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A nonparametric random coefficient approach for life expectancy growth using a hierarchical mixture likelihood model with application to regional data from North Rhine-Westphalia (Germany)

A nonparametric random coefficient approach for life expectancy growth using a hierarchical mixture likelihood model with application to regional data from North Rhine-Westphalia (Germany)
A nonparametric random coefficient approach for life expectancy growth using a hierarchical mixture likelihood model with application to regional data from North Rhine-Westphalia (Germany)
Background

Life expectancy is of increasing prime interest for a variety of reasons. In many countries, life expectancy is growing linearly, without any indication of reaching a limit. The state of North Rhine–Westphalia (NRW) in Germany with its 54 districts is considered here where the above mentioned growth in life expectancy is occurring as well. However, there is also empirical evidence that life expectancy is not growing linearly at the same level for different regions.

Methods

To explore this situation further a likelihood-based cluster analysis is suggested and performed. The modelling uses a nonparametric mixture approach for the latent random effect. Maximum likelihood estimates are determined by means of the EM algorithm and the number of components in the mixture model are found on the basis of the Bayesian Information Criterion. Regions are classified into the mixture components (clusters) using the maximum posterior allocation rule.

Results

For the data analyzed here, 7 components are found with a spatial concentration of lower life expectancy levels in a centre of NRW, formerly an enormous conglomerate of heavy industry, still the most densely populated area with Gelsenkirchen having the lowest level of life expectancy growth for both genders. The paper offers some explanations for this fact including demographic and socio-economic sources.

Conclusions

This case study shows that life expectancy growth is widely linear, but it might occur on different levels.
likelihood–based cluster analysis, random coefficient modelling, finite mixture model, life expectancy
1471-2288
1-13
Böhning, Dankmar
1df635d4-e3dc-44d0-b61d-5fd11f6434e1
Karasek, Sarah
8bbfdc0a-5817-409f-87ca-6ab941f3579d
Terschüren, Claudia
4499b89d-cecc-4146-a828-6fe7317344c7
Annuß, Rolf
c88ac87e-a26e-4a82-a689-cb6040c70a63
Fehr, Rainer
7b3d5566-d29e-4e2b-b519-0d28da73cb37
Böhning, Dankmar
1df635d4-e3dc-44d0-b61d-5fd11f6434e1
Karasek, Sarah
8bbfdc0a-5817-409f-87ca-6ab941f3579d
Terschüren, Claudia
4499b89d-cecc-4146-a828-6fe7317344c7
Annuß, Rolf
c88ac87e-a26e-4a82-a689-cb6040c70a63
Fehr, Rainer
7b3d5566-d29e-4e2b-b519-0d28da73cb37

Böhning, Dankmar, Karasek, Sarah, Terschüren, Claudia, Annuß, Rolf and Fehr, Rainer (2013) A nonparametric random coefficient approach for life expectancy growth using a hierarchical mixture likelihood model with application to regional data from North Rhine-Westphalia (Germany). BMC Medical Research Methodology, 13 (26), 1-13. (doi:10.1186/1471-2288-13-36).

Record type: Article

Abstract

Background

Life expectancy is of increasing prime interest for a variety of reasons. In many countries, life expectancy is growing linearly, without any indication of reaching a limit. The state of North Rhine–Westphalia (NRW) in Germany with its 54 districts is considered here where the above mentioned growth in life expectancy is occurring as well. However, there is also empirical evidence that life expectancy is not growing linearly at the same level for different regions.

Methods

To explore this situation further a likelihood-based cluster analysis is suggested and performed. The modelling uses a nonparametric mixture approach for the latent random effect. Maximum likelihood estimates are determined by means of the EM algorithm and the number of components in the mixture model are found on the basis of the Bayesian Information Criterion. Regions are classified into the mixture components (clusters) using the maximum posterior allocation rule.

Results

For the data analyzed here, 7 components are found with a spatial concentration of lower life expectancy levels in a centre of NRW, formerly an enormous conglomerate of heavy industry, still the most densely populated area with Gelsenkirchen having the lowest level of life expectancy growth for both genders. The paper offers some explanations for this fact including demographic and socio-economic sources.

Conclusions

This case study shows that life expectancy growth is widely linear, but it might occur on different levels.

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More information

Accepted/In Press date: 21 February 2013
Published date: 9 March 2013
Keywords: likelihood–based cluster analysis, random coefficient modelling, finite mixture model, life expectancy
Organisations: Statistics, Statistical Sciences Research Institute, Primary Care & Population Sciences

Identifiers

Local EPrints ID: 350653
URI: https://eprints.soton.ac.uk/id/eprint/350653
ISSN: 1471-2288
PURE UUID: 9b6a1796-57f6-45c7-8477-47e657a4161a
ORCID for Dankmar Böhning: ORCID iD orcid.org/0000-0003-0638-7106

Catalogue record

Date deposited: 04 Apr 2013 11:08
Last modified: 06 Oct 2018 00:32

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