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

Women's share of population ages 15+ living with HIV (%) in Other small states was 54.21 as of 2020. Its highest value over the past 30 years was 55.19 in 1997, while its lowest value was 52.59 in 2010.

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 54.62
1991 54.67
1992 54.68
1993 54.32
1994 54.92
1995 54.96
1996 54.84
1997 55.19
1998 55.07
1999 54.86
2000 54.03
2001 53.95
2002 53.76
2003 53.82
2004 53.53
2005 53.57
2006 53.29
2007 52.96
2008 52.78
2009 53.03
2010 52.59
2011 52.84
2012 52.93
2013 52.97
2014 53.10
2015 53.16
2016 53.28
2017 53.37
2018 53.91
2019 54.04
2020 54.21

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