The objective of disease mapping is to model data aggregated at the areal level. In some contexts, however, (e.g. residential histories, general practitioner catchment areas) when data is arising from a variety of sources, not necessarily at the same spatial scale, it is possible to specify spatial random effects, or covariate effects, at the areal level, by using a multiple membership principle (MM) (Petrof et al. 2020, Gramatica et al. 2021). A weighted average of conditional autoregressive (CAR) spatial random effects embeds spatial information for a spatially-misaligned outcome and estimate relative risk for both frameworks (areas and memberships). In this paper we investigate the theoretical underpinnings of these application of the multiple membership principle to the CAR prior, in particular with regard to parameterisation, properness and identifiability. We carry out simulations involving different numbers of memberships as compared to number of areas and assess impact of this on estimating parameters of interest. Both analytical and simulation study results show under which conditions parameters of interest are identifiable, so that we can offer actionable recommendations to practitioners. Finally, we present the results of an application of the multiple membership model to diabetes prevalence data in South London, together with strategic implications for public health considerations
翻译:疾病测绘的目标是模拟在区域层面汇总的数据,但在某些情况下(例如居住历史、全科医生集水区),当数据来自各种来源,不一定是相同的空间尺度,数据可以使用多个成员原则(MM)(Petrof等人,2020年,Gramatica等人,2021年),在地平层一级具体说明空间随机效应或共变效应(Petrof等人,2020年,Gramatica等人,2021年);有条件的自动递进(CAR)空间随机效应加权平均值,包含空间错位结果的空间信息,并估计两个框架(地区和成员)的相对风险;在本文中,我们先调查应用多成员原则给CAR的理论依据,特别是参数化、适当性和可识别性;我们进行成员数目不同的模拟,与区域数目相比,评估这对估计兴趣参数的影响;两个分析和模拟研究结果都表明在哪些条件下可以识别,因此我们可以向实践者提出可采取行动的建议;在本文之前,我们调查这些应用多成员原则给CAR的理论基础,特别是在参数的参数、适当性和可辨识度方面;我们一起提出多种成员制模式对多种健康状况的战略影响,同时提出伦敦的统计模式的战略影响。