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Estimates of the size of key populations at risk for HIV infection: men who have sex with men, female sex workers and injecting drug users in Nairobi, Kenya
  1. Jerry Okal1,
  2. Scott Geibel1,
  3. Nicolas Muraguri2,
  4. Helgar Musyoki2,
  5. Waimar Tun3,
  6. Dita Broz4,
  7. David Kuria5,
  8. Andrea Kim6,
  9. Tom Oluoch6,
  10. H Fisher Raymond7
  1. 1Population Council, Nairobi, Kenya
  2. 2National AIDS and STD Control Programme, Nairobi, Kenya
  3. 3Population Council, Washington, DC, USA
  4. 4Division of Global HIV/AIDS, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
  5. 5Gay and Lesbian Coalition of Kenya, Nairobi, Kenya
  6. 6Division of Global HIV/AIDS, Centers for Disease Control and Prevention, Nairobi, Kenya
  7. 7San Francisco Department of Public Health, HIV AIDS Statistics and Epidemiology, San Francisco, California, USA
  1. Correspondence to H Fisher Raymond, San Francisco Department of Public Health, HIV AIDS Statistics and Epidemiology, San Francisco, CA 94102, USA; hfisher.raymond{at}


Objectives Size estimates of populations at higher risk for HIV infection are needed to help policy makers understand the scope of the epidemic and allocate appropriate resources. Population size estimates of men who have sex with men (MSM), female sex workers (FSW) and intravenous drug users (IDU) are few or non-existent in Nairobi, Kenya.

Methods We integrated three population size estimation methods into a behavioural surveillance survey among MSM, FSW and IDU in Nairobi during 2010–2011. These methods included the multiplier method, ‘Wisdom of the Crowds’ and an approach that drew on published literature. The median of the three estimates was hypothesised to be the most plausible size estimate with the other results forming the upper and lower plausible bounds. Data were shared with community representatives and stakeholders to finalise ‘best’ point estimates and plausible bounds based on the data collected in Nairobi, a priori expectations from the global literature and stakeholder input.

Results We estimate there are approximately 11 042 MSM with a plausible range of 10 000–22 222, 29 494 FSW with a plausible range of 10 000–54 467 FSW and approximately 6107 IDU and plausibly 5031–10 937 IDU living in Nairobi.

Conclusions We employed multiple methods and used a wide range of data sources to estimate the size of three hidden populations in Nairobi, Kenya. These estimates may be useful to advocate for and to plan, implement and evaluate HIV prevention and care programmes for MSM, FSW and IDU. Surveillance activities should consider integrating population size estimation in their protocols.

  • Africa
  • Developing World
  • Epidemiology (General)
  • HIV

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