Guinea - Women's share of population ages 15+ living with HIV (%)

Women's share of population ages 15+ living with HIV (%) in Guinea was 65.60 as of 2020. Its highest value over the past 30 years was 65.60 in 2020, while its lowest value was 57.30 in 1990.

Definition: Prevalence of HIV is the percentage of people who are infected with HIV. Female rate is as a percentage of the total population ages 15+ who are living with HIV.

Source: UNAIDS estimates.

See also:

Year Value
1990 57.30
1991 58.10
1992 58.80
1993 59.50
1994 60.10
1995 60.60
1996 61.10
1997 61.50
1998 62.00
1999 62.40
2000 62.70
2001 63.10
2002 63.40
2003 63.70
2004 63.90
2005 64.00
2006 64.00
2007 64.00
2008 63.90
2009 63.70
2010 63.60
2011 63.50
2012 63.40
2013 63.60
2014 63.80
2015 64.20
2016 64.50
2017 64.80
2018 65.00
2019 65.20
2020 65.60

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

Classification

Topic: Health Indicators

Sub-Topic: Risk factors