Monash University, Bandar Sunway, Malaysia.
Chron Respir Dis. 2013 May;10(2):85-94. doi: 10.1177/1479972313482847.
Health forecasting can improve health service provision and individual patient outcomes. Environmental factors are known to impact chronic respiratory conditions such as asthma, but little is known about the extent to which these factors can be used for forecasting. Using weather, air quality and hospital asthma admissions, in London (2005-2006), two related negative binomial models were developed and compared with a naive seasonal model. In the first approach, predictive forecasting models were fitted with 7-day averages of each potential predictor, and then a subsequent multivariable model is constructed. In the second strategy, an exhaustive search of the best fitting models between possible combinations of lags (0-14 days) of all the environmental effects on asthma admission was conducted. Three models were considered: a base model (seasonal effects), contrasted with a 7-day average model and a selected lags model (weather and air quality effects). Season is the best predictor of asthma admissions. The 7-day average and seasonal models were trivial to implement. The selected lags model was computationally intensive, but of no real value over much more easily implemented models. Seasonal factors can predict daily hospital asthma admissions in London, and there is a little evidence that additional weather and air quality information would add to forecast accuracy.
健康预测可以改善医疗服务的提供和个体患者的预后。环境因素已知会影响哮喘等慢性呼吸道疾病，但对于这些因素在多大程度上可以用于预测知之甚少。本研究使用天气、空气质量和伦敦（2005-2006 年）的医院哮喘入院数据，开发了两个相关的负二项式模型，并与简单的季节性模型进行了比较。在第一种方法中，使用每个潜在预测因子的 7 天平均值拟合预测性预测模型，然后构建随后的多变量模型。在第二种策略中，对所有环境影响对哮喘入院的可能滞后（0-14 天）的最佳拟合模型进行了详尽的搜索。考虑了三个模型：一个基础模型（季节性影响），与 7 天平均值模型和选定滞后模型（天气和空气质量影响）进行对比。季节是哮喘入院的最佳预测因子。7 天平均值和季节性模型易于实施。选定滞后模型计算量大，但在更易于实施的模型上没有实际价值。季节性因素可以预测伦敦的每日医院哮喘入院人数，并且几乎没有证据表明额外的天气和空气质量信息会提高预测准确性。
