Modelling personal temperature exposure using household and outdoor temperature and questionnaire data: implications for epidemiological studies

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Abstract
The reliance of outdoor exposure data in epidemiological studies on temperature entails important uncertainties from personal exposure misclassification. We analysed ~88,000 concurrent person-hours of measured personal, household (kitchen and living room), and outdoor temperatures collected in the summer (MAY-SEP 2017) and winter (NOV 2017-JAN 2018) in rural and urban China. 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). Random forest (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. Multivariable linear regression and RF models incorporating questionnaire and device data performed satisfactorily in predicting personal exposure in both seasons (R2summer: 0.92; R2winter: 0.68-0.70). Using generalised additive mixed effect models, we found consistent U-shaped associations between measured and predicted personal temperature exposures and heart rate (lowest at ~14.5ºC), but a weak positive linear association with outdoor temperature. Personal and outdoor temperatures differ substantially in 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.

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