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The effects of deprivation and relative deprivation on self-reported morbidity in England: an area-level ecological study

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      Abstract

      Background

      Socioeconomic status gradients in health outcomes are well recognised and may operate in part through the psychological effect of observing disparities in affluence. At an area-level, we explored whether the deprivation differential between neighbouring areas influenced self-reported morbidity over and above the known effect of the deprivation of the area itself.

      Methods

      Deprivation differentials between small areas (population size approximately 1,500) and their immediate neighbours were derived (from the Index of Multiple Deprivation (IMD)) for Lower Super Output Area (LSOA) in the whole of England (n=32482). Outcome variables were self-reported from the 2001 UK Census: the proportion of the population suffering Limiting Long-Term Illness (LLTI) and ‘not good health’. Linear regression was used to identify the effect of the deprivation differential on morbidity in different segments of the population, controlling for the absolute deprivation. The population was segmented using IMD tertiles and P2 People and Places geodemographic classification. P2 is a commercial market segmentation tool, which classifies small areas according to the characteristics of the population. The classifications range in deprivation, with the most affluent type being ‘Mature Oaks’ and the least being ‘Urban Challenge’.

      Results

      Areas that were deprived compared to their immediate neighbours suffered higher rates of ‘not good health’ (β=0.312, p<0.001) and LLTI (β=0.278, p<0.001), after controlling for the deprivation of the area itself (‘not good health’—ß=0.655, p<0.001; LLTI—ß=0.548, p<0.001). The effect of the deprivation differential relative to the effect of deprivation was strongest in least deprived segments (e.g., for ‘not good health’, P2 segments ‘Mature Oaks’—β=0.638; ‘Rooted Households’—β=0.555).

      Conclusions

      Living in an area that is surrounded by areas of greater affluence has a negative impact on health in England. A possible explanation for this phenomenon is that negative social comparisons between areas cause ill-health. This ‘psychosocial effect’ is greater still in least deprived segments of the population, supporting the notion that psychosocial effects become more important when material (absolute) deprivation is less relevant.

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      Most cited references 42

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      Self-Rated Health and Mortality: A Review of Twenty-Seven Community Studies

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        Stress, Adaptation, and Disease: Allostasis and Allostatic Load

         BRUCE McEWEN (1998)
        Adaptation in the face of potentially stressful challenges involves activation of neural, neuroendocrine and neuroendocrine-immune mechanisms. This has been called "allostasis" or "stability through change" by Sterling and Eyer (Fisher S., Reason J. (eds): Handbook of Life Stress, Cognition and Health. J. Wiley Ltd. 1988, p. 631), and allostasis is an essential component of maintaining homeostasis. When these adaptive systems are turned on and turned off again efficiently and not too frequently, the body is able to cope effectively with challenges that it might not otherwise survive. However, there are a number of circumstances in which allostatic systems may either be overstimulated or not perform normally, and this condition has been termed "allostatic load" or the price of adaptation (McEwen and Stellar, Arch. Int. Med. 1993; 153: 2093.). Allostatic load can lead to disease over long periods. Types of allostatic load include (1) frequent activation of allostatic systems; (2) failure to shut off allostatic activity after stress; (3) inadequate response of allostatic systems leading to elevated activity of other, normally counter-regulated allostatic systems after stress. Examples will be given for each type of allostatic load from research pertaining to autonomic, CNS, neuroendocrine, and immune system activity. The relationship of allostatic load to genetic and developmental predispositions to disease is also considered.
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          Investigating neighborhood and area effects on health.

          The past few years have witnessed an explosion of interest in neighborhood or area effects on health. Several types of empiric studies have been used to examine possible area or neighborhood effects, including ecologic studies relating area characteristics to morbidity and mortality rates, contextual and multilevel analyses relating area socioeconomic context to health outcomes, and studies comparing small numbers of well-defined neighborhoods. Strengthening inferences regarding the presence and magnitude of neighborhood effects will require addressing a series of conceptual and methodological issues. Many of these issues relate to the need to develop theory and specific hypotheses on the processes through which neighborhood and individual factors may jointly influence specific health outcomes. Important challenges include defining neighborhoods or relevant geographic areas, identifying significant area or neighborhood characteristics, specifying the role of individual-level variables, incorporating life-course and longitudinal dimensions, combining a variety of research designs, and avoiding reductionism in the way in which "neighborhood" factors are incorporated into models of disease causation and quantitative analyses.analyses.
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            Author and article information

            Affiliations
            [1]Liverpool John Moores University, Centre for Public Health, Henry Cotton Campus, Webster Street, Liverpool, UK
            [2]University of Salford, School of Health Sciences, College of Health & Social Care, Allerton Building, Salford, UK
            [3]Liverpool John Moores University, Department of Mathematics and Statistics, Liverpool, UK
            Contributors
            Journal
            Int J Health Geogr
            Int J Health Geogr
            International Journal of Health Geographics
            BioMed Central
            1476-072X
            2013
            29 January 2013
            : 12
            : 5
            Copyright ©2013 Zhang et al.; licensee BioMed Central Ltd.

            This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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            Research

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