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.
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.
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