Background: Estimating HIV incidence is essential to monitoring progress in sub-Saharan African nations toward global epidemic control. One method for incidence estimation is to test nationally representative samples using laboratory-based incidence assays. An alternative method based on reported HIV testing history and the proportion of undiagnosed infections has recently been described.
Methods: We applied an HIV incidence estimation method which uses history of testing to nationally representative cross-sectional survey data from 12 sub-Saharan African nations with varying country-specific HIV prevalence. We compared these estimates with those derived from laboratory-based incidence assays. Participants were tested for HIV using the national rapid test algorithm and asked about prior HIV testing, date and result of their most recent test, and date of antiretroviral therapy initiation.
Results: The testing history-based method consistently produced results that are comparable and strongly correlated with estimates produced using a laboratory-based HIV incidence assay (ρ = 0.85). The testing history-based method produced incidence estimates that were more precise compared with the biomarker-based method. The testing history-based method identified sex-, age-, and geographic location-specific differences in incidence that were not detected using the biomarker-based method.
Conclusions: The testing history-based method estimates are more precise and can produce age-specific and sex-specific incidence estimates that are informative for programmatic decisions. The method also allows for comparisons of the HIV transmission rate and other components of HIV incidence among and within countries. The testing history-based method is a useful tool for estimating and validating HIV incidence from cross-sectional survey data.
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http://dx.doi.org/10.1097/QAI.0000000000003123 | DOI Listing |
Sci Rep
June 2024
Faculty of Engineering and IT, University of Technology Sydney, Ultimo, NSW, 2007, Australia.
This paper proposes a novel multi-hybrid algorithm named DHPN, using the best-known properties of dwarf mongoose algorithm (DMA), honey badger algorithm (HBA), prairie dog optimizer (PDO), cuckoo search (CS), grey wolf optimizer (GWO) and naked mole rat algorithm (NMRA). It follows an iterative division for extensive exploration and incorporates major parametric enhancements for improved exploitation operation. To counter the local optima problems, a stagnation phase using CS and GWO is added.
View Article and Find Full Text PDFGynecol Oncol
April 2024
Weill Cornell Medicine, New York, NY, United States of America.
Background: Patients with a personal or family history of cancer may have elevated risk of developing future cancers, which often remains unrecognized due to lapses in screening. This pilot study assessed the usability and clinical outcomes of a cancer risk stratification tool in a gynecologic oncology clinic.
Methods: New gynecologic oncology patients were prompted to complete a commercially developed personal and family history-based risk stratification tool to assess eligibility for genetic testing using National Comprehensive Cancer Network criteria and estimated lifetime breast cancer risk using the Tyrer-Cuzick model.
Sci Rep
February 2024
Faculty of Engineering and IT, University of Technology Sydney, Ultimo, NSW, 2007, Australia.
Differential evolution (DE) is a robust optimizer designed for solving complex domain research problems in the computational intelligence community. In the present work, a multi-hybrid DE (MHDE) is proposed for improving the overall working capability of the algorithm without compromising the solution quality. Adaptive parameters, enhanced mutation, enhanced crossover, reducing population, iterative division and Gaussian random sampling are some of the major characteristics of the proposed MHDE algorithm.
View Article and Find Full Text PDFSurg Oncol Clin N Am
April 2024
Division of Colorectal Surgery, Department of General Surgery, Kaiser Permanente San Jose Medical Center, Kaiser Permanente Northern California, 280 Hospital Parkway, Building B, San Jose, CA 95119, USA. Electronic address:
Cost-effectiveness analysis of precision oncology can help guide value-driven care. Next-generation sequencing is increasingly cost-efficient over single gene testing because diagnostic algorithms require multiple individual gene tests to determine biomarker status. Matched targeted therapy is often not cost-effective due to the high cost associated with drug treatment.
View Article and Find Full Text PDFJAMA Netw Open
February 2024
Program in Cancer Health Economics Research, Jonsson Comprehensive Cancer Center, and Department of Radiation Oncology, School of Medicine, University of California, Los Angeles.
Importance: The current method of BRCA testing for breast and ovarian cancer prevention, which is based on family history, often fails to identify many carriers of pathogenic variants. Population-based genetic testing offers a transformative approach in cancer prevention by allowing for proactive identification of any high-risk individuals and enabling early interventions.
Objective: To assess the lifetime incremental effectiveness, costs, and cost-effectiveness of population-based multigene testing vs family history-based testing.
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