年度交通成本(林吉特)细分

马来西亚低收入癌症患者的自付费用与生产力损失数据,重点分析交通成本等经济负担明细。

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2024-10-09 更新
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Annual transportation costs (MYR) breakdown.

基本信息

模态
表格
大小
0.000005632 GB
许可
CC BY 4.0
创建/更新时间
2024-10-09
版本
v1

资源简介

年度交通成本(林吉特)细分

【数据集背景】

本数据集源站标题为《Annual transportation costs (MYR) breakdown.》。源站首次发布:2024-10-09。

【数据内容】

源站原始描述:Cancer patients often grapple with substantial out-of-pocket (OOP) expenses and productivity loss, with the ramifications being particularly crucial for lower-income households. This study aims to estimate OOP costs incurred by cancer patients, assess their productivity loss, and analyse the financial coping mechanisms employed by individuals within the lower-income bracket. The study employed face-to-face interviews among cancer patients aged 40 years and above, currently undergoing treatment,…

【数据结构与技术规格】

文件数 1 个,合计 5.63 KB。Table 5.xls:application/vnd.ms-excel,5.63 KB。数据集 DOI:10.1371/journal.pone.0311815.t005。

【主题与分类】

源站学科分类:Ecology、Sociology、Biological Sciences not elsewhere classified、Science Policy。主题标签:tackle challenges faced、notable cost contributors、malaysian public healthcare、generalized linear model、ethnicity (&# 946、coping mechanisms employed、72 &# 8211、56 &# 8211、28 (&# 177、10 &# 8211。

【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/Annual_transportation_costs_MYR_breakdown_/27197511

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

规范引用:Aminuddin, Farhana; Raman, Sivaraj; Bahari, Mohd Shahri; Zaimi, Nur Amalina; Kunusagaran, Mohd Shaiful Jefri Mohd Nor Sham; Zainuddin, Nur Azmiah; et al. (2024). Annual transportation costs (MYR) breakdown.. PLOS ONE. Dataset

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陕川双基地 × 超十家三甲医院直连

从合规授权、采集治理、专业标注到数据集交付,我们提供全链路闭环服务,让高质量临床数据即拿即用。

⚡️ 需要原始数据或标注支持?联系我们获取定制方案。

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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)

命令行下载

curl -L -o "Table 5.xls" "https://ndownloader.figshare.com/files/49703568"

文件清单

  • Table 5.xls(5.63 KB)

文件总体积 5.63 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

Cancer patients often grapple with substantial out-of-pocket (OOP) expenses and productivity loss, with the ramifications being particularly crucial for lower-income households. This study aims to estimate OOP costs incurred by cancer patients, assess their productivity loss, and analyse the financial coping mechanisms employed by individuals within the lower-income bracket. The study employed face-to-face interviews among cancer patients aged 40 years and above, currently undergoing treatment, and belonging to the lower-income group. Participants were recruited from six public cancer referral hospitals. OOP expenses, encompassing medical and non-medical costs, along with productivity loss, were measured. A generalized linear model was applied to identify potential OOP determinants. Additionally, the coping mechanisms employed by individuals to finance their cancer OOP expenses were also determined. Among the 430 participants recruited, predominantly female (63.5%), and aged 60 or older (53.9%). The annual mean total cancer costs per patient were US$ 2,398.28 (±2,168.74), including 15% for medical costs US$ 350.95 (±560.24), 34% for non-medical costs US$820.24 (±818.24), and 51% for productivity loss costs US$1,227.09 (±1,809.09). Transportation, nutritional supplements, outpatient treatment, and medical supplies were notable cost contributors to total OOP expenditures. Ethnicity (β = 1.44; 95%CI = 1.15–1.79), household income (β = 1.40; 95%CI = 1.10–1.78), annual outpatient visits (β = 1.00; 95%CI = 1.00–1.01), age (β = 0.74; 95%CI = 0.56–0.98), and employment status (β = 0.54; 95%CI = 0.72–1.34) were identified as significant predictors of OOP costs among cancer patients. Notably, 91% of participants relied on household salaries and savings, while 15% resorted to interest-free borrowing, 11% sold possessions, and 0.5% borrowed with interest to finance their expenses. This study offers crucial insights into the economic impact of cancer on individuals and their families, providing policymakers with valuable information to tackle challenges faced in their journey. Despite substantial public healthcare subsidies, the study revealed that cancer costs can remain a

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0311815.t005(source: https://plos.figshare.com/articles/dataset/Annual_transportation_costs_MYR_breakdown_/27197511)。

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