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A mathematical model evaluates screening efficiency, estimating subgroup cancer risk from literature and comparing SOC-detected cancers with incremental MCED detections. Outcomes include true-positive to false-positive ratios, diagnostic yield, and cancer detection rate, with sensitivity analyses showing robust improvements and increased detection of cancers lacking an existing screening paradigm.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & 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BMC Health Services Research (2024) 24:1584  \n[https://doi.org/10.1186/s12913-024-12037-1](https://doi.org/10.1186/s12913-024-12037-1)  \nBMC Health Services Research  \n RESEARCH Open Access  \nEstimating the incremental population  \nhealth impact of a multi-cancer early detection (MCED) test to complement existing screening among elevated risk populations with multiple cancer risk factors: a mathematical modeling study  \nAshley Kim1*, Ze Cong1, Abdul-Rahman Jazieh2, Timothy R. Church3, Heidi Reichert4, Gina Nicholson4, Jon Fryzek4 and Sarah S. Cohen4  \nAbstract  \nBackground The added benefits of a multi-cancer early detection (MCED) test among individuals with multiple risk factors will help policy decision-makers allocate limited healthcare resources. This study sought to estimate the population health implications of adding an MCED test to standard-of-care (SOC) cancer screening tests among individuals aged 50–79 years with additional cancer risk factors (i. e., tobacco use, family history of cancer, and/or obesity) . Methods A mathematical model was developed to assess the potential screening efficiency of an MCED test in addition to current guideline-recommended screenings. Among the US population of 107 million adults aged 50–79 years, the size of, and cancer risk among specific subgroups (i. e., smokers, obese individuals, those with a family history of cancer) as well as the general population were estimated from the literature. Published estimates of screening uptake and/or performance were used to model the number of cancers detected by SOC screening alone, and the number of incremental cancers that could be detected by an MCED test. Screening efficiency outcomes included the true-positive:false-positive (TP:FP) ratio, diagnostic yield (DY), and cancer detection rate (CDR) . Sensitivity analyses were conducted by varying the values of key parameters.  \nResults Among all subgroups, the TP:FP ratios were higher with an MCED test than with SOC screening alone, and higher than in the general population, suggesting improved screening efficiency with an MCED test. The estimatedTP:FP ratios were 1:43 .3 (SOC)/1:1 .1 (MCED), 1:40 .4/1:0 . 8, 1:36 . 9/1:0 . 5 among former, ever, and current smokers, respectively, 1:38 . 3/1:0 . 9 (those with a family history of cancer), and 1:39 . 3/1:1 .1 (obese individuals) . Among the general population, the TP:FP ratios were 1:43 . 5/1:1 .1. Across all subpopulations, the DY and CDR increased by up to threefold with an MCED test, when compared to SOC screening alone, with up to 75% of cancers detected with an MCED test lacking a screening paradigm. These results were robust in sensitivity analyses.  \n*Correspondence: Ashley Kim [kim.ashley@gmail.com](kim.ashley@gmail.com)  \nFull list of author information is available at the end of the article  \n© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://creativecommons.org/licenses/by-nc-nd/4.0/](http://creativecommons.org/licenses/by-nc-nd/4.0/.)[.](http://creativecommons.org/licenses/by-nc-nd/4.0/.)  \nKi","cbCaiokrEMyIxWmu","https://ap.wps.com/l/cbCaiokrEMyIxWmu","pdf",1791163,12,"English","# Abstract\n## Background\n## Methods\n## Results\n## Conclusions\n# Background\n## Cancer risk in older adults and projected case growth\n## Risk factors and cancer prevention avenues","[{\"question\":\"What question does the study address about the MCED test?\",\"answer\":\"It estimates the population health impact of adding an MCED test to standard cancer screening for people aged 50–79 with multiple cancer risk factors.\"},{\"question\":\"How was the MCED test evaluated in the study?\",\"answer\":\"A mathematical modeling approach compared screening outcomes when MCED is added to guideline-recommended standard-of-care screening.\"},{\"question\":\"What were the main findings on screening efficiency and detection?\",\"answer\":\"True-positive to false-positive ratios were higher with MCED than with standard-of-care alone, and diagnostic yield and cancer detection rate increased by up to threefold, with many detected cancers lacking a screening paradigm.\"}]","Estimating the incremental population health impact of a multi-cancer early detection (MCED) test to complement existing screening among elevated risk populations with multiple cancer risk factors - a mathematical modeling study | PDF",1789999794]