforward method. All sites where sex work is known to occur, openly or hidden, are mapped with the help of informants and key people and at each of these sites women who engage in sex work are counted. In Diego-Suarez, Madagascar, the number of sex workers was estimated using the capture-recapture method. In a first phase, sex workers in predetermined geographic areas are ‘‘captured’’ and ‘‘marked’’ by giving them a token or an educational brochure on a particular day and time. A few weeks later, in the same places and at the same times, a second sample is ‘‘captured’’, which comprises of a certain number of people who were captured in the first round. Under the assumption that the proportion of marked people found in the second round is a reasonable estimate of the marked proportion in the unknown population, the size of the entire population can be estimated. In Indonesia the multiplier method was used. A detailed description of the method can be found elsewhere.21 In brief, data from one source (an official register of sex workers) were used as basis for the estimation. The numbers obtained from the register were compared with numbers obtained from other sources and the extent to which the numbers on the register were underestimated was assessed. The ‘‘multipliers’’ thus obtained were applied to the numbers of sex workers on the register. For West Africa estimates of the numbers of FSW were available for urban areas in Benin, Cameroon, Ghana, Ivory Coast, Ghana, Burkina Faso, and Niger.11–13 (Jacques Pe´pin, personal communication, 2005; Bea Vuylsteke, personal communication, 2005; Julio Soto, personal communication 2005). The FSW prevalence ranged from 0.7% to 4.3% (median 1.7%) in the capitals and from 0.4% to 0.7% (median 0.6%) in the smaller provincial towns. In Cotonou (Benin), Ouagadougou (Burkina Faso), and Niamey (Niger) 32%, 38%, and 70% of estimated numbers of FSW were indirect or hidden sex workers. In Benin, Burkina Faso, and Ivory Coast, 50%–70% of the FSW were between 20 and 29 years old. For East Africa, subnational estimates were available from two countries. Family Health International (FHI) conducted a mapping and census study in Addis Ababa and Nazareth, the latter being a popular holiday destination located at the crossroads of two major highways in the Oromia Region. The enumeration included three groups of women: (1) street based sex workers, (2) establishment based FSW working in hotels, bars, restaurants, red light houses, pastry shops, tea and coffee houses, beer shops, and (3) women working as waitresses in the same establishments. Among the waitresses 40–45% admitted to being involved in sex work besides their official job. In Addis Ababa and Nazareth, 35% and 30% of the FSW were considered as indirect FSW.14 15 The majority of FSW were aged 25–29 years old and the reported main motivation for getting into sex work was to generate an income for themselves and their family. In Kenya numbers of FSW were estimated in several locations in the west of the country and along the Mombasa-Kampala highway. The FSW prevalence was 3% in Kisumu, the capital of Nyanza Province,11 and nearly 7% in the four border and trading centres Busia, Mumias, Nzoia, and Webuye.5 The ‘‘Hotspot mapping of transactional sex on the Northern Corridor Mombasa-Kampala’’ project aimed to quantify the levels of commercial sex work along the major transport route linking the East African coast with the interior of Africa. The Kenyan part of this highway stretches over about 300 miles between Mombasa and Nairobi.16 The estimated number of FSW along this part of the highway, and excluding Mombasa and Nairobi, was 2700. The highway crosses an area with very low population density. General population data are missing and as such the prevalence of FSW was not calculated for this rural area, but it is expected to be relatively high. Estimates of female sex workers iii19 www.stijournal.com Table 1 Subnational estimated numbers of FSW and FSW prevalence per region Country Location Area FSW, n Adult women, n (15–49 years) % FSW Estimation method Year Reference Sub-Saharan Africa West African countries Benin Cotonou Capital 1915 133,912 1.4% Mapping & Census 1997 11 Capital 1750 149,648 1.2% Mapping & Census 2001 12 Porto-Novo Provincial town 274 55,405 0.5% Mapping & Census 2004 12 Abomey/Bohicon Provincial town 36 68,374 0.1% Mapping & Census 2004 12 Parakou Provincial town 236 50,896 0.5% Mapping & Census 2000 12 Kandi Provincial town 131 34,538 0.4% Mapping & Census 2004 12 Malanville Provincial town 105 8576 1.2% Mapping & Census 2004 12 Burkina Faso Ouagadougou Capital 8000 185,442 4.3% Mapping & Census 2000–03 PC* Cameroon Yaounde´ Capital 5600 252,210 2.2% Mapping & Census 1997 11 Ivory Coast Abidjan Capital 6000 867,266 0.7% Mapping & Census 2000 PC* San Pedro Provincial town 500 139,525 0.4% Mapping & Census NA PC* Bouake´ Provincial town 300 116,738 0.3% Mapping & Census 2000 PC* Korhogo Provincial town 347 36,327 1.0% Mapping & Census 2001 PC* Aboisso Provincial town 289 32,500 0.9% Mapping & Census 2004 PC* Daola Provincial town 497 38,478 1.2% Mapping & Census 2004 PC* Yamoussouka Provincial town 245 40,177 0.6% Mapping & Census 2004 PC* Ghana Accra-Tema Capital 5000 457,587 1.1% Mapping & Census 2003 13 Sekondi-Takoradi Provincial town 492 69,099 0.7% Mapping & Census 2003 PC* Niger Niamey Capital 11,249 427,680 2.6% Mapping & Census 2004 PC* East-African countries Ethiopia Addis Ababa Capital 12,453 599,886 2.1% Mapping & Census 2002 14 Nazareth Provincial town 1172 40,098 2.9% Mapping & Census 2002 15 Kenya Kisumu Provincial town 1374 45,158 3.0% Mapping&Census 1997 11 Busia, Mumias, Nzoia, Webuye (W. Province) 4 Provincial towns 1500 21,676 6.9% Mapping & Census 1999 5 Highway between Mombassa and Nairobi Truck stops 2700 – – Mapping & Census 2004 16 South-African countries Zambia Ndola Provincial town 2288 94,761 2.4% Mapping & Census 1997 11 Chirundu, Livingstone, Chipata, Nakonde, Kasumbalesa, Kapiri Mposhi 6 truck stops along the Southern highways 1500 55,375 2.7% Mapping & Census 2000 17 Madagascar Diego-Suarez Provincial town 2684 22,500 12.0% Capture-recapture 2001 18 Asia India Mumbai Capital of State 14,108 2,974,320 0.5% Mapping & Census 2001 19 (Maharashtra) Thane Provincial town 1335 306,549 0.4% Mapping & Census 2001 19 Pune Provincial town 2632 629,582 0.4% Mapping & Census 2001 19 Sangli District 1191 650,000 0.2% Mapping & Census 2001 19 Nepal Kathmandu District 1657 162,203 1.0% Mapping & Census 2001 20 Bhaktapur District 84 16,898 0.5% Mapping & Census 2001 20 Lalithpur District 267 37,612 0.7% Mapping & Census 2001 20 Indonesia Aceh Province 562 1,095,602 0.05% Multiplier method 2002 21 Sumatra Utara Province 18,659 3,246,372 0.6% Multiplier method 2002 21 Sumatra Barat Province 492 1,184,834 0.04% Multiplier method 2002 21 Riau Province 21,503 1,379,750 1.6% Multiplier method 2002 21 Jambi Province 3360 671,192 0.5% Multiplier method 2002 21 Sumatra Selatan Province 16,233 1,923,786 0.8% Multiplier method 2002 21 Bengkulu Province 1853 436,121 0.4% Multiplier method 2002 21 Jawa Barat Province 18,192 9,961,637 0.2% Multiplier method 2002 21 Lampung Province 5398 1,876,940 0.3% Multiplier method 2002 21 Bangka Belitung Province 726 250,965 0.3% Multiplier method 2002 21 Jakarta Province 32,448 2,331,465 1.4% Multiplier method 2002 21 Banten Province 7202 2,258,127 0.3% Multiplier method 2002 21 Jawa Tengah Province 24,455 8,706,534 0.3% Multiplier method 2002 21 Jogjakarta Province 4627 870,291 0.5% Multiplier method 2002 21 Jawa Timur Province 29,116 9,694,500 0.3% Multiplier method 2002 21 Kalimantan Barat Province 3350 1,119,862 0.3% Multiplier method 2002 21 Kalimantan Tengah Province 6410 517,267 1.2% Multiplier method 2002 21 Kalimantan Selatan Province 2819 832,088 0.3% Multiplier method 2002 21 Kalimantan Timur Province 13,021 683,740 1.9% Multiplier method 2002 21 Bali Province 4304 878,378 0.5% Multiplier method 2002 21 Nusa Tengara Barat Province 845 1,117,910 0.08% Multiplier method 2002 21 Nusa Tengara Timur Province 377 1,066,044 0.04% Multiplier method 2002 21 Sulawesi Utara Province 958 557,979 0.2% Multiplier method 2002 21 Sulawesi Tengah Province 977 606,778 0.2% Multiplier method 2002 21 Sulawesi Selatan Province 2614 2,245,021 0.1% Multiplier method 2002 21 Sulawesi Tenggara Province 1103 507,507 0.2% Multiplier method 2002 21 Gorontalo Province 696 232,282 0.3% Multiplier method 2002 21 Maluku Province 1762 324,303 0.5% Multiplier method 2002 21 Maluku Utara Province 1033 204,118 0.5% Multiplier method 2002 21 Papua Province 9386 617,374 1.5% Multiplier method 2002 21 Cambodia Phnom Penh Province 4727 170,404 2.8% Census 2003 PC* Siem Reap Province 674 35,473 1.9% Census 2003 PC* Kandal Province 308 17,198 1.8% Census 2003 PC* iii20 Vandepitte, Lyerla, Dallabetta, et al www.stijournal.com In Zambia, in Southern Africa, two studies have estimated the numbers of FSW, one in Ndola (the second largest city in the country), and one in six truck stops along the border with Zimbabwe and Malawi.11 17 The prevalence of FSW was estimated at 2.4% in Ndola and 2.7% in the truck stops. In Diego-Suarez, a provincial capital and port city in Madagascar, an attempt was made to enumerate the FSW population through the capture-recapture method.18 An estimated 12% of the adult female population in this town would be involved in sex work. Subnational estimates of numbers of FSW were found for four countries in Asia, including India, Nepal, Indonesia, and Cambodia. In the State of Maharashtra in India, mapping and census of FSW was conducted in the capital Mumbai, in two smaller towns and in the district of Sangli, located on a national highway.19 Enumeration of women selling sex was done at brothels, private houses, hotels, bars, or pick-up points (for example, parks, streets, truck stops). In Sangli, where the mapping and census was conducted in an urban and a rural area, 75% of brothels were found in rural sites. Rural FSW were largely a hidden group and although villagers were aware of their existence, their presence was not openly acknowledged. A similar mapping study was carried out in the Kathmandu Valley of Nepal, covering three districts.20 In Indonesia, estimates of FSW were obtained for all provinces, using the multiplier method.2