South Sudan - Prevalence of HIV, male (% ages 15-24)

Prevalence of HIV, male (% ages 15-24) in South Sudan was 0.600 as of 2020. Its highest value over the past 30 years was 0.600 in 2020, while its lowest value was 0.300 in 1990.

Definition: Prevalence of HIV, male is the percentage of males who are infected with HIV. Youth rates are as a percentage of the relevant age group.

Source: UNAIDS estimates.

See also:

Year Value
1990 0.300
1991 0.300
1992 0.400
1993 0.400
1994 0.500
1995 0.500
1996 0.600
1997 0.600
1998 0.600
1999 0.600
2000 0.600
2001 0.600
2002 0.600
2003 0.600
2004 0.600
2005 0.500
2006 0.500
2007 0.500
2008 0.500
2009 0.600
2010 0.600
2011 0.600
2012 0.600
2013 0.600
2014 0.600
2015 0.600
2016 0.600
2017 0.600
2018 0.600
2019 0.600
2020 0.600

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

General Comments: In many developing countries most new infections occur in young adults, with young women being especially vulnerable.

Classification

Topic: Health Indicators

Sub-Topic: Risk factors