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HIV and AIDS Estimates, Country factsheets - Albania 2016
UNAIDS; AIDSinfo
(2019)
C2
Accessed: 29.09.2019
HIV and AIDS Estimates - Country factsheets Armenia 2016
UNAIDS; AIDSinfo
(2019)
C2
Accessed: 29.09.2019
Country factsheets Kazakhstan 2016 - HIV and AIDS Estimates
UNAIDS; AIDSinfo
(2019)
C2
Accessed: 26.09.2019
Country factsheets Kyrgyzstan 2016 - HIV and AIDS Estimates
UNAIDS; AIDSinfo
(2019)
C2
Accessed: 26.09.2019
Country factsheets Uzbekistan 2016 - HIV and AIDS Estimates
UNAIDS; AIDSinfo
(2019)
C2
Accessed: 24.09.2019
Country Factsheets Azerbaijan 2016 - HIV and AIDS Estimates
UNAIDS; AIDSinfo
(2019)
C2
Accessed: 27.09.2019
Country factsheets Montenegro 2016 - HIV and AIDS Estimates
UNAIDS; AIDSinfo
(2019)
C2
Accessed: 24.09.2019
Country factsheets Tajikistan 2016 - HIV and AIDS Estimates
UNAIDS; AIDSinfo
(2019)
C2
Accessed: 04.10.2019
Country factsheets Ukraine 2016 - HIV and AIDS Estimates
UNAIDS; AIDSinfo
(2019)
C2
Accessed: 04.10.2019
dashboard
Country Factsheets Bosnia and Herzegovina 2016 - HIV and AIDS Estimates
UNAIDS; AIDSinfo
(2019)
C2
Accessed: 26.09.2019
Country factsheets Republic of Moldova 2016 - HIV and AIDS Estimates
recommended
UNAIDS; AIDSinfo
(2019)
C2
Accessed: 24.09.2019
Country factsheets: The former Yugoslav republic of Macedonia 2016 - HIV and AIDS Estimates
UNAIDS; AIDSinfo
(2019)
C2
Accessed: 26.09.2019
Background: Comparable estimates of health spending are crucial for the assessment of health systems and to optimally deploy health resources. The methods used to track health spending continue to e
...
volve, but little is known about the distribution of spending across diseases. We developed improved estimates of health spending by source, including development assistance for health, and, for the first time, estimated HIV/AIDS spending on prevention and treatment and by source of funding, for 188 countries.
more
Background: Sustainable Development Goal (SDG) 3 aims to “ensure healthy lives and promote well-being for all at all ages”. While a substantial effort has been made to quantify progress towards SDG3, less research has focused on tracking spendin
...
g towards this goal. We used spending estimates to measure progress in financing the priority areas of SDG3, examine the association between outcomes and financing, and identify where resource gains are most needed to achieve the SDG3 indicators for which data are available. Methods: We estimated domestic health spending, disaggregated by source (government, out-of-pocket, and prepaid private) from 1995 to 2017 for 195 countries and territories. For disease-specific health spending, we estimated spending for HIV/AIDS and tuberculosis for 135 low-income and middle-income countries, and malaria in 106 malaria-endemic countries, from 2000 to 2017. We also estimated development assistance for health (DAH) from 1990 to 2019, by source, disbursing development agency, recipient, and health focus area, including DAH for pandemic preparedness. Finally, we estimated future health spending for 195 countries and territories from 2018 until 2030. We report all spending estimates in inflation-adjusted 2019 US$, unless otherwise stated.
more
Objective: There are an estimated 38 million people with HIV (PWH), with significant economic consequences. We aimed to collate global lifetime costs for managing HIV.
Design: We conducted a system
...
atic review (PROSPERO: CRD42020184490) using five databases from 1999 to 2019.
Methods: Studies were included if they reported primary data on lifetime costs for PWH. Two reviewers independently assessed the titles and abstracts, and data were extracted from full texts: lifetime cost, year of currency, country of currency, discount rate, time horizon, perspective, method used to estimate cost and cost items included. Descriptive statistics were used to summarize the discounted lifetime costs [2019 United States dollars (USD)].
more
This report investigates the impact of potential misclassification of samples on HIV prevalence estimates for 23 surveys conducted from 2010-2014. In addition to visual inspection of laboratory resu
...
lts, we examined how accounting for potential misclassification of HIV status through Bayesian latent class models affected the prevalence estimates. Two types of Bayesian models were specified: a model that only uses the individual dichotomous test results and a continuous model that uses the quantitative information of the EIA (i.e., the signal-to-cutoff values). Overall, we found that adjusted prevalence estimates matched the surveys’ original results, with overlapping uncertainty intervals. This suggested that misclassification of HIV status should not affect the prevalence estimates in most surveys. However, our analyses suggested that two surveys may be problematic. The prevalence could have been overestimated in the Uganda AIDS Indicator Survey 2011 and the Zambia Demographic and Health Survey 2013-14, although the magnitude of overestimation remains difficult to ascertain. Interpreting results from the Uganda survey is difficult because of the lack of internal quality control and potential violation of the multivariate normality assumption of the continuous Bayesian latent class model. In conclusion, despite the limitations of our latent class models, our analyses suggest that prevalence estimates from most of the surveys reviewed are not affected by sample misclassification.
more
Indicators for monitoring the 2016 United Nations Political Declaration on Ending AIDS
UNAIDS supports countries to collect information on their national HIV responses through the Global AIDS M ... onitoring (GAM) framework—an annual collection of 72 indicators on the response to HIV in a country. These data form part of the data set used to report back to the General Assembly.
Different from the HIV epidemiological estimates that countries produce for data on the state of the epidemic in a country—that is, data for making estimates on the number of people living with HIV, AIDS-related deaths, etc.—GAM collects information on HIV programmes, including the number of people living with HIV who know their HIV status and people on HIV treatment, and on stigma and discrimination. A full list of the indicators is given in the GAM guidelines. more
UNAIDS supports countries to collect information on their national HIV responses through the Global AIDS M ... onitoring (GAM) framework—an annual collection of 72 indicators on the response to HIV in a country. These data form part of the data set used to report back to the General Assembly.
Different from the HIV epidemiological estimates that countries produce for data on the state of the epidemic in a country—that is, data for making estimates on the number of people living with HIV, AIDS-related deaths, etc.—GAM collects information on HIV programmes, including the number of people living with HIV who know their HIV status and people on HIV treatment, and on stigma and discrimination. A full list of the indicators is given in the GAM guidelines. more
Eastern Europe and Central Asia 2018
UNAIDS; AIDSinfo
(2019)
C2
Regional Factsheets
HIV and AIDS Estimates
Accessed: 29.09.2019
