Department of Occupational and Environmental Health, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China; Key Laboratory of Environment and Genes Related to Diseases, Ministry of Education, Xi'an, China; Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK; School of Public Health, Shaanxi University of Chinese Medicine, Xi'an, China; The Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, China.
Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
Environ Int. 2024 Oct;192:109060. doi: 10.1016/j.envint.2024.109060. Epub 2024 Oct 9.
Non-optimal temperature is a leading risk factor for global disease burden. Most epidemiological studies assessed only outdoor temperature, with important uncertainties on personal exposure misclassification. The CKB-Air study measured personal, household (kitchen and living room), and outdoor temperatures in the summer (MAY-SEP 2017) and winter (NOV 2017-JAN 2018) in 477 participants in China. After data cleaning, ∼88,000 person-hours of data were recorded across each microenvironment. Using multivariable linear regression (MLR) and random forest (RF) models, we identified key predictors and constructed personal temperature exposure prediction models. We used generalised additive mixed effect models to examine the relationships of personal and outdoor temperatures with heart rate. The 24-hour mean (SD) personal and outdoor temperatures were 29.2 (3.8) °C and 27.6 (6.4) °C in summer, and 12.0 (4.0) °C and 7.5 (4.2) °C in winter, respectively. The temperatures across microenvironments were strongly correlated (Spearman's ρ: 0.86-0.92) in summer. In winter, personal temperature was strongly related to household temperatures (ρ: 0.74-0.79) but poorly related to outdoor temperature (ρ: 0.30). RF algorithm identified household and outdoor temperatures and study date as top predictors of personal temperature exposure for both seasons, and heating-related factors were important in winter. The final MLR and RF models incorporating questionnaire and device data performed satisfactorily in predicting personal exposure in both seasons (R: 0.92; R: 0.68-0.70). We found consistent U-shaped associations between measured and predicted personal temperature exposures and heart rate (lowest at ∼ 14.5 °C), but a weak positive linear association with outdoor temperature. Personal and outdoor temperatures differ substantially winter, but prediction models incorporating household and outdoor temperatures and questionnaire data performed satisfactorily. Exposure misclassification from using outdoor temperature may produce inappropriate epidemiological findings.
非最佳温度是全球疾病负担的主要危险因素。大多数流行病学研究仅评估了室外温度，但存在重要的个人暴露分类错误的不确定性。CKB-Air 研究在中国 477 名参与者中测量了夏季（2017 年 5 月至 9 月）和冬季（2017 年 11 月至 2018 年 1 月）的个人、家庭（厨房和客厅）和室外温度。在数据清理后，每个微环境记录了约 88000 个人小时的数据。使用多元线性回归（MLR）和随机森林（RF）模型，我们确定了关键预测因素，并构建了个人温度暴露预测模型。我们使用广义加性混合效应模型研究了个人和室外温度与心率的关系。夏季 24 小时平均（SD）个人和室外温度分别为 29.2（3.8）℃和 27.6（6.4）℃，冬季分别为 12.0（4.0）℃和 7.5（4.2）℃。夏季微环境之间的温度具有很强的相关性（Spearman's ρ：0.86-0.92）。冬季，个人温度与家庭温度密切相关（ρ：0.74-0.79），但与室外温度相关性较差（ρ：0.30）。RF 算法确定了家庭和室外温度以及研究日期是两个季节个人温度暴露的主要预测因素，而冬季与供暖相关的因素很重要。包含问卷和设备数据的最终 MLR 和 RF 模型在两个季节的个人暴露预测中表现良好（R：0.92；R：0.68-0.70）。我们发现，在所测量和预测的个人温度暴露与心率之间存在一致的 U 形关联（最低在约 14.5℃），但与室外温度呈弱正线性关联。冬季个人和室外温度差异很大，但包含家庭和室外温度以及问卷数据的预测模型表现良好。使用室外温度进行暴露分类错误可能会产生不适当的流行病学发现。
