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Spatial modeling with INLA for analysis of unequal care in Skåne

Wierzchoslawska, Julia LU (2023) In Bachelor's Theses in Mathematicas Sciences MASK11 20211
Mathematical Statistics
Abstract (Swedish)
The objective of this thesis is to extend on a previous analysis of health care accessibility for patients diagnosed with a chronic disease in Region Skåne. The previous analysis resulted in a logistic mixed effects model having municipality as a random effect and age as a first-degree spline-function. This thesis extends on the random effects from the previous model in order to analyse the spatial dependencies on municipal and postal-code spatial levels.
The models being compared are Bayesian structured additive regression models with latent Gaussian Markov Random Fields. The spatial dependencies are modeled using a Conditional Autoregressive model, and a Random Walk is used to approximate a spline-function for age in this framework.... (More)
The objective of this thesis is to extend on a previous analysis of health care accessibility for patients diagnosed with a chronic disease in Region Skåne. The previous analysis resulted in a logistic mixed effects model having municipality as a random effect and age as a first-degree spline-function. This thesis extends on the random effects from the previous model in order to analyse the spatial dependencies on municipal and postal-code spatial levels.
The models being compared are Bayesian structured additive regression models with latent Gaussian Markov Random Fields. The spatial dependencies are modeled using a Conditional Autoregressive model, and a Random Walk is used to approximate a spline-function for age in this framework. To perform approximate Bayesian inference Integrated Nested Laplace Approximation (INLA) is used. It is shown that both on a municipal and postal-code level a Random Walk of order two is preferred for approximating the spline-function. The difference lies in the spatial dependencies, where on municipal level modeling them as i.i.d. is sufficient, which is comparable to the previous analysis. Regarding spatial dependencies with more intricate geographic boarders, such as on the postal-code level, modeling using a Conditional Autoregressive model is preferred. (Less)
Please use this url to cite or link to this publication:
author
Wierzchoslawska, Julia LU
supervisor
organization
course
MASK11 20211
year
type
M2 - Bachelor Degree
subject
publication/series
Bachelor's Theses in Mathematicas Sciences
report number
LUNFMS-4059-2021
ISSN
1654-6229
other publication id
2021:K31
language
English
id
9111119
date added to LUP
2023-02-27 15:41:22
date last changed
2023-03-01 14:09:18
@misc{9111119,
  abstract     = {{The objective of this thesis is to extend on a previous analysis of health care accessibility for patients diagnosed with a chronic disease in Region Skåne. The previous analysis resulted in a logistic mixed effects model having municipality as a random effect and age as a first-degree spline-function. This thesis extends on the random effects from the previous model in order to analyse the spatial dependencies on municipal and postal-code spatial levels. 
 The models being compared are Bayesian structured additive regression models with latent Gaussian Markov Random Fields. The spatial dependencies are modeled using a Conditional Autoregressive model, and a Random Walk is used to approximate a spline-function for age in this framework. To perform approximate Bayesian inference Integrated Nested Laplace Approximation (INLA) is used. It is shown that both on a municipal and postal-code level a Random Walk of order two is preferred for approximating the spline-function. The difference lies in the spatial dependencies, where on municipal level modeling them as i.i.d. is sufficient, which is comparable to the previous analysis. Regarding spatial dependencies with more intricate geographic boarders, such as on the postal-code level, modeling using a Conditional Autoregressive model is preferred.}},
  author       = {{Wierzchoslawska, Julia}},
  issn         = {{1654-6229}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{Bachelor's Theses in Mathematicas Sciences}},
  title        = {{Spatial modeling with INLA for analysis of unequal care in Skåne}},
  year         = {{2023}},
}