CHICAS Research Group, Lancaster Medical School, Lancaster University, Bailrigg, Lancaster, UK.
Int J Health Geogr. 2020 Mar 4;19(1):6. doi: 10.1186/s12942-020-00200-w.
Life expectancy at birth (LEB), one of the main indicators of human longevity, has often been used to characterise the health status of a population. Understanding its relationships with the deprivation is key to develop policies and evaluate interventions that are aimed at reducing health inequalities. However, methodological challenges in the analysis of LEB data arise from the fact that different Government agencies often provide spatially aggregated information on LEB and the index of multiple deprivation (IMD) at different spatial scales. Our objective is to develop a geostatistical framework that, unlike existing methods of inference, allows to carry out spatially continuous prediction while dealing with spatial misalignment of the areal-level data. METHODS : We developed a model-based geostatistical approach for the joint analysis of LEB and IMD, when these are available over different partitions of the study region. We model the spatial correlation in LEB and IMD across the areal units using inter-point distances based on a regular grid covering the whole of the study area. The advantages and strengths of the new methodology are illustrated through an analysis of LEB and IMD data from the Liverpool district council. RESULTS : We found that the effect of IMD on LEB is stronger in males than in females, explaining about 63.35% of the spatial variation in LEB in the former group and 38.92% in the latter. We also estimate that LEB is about 8.5 years lower between the most and least deprived area of Liverpool for men, and 7.1 years for women. Finally, we find that LEB, both in males and females, is at least 80% likely to be above the England wide average only in some areas falling in the electoral wards of Childwall, Woolton and Church. CONCLUSION : The novel model-based geostatistical framework provides a feasible solution to the spatial misalignment problem. More importantly, the proposed methodology has the following advantages over existing methods used model LEB: (1) it can deliver spatially continuous inferences using spatially aggregated data; (2) it can be applied to any form of misalignment with information provided at a range of spatial scales, from areal-level to pixel-level.
出生预期寿命（LEB）是衡量人类长寿的主要指标之一，常被用于描述人口的健康状况。了解其与贫困的关系是制定旨在减少健康不平等的政策和评估干预措施的关键。然而，LEB 数据分析中存在方法学挑战，这是因为不同的政府机构通常在不同的空间尺度上提供 LEB 和多重剥夺指数（IMD）的空间聚合信息。我们的目标是开发一个地质统计学框架，该框架与现有推断方法不同，允许在处理区域水平数据的空间错位时进行空间连续预测。
我们开发了一种基于模型的地质统计学方法，用于联合分析 LEB 和 IMD，当这些数据可用于研究区域的不同分区时。我们使用基于覆盖整个研究区域的规则网格的点间距离来对 LEB 和 IMD 在区域单元之间的空间相关性进行建模。通过对利物浦区议会的 LEB 和 IMD 数据进行分析，说明了新方法的优点和优势。
我们发现，IMD 对 LEB 的影响在男性中比在女性中更强，前者组中 LEB 的空间变化约有 63.35%可以用 IMD 来解释，而后者组中只有 38.92%。我们还估计，在利物浦最贫困和最不贫困的地区之间，男性的 LEB 要低 8.5 岁，女性的 LEB 要低 7.1 岁。最后，我们发现，在男性和女性中，LEB 至少有 80%的可能性在英格兰平均水平以上，仅在落在柴尔沃尔、伍尔顿和丘奇选区的一些地区。
基于模型的地质统计学框架为空间错位问题提供了可行的解决方案。更重要的是，与用于建模 LEB 的现有方法相比，所提出的方法具有以下优点：（1）它可以使用空间聚合数据提供空间连续的推断；（2）它可以应用于任何形式的错位，包括从区域水平到像素水平的各种空间尺度。
