A case study of nonlinear programming approach for repeated testing of HIV in a population stratified by subpopulations according to different risks of new infections. (December 2018)

• Record Type: Journal Article
• Authors: Yan, Ping; Zhang, Fan
• Is Part Of: Operations research for health care. Volume 19(2018)
• Page Start: 120
• Page End: 133
• Publication Date: 2018-12
• Subjects: Medical care -- Mathematical models -- Periodicals; Medical policy -- Mathematical models -- Periodicals; Health services administration -- Mathematical models -- Periodicals; Operations research -- Periodicals; Operations Research -- Periodicals; Health Services Research -- Periodicals; Health Policy -- Periodicals; Delivery of Health Care -- Periodicals; 362.106805
• DOI: 10.1016/j.orhc.2018.03.007
• Languages: English
• ISSNs: 2211-6923

Abstract

Abstract: Motivated by the vision of the Joint United Nations Programme on HIV/AIDS that 90% of people living with HIV will be diagnosed by year 2020, we present an optimization framework regarding repeated testing of an infectious disease which is transmitted unevenly in the population. A subset of HIV surveillance data in Canada with detailed and compatible variables is pooled for statistical analysis. The study population is Men having Sex with Men (MSM) in Canada from the pooled data. Estimated parameters regarding the HIV epidemic in the study population show that, across age strata, the number of new infections is distributed differently from the number of people living with HIV. A nonlinear programming algorithm is developed regarding which strata should be considered for repeated testing. Among strata in which repeated testing is considered, the optimal frequency of testing is calculated by stratum to minimize the expected number of tests per year. Scenarios and options that all fulfil the UNAIDS vision are presented. In addition to minimizing the expected number of tests per year, other considerations are also examined such as annual testing in selected strata and the tolerance to imperfect implementation of the testing program with low coverage or uptake rates.