PubMed · 14374353
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M D JEFFREYS. 1955. A census factor.. https://pubmed.ncbi.nlm.nih.gov/14374353/
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STUDY OBJECTIVE: To investigate whether the Index of Multiple Deprivation 2000 (IMD) is more strongly related to inequalities in health in rural areas than traditional deprivation indices. To explore the contribution of the IMD domain "geographical access to services" to understanding rural health variations. DESIGN: A geographically based cross sectional study. SETTING: Nine counties in the south west region of England. PARTICIPANTS: All those aged below 65 who reported a limiting long term illness in the 1991 census, and all those who died during 1991-96, aged less than 65 years. MAIN RESULTS: The IMD is comparable with the Townsend score in its overall correlation with premature mortality (r(2) = 0.44 v 0.53) and morbidity (r(2) = 0.79 v 0.76). Correlation between the Townsend score and population health is weak in rural areas but the IMD maintains a strong correlation with rates of morbidity (r(2) = 0.70). The "geographical access to services" domain of the IMD is not strongly correlated with rates of morbidity in rural areas (r(2) = 0.04), and in urban areas displays a negative correlation (r(2) = -0.47). CONCLUSIONS: The IMD has a strong relation with health in both rural and urban areas. This is likely to be the result of the inclusion of data in the IMD on the numbers of people claiming benefits related to ill health and disability. The domain "geographical access to services" is not associated with health in rural areas, although it displays some association in urban areas. This domain is potentially important but, as yet, inadequately specified in the IMD for the purposes of health research.
In the areas of epidemiology, psychology, sociology, and other social and behavioural sciences, researchers often encounter situations where there are not only many variables contributing to a particular phenomenon, but there are also strong relationships among many of the predictor variables of interest. By using the traditional multiple regression on all the predictor variables, it is possible to have problems with interpretation and multicollinearity. As an alternative to multiple regression, we explore the use of a latent variable model that can address the relationship among the predictor variables. We consider two different methods for estimation and prediction for this model: one that uses multiple regression on factor score estimates and the other that uses structural equation modelling. The first method uses multiple regression but on a set of predicted underlying factors (i.e. factor scores), and the second method is a full-information maximum-likelihood technique that incorporates the complete covariance structure of the data. In this tutorial, we will explain the model and each estimation method, including how to carry out prediction. A data example will be used for demonstration, where respiratory disease death rates by county in Minnesota are predicted by five county-level census variables. A simulation study is performed to evaluate the efficiency of prediction using the two latent variable modelling techniques compared to multiple regression.
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