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Spatial and temporal variation of mortality and deprivation 2: statistical modelling.

M L Senior, H C Williams, G Higgs

    Environment & Planning A
    |September 26, 2002
    PubMed
    Summary
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    This study uses statistical models to analyze how mortality relates to deprivation over time. It reveals significant temporal changes in these mortality-deprivation relationships, offering a formal approach to understanding health inequalities.

    Area of Science:

    • Public Health
    • Biostatistics
    • Sociology

    Background:

    • Tabular analyses are common in medical literature for examining mortality and deprivation.
    • A formal statistical approach is needed to analyze the dynamic relationship between mortality and deprivation over time.

    Purpose of the Study:

    • To apply generalized linear models for a formal statistical analysis of mortality-deprivation relationships.
    • To investigate the changes in mortality-deprivation relationships over time using longitudinal data.
    • To explore nonlinear effects of deprivation on mortality and connect them to tabular methods.

    Main Methods:

    • Utilized generalized linear models, specifically Poisson and logit fixed-effects models.
    • Employed ward-level data from Wales, consistent with prior tabular analyses.
    Keywords:
    Demographic FactorsDeveloped CountriesDifferential MortalityEconomic FactorsEuropeGeographic FactorsInequalitiesModels, TheoreticalMortalityNorthern EuropePopulationPopulation DynamicsPovertyResearch MethodologySocioeconomic FactorsTime FactorsUnited KingdomWales

    Related Experiment Videos

  • Incorporated dummy variables for deprivation categories to model nonlinear effects.

    • Main Results:

    • Estimated cross-sectional and repeated-measures Poisson models to analyze mortality-deprivation at single time points and over time.
    • Estimated logit models to specifically focus on temporal changes in these relationships.
    • Established a connection between formal statistical models and tabular approaches for analyzing nonlinear deprivation effects.

    Conclusions:

    • Generalized linear models provide a robust statistical framework for analyzing mortality-deprivation dynamics.
    • The study quantifies temporal changes in mortality-deprivation relationships, advancing understanding of health inequalities.
    • The findings bridge formal statistical modeling with established tabular methods for analyzing deprivation impacts on health outcomes.