Alleviating Spatial Confounding for Areal Data Problems by Displacing the Geographical Centroids

Prates, Marcos O.; Assunção, Renato M.; Rodrigues, Érica Castilho

doi:10.1214/18-ba1123

Cited by 29 publications

(53 citation statements)

References 19 publications

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“…An alternative to the RSR method is to formulate spatially misaligned ecological model, misaligned in a sense that the spatial prior of the random effects and the included covariates are varying at different spatial scales and/or configurations ( Lee and Sarran, 2015 , Prates et al, 2019 , Azevedo et al, 2021 ). This approach to deconfounding is also consistent with the Paciorek (2010) work on spatial confounding, which offered insight into the importance of scale for spatial-confounding bias and precision of spatial regression estimators.…”

Section: Disease Mapping With Areal-level Covariates: Ecological Regressionmentioning

confidence: 99%

Bayesian disease mapping: Past, present, and future

MacNab

2022

Spatial Statistics

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On the occasion of the Spatial Statistics’ 10th Anniversary, I reflect on the past and present of Bayesian disease mapping and look into its future. I focus on some key developments of models, and on recent evolution of multivariate and adaptive Gaussian Markov random fields and their impact and importance in disease mapping. I reflect on Bayesian disease mapping as a subject of spatial statistics that has advanced to date, and continues to grow, in scope and complexity alongside increasing needs of analytic tools for contemporary health science research, such as spatial epidemiology, population and public health, and medicine. I illustrate (potential) utility and impact of some of the disease mapping models and methods for analyzing and monitoring communicable disease such as the COVID-19 infection risks during an ongoing pandemic.

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Section: Disease Mapping With Areal-level Covariates: Ecological Regressionmentioning

confidence: 99%

Bayesian disease mapping: Past, present, and future

MacNab

2022

Spatial Statistics

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“…Spatial confounding (meaning that the spatial random effect is collinear with the observed covariates) may attenuate or bias regression coefficients on the observed covariates, and inflate the variance of the estimates of these coefficients (Prates et al. 2019 ). As it can be seen in Sect.…”

Section: Methodsmentioning

confidence: 99%

Identifying extreme COVID-19 mortality risks in English small areas: a disease cluster approach

Adin

Congdon

Santafé

et al. 2022

Stoch Environ Res Risk Assess

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The COVID-19 pandemic is having a huge impact worldwide and has highlighted the extent of health inequalities between countries but also in small areas within a country. Identifying areas with high mortality is important both of public health mitigation in COVID-19 outbreaks, and of longer term efforts to tackle social inequalities in health. In this paper we consider different statistical models and an extension of a recent method to analyze COVID-19 related mortality in English small areas during the first wave of the epidemic in the first half of 2020. We seek to identify hotspots, and where they are most geographically concentrated, taking account of observed area factors as well as spatial correlation and clustering in regression residuals, while also allowing for spatial discontinuities. Results show an excess of COVID-19 mortality cases in small areas surrounding London and in other small areas in North-East and and North-West of England. Models alleviating spatial confounding show ethnic isolation, air quality and area morbidity covariates having a significant and broadly similar impact on COVID-19 mortality, whereas nursing home location seems to be slightly less important.

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“…In a spatial casual analysis, the treatment variables, covariates and random effects may all have spatial patterns. One way to resolve this conflict is to restrict the spatial random effects to be orthogonal to the observed treatment variables (Reich et al, 2006;Hughes & Haran, 2013;Hanks et al, 2015;Page et al, 2017;Prates et al, 2019). However, Khan & Calder (2020) showed that this can lead to poor performance for treatment estimates.…”

Section: Summary and Future Workmentioning

confidence: 99%

A Review of Spatial Causal Inference Methods for Environmental and Epidemiological Applications

Reich

Yang

Guan

et al. 2021

Int Statistical Rev

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The scientific rigor and computational methods of causal inference have had great impacts on many disciplines but have only recently begun to take hold in spatial applications. Spatial causal inference poses analytic challenges due to complex correlation structures and interference between the treatment at one location and the outcomes at others. In this paper, we review the current literature on spatial causal inference and identify areas of future work. We first discuss methods that exploit spatial structure to account for unmeasured confounding variables. We then discuss causal analysis in the presence of spatial interference including several common assumptions used to reduce the complexity of the interference patterns under consideration. These methods are extended to the spatiotemporal case where we compare and contrast the potential outcomes framework with Granger causality and to geostatistical analyses involving spatial random fields of treatments and responses. The methods are introduced in the context of observational environmental and epidemiological studies and are compared using both a simulation study and analysis of the effect of ambient air pollution on COVID-19 mortality rate. Code to implement many of the methods using the popular Bayesian software OpenBUGS is provided.

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Alleviating Spatial Confounding for Areal Data Problems by Displacing the Geographical Centroids

Cited by 29 publications

References 19 publications

Bayesian disease mapping: Past, present, and future

Bayesian disease mapping: Past, present, and future

Identifying extreme COVID-19 mortality risks in English small areas: a disease cluster approach

A Review of Spatial Causal Inference Methods for Environmental and Epidemiological Applications

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