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

Abstract

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.

Bayesian Factor Analysis for Spatially Correlated Data, With Application to Summarizing Area-Level Material Deprivation From Census Data

“…Thus one natural extension would permit multivariate factors with a spatial correlation structure. Second, as suggested earlier, the advantage gained by using a model versus an empirical index could be quanti ed through validation studies, such as the one carried out by Barnett et al (2002). The analyses by Barnett et al (2002) also suggest that the components of material deprivation may differ between urban and rural areas, suggesting the need to incorporate covariates, which could enter either at the rst or the second level (Sammel and Ryan 1996).…”

“…Second, as suggested earlier, the advantage gained by using a model versus an empirical index could be quanti ed through validation studies, such as the one carried out by Barnett et al (2002). The analyses by Barnett et al (2002) also suggest that the components of material deprivation may differ between urban and rural areas, suggesting the need to incorporate covariates, which could enter either at the rst or the second level (Sammel and Ryan 1996). To validate measurement of deprivation per se, one can use external indicators measured at the neighborhood level (as in Raudenbush and Sampson 1999); to validate the index as a surrogate of morbidity or health services use, data on the speci c health outcome is needed (e.g., Krieger et al 2002).…”

“…The indices are derived from publicly available data and can be calibrated using data on morbidity and/or speci c health outcomes, such as cancer, that are directly related to use of services but typically are available only from sources, such as registries, that are not publicly available. Thus one objective in this area of research is to develop indices with high construct validity as it relates to use of services (see, e.g., Carstairs 1995;Malmström, Sundquist, Bajekal, and Johansson 1998;Barnett, Roderick, Martin, Diamond, and Wrigley 2002). Barnett et al (2002) provided a recent review of the key issues related to index use in resource allocation, and carried out a detailed analysis showing the relationships between mortality, morbidity, and several deprivation indices.…”

“…Thus one objective in this area of research is to develop indices with high construct validity as it relates to use of services (see, e.g., Carstairs 1995;Malmström, Sundquist, Bajekal, and Johansson 1998;Barnett, Roderick, Martin, Diamond, and Wrigley 2002). Barnett et al (2002) provided a recent review of the key issues related to index use in resource allocation, and carried out a detailed analysis showing the relationships between mortality, morbidity, and several deprivation indices. A related and recently opened line of research in the United States concerns the use of area-based measuresin particular, those based on census variables-to quantify and monitor socioeconomic inequalities in health (Krieger et al 2001(Krieger et al , 2002.…”

Versorgungsbedarf, Angebot und Inanspruchnahme ambulanter hausärztlicher Leistungen im kleinräumigen regionalen Vergleich

“…Diese Angaben zur Prävalenz häufiger Krankheitsbilder beruhen nicht notwendigerweise auf bereits gestellten Diagnosen, sondern vielmehr auf der subjektiven Einschätzung der Befragten zu ihrer Morbidität. Damit ist dieser Indikator laut Barnett, Roderick et al (2002) vor allem für die Einschätzung der Morbiditäts-last von jüngeren Altersgruppen besser geeignet als die Mortalität, weil damit der nicht lebensbedrohliche Zustand einer chronischen Erkrankung, der nichtsdestotrotz einen Bedarf an Versorgungsleistungen generiert, analysiert werden kann.…”