数据表1：医疗能力、卫生支出与公民社会作为COVID-19病死率预测因素——全球分析

数据集获取（Figshare）

• 源站标题：Data_Sheet_1_Healthcare Capacity, Health Expenditure, and Civil Society as Predictors of COVID-19 Case Fatalities: A Global Analysis.docx
• DOI：10.3389/fpubh.2020.00347.s001
• 发布日期：2020-07-03
• 文件数：1 个
• 文件总体积：16.04 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Healthcare_Capacity_Health_Expenditure_and_Civil_Society_as_Predictors_of_COVID-19_Case_Fatalities_A_Global_Analysis_docx/12608225

命令行下载

curl -L -o "Data_Sheet_1_Healthcare Capacity, Health Expenditure, and Civil Society as Predictors of COVID-19 Case Fatalities: A Global Analysis.docx" "https://ndownloader.figshare.com/files/23657147"

文件清单

• Data_Sheet_1_Healthcare Capacity, Health Expenditure, and Civil Society as Predictors of COVID-19 Case Fatalities: A Global Analysis.docx（16.04 KB）

文件总体积 16.04 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: The rapid growth in cases of COVID-19 has challenged national healthcare capacity, testing systems at an advanced ICU, and public health infrastructure level. This global study evaluates the association between multi-factorial healthcare capacity and case fatality of COVID-19 patients by adjusting for demographic, health expenditure, population density, and prior burden of non-communicable disease. It also explores the impact of government relationships with civil society as a predictor of infection and mortality rates.
Methods: Data were extracted from the Johns Hopkins University database, World Bank records and the National Civic Space Ratings 2020 database. This study used data from 86 countries which had at least 1,000 confirmed cases on 30th April 2020. Negative binomial regression model was used to assess the association between case fatality (a ratio of total number of confirmed deaths to total number of confirmed cases) and healthcare capacity index adjusting for other covariates.
Findings: Regression analysis shows that greater healthcare capacity was related to lesser case-fatality [incidence rate ratio (IRR) 0.5811; 95% confidence interval (CI) 0.4727–0.7184; p Interpretation: Based on preliminary data, this research suggests that building effective multidimensional healthcare capacity is the most promising means to mitigate future case fatalities. The data also suggests that government’'s ability to implement public health measures to a degree determines mortality outcomes.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fpubh.2020.00347.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Healthcare_Capacity_Health_Expenditure_and_Civil_Society_as_Predictors_of_COVID-19_Case_Fatalities_A_Global_Analysis_docx/12608225）。