Title |
Modelling the Potential Population Impact and Cost-Effectiveness of Self-Testing for HIV: Evaluation of Data Requirements
|
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Published in |
AIDS and Behavior, June 2014
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DOI | 10.1007/s10461-014-0824-x |
Pubmed ID | |
Authors |
Valentina Cambiano, Sue Napierala Mavedzenge, Andrew Phillips |
Abstract |
HIV testing uptake has increased dramatically in recent years in resource limited settings. Nevertheless, over 50 % of the people living with HIV are still unaware of their status. HIV self-testing (HIVST) is a potential new approach to facilitate further uptake of testing which requires consideration, taking into account economic factors. Mathematical models and associated economic analysis can provide useful assistance in decision-making processes, offering insight, in this case, into the potential long-term impact at a population level and the price-point at which free or subsidized HIVST would be cost-effective in a given setting. However, models are based on assumptions, and if the required data are sparse or limited, this uncertainty will be reflected in the results from mathematical models. The aim of this paper is to describe the issues encountered in modeling the cost-effectiveness of introducing HIVST, to indicate the evidence needed to support various modeling assumptions, and thus which data on HIVST would be most beneficial to collect. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 1 | 50% |
Switzerland | 1 | 50% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 2 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 1 | 1% |
Canada | 1 | 1% |
South Africa | 1 | 1% |
Unknown | 91 | 97% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Master | 21 | 22% |
Researcher | 19 | 20% |
Student > Ph. D. Student | 13 | 14% |
Other | 6 | 6% |
Student > Bachelor | 6 | 6% |
Other | 11 | 12% |
Unknown | 18 | 19% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 22 | 23% |
Nursing and Health Professions | 11 | 12% |
Social Sciences | 10 | 11% |
Agricultural and Biological Sciences | 6 | 6% |
Economics, Econometrics and Finance | 4 | 4% |
Other | 13 | 14% |
Unknown | 28 | 30% |