Bayesian inference in multivariate spatio-temporal areal models using INLA: analysis of gender-based violence in small areas

Vicente, Gonzalo; Goicoa, T.; Ugarte, M. D.

doi:10.1007/s00477-020-01808-x

Cited by 22 publications

(10 citation statements)

References 41 publications

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“…Age-effects could be included in (7) or (8) as additional components ( Held and Besag, 1998 , Goicoa et al, 2016 , Martinez-Beneito et al, 2017 ) or modelled by a spline for non-linear age-effects ( MacNab, 2003a ). Multivariate spatiotemporal disease mapping is another direction ( Tzala and Best, 2008 , Jack et al, 2019 , Vicente et al, 2020 , Vicente et al, 2021 , among others).…”

Section: Multidimensional Disease Mappingmentioning

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: Multidimensional Disease Mappingmentioning

confidence: 99%

Bayesian disease mapping: Past, present, and future

MacNab

2022

Spatial Statistics

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“…This study aimed to analyze whether there was a shared spatial distribution of police calls reporting street-level crime and IPVAW. A Bayesian joint model was performed in line with previous research that incorporates a multivariate spatial analysis to study crime outcomes [40][41][42][43]51,52]. In addition, two different Poisson regression models were conducted to assess the spatial similarity of relative risks.…”

Section: Discussionmentioning

confidence: 99%

The Spatial Overlap of Police Calls Reporting Street-Level and Behind-Closed-Doors Crime: A Bayesian Modeling Approach

Marco

Gracia

López‐Quílez

et al. 2021

IJERPH

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Traditionally, intimate-partner violence has been considered a special type of crime that occurs behind closed doors, with different characteristics from street-level crime. The aim of this study is to analyze the spatial overlap of police calls reporting street-level and behind-closed-doors crime. We analyzed geocoded police calls in the 552 census-block groups of the city of Valencia, Spain, related to street-level crime (N = 26,624) and to intimate-partner violence against women (N = 11,673). A Bayesian joint model was run to analyze the spatial overlap. In addition, two Bayesian hierarchical models controlled for different neighborhood characteristics to analyze the relative risks. Results showed that 66.5% of the total between-area variation in risk of reporting street-level crime was captured by a shared spatial component, while for reporting IPVAW the shared component was 91.1%. The log relative risks showed a correlation of 0.53, with 73.6% of the census-block groups having either low or high values in both outcomes, and 26.4% of the areas with mismatched risks. Maps of the shared component and the relative risks are shown to detect spatial differences. These results suggest that although there are some spatial differences between police calls reporting street-level and behind-closed-doors crime, there is also a shared distribution that should be considered to inform better-targeted police interventions.

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“…In addition, these models are computationally convenient for modeling multivariate spatial patterns, which makes them an appropriate choice for fitting the data in regular Bayesian inference packages such as WinBUGS, OpenBUGS, Nimble... Moreover, M-models have already been implemented for the joint analysis of several correlated spatial patterns in INLA, either for a separable model for a large number of spatial patterns [41], or for an inseparable joint analysis of three spatial patterns [42]. Thus INLA should be also born in mind as a potential tool for this kind of analyses.…”

Section: Discussionmentioning

confidence: 99%

Beyond standardized mortality ratios; some uses of smoothed age-specific mortality rates on small areas studies.

Pérez-Panadés

Botella-Rocamora

Martínez-Beneito

2020

Preprint

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Background: 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.Methods: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.Results: 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 consideringage-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.Conclusion: 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

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Bayesian inference in multivariate spatio-temporal areal models using INLA: analysis of gender-based violence in small areas

Cited by 22 publications

References 41 publications

Bayesian disease mapping: Past, present, and future

Bayesian disease mapping: Past, present, and future

The Spatial Overlap of Police Calls Reporting Street-Level and Behind-Closed-Doors Crime: A Bayesian Modeling Approach

Beyond standardized mortality ratios; some uses of smoothed age-specific mortality rates on small areas studies.

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