异质性分布式滞后模型估计母亲空气污染暴露的个性化效应

数据集获取（Figshare）

• 源站标题：Heterogeneous Distributed Lag Models to Estimate Personalized Effects of Maternal Exposures to Air Pollution
• DOI：10.6084/m9.figshare.24139608.v2
• 发布日期：2023-11-09
• 文件数：5 个
• 文件总体积：1.52 MB
• 源站页面：https://tandf.figshare.com/articles/dataset/Heterogeneous_Distributed_Lag_Models_to_Estimate_Personalized_Effects_of_Maternal_Exposures_to_Air_Pollution/24139608

命令行下载

curl -L -o "._HDLM supplement.pdf" "https://ndownloader.figshare.com/files/42352950"
curl -L -o "._README.rtf" "https://ndownloader.figshare.com/files/42352959"
curl -L -o "._dlmtree_0.9.0.tar.gz" "https://ndownloader.figshare.com/files/42352965"
curl -L -o "HDLM Supplement.zip" "https://ndownloader.figshare.com/files/42352968"
curl -L -o "uasa_a_2258595_sm9476.pdf" "https://ndownloader.figshare.com/files/42352971"

文件清单

• ._HDLM supplement.pdf（416 B）
• ._README.rtf（613 B）
• ._dlmtree_0.9.0.tar.gz（259 B）
• HDLM Supplement.zip（1.40 MB）
• uasa_a_2258595_sm9476.pdf（121.72 KB）

文件总体积 1.52 MB（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

Children’s health studies support an association between maternal environmental exposures and children’s birth outcomes. A common goal is to identify critical windows of susceptibility—periods during gestation with increased association between maternal exposures and a future outcome. The timing of the critical windows and magnitude of the associations are likely heterogeneous across different levels of individual, family, and neighborhood characteristics. Using an administrative Colorado birth cohort we estimate the individualized relationship between weekly exposures to fine particulate matter (PM2.5) during gestation and birth weight. To achieve this goal, we propose a statistical learning method combining distributed lag models and Bayesian additive regression trees to estimate critical windows at the individual level and identify characteristics that induce heterogeneity from a high-dimensional set of potential modifying factors. We find evidence of heterogeneity in the PM2.5—birth weight relationship, with some mother—child dyads showing a three times larger decrease in birth weight for an IQR increase in exposure (5.9–8.5 μg/m3 PM2.5) compared to the population average. Specifically, we find increased vulnerability for non-Hispanic mothers who are either younger, have higher body mass index or lower educational attainment. Our case study is the first precision health study of critical windows. Supplementary materials for this article are available online.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.24139608.v2（source: https://tandf.figshare.com/articles/dataset/Heterogeneous_Distributed_Lag_Models_to_Estimate_Personalized_Effects_of_Maternal_Exposures_to_Air_Pollution/24139608）。