Prevalence of HIV, total (% of population ages 15-49) - Country Ranking - Asia

Definition: Prevalence of HIV refers to the percentage of people ages 15-49 who are infected with HIV.

Source: UNAIDS estimates.

See also: Thematic map, Time series comparison

Find indicator:
Rank Country Value Year
1 Thailand 1.10 2018
2 Myanmar 0.80 2018
3 Cambodia 0.50 2018
4 Georgia 0.40 2018
4 Indonesia 0.40 2018
4 Malaysia 0.40 2018
7 Vietnam 0.30 2018
7 Bhutan 0.30 2018
7 Lao PDR 0.30 2018
10 Kazakhstan 0.20 2018
10 Kyrgyz Republic 0.20 2018
10 Armenia 0.20 2018
10 Israel 0.20 2018
10 Tajikistan 0.20 2018
10 Oman 0.20 2018
10 Uzbekistan 0.20 2018
10 Singapore 0.20 2018
18 Syrian Arab Republic 0.10 2018
18 Pakistan 0.10 2018
18 Philippines 0.10 2018
18 Nepal 0.10 2018
18 Yemen 0.10 2018
18 Jordan 0.10 2018
18 Japan 0.10 2018
18 Iran 0.10 2018
18 Bangladesh 0.10 2018
18 Afghanistan 0.10 2018
18 Kuwait 0.10 2018
18 Lebanon 0.10 2018
18 Sri Lanka 0.10 2018
18 Mongolia 0.10 2018

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Limitations and Exceptions: The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information.

Statistical Concept and Methodology: HIV prevalence rates reflect the rate of HIV infection in each country's population. Low national prevalence rates can be misleading, however. They often disguise epidemics that are initially concentrated in certain localities or population groups and threaten to spill over into the wider population. In many developing countries most new infections occur in young adults, with young women especially vulnerable. Data on HIV are from the Joint United Nations Programme on HIV/AIDS (UNAIDS). Changes in procedures and assumptions for estimating the data and better coordination with countries have resulted in improved estimates of HIV and AIDS. The models, which are routinely updated, track the course of HIV epidemics and their impact, making full use of information in HIV prevalence trends from surveillance data as well as survey data. The models take into account reduced infectivity among people receiving antiretroviral therapy (which is having a larger impact on HIV prevalence and allowing HIV-positive people to live longer) and allow for changes in urbanization over time in generalized epidemics. The estimates include plausibility bounds, which reflect the certainty associated with each of the estimates.

Aggregation method: Weighted average

Periodicity: Annual