中国宁夏2015-2020年脑卒中住院患者住院时长与费用关系研究Table 2

数据集获取（Figshare）

• 源站标题：Table_2_The direct and indirect effects of length of hospital stay on the costs of inpatients with stroke in Ningxia, China, between 2015 and 2020: A retrospective study using quantile regression and structural equation models.docx
• DOI：10.3389/fpubh.2022.881273.s002
• 发布日期：2022-08-12
• 文件数：1 个
• 文件总体积：17.04 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_2_The_direct_and_indirect_effects_of_length_of_hospital_stay_on_the_costs_of_inpatients_with_stroke_in_Ningxia_China_between_2015_and_2020_A_retrospective_study_using_quantile_regression_and_structural_equation_models_docx/20481120

命令行下载

curl -L -o "Table_2_The direct and indirect effects of length of hospital stay on the costs of inpatients with stroke in Ningxia, China, between 2015 and 2020: A .docx" "https://ndownloader.figshare.com/files/36661968"

文件清单

• Table_2_The direct and indirect effects of length of hospital stay on the costs of inpatients with stroke in Ningxia, China, between 2015 and 2020: A .docx（17.04 KB）

文件总体积 17.04 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

ImportanceLength of hospital stay (LOHS) is the main cost-determining factor of hospitalization for stroke patients. However, previous analyses involving LOHS did not consider confounding or indirect factors, or the effects of other factors on LOHS and inpatient costs.
ObjectiveTo investigate the direct and indirect effects of LOHS on the hospitalization costs of inpatients with ischemic and hemorrhagic stroke.
Design, setting, and participantsThis was a population-based, retrospective, and observational study that analyzed data acquired from the Nationwide Inpatient Sample between 2015 and 2020 relating to ischemic and hemorrhagic stroke in Ningxia, China.
Main outcomes and measuresHospitalizations were identified by the International Classification of Diseases 10th Revision (ICD-10). Inpatient costs were described by the median M (P25, P75). We used a quantile regression model to estimate the linear relationships between a group of independent variables X and the quantile of the explained variable hospitalization cost (Y). A structural equation model (SEM) was then used to investigate the direct and indirect effects of LOHS on inpatient costs.
ResultsThe study included 129,444 patients with ischemic stroke and 15,525 patients with hemorrhagic stroke. The median LOHS was 10 (8–13) days for ischemic stroke and 15 (10–22) days for hemorrhagic stroke. The median M (P25, P75) of inpatient costs was $1020 (742–1545) for ischemic stroke and 2813 (1576–6191) for hemorrhagic stroke. The total effect of LOHS on inpatient costs was 0.795 in patients with ischemic stroke. The effect of yearof discharge (X4) and CCI (X8) on inpatient costs was dominated by an indirect effect through the LOHS. The indirect effect was −0.071 (84.52% of the total effect value) and 0.034 (69.39% of the total effect value), respectively. The total effect of LOHS on inpatient costs in patients with hemorrhagic stroke was 0.754. The influence of CCI on inpatient costs was dominated by an indirect effect through LOHS; the indirect effect value was −0.028 (77.78% of the total effect value). The payment type, surgery, method of discharge, and hospital level also exerted an impact on inpatient costs

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fpubh.2022.881273.s002（source: https://frontiersin.figshare.com/articles/dataset/Table_2_The_direct_and_indirect_effects_of_length_of_hospital_stay_on_the_costs_of_inpatients_with_stroke_in_Ningxia_China_between_2015_and_2020_A_retrospective_study_using_quantile_regression_and_structural_equation_models_docx/20481120）。