用于成本核算的输入成本参数

结核病筛查成本参数数据集,包含CXR和AI辅助解释的不同筛查策略的实施成本与成本效果数据。

Figshare
2025-06-11 更新
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表格ultraportable chest xlung health campshealth system perspectiveactive case findingabnormality &amp;# 88052 weeks </five screening scenarioscost per casepeople 15 yearsindividuals reporting symptomsdiv >< pconfirmed case detected
Input cost parameters used for costing.

基本信息

模态
表格
大小
0.000009728 GB
许可
CC BY 4.0
创建/更新时间
2025-06-11
版本
v1

资源简介

用于成本核算的输入成本参数

【数据集背景】

本数据集源站标题为《Input cost parameters used for costing.》。源站首次发布:2025-06-11。

【数据内容】

源站原始描述:Availability of ultraportable chest x-ray (CXR) and advancements in artificial intelligence (AI)-enabled CXR interpretation are promising developments in tuberculosis (TB) active case finding (ACF) but costing and cost-effectiveness analyses are limited. We provide implementation cost and cost-effectiveness estimates of different screening algorithms using symptoms, CXR and AI in Nigeria. People 15 years and older were screened for TB symptoms and offered a CXR with AI-enabled interpretation usi…

【数据结构与技术规格】

文件数 1 个,合计 9.73 KB。Table 1.xls:application/vnd.ms-excel,9.73 KB。数据集 DOI:10.1371/journal.pdig.0000894.t001。

【主题与分类】

源站学科分类:Medicine、Pharmacology、Biotechnology、Sociology、Biological Sciences not elsewhere classified、Information Systems not elsewhere classified。主题标签:ultraportable chest x、lung health camps、health system perspective、active case finding、abnormality &amp;# 8805、2 weeks </、five screening scenarios、cost per case、people 15 years、individuals reporting symptoms。

【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/Input_cost_parameters_used_for_costing_/29296915

使用许可:CC BY 4.0(https://creativecommons.org/licenses/by/4.0/)。

规范引用:Garg, Tushar; John, Stephen; Abdulkarim, Suraj; Ahmed, Adamu D.; Kirubi, Beatrice; Rahman, Md. Toufiq; et al. (2025). Input cost parameters used for costing.. PLOS Digital Health. Dataset

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下载信息

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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)

命令行下载

curl -L -o "Table 1.xls" "https://ndownloader.figshare.com/files/55314157"

文件清单

  • Table 1.xls(9.73 KB)

文件总体积 9.73 KB(源站 API 实测)。

数据说明

Availability of ultraportable chest x-ray (CXR) and advancements in artificial intelligence (AI)-enabled CXR interpretation are promising developments in tuberculosis (TB) active case finding (ACF) but costing and cost-effectiveness analyses are limited. We provide implementation cost and cost-effectiveness estimates of different screening algorithms using symptoms, CXR and AI in Nigeria. People 15 years and older were screened for TB symptoms and offered a CXR with AI-enabled interpretation using qXR v3 (Qure.ai) at lung health camps. Sputum samples were tested on Xpert MTB/RIF for individuals reporting symptoms or with qXR abnormality scores ≥0.30. We conducted a retrospective costing using a combination of top-down and bottom-up approaches while utilizing itemized expense data from a health system perspective. We estimated costs in five screening scenarios: abnormality score ≥0.30 and ≥0.50; cough ≥ 2 weeks; any symptom; abnormality score ≥0.30 or any symptom. We calculated total implementation costs, cost per bacteriologically-confirmed case detected, and assessed cost-effectiveness using incremental cost-effectiveness ratio (ICER) as additional cost per additional case. Overall, 3205 people with presumptive TB were identified, 1021 were tested, and 85 people with bacteriologically-confirmed TB were detected. Abnormality ≥ 0.30 or any symptom (US$65704) had the highest costs while cough ≥ 2 weeks was the lowest (US$40740). The cost per case was US$1198 for cough ≥ 2 weeks, and lowest for any symptom (US$635). Compared to baseline strategy of cough ≥ 2 weeks, the ICER for any symptom was US$191 per additional case detected and US$ 2096 for Abnormality ≥0.30 OR any symptom algorithm. Using CXR and AI had lower cost per case detected than any symptom screening criteria when asymptomatic TB was higher than 30% of all bacteriologically-confirmed TB detected. Compared to traditional symptom screening, using CXR and AI in combination with symptoms detects more cases at lower cost per case detected and is cost-effective. TB programs should explore adoption of CXR and AI for screening in ACF.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pdig.0000894.t001(source: https://plos.figshare.com/articles/dataset/Input_cost_parameters_used_for_costing_/29296915)。

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