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。

基本信息
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 &# 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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使用方式
数据集获取(Figshare)
- 源站标题:Input cost parameters used for costing.
- DOI:10.1371/journal.pdig.0000894.t001
- 发布日期:2025-06-11
- 文件数:1 个
- 文件总体积:9.73 KB
- 源站页面:https://plos.figshare.com/articles/dataset/Input_cost_parameters_used_for_costing_/29296915
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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