3A_2_Modelling health-harming behaviours in a socially ranked geographic space

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  • The policy priority in the National Health Service (NHS) operating framework for 2009/10 calls for Primary Care Trusts (PCTs) to commission services based on the identification of local health outcomes connected to health-harming behaviours. To enable detection of local health needs they must first be successfully measured for different population groups. Indeed, measurement is fundamental to evidence-based policy, strategy and service delivery for both local and national scales but measuring health outcomes of behaviours is problematic. Current measurement is restricted to the use of administrative geographies, associated with the formal hierarchical scales of government and public sector organisations (Moon 1990). These formal boundaries produce a workable organisational structure practical for controlling budgets. Despite this, their usefulness for understanding the health needs of different groups of people is limited, due to their susceptibility to the ecological fallacy and their inherent arbitrary nature. As cities grow and become more crowded, the diverse nature of the population emerges as an observable social pattern. This diversity has repercussions for health intervention planning, since health outcomes are not distributed equally. Understanding the nature of the diversity within the realm of functional scales will promote the identification of neighbourhoods where social groups are sharply differentiated in health and lifestyle status. This paper considers how geodemographic classifications, whilst not without limitations, can provide a geographic and analytical framework to investigate the social-spatial differentiations of health status that emerge when using functional geographies.
  • There are many different types of health measures – which are not without there limitations.
  • To define health status, a commercial geodemographic classification (Mosaic: Experian, Nottingham, UK) was adjoined to the National Hospital Episodes database. Each patient episode was categorised by diagnosis defined by the International Classification of Diseases (ICD). Neighbourhood quotients were then calculated by averaging the total number of episodes for a neighbourhood sub-group compared to the average number of total episodes for that sub-group in England (Webber, 2004).
  • This enabled many types of health risks of being admitted to hospital to be investigated, even though not all illnesses can be attributed to lifestyle or behavioural choices. Those unrelated to lifestyle choices were identified and excluded from the analytical framework as they would be unsuitable for geodemographic analysis. To identify lifestyle related illness an index was developed based upon the extent of variation, for each individual illness, across different neighbourhoods. It is known as Total Weighted Deviation (TWD) and was first proposed by Webber (2004). It evaluates the suitability of population sub-groups to predict variation in illnesses, see equation 1. Hospital Episode Statistics: A database of patient-based records of finished consultant episodes by diagnosis, operation and speciality from NHS hospitals in England (ONS, 2010)
  •  The next step was to define lifestyle status. Bourdieu recognized that a number of constituents interlock together to create social space (1997), comprising different capitals: economic, cultural and social, which fluctuate in volume and composition over time. It is these differences that distinguish population groups. Using this viewpoint and combining it with information derived from a geodemographic base, social space can be explored. Lifestyle status was defined using extracted variables from the geodemographic classification. These proxy measures were likened to the different conceptions of Bourdieu’s capital: economic capital was measured using unemployment, salary and housing tenure, cultural capital with education attainment and the neighbourhood classes represented the different types of social capital. The domain of cultural capital based on the premise of cultural capital being knowledge and skills acquired by socialisation and education (Painter 2000)
  • It appraises the usefulness of a spatial framework based on geodemographic classifications to facilitate the examination of health-harming behaviours. Using Bourdieu’s perception of social space as a starting point (Bourdieu, 1987), social space is explored using a measure of lifestyle status as a factor of the three different capitals; economic, cultural and social. It considers how health behaviour can be predicted by means of a composite measure of health status.
  • The use of socially similar neighbourhoods as the functional unit of analysis helps our understanding of the local social context and aids the exploration of health status. In Figure 2 a visualisation of the local population of one London borough, Camden, summarises the population health status alongside their lifestyle status. The pattern that emerges is familiar as it replicates the social gradient of health inequalities first proposed by Marmot and Wilkinson (2004). This pattern reinforces the notion that societal differences in health and lifestyle status persist, albeit to different extents, through the social spectrum.
  • In the literature there are concerns relating to the limitations of geodemographic typologies, which have not yet been discussed in this paper but briefly include over generalisation and reductionist (Goss, 1995; Curry, 1997), uncertainty (the levels of which are difficult to ascertain), subjection to the ecological fallacy (Weeks, 2004) as well as the misunderstanding that they are true depictions of reality and symbolise discrete distinct groups (they actually indicate social averages). These limitations withstanding, the location effects that arise from the geographical proximity of socially similar populations are evident in geodemographic clustering (Longley and Webber, 2003) and for the purposes of health care planning which includes social marketing they offer one method of enriching data at the local scale.
  • The different capitals modelled in this paper are cognisant of social determinants of health (Dahlgren and Whitehead, 1991) which include factors such as personal behaviour, lifestyles, community influences, living and working conditions and educational attainment. Measurement of these elements at the functional scale of health geographies aids recognition of behavioural patterns within our lived space. For health care policy and practice, intricate knowledge of both the geographical and social space, where and how we inhabit space, will aid clearer understanding of population groups, a fundamental requirement to effective health targeting and intervention planning. Furthermore the 3 components of social space proposed by Sorre (1957) are shape, localisation and division. In this paper a geodemographic framework did enable observation of these components since to some extent they allude to the homogenous characteristics and social groupings of people with the potential to experience the same life and health status
  • 3A_2_Modelling health-harming behaviours in a socially ranked geographic space

    1. 1. Modelling health-harming behaviours in a socially ranked geographic space <br />Dr Catherine (Kate) Emma Jones  Department of Geography, University of Portsmouth kate.jones@ucl.ac.uk<br />
    2. 2. What......Aims?<br /><ul><li>To explore the geo-visualisation of patterns of risk associated with health & lifestyle behaviours within social space:
    3. 3. Develop local geoodemographic framework at the functional scale
    4. 4. Creating composite geodemographic indicators of </li></ul>Health Status<br />Lifestyle Status<br />Social differentiation<br /><ul><li>To identify patterns of health and lifestyles behaviours
    5. 5. Create a continuous scaled social ranking of inequalities</li></li></ul><li>Why.....Policy?<br /><ul><li>Commission services based on identification of local health outcomes connected to health-harming behaviours
    6. 6. The policy priority in the National Health Service (NHS) operating framework for 2009/10 for Primary Care Trusts
    7. 7. Reducing Health Inequalities is NHS priority 1 (Marmot, 2010)
    8. 8. Measurement of health is fundamental </li></ul>evidence-based policy, strategy and service delivery <br />Required for both local and national scales<br />measuring health outcomes of behaviours is problematic<br />
    9. 9. Why.....Literature?<br />Limitations of existing health measures<br /><ul><li>Based on arbitrary formal admin boundaries which are ‘containers’
    10. 10. Do not account for social gradients
    11. 11. Complex statistical models difficult to implement
    12. 12. Limitations of data availability
    13. 13. Often do not account for conditions of daily life and the structural influences upon them
    14. 14. Complex model of social determinants of health which include the physical, social and personal resources
    15. 15. Do not account for the aggregate of resources available within people’s networks</li></li></ul><li>How.....Method Summary?<br /><ul><li>Use geodemographic classifications to define 3 domains to represent components of the social determinants of health:</li></ul>Health status based on risk of hospitalisation to lifestyle related conditions<br />Lifestyle status<br />Social Status<br /><ul><li>For study area of Camden, North London, UK</li></ul>Social<br />Economic<br />Cultural <br />Bordieu’s 3 types of capital<br />
    16. 16. Case Study Area<br />
    17. 17. Defining Health Status – step 1<br />Link Geodemographic Classification to Hospital Episode Statistics (HES)<br />To explore patterns of different illness by different geodemographic type<br />Index score= <br />% target population x 100<br /> % base population<br />Standardise postcodes<br />HES <br />Append Neighbourhood Type using postcode<br />Create neighbourhood index values <br />Count responses per Type<br />Geodem<br />Assign to postcode grid reference<br />Map Scores and create profiles<br />
    18. 18. Defining Health Status – step 1<br />
    19. 19. Defining Health Status – step 2<br />Explore variation within different health conditions for different population sub-groups<br />To understand which health conditions are effectively differentiated <br />Ds= Disease/condition index score<br />p = percentage of population <br />n = neighbourhood Type<br />
    20. 20. Defining Health Status – step 3<br />Average the risk across all the health conditions <br />Good health status<br /> Educated, young, singles transient <br />Older people in social housing<br /> Career professionals <br />People living in social housing<br />Poor health status<br />
    21. 21. Define Lifestyle Status<br />Defined using extracted variables from the geodemographic classification<br />Proxy measures likened to the different conceptions of Bourdieu’s capital: <br />Economic capital <br />unemployment, salary and housing tenure<br />Cultural capital <br />education attainment <br />[based on the premise of cultural capital being knowledge and skills acquired by socialisation and education (Painter 2000)]<br />Social capital <br />The individual neighbourhood population types<br />
    22. 22. Social Gradient of Lifestyle and Health Status<br />
    23. 23. Representations of lifestyle and health status for Camden’s neighbourhoods <br />(Note: Large red circles are indicative of a positive healthy lifestyle whereas small yellow circles indicate the most unhealthy health and lifestyle status)<br />
    24. 24. Outcomes.....Limitations?<br />Limitations of geodemographic typologies<br />Over generalisation and reductionist (Goss, 1995; Curry, 1997)<br />Uncertainty (the levels of which are difficult to ascertain)<br />Subjection to the ecological fallacy (Weeks, 2004)<br />Misunderstanding they are true depictions of reality and symbolise discrete distinct groups<br />actually indicate social averages<br />
    25. 25. Conclusions.......Potential?<br />Location effects arising from geographical proximity of socially similar populations are evident in geodemographic clustering (Longley&Webber, 2003) <br />They offer one method of enriching data at the local scale. <br />Builds on Gatrell’s (2000,2004) ideas of social and geographical space <br />Bourdieu’s belief that the diverse nature of social space requires distributions to account for “socially ranked geographical space” (Bourdieu, 1986, pg 124)<br />Geodemographics used to define ranked view of the geographic & social space <br />Provides the mechanism for maximising homogeneity of social processes within different neighbourhoods. <br />Supports Moon (1990) and his desire for functional geography to be the basis for the community, neighbourhood or locality. <br />Offer an alternative spatial framework for neighbourhood representations of social space. <br />
    26. 26. Acknowledgements....thank you!<br />Knowledge Transfer Partnership between University College London and Camden Primary Care Trust (PCT) <br />KTP programme number 37. <br />Former PhD Supervisors Dr Muki Haklay and Prof Paul Longley <br />With thanks also to Experian and Richard Webber for providing some of the data for this study.<br />

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