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A simple multilevel approach for analysing geographical inequalities in public health reports: : The case of municipality differences in obesity

Merlo, Juan LU orcid ; Wagner, Philippe LU and Leckie, George LU (2019) In Health & Place 58.
Abstract
The epidemiological analysis of geographical inequalities in individual outcomes is a fundamental theme in public health research. However, many traditional studies focus on analysing area differences in averages outcomes, disregarding individual variation around such averages. In doing so, these studies may produce misleading information and lead researchers to draw incorrect conclusions. Analysing individual and municipality differences in body mass index (BMI) and overweight/obesity status, we apply an analytical approach based on the multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA). This analytical approach may be viewed as a reorganization of existing multilevel modelling concepts in order to... (More)
The epidemiological analysis of geographical inequalities in individual outcomes is a fundamental theme in public health research. However, many traditional studies focus on analysing area differences in averages outcomes, disregarding individual variation around such averages. In doing so, these studies may produce misleading information and lead researchers to draw incorrect conclusions. Analysing individual and municipality differences in body mass index (BMI) and overweight/obesity status, we apply an analytical approach based on the multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA). This analytical approach may be viewed as a reorganization of existing multilevel modelling concepts in order to provide a systematic approach to simultaneously considering both differences between area averages and individual heterogeneity around those averages. In doing so, MAIHDA provides an improved approach to the quantification and understanding of geographical inequalities as compared with traditional approaches. (Less)
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author
; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Health & Place
volume
58
article number
102145
publisher
Elsevier
external identifiers
  • scopus:85066937264
  • pmid:31195211
ISSN
1873-2054
DOI
10.1016/j.healthplace.2019.102145
project
Multilevel analysis of individual heterogeneity
language
English
LU publication?
yes
id
221b3fcc-bd9e-4240-8cea-c30a2a2e7520
date added to LUP
2019-06-17 08:32:40
date last changed
2023-04-09 15:44:23
@article{221b3fcc-bd9e-4240-8cea-c30a2a2e7520,
  abstract     = {{The epidemiological analysis of geographical inequalities in individual outcomes is a fundamental theme in public health research. However, many traditional studies focus on analysing area differences in averages outcomes, disregarding individual variation around such averages. In doing so, these studies may produce misleading information and lead researchers to draw incorrect conclusions. Analysing individual and municipality differences in body mass index (BMI) and overweight/obesity status, we apply an analytical approach based on the multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA). This analytical approach may be viewed as a reorganization of existing multilevel modelling concepts in order to provide a systematic approach to simultaneously considering both differences between area averages and individual heterogeneity around those averages. In doing so, MAIHDA provides an improved approach to the quantification and understanding of geographical inequalities as compared with traditional approaches.}},
  author       = {{Merlo, Juan and Wagner, Philippe and Leckie, George}},
  issn         = {{1873-2054}},
  language     = {{eng}},
  month        = {{06}},
  publisher    = {{Elsevier}},
  series       = {{Health & Place}},
  title        = {{A simple multilevel approach for analysing geographical inequalities in public health reports: : The case of municipality differences in obesity}},
  url          = {{http://dx.doi.org/10.1016/j.healthplace.2019.102145}},
  doi          = {{10.1016/j.healthplace.2019.102145}},
  volume       = {{58}},
  year         = {{2019}},
}