医疗保险受益人结直肠癌筛查中多靶点粪便DNA检测的成本效益分析
基于微观模拟模型的医疗保险受益人结直肠癌筛查中多靶点粪便DNA检测的成本效益数据集。

基本信息
资源简介
医疗保险受益人结直肠癌筛查中多靶点粪便DNA检测的成本效益分析
【数据集背景】
本数据集源站标题为《Cost-effectiveness of a multitarget stool DNA test for colorectal cancer screening of Medicare beneficiaries》。配套论文《Cost-effectiveness of a multitarget stool DNA test for colorectal cancer screening of Medicare beneficiaries》发表于 PLOS ONE(2019),DOI:10.1371/journal.pone.0220234。源站首次发布:2019-09-04。
【数据内容】
源站原始描述:Background In 2014, the Centers for Medicare and Medicaid Services (CMS) began covering a multitarget stool DNA (mtSDNA) test for colorectal cancer (CRC) screening of Medicare beneficiaries. In this study, we evaluated whether mtSDNA testing is a cost-effective alternative to other CRC screening strategies reimbursed by CMS, and if not, under what conditions it could be. Methods We use three independently-developed microsimulation models to simulate a cohort of previously unscreened US 65-year-o…
【数据结构与技术规格】
文件数 9 个,合计 395.55 KB。pone.0220234.s001.tif:image/tiff,108.18 KB。pone.0220234.s002.tif:image/tiff,65.79 KB。pone.0220234.s003.tif:image/tiff,92.48 KB。数据集 DOI:10.1371/journal.pone.0220234。
【主题与分类】
源站学科分类:Medicine、Biotechnology、Evolutionary Biology、Biological Sciences not elsewhere classified、Cancer、Science Policy。主题标签:LYG、triennial mtSDNA testing、multitarget stool DNA test、colorectal cancer screening、CRC screening strategies、CMS、independently-developed microsimulation models、strategy、2017 reimbursement rate、threshold adherence rates。
【适用方向】
结构化表格数据可直接用于统计建模、特征工程与队列分析。
【获取与许可】
源站页面:https://plos.figshare.com/articles/dataset/Cost-effectiveness_of_a_multitarget_stool_DNA_test_for_colorectal_cancer_screening_of_Medicare_beneficiaries/9769055
使用许可:CC BY 4.0(https://creativecommons.org/licenses/by/4.0/)。
规范引用:Naber, Steffie K.; Knudsen, Amy B.; Zauber, Ann G.; Rutter, Carolyn M.; Fischer, Sara E.; Pabiniak, Chester J.; et al. (2019). Cost-effectiveness of a multitarget stool DNA test for colorectal cancer screening of Medicare beneficiaries. PLOS ONE. Dataset
【使用注意】
· 源站最后修改于 2023-06-01,引用时请注意版本时效。
下载信息
注册下载
需要注册 Kaggle 账号并登录后下载,适合需要跟踪下载记录和使用 API 的用户。
暂未开放公开下载
无需注册即可直接获取公开样本或文档,适合快速预览和评估数据集质量。
免登录有偿下载
公开数据集受托下载与技术交付服务。
提供高速下载与技术交付服务(收技术服务费,非数据销售)
暂未开放当前数据集主要来源为 Kaggle 公开托管,完整影像包建议通过原始链接或 Kaggle API 获取。
使用方式
数据集获取(Figshare)
- 源站标题:Cost-effectiveness of a multitarget stool DNA test for colorectal cancer screening of Medicare beneficiaries
- DOI:10.1371/journal.pone.0220234
- 发布日期:2019-09-04
- 文件数:9 个
- 文件总体积:395.55 KB
- 源站页面:https://plos.figshare.com/articles/dataset/Cost-effectiveness_of_a_multitarget_stool_DNA_test_for_colorectal_cancer_screening_of_Medicare_beneficiaries/9769055
命令行下载
curl -L -o "pone.0220234.s001.tif" "https://ndownloader.figshare.com/files/17500439"
curl -L -o "pone.0220234.s002.tif" "https://ndownloader.figshare.com/files/17500445"
curl -L -o "pone.0220234.s003.tif" "https://ndownloader.figshare.com/files/17500448"
curl -L -o "pone.0220234.s004.docx" "https://ndownloader.figshare.com/files/17500451"
curl -L -o "pone.0220234.s005.docx" "https://ndownloader.figshare.com/files/17500454"
curl -L -o "pone.0220234.s006.docx" "https://ndownloader.figshare.com/files/17500460"
curl -L -o "pone.0220234.s007.docx" "https://ndownloader.figshare.com/files/17500463"
curl -L -o "pone.0220234.s008.docx" "https://ndownloader.figshare.com/files/17500466"
文件清单
pone.0220234.s001.tif(108.18 KB)pone.0220234.s002.tif(65.79 KB)pone.0220234.s003.tif(92.48 KB)pone.0220234.s004.docx(16.82 KB)pone.0220234.s005.docx(14.24 KB)pone.0220234.s006.docx(19.58 KB)pone.0220234.s007.docx(17.04 KB)pone.0220234.s008.docx(16.73 KB)pone.0220234.s009.docx(44.70 KB)
文件总体积 395.55 KB(源站 API 实测)。
数据加载示例(图像类)
from PIL import Image
import glob, os
files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)
# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path) # 要求 子目录=类别
目录组织与标注格式以源站说明和下载后实际文件为准。
数据说明
Background
In 2014, the Centers for Medicare and Medicaid Services (CMS) began covering a multitarget stool DNA (mtSDNA) test for colorectal cancer (CRC) screening of Medicare beneficiaries. In this study, we evaluated whether mtSDNA testing is a cost-effective alternative to other CRC screening strategies reimbursed by CMS, and if not, under what conditions it could be.
Methods
We use three independently-developed microsimulation models to simulate a cohort of previously unscreened US 65-year-olds who are screened with triennial mtSDNA testing, or one of six other reimbursed screening strategies. Main outcome measures are discounted life-years gained (LYG) and lifetime costs (CMS perspective), threshold reimbursement rates, and threshold adherence rates. Outcomes are expressed as the median and range across models.
Results
Compared to no screening, triennial mtSDNA screening resulted in 82 (range: 79–88) LYG per 1,000 simulated individuals. This was more than for five-yearly sigmoidoscopy (80 (range: 71–89) LYG), but fewer than for every other simulated strategy. At its 2017 reimbursement rate of $512, mtSDNA was the most costly strategy, and even if adherence were 30% higher than with other strategies, it would not be a cost-effective alternative. At a substantially reduced reimbursement rate ($6–18), two models found that triennial mtSDNA testing was an efficient and potentially cost-effective screening option.
Conclusions
Compared to no screening, triennial mtSDNA screening reduces CRC incidence and mortality at acceptable costs. However, compared to nearly all other CRC screening strategies reimbursed by CMS it is less effective and considerably more costly, making it an inefficient screening option.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pone.0220234(source: https://plos.figshare.com/articles/dataset/Cost-effectiveness_of_a_multitarget_stool_DNA_test_for_colorectal_cancer_screening_of_Medicare_beneficiaries/9769055)。
数据缺失?
依托陕西、四川两大基地,我们与超过十家三甲医院建立直接合作关系,覆盖合规授权、采集治理、专业标注、数据交付的全流程,为AI医疗团队提供即拿即用的高质量临床数据。
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