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Modelling small-area inequality in premature mortality using years of life lost rates

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  • Peter Congdon

Abstract

Analysis of premature mortality variations via standardized expected years of life lost (SEYLL) measures raises questions about suitable modelling for mortality data, especially when developing SEYLL profiles for areas with small populations. Existing fixed effects estimation methods take no account of correlations in mortality levels over ages, causes, socio-ethnic groups or areas. They also do not specify an underlying data generating process, or a likelihood model that can include trends or correlations, and are likely to produce unstable estimates for small-areas. An alternative strategy involves a fully specified data generation process, and a random effects model which “borrows strength” to produce stable SEYLL estimates, allowing for correlations between ages, areas and socio-ethnic groups. The resulting modelling strategy is applied to gender-specific differences in SEYLL rates in small-areas in NE London, and to cause-specific mortality for leading causes of premature mortality in these areas. Copyright Springer-Verlag Berlin Heidelberg 2013

Suggested Citation

  • Peter Congdon, 2013. "Modelling small-area inequality in premature mortality using years of life lost rates," Journal of Geographical Systems, Springer, vol. 15(2), pages 149-167, April.
    • Handle: RePEc:kap:jgeosy:v:15:y:2013:i:2:p:149-167
    DOI: 10.1007/s10109-012-0167-y
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    References listed on IDEAS

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    More about this item

    Keywords

    Premature mortality; Small-area mortality; Standardized expected years of life lost; Spatial; Bayesian; I14; C11; C21; C51;
    All these keywords.

    JEL classification:

    • I14 - Health, Education, and Welfare - - Health - - - Health and Inequality
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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