Direcció General de Salut Pública i Addiccions, Conselleria de Sanitat Universal i Salut Pública, Avda/Cataluña, 21, 46020, Valencia, Spain.
Departament d'Estadística i Investigació Operativa, Universitat de València, C/Dr. Moliner, 50, 46100, Burjassot, Valencia, Spain.
Int J Health Geogr. 2020 Dec 4;19(1):54. doi: 10.1186/s12942-020-00251-z.
Most epidemiological risk indicators strongly depend on the age composition of populations, which makes the direct comparison of raw (unstandardized) indicators misleading because of the different age structures of the spatial units of study. Age-standardized rates (ASR) are a common solution for overcoming this confusing effect. The main drawback of ASRs is that they depend on age-specific rates which, when working with small areas, are often based on very few, or no, observed cases for most age groups. A similar effect occurs with life expectancy at birth and many more epidemiological indicators, which makes standardized mortality ratios (SMR) the omnipresent risk indicator for small areas epidemiologic studies.
To deal with this issue, a multivariate smoothing model, the M-model, is proposed in order to fit the age-specific probabilities of death (PoDs) for each spatial unit, which assumes dependence between closer age groups and spatial units. This age-space dependence structure enables information to be transferred between neighboring consecutive age groups and neighboring areas, at the same time, providing more reliable age-specific PoDs estimates.
Three case studies are presented to illustrate the wide range of applications that smoothed age specific PoDs have in practice . The first case study shows the application of the model to a geographical study of lung cancer mortality in women. This study illustrates the convenience of considering age-space interactions in geographical studies and to explore the different spatial risk patterns shown by the different age groups. Second, the model is also applied to the study of ischaemic heart disease mortality in women in two cities at the census tract level. Smoothed age-standardized rates are derived and compared for the census tracts of both cities, illustrating some advantages of this mortality indicator over traditional SMRs. In the latest case study, the model is applied to estimate smoothed life expectancy (LE), which is the most widely used synthetic indicator for characterizing overall mortality differences when (not so small) spatial units are considered.
Our age-space model is an appropriate and flexible proposal that provides more reliable estimates of the probabilities of death, which allow the calculation of enhanced epidemiological indicators (smoothed ASR, smoothed LE), thus providing alternatives to traditional SMR-based studies of small areas.
大多数流行病学风险指标强烈依赖于人口的年龄构成，这使得原始（未经标准化）指标的直接比较具有误导性，因为研究的空间单元的年龄结构不同。年龄标准化率（ASR）是克服这种混淆效应的常用方法。ASR 的主要缺点是它们依赖于特定年龄的比率，而在处理小区域时，对于大多数年龄组，这些比率通常基于很少或没有观察到的病例。出生时预期寿命和更多的流行病学指标也存在类似的影响，这使得标准化死亡率比（SMR）成为小区域流行病学研究的普遍风险指标。
为了解决这个问题，提出了一种多元平滑模型，即 M 模型，以便拟合每个空间单元的特定年龄死亡率（PoD）的概率，该模型假设较近年龄组和空间单元之间存在依赖性。这种年龄-空间依赖结构使信息能够在相邻的连续年龄组和相邻区域之间传递，同时提供更可靠的特定年龄 PoD 估计。
呈现了三个案例研究，以说明平滑特定年龄 PoD 在实践中的广泛应用。第一个案例研究展示了该模型在女性肺癌死亡率的地理研究中的应用。该研究说明了在地理研究中考虑年龄-空间相互作用以及探索不同年龄组显示的不同空间风险模式的便利性。其次，该模型还应用于两个城市的女性缺血性心脏病死亡率的研究。为两个城市的普查区导出并比较了平滑的年龄标准化率，说明了这种死亡率指标相对于传统 SMR 的一些优势。在最新的案例研究中，该模型用于估计平滑的预期寿命（LE），这是最广泛用于描述考虑（非如此小）空间单元时整体死亡率差异的综合指标。
我们的年龄-空间模型是一种合适且灵活的方法，它提供了更可靠的死亡率概率估计，允许计算增强的流行病学指标（平滑的 ASR、平滑的 LE），从而为小区域基于传统 SMR 的研究提供了替代方案。
