室内空气污染与短期肺功能恶化的测量误差感知因果分布滞后分位数建模

中文摘要 AI 辅助

低成本室内空气质量传感器可支持个性化哮喘预防，但其非线性测量误差、延迟暴露效应、时变混杂以及异质性下尾响应限制了风险估计。我们提出了CAUSALQUANT-ASTHMA，一个用于短期峰值呼气流速分析的测量误差感知因果分位数分布滞后框架。稀疏参考测量训练非线性校准模型；稳定的序贯广义倾向权重处理测量的暴露分配；易感性调制的平滑非交叉分位数模型估计滞后特定和持续暴露对比。由于没有授权队列同时提供密集室内传感、参考共置和结局兼容的纵向数据，评估使用了五个具有已知反事实真值的半合成面板，每次实现包含150名患者和12,600个患者日。在八种方法中，CAUSALQUANT实现了剂量反应积分绝对误差0.304±0.094，比最强的测量误差和倾向加权基线改善了24.2%。它还获得了最低的总体分位数损失1.065，同时保持零分位数交叉和名义80%区间的78.1%覆盖率。传感器校准将保留暴露均方根误差降低了33.7%。压力测试量化了在传感器噪声、缺失个人测量和隐藏混杂下的退化。这些发现确立了方法学可行性和可重复性，而非临床有效性；在患者级解释或部署之前，需要进行前瞻性、治理批准的外部验证。

英文摘要

Low-cost indoor air-quality sensors could support personalized asthma prevention, but their nonlinear measurement error, delayed exposure effects, time-varying confounding, and heterogeneous lower-tail responses limit risk estimation. We present CAUSALQUANT-ASTHMA, a measurement-error-aware causal quantile distributed-lag framework for short-horizon peak expiratory flow analysis. Sparse reference measurements train a nonlinear calibration model; stabilized sequential generalized-propensity weights address measured exposure assignment; and a susceptibility-modulated, smooth, noncrossing quantile model estimates lag-specific and sustained-exposure contrasts. Because no authorized cohort simultaneously provided dense indoor sensing, reference co-location, and outcome-compatible longitudinal data, evaluation used five semi-synthetic panels with known counterfactual truth, 150 patients and 12,600 patient-days per realization. Across eight methods, CAUSALQUANT achieved a dose-response integrated absolute error of 0.304 plus or minus 0.094, improving 24.2 percent over the strongest measurement-error and propensity-weighted baseline. It also obtained the lowest overall pinball loss, 1.065, while maintaining zero quantile crossings and 78.1 percent coverage for the nominal 80 percent interval. Sensor calibration reduced held-out exposure RMSE by 33.7 percent. Stress tests quantified degradation under sensor noise, missing personal measurements, and hidden confounding. These findings establish methodological feasibility and reproducibility, not clinical effectiveness; prospective, governance-approved external validation is required before patient-level interpretation or deployment.

发表机构

• United Arab Emirates University（阿联酋大学）
• National University of Modern languages（国立现代语言大学）

机构由 AI 辅助整理，请以论文原文为准。