Zimbabwe | Prevalence of HIV, female (% ages 15-24)
Prevalence of HIV, female is the percentage of females who are infected with HIV. Youth rates are as a percentage of the relevant age group. 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.
Publisher
The World Bank
Origin
Republic of Zimbabwe
Records
63
Source
|
value
Min
Max
|
year |
|---|---|
| 1960 | |
| 1961 | |
| 1962 | |
| 1963 | |
| 1964 | |
| 1965 | |
| 1966 | |
| 1967 | |
| 1968 | |
| 1969 | |
| 1970 | |
| 1971 | |
| 1972 | |
| 1973 | |
| 1974 | |
| 1975 | |
| 1976 | |
| 1977 | |
| 1978 | |
| 1979 | |
| 1980 | |
| 1981 | |
| 1982 | |
| 1983 | |
| 1984 | |
| 1985 | |
| 1986 | |
| 1987 | |
| 1988 | |
| 1989 | |
| 16 | 1990 |
| 18.6 | 1991 |
| 20.6 | 1992 |
| 21.7 | 1993 |
| 22.1 | 1994 |
| 21.8 | 1995 |
| 20.9 | 1996 |
| 19.6 | 1997 |
| 18.1 | 1998 |
| 16.6 | 1999 |
| 15.1 | 2000 |
| 13.8 | 2001 |
| 12.6 | 2002 |
| 11.6 | 2003 |
| 10.8 | 2004 |
| 10.2 | 2005 |
| 9.8 | 2006 |
| 9.4 | 2007 |
| 9.1 | 2008 |
| 8.8 | 2009 |
| 8.7 | 2010 |
| 8.5 | 2011 |
| 8.2 | 2012 |
| 8 | 2013 |
| 7.7 | 2014 |
| 7.4 | 2015 |
| 7 | 2016 |
| 6.6 | 2017 |
| 6.1 | 2018 |
| 5.6 | 2019 |
| 5.2 | 2020 |
| 4.7 | 2021 |
| 2022 |
Zimbabwe | Prevalence of HIV, female (% ages 15-24)
Prevalence of HIV, female is the percentage of females who are infected with HIV. Youth rates are as a percentage of the relevant age group. 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.
Publisher
The World Bank
Origin
Republic of Zimbabwe
Records
63
Source