District-Level Life Expectancy in Germany.


Journal

Deutsches Arzteblatt international
ISSN: 1866-0452
Titre abrégé: Dtsch Arztebl Int
Pays: Germany
ID NLM: 101475967

Informations de publication

Date de publication:
20 Jul 2020
Historique:
received: 09 10 2019
revised: 09 10 2019
accepted: 23 04 2020
entrez: 22 10 2020
pubmed: 23 10 2020
medline: 20 11 2020
Statut: ppublish

Résumé

Identifying regions with low life expectancy is important to policy makers, in particular for allocating resources in the health system. Life expectancy estimates for small regions are, however, often unreliable and lead to statistical uncertainties when the underlying populations are relatively small. We combine the most recent German data available (2015-2017) with a Bayesian model that includes several methodological advances. This allows us to estimate male and female life expectancy with good precision for all 402 German districts and to quantify the uncertainty of those estimates. Across districts, life expectancy varies between 75.8 and 81.2 years for men and from 81.8 to 85.7 years for women. The spatial pattern is similar for women and men. Rural districts in eastern Germany and some districts of the Ruhr region have relatively low life expectancy. Districts with relatively high life expectancies cluster in Baden-Wuerttemberg and southern Bavaria. Exploratory analysis shows that average income, population density, and number of physicians per 100 000 inhabitants are not strongly correlated with life expectancy at district level. In contrast, indicators that point to particularly disadvantaged segments of the population (unemployment rate, welfare benefits) are better predictors of life expectancy. We do not find a consistent urban-rural gap in life expectancy. Our results suggest that policies that improve living standards for poorer segment of the population are the most likely to reduce the existing differences in life expectancy.

Sections du résumé

BACKGROUND BACKGROUND
Identifying regions with low life expectancy is important to policy makers, in particular for allocating resources in the health system. Life expectancy estimates for small regions are, however, often unreliable and lead to statistical uncertainties when the underlying populations are relatively small.
METHODS METHODS
We combine the most recent German data available (2015-2017) with a Bayesian model that includes several methodological advances. This allows us to estimate male and female life expectancy with good precision for all 402 German districts and to quantify the uncertainty of those estimates.
RESULTS RESULTS
Across districts, life expectancy varies between 75.8 and 81.2 years for men and from 81.8 to 85.7 years for women. The spatial pattern is similar for women and men. Rural districts in eastern Germany and some districts of the Ruhr region have relatively low life expectancy. Districts with relatively high life expectancies cluster in Baden-Wuerttemberg and southern Bavaria. Exploratory analysis shows that average income, population density, and number of physicians per 100 000 inhabitants are not strongly correlated with life expectancy at district level. In contrast, indicators that point to particularly disadvantaged segments of the population (unemployment rate, welfare benefits) are better predictors of life expectancy.
CONCLUSIONS CONCLUSIONS
We do not find a consistent urban-rural gap in life expectancy. Our results suggest that policies that improve living standards for poorer segment of the population are the most likely to reduce the existing differences in life expectancy.

Identifiants

pubmed: 33087229
pii: arztebl.2020.0493
doi: 10.3238/arztebl.2020.0493
pmc: PMC7588608
doi:
pii:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

493-499

Commentaires et corrections

Type : CommentIn

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Auteurs

Roland Rau (R)

Department of Sociology and Demography, University of Rostock; Max Planck Institute forDemographic Research, Rostock.

Carl P Schmertmann (CP)

Center for Demography and Population Health, Florida State University, Tallahassee, USA.

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Classifications MeSH