Swiss Tropical and Public Health Institute, Socinstr. 57, 4051 Basel, Switzerland.
J Environ Radioact. 2012 Oct;112:83-9. doi: 10.1016/j.jenvrad.2012.03.014. Epub 2012 Jun 8.
Indoor radon is regularly measured in Switzerland. However, a nationwide model to predict residential radon levels has not been developed. The aim of this study was to develop a prediction model to assess indoor radon concentrations in Switzerland. The model was based on 44,631 measurements from the nationwide Swiss radon database collected between 1994 and 2004. Of these, 80% randomly selected measurements were used for model development and the remaining 20% for an independent model validation. A multivariable log-linear regression model was fitted and relevant predictors selected according to evidence from the literature, the adjusted R², the Akaike's information criterion (AIC), and the Bayesian information criterion (BIC). The prediction model was evaluated by calculating Spearman rank correlation between measured and predicted values. Additionally, the predicted values were categorised into three categories (50th, 50th-90th and 90th percentile) and compared with measured categories using a weighted Kappa statistic. The most relevant predictors for indoor radon levels were tectonic units and year of construction of the building, followed by soil texture, degree of urbanisation, floor of the building where the measurement was taken and housing type (P-values <0.001 for all). Mean predicted radon values (geometric mean) were 66 Bq/m³ (interquartile range 40-111 Bq/m³) in the lowest exposure category, 126 Bq/m³ (69-215 Bq/m³) in the medium category, and 219 Bq/m³ (108-427 Bq/m³) in the highest category. Spearman correlation between predictions and measurements was 0.45 (95%-CI: 0.44; 0.46) for the development dataset and 0.44 (95%-CI: 0.42; 0.46) for the validation dataset. Kappa coefficients were 0.31 for the development and 0.30 for the validation dataset, respectively. The model explained 20% overall variability (adjusted R²). In conclusion, this residential radon prediction model, based on a large number of measurements, was demonstrated to be robust through validation with an independent dataset. The model is appropriate for predicting radon level exposure of the Swiss population in epidemiological research. Nevertheless, some exposure misclassification and regression to the mean is unavoidable and should be taken into account in future applications of the model.
瑞士经常对室内氡进行测量。然而，尚未建立一种用于预测居民住宅氡水平的全国性模型。本研究旨在开发一种预测模型，以评估瑞士的室内氡浓度。该模型基于 1994 年至 2004 年期间从全国性瑞士氡数据库中收集的 44631 次测量值。其中，随机选择 80%的测量值用于模型开发，其余 20%用于独立模型验证。拟合了多变量对数线性回归模型，并根据文献中的证据、调整后的 R²、Akaike 信息准则（AIC）和贝叶斯信息准则（BIC）选择了相关的预测因子。通过计算测量值和预测值之间的斯皮尔曼等级相关来评估预测模型。此外，还根据测量类别将预测值分为三个类别（第 50 个、第 50-90 个和第 90 个百分位），并使用加权 Kappa 统计量与测量类别进行比较。室内氡水平的最相关预测因子是构造单元和建筑物的建造年份，其次是土壤质地、城市化程度、测量地点的建筑物楼层和住房类型（所有 P 值均<0.001）。最低暴露类别中的平均预测氡值（几何平均值）为 66 Bq/m³（四分位距 40-111 Bq/m³），中等类别为 126 Bq/m³（69-215 Bq/m³），最高类别为 219 Bq/m³（108-427 Bq/m³）。发展数据集的预测值与测量值之间的斯皮尔曼相关性为 0.45（95%-CI：0.44；0.46），验证数据集的相关性为 0.44（95%-CI：0.42；0.46）。发展数据集的 Kappa 系数为 0.31，验证数据集的 Kappa 系数为 0.30。该模型总体解释了 20%的变异性（调整后的 R²）。总之，该基于大量测量值的住宅氡预测模型通过使用独立数据集进行验证，证明了其稳健性。该模型适用于在流行病学研究中预测瑞士人口的氡暴露水平。然而，在未来模型的应用中，不可避免地会存在一些暴露分类错误和向均值回归，应予以考虑。
