结核病患者共病亚组对医疗使用和临床结果的影响：基于人群的队列分析表1

数据集获取（Figshare）

• 源站标题：Table_1_Impact of Multimorbidity Subgroups on the Health Care Use and Clinical Outcomes of Patients With Tuberculosis: A Population-Based Cohort Analysis.docx
• DOI：10.3389/fpubh.2021.756717.s001
• 发布日期：2021-10-08
• 文件数：1 个
• 文件总体积：34.42 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_Impact_of_Multimorbidity_Subgroups_on_the_Health_Care_Use_and_Clinical_Outcomes_of_Patients_With_Tuberculosis_A_Population-Based_Cohort_Analysis_docx/16768141

命令行下载

curl -L -o "Table_1_Impact of Multimorbidity Subgroups on the Health Care Use and Clinical Outcomes of Patients With Tuberculosis: A Population-Based Cohort Analy.docx" "https://ndownloader.figshare.com/files/31025389"

文件清单

• Table_1_Impact of Multimorbidity Subgroups on the Health Care Use and Clinical Outcomes of Patients With Tuberculosis: A Population-Based Cohort Analy.docx（34.42 KB）

文件总体积 34.42 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: Multimorbidity is defined as the existence of two or more chronic health conditions in the same individual. While patients with tuberculosis commonly have multiple conditions at diagnosis, such as HIV, diabetes, and depression, to the authors’’ knowledge, there is limited information on the patterns of multimorbidity, and how the types and combinations of conditions could impact the healthcare utilization, expenditure, and TB outcomes.
Methods: An observational cohort study of adult patients diagnosed with tuberculosis was conducted using the Chinese Center for Disease Control and Prevention (CDC)''s National TB Information System (NTBIS) linked to the Ningbo Regional Health Care Database (NRHCD) (2015–2020). Latent class analysis was used to identify comorbidity groups among the subset with ≥2 conditions including TB. Group-level health care use, expenditure, and treatment outcomes were compared with patients without chronic conditions using multivariate regression models.
Results: A total of 9,651 patients with TB were identified, of whom approximately 61.4% had no chronic conditions, 17.4% had 1 chronic condition, and 21.3% had ≥2 chronic conditions. Among those with ≥1 chronic condition other than TB, 4 groups emerged: (1) general morbidity (54.4%); (2) cardiovascular morbidity without complications (34.7%); (3) cardiovascular morbidity with complications (5.0%); (4) respiratory morbidity (5.9%). The respiratory morbidity group experienced the highest expenditures, at 16,360 CNY more overall (95% CI, CNY 12,615–21,215) after adjustment compared with TB patients without chronic conditions. The respiratory morbidity and cardiovascular morbidity with complications group also had the lowest odds of favorable TB outcomes [adjusted odds ratio (aOR), 0.68; 95% CI, 0.49–0.93] and (aOR 0.59, 95% CI 0.42–0.83), respectively. The cardiovascular morbidity without complications group had the highest odds of successful TB treatment (aOR, 1.40; 95% CI, 1.15–1.71).
Conclusions: Multimorbidity is common among patients with TB. The current study identified four distinct comorbidity subgroups, all of which experienced high, yet differential, rates of health care use.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fpubh.2021.756717.s001（source: https://frontiersin.figshare.com/articles/dataset/Table_1_Impact_of_Multimorbidity_Subgroups_on_the_Health_Care_Use_and_Clinical_Outcomes_of_Patients_With_Tuberculosis_A_Population-Based_Cohort_Analysis_docx/16768141）。