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A simple multilevel approach for analysing geographical inequalities in public health reports: The case of municipality differences in obesity
Lund Univ, Fac Med, Res Unit Social Epidemiol, Jan Waldenstroms St 35, SE-20502 Malmo, Sweden.ORCID iD: 0000-0001-8379-9708
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Medicinska och farmaceutiska vetenskapsområdet, centrumbildningar mm, Centre for Clinical Research, County of Västmanland. Lund Univ, Fac Med, Res Unit Social Epidemiol, Jan Waldenstroms St 35, SE-20502 Malmo, Sweden;.
Lund Univ, Fac Med, Res Unit Social Epidemiol, Jan Waldenstroms St 35, SE-20502 Malmo, Sweden;Univ Bristol, Ctr Multilevel Modelling, 35 Berkeley Sq, Bristol BS8 1JA, Avon, England.
2019 (English)In: Health and Place, ISSN 1353-8292, E-ISSN 1873-2054, Vol. 58, article id UNSP 102145Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
ELSEVIER SCI LTD , 2019. Vol. 58, article id UNSP 102145
Keywords [en]
Public health, Geographical differences, Multilevel analysis, Discriminatory accuracy, Obesity
National Category
Public Health, Global Health, Social Medicine and Epidemiology
Identifiers
URN: urn:nbn:se:uu:diva-393838DOI: 10.1016/j.healthplace.2019.102145ISI: 000482101600003PubMedID: 31195211OAI: oai:DiVA.org:uu-393838DiVA, id: diva2:1355315
Funder
Swedish Research Council, 2017-01321Available from: 2019-09-27 Created: 2019-09-27 Last updated: 2019-09-27Bibliographically approved

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