评估幼年关节炎登记中缺失种族和族裔数据的差异：数据表1

数据集获取（Figshare）

• 源站标题：Datasheet1_Assessing disparities through missing race and ethnicity data: results from a juvenile arthritis registry.pdf
• DOI：10.3389/fped.2024.1430981.s001
• 发布日期：2024-07-24
• 文件数：1 个
• 文件总体积：38.04 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Datasheet1_Assessing_disparities_through_missing_race_and_ethnicity_data_results_from_a_juvenile_arthritis_registry_pdf/26361607

命令行下载

curl -L -o "Datasheet1_Assessing disparities through missing race and ethnicity data: results from a juvenile arthritis registry.pdf" "https://ndownloader.figshare.com/files/47886295"

文件清单

• Datasheet1_Assessing disparities through missing race and ethnicity data: results from a juvenile arthritis registry.pdf（38.04 KB）

文件总体积 38.04 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

IntroductionEnsuring high-quality race and ethnicity data within the electronic health record (EHR) and across linked systems, such as patient registries, is necessary to achieving the goal of inclusion of racial and ethnic minorities in scientific research and detecting disparities associated with race and ethnicity. The project goal was to improve race and ethnicity data completion within the Pediatric Rheumatology Care Outcomes Improvement Network and assess impact of improved data completion on conclusions drawn from the registry.
MethodsThis is a mixed-methods quality improvement study that consisted of five parts, as follows: (1) Identifying baseline missing race and ethnicity data, (2) Surveying current collection and entry, (3) Completing data through audit and feedback cycles, (4) Assessing the impact on outcome measures, and (5) Conducting participant interviews and thematic analysis.
ResultsAcross six participating centers, 29% of the patients were missing data on race and 31% were missing data on ethnicity. Of patients missing data, most patients were missing both race and ethnicity. Rates of missingness varied by data entry method (electronic vs. manual). Recovered data had a higher percentage of patients with Other race or Hispanic/Latino ethnicity compared with patients with non-missing race and ethnicity data at baseline. Black patients had a significantly higher odds ratio of having a clinical juvenile arthritis disease activity score (cJADAS10) of ≥5 at first follow-up compared with White patients. There was no significant change in odds ratio of cJADAS10 ≥5 for race and ethnicity after data completion. Patients missing race and ethnicity were more likely to be missing cJADAS values, which may affect the ability to detect changes in odds ratio of cJADAS ≥5 after completion.
ConclusionsAbout one-third of the patients in a pediatric rheumatology registry were missing race and ethnicity data. After three audit and feedback cycles, centers decreased missing data by 94%, primarily via data recovery from the EHR. In this sample, completion of missing data did not change the findings related to differential outcomes by race. Recovered data were not un

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fped.2024.1430981.s001（source: https://frontiersin.figshare.com/articles/dataset/Datasheet1_Assessing_disparities_through_missing_race_and_ethnicity_data_results_from_a_juvenile_arthritis_registry_pdf/26361607）。