Journal of Public Health | Vol. 32, No. 4, pp. 572 –581 | doi:10.1093/pubmed/fdq025 | Advance Access Publication 21 April 2010
Do geodemographic typologies explain variations in uptake
in colorectal cancer screening? An assessment using routine
screening data in the south of England
Kelechi E. Nnoaham 1, Alison Frater 1,2, Paul Roderick 2, Graham Moon 3, Stephen Halloran 4
1
South Central Strategic Health Authority, Newbury, Berkshire RG14 2PZ, UK
Public Health Sciences and Medical Statistics, University of Southampton, Southampton SO166YD, UK
3
Centre for Geographical Health Research, School of Geography, University of Southampton, Southampton SO17 1BJ, UK
4
BCSP South Regional Hub, University of Surrey and Royal Surrey County Hospital, Surrey GU2 7XX, UK
Address correspondence to Kelechi E. Nnoaham, E-mail: [email protected], [email protected]
2
A B S T R AC T
Background Uptake of colorectal cancer (CRC) screening in UK is less than 60%. Geodemographic typologies are useful in describing patterns
of individual preventive health behaviour but little is known of their value in assessing uptake of CRC screening, or how this compares to
traditional measures of area deprivation.
Methods We used data on CRC screening uptake in the South Central, South-East Coast and South-West England National Health Service
regions in multilevel logistic regression to describe the effects of individual composition and contextual factors (area deprivation and
geodemographic segments) on non-response to screening invitation. The relative impact of geodemographic segmentation and the index of
multiple deprivation (IMD) 2007 was compared. The potential population impact of a targeted increase in uptake in specific geodemographic
segments was examined.
Results About 88 891 eligible adults were invited to be screened from 2006 to 2008. Uptake rate was 57.3% (CI: 57.0–57.7) and was lower
amongst younger persons, men, residents of more deprived areas and people in specific geodemographic segments. Age and gender were
significant determinants of uptake and contextual factors explained an additional 3% of the variation. Geodemographic segmentation
reduced this residual contextual variation in uptake more than the IMD 2007 (72% vs. 53% reduction). The three geodemographic types that
best predicted non-response were characterized by both ethnic mix and a higher than average proportion of single pensioner households
renting council properties. Achieving average uptake in the 2.3% of the study population in these geodemographic segments would only
increase the total population uptake rate by 0.5% (57.3–57.8%).
Conclusion Variation in the CRC screening uptake in Southern England is principally explained by characteristics of individuals but contextual
factors also have a small but significant effect. This effect is captured in greater detail by geodemographic segmentation than by IMD 2007.
This information could be used to inform the design of interventions aiming to improve uptake.
Keywords colorectal cancer, geodemographics, multilevel model, screening
Background
Colorectal cancer (CRC) is the third most prevalent cancer
and the second leading cause of cancer death in the UK.1
Of 30 000 new cases and 20 000 deaths each year in the
UK, over 80% are in those aged 60 and over.
The UK National Screening Committee recommended
the implementation of a national screening programme for
572
Kelechi E. Nnoaham , Specialist Registrar in Public Health
Alison Frater , Consultant in Public Health
Paul Roderick , Professor of Public Health
Graham Moon , Professor of Spatial Analysis in Human Geography
Stephen Halloran , Consultant Biochemist and Director
# The Author 2010, Published by Oxford University Press on behalf of Faculty of Public Health. All rights reserved.
GE O D EM O GR A P H I C TYPO LO G I ES A ND SC R EE NI N G
colorectal cancer following results from randomized trials
showing that screening with faecal occult blood testing
(FOBt) results in a 16% reduction in the risk of death from
CRC.2 The programme began by offering screening to 60 –
69 year olds in England and will be extended in 2010 to
people up to age 75. Cost-effectiveness estimates underpinning the rationale for the programme have been based on
uptake rates of about 60%.3 This has not been achieved in
many parts of the country, particularly amongst men and
minority ethnic groups.4 Suboptimal uptake in comparison
with breast and cervical screening has been documented in
other CRC programmes for example in the Netherlands.5
The response to invitations to attend for screening is governed by a number of individual and contextual factors.6
Certain individual characteristics such as age and gender
show consistency across geographical settings in respect of
how they specifically influence uptake of CRC screening.7,8
Individual socioeconomic deprivation is associated consistently with lower uptake of CRC screening.7 Lower uptake
rates are also reported for minority ethnic populations even
after adjustment for demographic variables and socioeconomic status.9,10 As these factors influence the individual
response to CRC screening invitation, their distribution in
populations might be expected to explain variations in
uptake of screening between populations. Indeed, most previous evaluations of CRC screening have examined how
individual characteristics explain variations in uptake but few
have attempted to examine population-level factors such as
those found to influence the uptake of breast and cervical
cancer screening.11,12
Studying population-level variation in uptake of CRC
screening is a potentially useful way to assess variations in
uptake because it recognizes contextual-level impacts such as
local programme delivery practices, and also acts as a surrogate for unmeasured and unknown individual factors. One
approach to studying population-level variation is the use of
composite indices of area deprivation, such as the index of
multiple deprivation (IMD).13 Area deprivation is clearly
associated with uptake of most screening programmes but
the relationship is complex and there is little evidence to
suggest that targeting health promotion or education based
on area deprivation increases uptake of screening.14
An alternative approach to the study of context is geodemographic segmentation. Traditionally, in biomedical disciplines, the notion of segmentation has involved identifying
subgroups within a population that may have similar
individual- or area-level attributes such as age, gender and
socioeconomic status. Geodemographic segmentation has
extended this approach to include data on behaviours,
beliefs, habits and preferences to provide a more robust
573
understanding of subgroups within the population. It
focuses on the distinctiveness of areas and potentially offers
an improvement on the use of composite indices of area
deprivation. Geodemographic segmentation systems use a
range of carefully selected demographic and contextual indicator variables to classify small geographical areas by the
predominant characteristics of the locality and its residents.13
A number of geodemographic segmentation systems are in
use, including ACORN, MOSAIC, output area classification
and people and places (P2) system. Most systems incorporate
both census and non-census data, for example including
information on house prices, unemployment, share ownership, product purchases and council tax band.15 It has been
argued that the different combinations of these variables
summarized as geodemographic segments provide an
enhanced understanding of local conditions that is not routinely captured by traditional composite deprivation indices
such as the IMD.13
Geodemographic segmentation systems are increasingly
being used in health settings to describe variations in health
service use,16 design tailored interventions for specific
groups in the population17,18 and assess the penetration of
policy implementation.13 This trend is closely linked to the
growing emphasis on social marketing principles and techniques, which are becoming key components of national
public health policy to improve health and reduce health
inequalities.19 The approach builds on private sector target
marketing techniques and conceptualizes a social intervention (in this case screening), as a product being promoted
and subsequently delivered to target customers in target
locations.20 The proponents of social marketing techniques
in health care argue that intelligence gleaned from the geodemographic segmentation of a population can be combined
with other threads of social marketing, such as consumer
insights and motivations, to establish robust consumer
profiles.21 These profiles can in turn inform promotional
activities aimed at enhancing the social acceptability of a
health-care intervention amongst the target audience.
Enhanced knowledge of the characteristics of the customer
enables ‘smart’ targeting and, potentially, raised product
uptake. The success of such social marketing in the context
of screening will thus rely both on how well geodemographic segmentation systems can identify and quantify
variations in the population characteristics associated with
screening21 and their effectiveness in targeting health
promotion campaigns, which are informed by such findings.
This paper aims to assess how the level of information
that can be gained from geodemographic segmentation
compares with the use of the IMD. More specifically the
paper (i) investigates the contribution of both individual and
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J O U RN A L O F P U B L I C H E A LTH
contextual factors to total variation in CRC screening uptake
and (ii) compares the relative contributions of area deprivation and geodemographic segmentation to this variation.
Methods
Data on the uptake of colorectal cancer screening from
2006 to 2008 were obtained from the southern hub of the
National Bowel Cancer Screening Programme and included
data from the South-East Coast, South Central and
South-West Strategic Health Authorities of England. This
data set comprised 88 891 records with unique subject identifiers, age, gender, postcodes of residence and response to
screening invitation.
Postcodes in the data set were linked to 2001 census
lower super output areas (LSOAs) using the geoconvert
online facility freely available to UK academics and thence
to (i) continuous deprivation scores in the IMD 200722 and
to (ii) the P2 geodemographic segmentation system commercially available from Beacon Dodsworth.23
P2 was constructed using 2001 census data (age, household information, transport, education, employment,
housing tenure, affluence and ethnicity) and other noncensus information.23 Figure 1 shows how the UK and our
study populations are relatively distributed across P2 segments. P2 was selected for analysis because it is freely available to the National Health Service and has been used in a
number of health-related applications. Users include the
North West Public Health Observatory. The construction of
P2 follows the general principles evident to all geodemographic segmentation systems and it serves as a general
exemplar of their utility.
The linked data set was imported into MLwiN 2.10 Beta
for multilevel analyses.24 To identify potential non-linear
effects, four age categories (60– 62, 63 – 65, 66 – 68 and 69–
71) were created and continuous deprivation scores were
converted to deprivation quartiles with deprivation worsening from quartile 1 through to 4 (Table 1).
Multilevel analysis
The multilevel structure of the data set comprised individuals (level 1; n ¼ 88 891) nesting within LSOAs (level 2;
n ¼ 1412). We developed a two-level model with a binary
outcome (‘non-response’ to invitation for CRC screening)
for persons i living within LSOAs j. The likelihood of nonresponse was related to a number of categorical predictor
variables (age, gender, deprivation and geodemographic segmentation type) as well as to a random effect for each of
the two levels.
For gender, age and deprivation, we used female gender,
age 60 – 62 and the ‘least deprived’ category, respectively, as
the reference categories in the models. For the P2 geodemographic segmentation, we used the modal Tree (‘mature
oaks’) as the reference category. Thus, the reference category
for the model was a 60– 62-year-old woman living in a lowest
quartile IMD LSOA with a ‘Mature Oaks’ P2 tree type.
Analysis involved fitting five models. The intercept-only
(null) model included no predictor variables and only
described area (LSOA) components of variation in nonresponse to screening invitation. In Model 1, we included
age and gender (individual-level variables) to adjust for
age- and gender-related differences in non-response to
screening invitation. Model 2 extended Model 1 by adding
deprivation alone to age and gender; Model 3 added only
M. Urban challenge
L. Disadvantaged households
K Weathered communities
J. Urban producers
I. Multicultural centres
H. New starters
G. Suburban stability
UK population
F. Senior neighbourhoods
Study population
E. Qualified metropolitans
D. Rooted households
C. Blossoming families
B. Country orchards
A. Mature oaks
25
20
15
10
5
0
5
Percentage of population in P
Fig. 1 Comparative distribution of P2 trees in the study and UK populations.
10
2
15
GE O D EM O GR A P H I C TYPO LO G I ES A ND SC R EE NI N G
575
Table 1 Predictor and outcome variables, description and coding
Variable
Measure
Outcome variable
Non-response
Non-response to colorectal cancer screening invitation [No ¼ 0 (i.e. respondent); Yes ¼ 1 (i.e. non-respondent)]
Individual predictors
Age
Age groups [(0) 60– 62 years; (1) 63– 65 years; (2) 66– 68 years; (3) 69– 71 years]
Gender
Gender (female ¼ 0; male ¼ 1)
Population predictors
Deprivation
Deprivation categories by IMD scores (based on distribution of the southern hub population): (0) 1.18218 and 7.87399; (1)
.7.87399 and 13.01443; (2) .13.01443 and 20.57127; (3) .20.57127 and 74.28716
Geodemographic
P2 geodemographic segmentation type with 13 þ 1 trees: (0) mature oaks; (1) country orchards; (2) blossoming families;
type
(3) rooted households; (4) qualified metropolitans; (5) senior neighbourhoods; (6) suburban stability; (7) new starters;
(8) multicultural centres; (9) urban producers; (10) weathered communities; (11) disadvantaged households; (12) urban
challenge; (13) unclassified
geodemographic types whereas Model 4 added both deprivation and geodemographic type. This enabled us to assess
multicollinearity and the relative importance of deprivation
and geodemographic segmentation.
We computed the extra-binomial variation in the probability of non-response to a screening invitation between individuals within LSOAs as well as the level-two variance
(variation across LSOAs). Measures of association (fixed
effects) were expressed in the models as beta coefficients and
standard errors, which were used to derive odds ratios (ORs)
and 95% confidence intervals (CIs). Measures of variation
(random effects) were expressed as the variance partition
coefficient (VPC) and percentage change in variance from
the intercept-only model for each subsequent model.
The VPC expresses the extent to which members of one
LSOA resemble each other more than they resemble
members of other LSOAs with respect to non-response to
screening invitation. It is a useful indicator of how much of
the variation in non-response to screening is explained by
contextual factors. The latent variable approach described by
Snijders and Bosker25 was used to estimate the VPC for the
binary response multilevel model. A large VPC would
suggest that differences in LSOA populations are responsible for a large part of the variation in non-response to
screening invitation between persons in the study.
Alternatively, a near-zero VPC would suggest that
LSOA-level characteristics exert little effect on variations in
non-response in the study population.25
Increasing uptake in low-uptake geodemographic
groups
We examined what might be the consequence of using P2
geodemographic segmentation to target efforts to increase
uptake of CRC screening in our study population. To do
this, we estimated the impact of increasing to the study
population average the uptake rates in the three geodemographic trees with the lowest uptake rates, whilst keeping the
rate in other trees unchanged. The effect of this increase on
the total number of people taking up CRC screening was
observed and a new population uptake rate under this scenario calculated (with 95% CIs).
Results
The average uptake rate of bowel cancer screening invitation
over the period was 57.3% (CI: 57.0 – 57.7) and women had
a higher uptake rate than men (Table 2). There was a statistically significant trend for the uptake rate to increase with
age (x 2 test for trend P , 0.0001) and to decrease with
increasing deprivation (x 2 test for trend P , 0.0001).
Multilevel models
The results of the multilevel modelling are shown in
Table 3. The intercept-only model suggests there is significant variation in the likelihood of non-response to colorectal
cancer screening invitation across LSOAs in the population
studied (t ¼ 0.098). The VPC indicates that about 3% of
the total variation in the probability of non-response to
CRC screening invitation is attributable to population-level
factors. After accounting for individual-level characteristics,
this variation remained unchanged (Model 1). Unexplained
residual variation in non-response remained even after
accounting for both individual and LSOA-level characteristics (Model 4).
From the proportional changes in variance estimated, it is
observed that deprivation explained 53% of the variation at
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J O U RN A L O F P U B L I C H E A LTH
Table 2 Uptake of screening by category of age, gender and deprivation
Variables
Total invited (n)
Accepted
Did not respond
Percentage uptake (CI)
Female
44 999
26 839
18 160
59.6 (59.2 –60.1)
Male
43 892
24 129
19 763
55.0 (54.5 –55.4)
Gender
Age
60– 62
16 341
9162
7179
56.1 (55.3 –56.8)
63– 65
19 207
10 871
8336
56.6 (55.9 –57.3)
66– 68
26 435
15 285
11 150
57.8 (57.2 –58.4)
69– 71
26 908
15 650
11 258
58.2 (57.6 –58.8)
Deprivation
Top imd
22 272
13 977
8295
62.8 (62.1 –63.4)
Mid imd
22 204
13 422
8782
60.4 (59.8 –61.1)
Bottom imd
22 250
12 771
9479
57.4 (56.7 –58.0)
Worst imd
22 164
10 797
11 367
48.7 (48.1 –49.4)
17 289
11 015
6274
63.7 (63.3 –64.1)
8155
4911
3244
60.2 (59.7 –60.8)
P2 geodemographic segmentation
Mature oaks
Country orchards
Blossoming families
4448
2683
1765
60.3 (59.6 –61.1)
Rooted households
15 305
9159
6146
59.8 (59.4 –60.2)
Qualified metropolitans
13 690
8137
5553
59.4 (59.0 –59.9)
Senior neighbourhoods
1279
708
571
55.4 (54.0 –56.7)
13 998
7706
6292
55.1 (54.6 –55.5)
3055
1394
1661
45.6 (44.7 –46.5)
226
70
156
31.0 (27.9 –34.0)
Urban producers
6720
3192
3528
47.5 (46.9 –48.1)
Weathered communities
2868
1268
1600
44.2 (43.3 –45.1)
Disadvantaged households
1232
488
744
39.6 (38.2 –41.0)
213
372
36.4 (34.4 –38.4)
50 968
37 923
57.3 (57.0 –57.7)
Suburban stability
New starters
Multicultural centres
Urban challenge
Total
585
88 891 a
a
Forty-one persons with unclassified P2 trees.
the LSOA level and P2 geodemographic segmentation
explained 72% of the LSOA-level variation in uptake of
CRC screening. Inspection of the reductions in LSOA-level
variance reveals that the P2 geodemographic segmentation
achieves a greater variance reduction over the
individual-only model than the IMD. Thus, it can be
inferred that the set of contextual factors captured by P2
geodemographic segmentation types (or ‘trees’) is more
effective at capturing LSOA variation in non-response to
CRC screening invitation than the IMD.
Figure 2 shows a plot of P2 trees and predicted values
(with 95% CIs) of their probability of non-response from
Model 4. It suggests that individuals in segments described
by the terms ‘urban challenge’, ‘disadvantaged households’
and ‘multicultural centres’ had the highest probabilities of
non-response to CRC screening invitation. The probability
of non-response was however significantly different from
other segments (excluding the above three) only for ‘multicultural centres’, although the overlap of CIs amongst the
three ‘trees’ is marginal partly due to the width of the CIs
for the small population in those groups. There is significant
overlap between the other ‘trees’ but with some indication
of discrimination between high and low responding
segments.
Gender, age and area deprivation as predictors
of non-response
After adjusting for the effect of age (Table 3), men were
more likely than women to fail to take up invitation to CRC
screening (OR: 1.21; CI: 1.19 – 1.24). Addition of the
population-level variables did not have any effect on the
contribution of gender to non-response to CRC screening
invitation.
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Table 3 Multilevel regression analysis of non-response to colorectal cancer screening invitation
Individual-level
Individual- and population-level variables
variabless
Base model
Model 1 (gender and
Model 2 (gender, age
Model 3 (gender, age
Model 4 (gender,
age)
and deprivation)
and P2 types)
age, P2 types and
deprivation)
OR
LCL
UCL
OR
LCL
UCL
OR
LCL
UCL
OR
LCL
UCL
Male
1.21
1.19
1.24
1.21
1.18
1.24
1.21
1.18
1.24
1.21
1.18
1.24
Agecat 2
0.98
0.94
1.02
0.98
0.94
1.02
0.99
0.94
1.03
0.99
0.94
1.03
Agecat 3
0.93
0.89
0.97
0.93
0.89
0.97
0.93
0.89
0.97
0.93
0.89
0.97
Agecat 4
0.92
0.88
0.96
0.91
0.88
0.95
0.91
0.87
0.95
0.91
0.88
0.95
Imdcat1
1.11
1.05
1.16
1.05
1.00
1.10
Imdcat2
1.29
1.23
1.34
1.13
1.07
1.19
Imdcat3
1.83
1.78
1.89
1.22
1.14
1.30
Fixed effects
Qualified metropol
1.20
1.14
1.26
1.12
1.06
1.19
Weathered comm
2.23
2.13
2.33
1.87
1.75
1.99
Country orchards
1.17
1.10
1.24
1.12
1.04
1.19
New starters
2.09
2.00
2.18
1.78
1.68
1.89
Rooted household
1.18
1.12
1.24
1.14
1.08
1.20
Suburban stability
1.45
1.39
1.51
1.29
1.21
1.36
Urban producers
1.97
1.90
2.04
1.66
1.56
1.75
Blossoming family
1.17
1.08
1.25
1.16
1.08
1.24
Senior neighbourhood
1.41
1.08
1.25
1.35
1.08
1.24
Urban challenge
3.08
1.27
1.56
2.58
1.20
1.49
Disadvantage household
2.68
2.88
3.28
2.25
2.37
2.79
Multicultural centres
3.97
2.54
2.82
3.32
2.09
2.40
Unclassified
1.31
3.64
4.30
1.28
2.98
3.65
Intercept
20.255
20.30
20.560
20.606
20.627
Random effects
LSOA variance (SE)
0.098 (0.007)
0.099 (0.007)
0.046 (0.004)
0.027 (0.003)
0.029 (0.003)
VPC (%)
2.9
2.9
1.4
0.8
0.8
Change in LSOA
Reference
0
53
72
74
variance (%)
Similarly, compared to people aged 60– 62 years, after
accounting for the effect of gender, older subjects were less
likely to fail to attend for screening (OR: 0.92; CI: 0.88 –
0.96 for age group 69– 71 years). Although subjects aged
69 –71 years were more likely to accept screening invitations,
their uptake was not significantly different from those aged
63 –68 years. The addition of the population-level variables
did not affect the contribution of age to non-response to
CRC screening invitation.
Model 2 in Table 3 indicates that after controlling for the
influences of gender and age, compared with the least
deprived invitees in the population, people in more deprived
LSOAs were less likely to take up screening invitation (OR:
1.83; CI: 1.78 –1.89 for the most deprived quintile). There
was also a significant trend for non-response to be more
likely with increasing deprivation.
Collinearity of deprivation and geodemographic
types
As shown in Table 3, when P2 geodemographic segmentation
types were added to the gender–age deprivation model,
the ORs for the deprivation categories were changed but the
ORs for age and gender remained unchanged. Similarly, the
exclusion of deprivation from the gender– age deprivation P2
model changed the ORs for the P2 categories. These changes
in the ORs with the addition of P2 geodemographic type or
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J O U RN A L O F P U B L I C H E A LTH
0.73
Qualified metropolitans
Probability of non-response
Weathered communities
0.65
Country orchards
New starters
Rooted households
0.57
Suburban stability
Urban producers
0.49
Mature oaks
Blossoming families
0.41
Senior neighbourhoods
Urban challenge
0.33
Disadvantaged households
P2 geodemographic trees
Multicultural centres
Fig. 2 Predicted values of non-response probability for P2 segments.
Table 4 Geodemographic types and key characteristics
Geodemographic
Characteristics
segmentation
P2 trees
‘Multicultural
Broad ethnic mix, most renting council or housing association property, and in
centres’
the lowest quartile of income for the UK. Make up 6.3% of UK population
‘Disadvantaged
Single pensioner households, most renting council or housing association
households’
property, and in the lowest quartile of income for the UK. Make up 6.4% of UK population
‘Urban challenge’
Single pensioner households, most renting council or housing association property; smokers, long-term limiting illness;
in the lowest quartile of income for the UK. Make up 2.86% of UK population
exclusion of deprivation indicate the presence of some collinearity between those variables, suggesting that deprivation
(measured by the IMD 2007) and geodemographic segmentation types, measure similar constructs. If the collinearity
were perfect, both variables might be expected to explain
LSOA variability in non-response to similar extents. However,
as seen from a direct comparison of models adding P2 geodemographic type as the only population-level variable explains
more variability in non-response than does the model adding
deprivation as the only population-level variable.
Geodemographic types associated with
non-response
Although there was no inherent ordering of the categories,
the P2 geodemographic segments with the highest significant
odds of non-response were identified. As shown in Table 4,
people belonging to three P2 geodemographic types were
most likely to fail to take up CRC screening invitation. Key
demographic and neighbourhood characteristics of these
segments are included in Table 5.
Table 5 shows that subjects in the three P2 geodemographic segments with the lowest uptake rates (n ¼ 2043)
represent just 2.3% of the study population. If the uptake
rate in these P2 geodemographic segments were hypothetically increased to equal the rate for the study population
(57.3%), there would be 400 more subjects in the study
population taking up invitation for screening (difference of
total ‘new uptake’ and total ‘observed uptake’ in Table 5).
For the study population, this would result in a nonsignificant increase of 0.5% in the uptake of CRC screening
to 57.8% (CI: 57.5 – 58.1).
Using the same method, if targeting were based on
gender, such that the uptake rate in men was increased to
equal that in women, the wider study population uptake
would increase by 2.3 to 59.6%.
Discussion
Main findings of this study
We have used a multilevel analytical framework in this study
to demonstrate that in the absence of information on
individual-level socioeconomic status, both area-level geodemographic segmentation and composite indices of deprivation can add explanatory power to age and gender in
GE O D EM O GR A P H I C TYPO LO G I ES A ND SC R EE NI N G
579
Table 5 Predicted uptake lower than average uptake geodemographic groups if improved to average for whole population
P2 trees
‘Urban challenge’
‘Disadvantaged households’
‘Multicultural centres’
Total
Total in
Percentage of total population
Observed
New
segment
(88 891) in segment
uptake (n)
uptake (n)a
585
0.66
213
335
1232
1.39
488
706
226
0.25
70
130
2043
2.30
771
1171
a
if uptake rate in segment was equal to average for population (i.e. 57.3%).
explaining area variations in the uptake of CRC screening.
We found that geodemographic segmentation of the population studied using the P2 system was better than the IMD
(2007) to the extent that it explained more of the variation
in the uptake of CRC screening across LSOAs in the study
population.
The multilevel models also suggest that the likelihood of
failing to take up invitation for CRC screening in the study
population increased with younger invitee age, male gender
and, after controlling for age and gender, higher deprivation
and belonging to specific P2 geodemographic segments. P2
geodemographic segments whose presence in a population
predicted lower CRC screening uptake were characterized by
a higher than average concentration of single pensioner
households renting council or housing association properties
and were principally of a broad ethnic mix. These are
uncommon segments in the study population and a focus
on them would offer only limited improvements to uptake.
What is already known on this topic
Individual attributes such as age, gender, ethnicity and individual socioeconomic status are known determinants of
screening behaviour but little is known of the contextual
variables that particularly influence CRC screening behaviour. When such variables have been studied,11 much focus
has been on area deprivation but it is not clear that using
area deprivation to target health education interventions
aimed at changing screening behaviour is effective. There
have been suggestions that geodemographic segmentation
systems offer added value, through more detailed elucidation
of social context.26
What this study adds
It has been suggested that the use of geodemographic segmentation systems, which supplement census level data with
up-to-date demographic and lifestyle data, would give a finer
resolution to investigations of the determinants of colorectal
screening uptake.27 Our study is the first to explicitly
employ a geodemographic segmentation system to describe
the effects of individual composition and contextual factors
on colorectal screening uptake. The geodemographic characteristics of subjects from segmentation types more likely to
decline a screening invitation highlight specific personal and
population attributes. This study has shown that geodemographic segmentation is more effective than a composite
index of deprivation as a measure capturing contextual variation but also that internal discrimination between geodemographic segments can be limited.
Limitations of this study
There are important limitations of the analysis in this study.
In a general sense, it must be recognized that the conclusions derive from an analysis of the P2 geodemographic
segmentation system. Other segmentation systems may
behave differently, however, as noted earlier, geodemographic segmentation systems tend to be built in similar
ways and P2 serves as an exemplar. More specific limitations
are 4-fold. First, the three geodemographic segments significantly associated with lower uptake of CRC screening in the
study population are attributable to only a small proportion
of the total population. Substantial improvements in screening uptake in these geodemographic segments may not
therefore have a significant impact on total population
uptake rates though the relative mortality benefit for people
in these groups may be considerable. Second, the small size
of the population-level effect in the study (VPC 3%) may
relate to the fact that the invitees in the study population,
who represent only 1% of the UK population aged 60– 74,
live within a region that lacks the extremes of contextual
variability. Additionally, the small magnitude of the population effect may also point to screening uptake decisions
being a consequence of the characteristics of individuals
rather than places. To this end, a third and common limitation for studies of this nature is the absence of individual
socioeconomic data on the study population; our analysis
could not control for individual socioeconomic or other
580
J O U RN A L O F P U B L I C H E A LTH
characteristics associated with uptake. Finally, a more conceptual limitation in the analysis relates to the fact that the
characteristics of persons and the contexts in which they live
are tightly interrelated28 and these tight interrelationships are
often difficult to capture in quantitative studies such as this.
5 van Rijn AF, van Rossum LGM, Deutekom M et al. Low priority main
reason not to participate in a colorectal cancer screening program with
a faecal occult blood test. J Public Health 2008;30(4):461–5.
Implications for future research
7 Giorgi Rossi P, Federici A, Bartolozzi F et al. Understanding noncompliance to colorectal cancer screening: a case control study,
nested in a randomised trial. BMC Public Health 2005;5:139.
Work is underway to investigate the potential of geodemographic segmentation for describing characteristics of
responders and non-responders in a target population with
greater contextual variation than those living in the area
covered by the southern hub. Further research will assess
the cost-effectiveness of health promotion interventions
designed using the information gleaned from geodemographic segmentation of non-responders to colorectal
screening invitation. Such interventions may, for example,
involve tailored invitations to screening or invitation delivery
in specific settings. Further research is also needed to understand and target the significant gender differences in uptake.
Conclusion
We found significant variations between LSOAs in our study
population in the likelihood of non-response to CRC screening invitation. Younger age, higher area deprivation and
male gender were associated with a greater likelihood of
non-response to screening invitation. Age and gender did
not fully explain the variation in non-response to screening
invitation between LSOAs; geodemographic segmentation
added a small but significant amount of explanatory power.
People in LSOAs with a more diverse ethnic mix and
single pensioner households living in public rented flats
appeared to have a higher probability of failing to take up
invitation for colorectal screening in our study area. Further
work is needed to assess the full potential of this approach
in areas of the country with greater heterogeneity of social
and geodemographic context.
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