HIV testing and status awareness among key populations (A-D)
Export Indicator
Progress providing HIV testing services to members of key populations.
This indicator is divided into four sub-indicators:
A. HIV testing among sex workers.
B. HIV testing among men who have sex with men.
C. HIV testing among people who inject drugs.
D. HIV testing among transgender people.
Ensuring that people living with HIV receive the care and treatment required to live healthy, productive lives and reduce the chance of transmitting HIV, requires that they know their HIV status. In many countries, targeting testing and counselling for locations and populations with the highest HIV burden is the most efficient way to reach people living with HIV and ensure that they know their HIV status. This indicator captures the effectiveness of HIV testing interventions in reaching populations at higher risk of HIV infection.
Respondent knows they are living with HIV (answer to Question 3 is “positive”)
The number of respondents in the yellow boxes is the numerator.
Number of people in key populations who answered question Question 1 (below).
Numerator/denominator
Every two years
A, C: Gender (female, male and transgender).
D: gender (transman, transwoman, other)
A-D: Age (<25 and 25+ years).
If there are subnational data available, please provide the disaggregation by administrative area, city, or site in the space provided. Submit the digital version of any available survey reports using the upload tool.
HIV-positive respondents may be less willing to accurately report their HIV status than HIV-negative respondents, leading to under-reporting of testing coverage among people living with HIV.
Surveying key populations can be challenging. Consequently, the data obtained may not be based on a representative national sample of the key populations at higher risk being surveyed. If there are concerns that the data are not based on a representative sample, the interpretation of the survey data should reflect these concerns. If there are different sources of data, the best available estimate should be used.
BBS-Lite is less technically demanding and may be undertaken with fewer resources than larger-scale, more comprehensive bio-behavioural surveys. It can also be repeated more frequently and yield results more rapidly. The results supplement data from other sources. The BBS-Lite involves non-probability sampling methods, and therefore in many cases the results are most useful for understanding the local situation for programming purposes.