Hard_Edges_Mapping_SMD_FINAL_VERSION_Web

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24 Hard edges Map 1: Combined SMD prevalence index by Local Authority district Source: Authors’ analysis of SP, OASys and NDTMS data and 2011 census SMD 2 or 3 domains 150 to 307 99 to 150 67 to 99 45 to 67 0 to 45
25 www.lankellychase.org.uk The mapping exercise above is suggestive of an association between SMD prevalence rates and areas of the country where poverty tends to be concentrated, and this was confirmed by an analysis of these SMD indices by levels of low income deprivation at local authority level (see Figure 9 below). The combined SMD prevalence index falls from 168 in the poorest fifth of local authorities, to 50 in the richest fifth. The pattern is strikingly similar across the three component indices, though with the NDTMS index showing the sharpest variation, and OASys almost as sharp a pattern, which is then somewhat less pronounced with regard to SP. 13 Finally, we were able to use multiple regression analysis to explore the relationships at local authority level between SMD prevalence (using all three component indices and the composite index) and a range of demographic, social, economic, health and institutional factors. We found that, other things being equal, 14 the factors associated with higher levels of SMD included: Measures by IMD Low s of Local Authorities • Demographic factors: having a high proportion of the population aged 16–24 and/or large numbers of single person households. • Economic factors: high rates of unemployment and/or poverty. • Housing factors: housing markets with concentrations of smaller properties (e.g. bedsits and small flats). However, indicators of housing pressure (overcrowding) or low quality (lack of central heating) were not associated with areas of high SMD. • Health factors: a poor health profile amongst the local population. • Institutional factors: concentrations of institutional populations, especially those living in mental health hospitals or units, or in homeless hostels, as we might expect. There was also an association with local concentrations of holiday accommodation, tying in with the overrepresentation of seaside towns noted above. 13 The flatter pattern for the SP index partly reflects the fact that this is averaged across shire county areas. 14 In addition to the factors listed, other variables tested and discarded as non-significant included older age groups, employment rates, students, occupational class, housing tenure, non-white ethnicities, density/sparsity of population, migrant groups (e.g. new EU), and defence establishments. Figure 9: SMD prevalence measures by IMD low income quintiles of Local Authorities 200 180 160 RELATIVE PREVALENCE 140 120 100 80 60 Combined 40 NDTMS-based 20 0 OASys-based SP-based MOST LOW INCOME 2ND MOST LOW INCOME MIDDLE QUINTILES 2ND LEAST LOW INCOME LEAST LOW INCOME IMD QUINTILES Source: Authors’ analysis of SP, OASys and NDTMS data and 2011 census
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