Department of Public Health, Environments and Society, London School of Hygiene and Tropical Medicine (LSHTM), 15-17 Tavistock Place, London, WC1H 9SH, UK.
Centre for Statistical Methodology, London School of Hygiene & Tropical Medicine (LSHTM), Keppel Street, London, WC1E 7HT, UK.
BMC Med Res Methodol. 2022 Apr 30;22(1):129. doi: 10.1186/s12874-022-01612-x.
The increased availability of data on health outcomes and risk factors collected at fine geographical resolution is one of the main reasons for the rising popularity of epidemiological analyses conducted at small-area level. However, this rich data setting poses important methodological issues related to modelling complexities and computational demands, as well as the linkage and harmonisation of data collected at different geographical levels.
This tutorial illustrated the extension of the case time series design, originally proposed for individual-level analyses on short-term associations with time-varying exposures, for applications using data aggregated over small geographical areas. The case time series design embeds the longitudinal structure of time series data within the self-matched framework of case-only methods, offering a flexible and highly adaptable analytical tool. The methodology is well suited for modelling complex temporal relationships, and it provides an efficient computational scheme for large datasets including longitudinal measurements collected at a fine geographical level.
The application of the case time series for small-area analyses is demonstrated using a real-data case study to assess the mortality risks associated with high temperature in the summers of 2006 and 2013 in London, UK. The example makes use of information on individual deaths, temperature, and socio-economic characteristics collected at different geographical levels. The tutorial describes the various steps of the analysis, namely the definition of the case time series structure and the linkage of the data, as well as the estimation of the risk associations and the assessment of vulnerability differences. R code and data are made available to fully reproduce the results and the graphical descriptions.
The extension of the case time series for small-area analysis offers a valuable analytical tool that combines modelling flexibility and computational efficiency. The increasing availability of data collected at fine geographical scales provides opportunities for its application to address a wide range of epidemiological questions.
健康结果和风险因素数据在精细地理分辨率上的可获得性增加，是在小区域层面进行流行的流行病学分析的主要原因之一。然而，这种丰富的数据设置带来了重要的方法学问题，涉及到建模复杂性和计算需求，以及在不同地理层面收集的数据的链接和协调。
本教程说明了病例时间序列设计的扩展，该设计最初是为个体层面的短期暴露与时间变化的关联分析而提出的，适用于使用小地理区域汇总数据的应用。病例时间序列设计将时间序列数据的纵向结构嵌入到仅病例方法的自我匹配框架内，提供了一种灵活且高度适应的分析工具。该方法非常适合建模复杂的时间关系，并且为包括在精细地理水平上收集的纵向测量在内的大型数据集提供了高效的计算方案。
使用真实数据案例研究来评估英国伦敦 2006 年和 2013 年夏季高温与死亡率之间的关联，演示了病例时间序列在小区域分析中的应用。该示例利用了在不同地理层面收集的个体死亡、温度和社会经济特征的信息。本教程描述了分析的各个步骤，即病例时间序列结构的定义和数据的链接，以及风险关联的估计和脆弱性差异的评估。提供了 R 代码和数据，以完全复制结果和图形描述。
病例时间序列在小区域分析中的扩展提供了一种有价值的分析工具，它结合了建模灵活性和计算效率。在精细地理尺度上收集的数据的可用性不断增加，为应用它来解决广泛的流行病学问题提供了机会。
