Hard_Edges_Mapping_SMD_FINAL_VERSION_Web

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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