 PATH.png)
The influence of individual and area disadvantage over the life course on mortality risk 1996-2001: a multilevel analysis
Chris White, Alison Whitworth and Anne-Marie Jones, Office for National Statistics, and Dick Wiggins, City University London
[Project number 20066]
Spatial variations in health represent an important dimension of health inequality in Britain, having implications for resource allocation at the administrative level. Dorling has undertaken a detailed investigation into regional differences in mortality and found sharp rises in relative risk. A similar pattern was observed in the prevalence of limiting long-term illness, carried out in the wake of the 1991 census.
Geographical differences in health and mortality could be simply due to the personal characteristics, behaviours and exposure histories of the individuals that make up the area population (i.e. compositional effects). Alternatively, they could be due to the ecological and environmental fabric of the area itself (contextual effects). A crucial step in clarifying the balance of these effects is to determine whether the scale of variation between areas goes beyond what would be expected on the basis of individual characteristics alone. Such an understanding can only be achieved through the application of models that take account of the hierarchical nature of the data.
This project aims to demonstrate the use of multilevel modelling techniques in the analysis of longitudinal survey and vital events data, using the statistical packages MLWin and Stata, and contribute methodologically and substantively to the literature on geographical health inequalities.
The objectives of the project are to:
1. Build up multilevel modelling skills capacity within the Health Variations
team;
2. Model the effect of individual and county district level disadvantage
across the life course on mortality risk 1996-2001, using multilevel logistic
regression.
3. Provide a discussion of the advantages and disadvantages of multilevel
modelling in the analysis of socio-demographic and mortality data.
4. Assess conformity with multilevel models constructed to measure the
relationship between individual and area disadvantage and limiting long-term
illness.
5. Assess software performance.
6. Model the effect of individual and ward level disadvantage across the
life course on risk of LLTI in 2001, using multilevel logistic regression.
7. Model the effect of individual and ward level disadvantage across the
life course on self-reported health in 2001.
8. Explore application of multivariate multilevel models using LLTI and
self-reported heath at 2001.
Objectives 6, 7 and 8 will not be tackled until objectives 1-5 have been
achieved.
The LS is the only data source available to provide socio-demographic
and event histories spanning 30 years. The ability to measure socioeconomic
and spatial transitions allows a more sensitive profile of individual
disadvantage to be constructed across time, with greater predictive power
than assigning a measure of social standing at a point in time. The aims
and objectives of this project are inconceivable in the absence of a representative
longitudinal data source such as the ONS LS.