Interrelations between three proxies of health care need at the small area level: an urban/rural comparison.

STUDY OBJECTIVE To examine the relations between geographical variations in mortality, morbidity, and deprivation at the small area level in the south west of England and to assess whether these relations vary between urban and rural areas. DESIGN A geographically based cross sectional study using 1991 census data on premature limiting long term illness (LLTI) and socioeconomic characteristics, and 1991-1996 data on all cause premature mortality. The interrelations between the three widely used proxies of health care need are examined using correlation coefficients and scatterplots. The distribution of standardised LLTI residuals from a regression analysis on mortality are mapped and compared with the distribution of urban and rural areas. Multilevel Poisson modelling investigates whether customised deprivation profiles improve upon a generic deprivation index in explaining the spatial variation in morbidity and mortality after controlling for age and sex. These relations are examined separately for urban, fringe, and rural areas. SETTING Nine counties in the south west of England. PARTICIPANTS Those aged between 0-64 who reported having a LLTI in the 1991 census, and those who died during 1991-1996 aged 0-74. MAIN RESULTS Relations between both health outcomes and generic deprivation indices are stronger in urban than rural areas. The replacement of generic with customised indices is an improvement in all area types, especially for LLTI in rural areas. The relation between mortality and morbidity is stronger in urban than rural areas, with levels of LLTI appearing to be greater in rural areas than would be predicted from mortality rates. Despite the weak direct relations between mortality and morbidity, there are strong relations between the customised deprivation indices computed to predict these outcomes in all area types. CONCLUSIONS The improvement of the customised deprivation indices over the generic indices, and the similarity between the mortality and morbidity customised indices within area types highlights the importance of modelling urban and rural areas separately. Stronger relations between mortality and morbidity have been revealed at the local authority level in previous research providing empirical evidence that the inadequacy of mortality as a proxy for morbidity becomes more marked at lower levels of aggregation, especially in rural areas. Higher levels of LLTI than expected in rural areas may reflect different perceptions or differing patterns of illness. The stronger relations between the three proxies in urban than rural areas suggests that the choice of indicator will have less impact in urban than rural areas and strengthens the argument to develop better measures of health care need in rural areas.