Leeds Institute for Data Analytics, University of Leeds, Leeds, LS2 9JT, UKUnited Kingdom
School of Geography, University of Leeds, Leeds, LS2 9JT, UKUnited Kingdom
Centre for Sports Medicine, School of Medicine, University of Nottingham, Queens Medical Centre, Nottingham, NG7 2UH, UKUnited Kingdom
Academic Unit for Health Economics, Leeds Institute of Health Sciences, University of Leeds, LS2 9JT, UKUnited Kingdom
Nutritional Epidemiology Group, School of Food Science and Nutrition, University of Leeds, Leeds, LS2 9JT, UKUnited Kingdom
Also available as PDF (figures and tables appear in the PDF).
Diet can influence health outcomes and chronic disease risk, therefore a better understanding of factors influencing diet is important in promotion of healthier dietary choices. Many factors influence food choice, including the environment in which we live. This study aims to explore differences in dietary pattern consumption by two spatial measures: Government Office Region (a large regional unit of geography) and Output Area Classification (a small area geography combined with demographic characteristics). A cross-sectional analysis using data from the UK Women’s Cohort Study was carried out. This cohort included ~35000 middle aged women recruited between 1995 and 1999. Dietary patterns were derived using a k-means cluster analysis from diet data collected using a validated 217 item Food Frequency Questionnaire. Multinomial logit regression was used to test whether the area in which the women live, predicts their dietary pattern consumption. Results show that dietary patterns vary significantly by both spatial measures. The Government Office Region, the North West of England has the highest proportion of individuals consuming the least healthy, monotonous diets, while Greater London has the highest proportion of vegetarian diets. Individuals living in Super groups ‘Countryside’ and ‘Prospering Suburbs’ consume healthier, more diverse diets. Those in ‘Constrained by Circumstance’ and ‘Blue Collar Communities’ consume monotonous, less healthy diets. Using a combination of spatial scales such as Government Office Region and Output Area Classification Supergroup could have a beneficial impact on targeting of public health dietary interventions and subsequent health.
Keywords: Dietary pattern; Diet; Geodemographic classification; Geography; Region; UK women’s cohort study; Public health
At baseline, between 1995 and 1998, 35,372 women
were recruited into the UKWCS on a volunteer basis from
a World Cancer Research Fund (WCRF) mailing list. The re-
sponse rate was 58%, from 61,000 invitations to take part. These
women all completed at baseline first contact, a 217 item vali-
7 17 dated,17 Food Frequency Questionnaire (FFQ), aiming to assess
usual diet over the past 12 months, along with a more general
questionnaire. It was not possible to assign a dietary pattern to
all women due to some providing incomplete FFQs and there-
fore 1902 women were excluded from the sample. Individuals
consuming <500 and >6000 kcal/day were excluded from the
analysis as these were considered to be outliers (n=70). A valid
postcode was not available for all these women, so following 13-15
data cleaning these 1128 women were excluded. Finally, 62
women living in Northern Ireland were also excluded due to in-
sufficient numbers in this area for meaningful analysis. This left
a sample of 32,205 for cross-sectional dietary pattern analysis.
Ethical approval was obtained from 174 National
Health Service (NHS) local research ethics committees during
1994 and 1995.
The dietary patterns in the UKWCS were identified in
a previous study by Greenwood et al.7 Seven dietary patterns
were identified from FFQ data using a k-means cluster analy-
sis. The patterns were named to reflect the types and quantities
of food consumed in each pattern. The patterns are as follows:
“Monotonous Low Quantity Omnivore” – a diet high in white
bread, sugar and milk; “Health Conscious” – a diet high in fruit
and vegetables and wholegrains; “Traditional Meat Chips and
Pudding Eater” – typified by a white bread, meat, chips and
high fat, creamy foods; “Conservative Omnivore” – a diet lack-
ing high quantities of any food, but with moderate quantities
of most foods, especially potatoes, meat, fish, eggs, fruit and
vegetables; “Higher Diversity Traditional Omnivore” – a diet
similar to the Traditional Meat Chips and Pudding eater but with
higher diversity; “Low Diversity Vegetarian” – a meat free diet
high in wholemeal bread, soya, pulses, fruit and vegetables; and
“High Diversity Vegetarian” – a meat free diet with lots of va-
riety including wholemeal bread, cereals, wholemeal pasta and
rice, soya, spreads, nuts, pulses, fruit, vegetables and more (see
Appendix A - Summary of dietary patterns for more details of
the dietary patterns). The “Health Conscious” dietary pattern is
the healthiest pattern and the “Monotonous Low Quantity Om-
nivore” the least healthy. The healthiness of the dietary patterns
was determined by scoring each pattern against the UK dietary
recommendations ‘The Eatwell Plate’.
This study first reports diet according to the nine GORs
of England. Scotland and Wales are included as entire countries
(without further regional breakdown). Northern Ireland has not
been included (as described above). Therefore 11 regions are
presented. The OAC20 is used for the geodemographic analysis.
This classification has been created using the geographical unit
Output Area – which consists of a minimum of 40 households
and contains an average of 250 people – combined with 41 vari-
ables reported in the 2001 census. This was the census closest in
time to the majority of the UKWCS data collection. OAC is a
three tier classification. The first tier are named ‘Supergroups’ of
which there are seven. The second tier ‘Groups’ of which there
are 21 and the third tier ‘Subgroups’ of which there are 52. The
OAC categories were generated using an adapted k-means clus-
tering procedure.20 Results at Supergroup level are presented in
this paper. The seven OAC Supergroups are as follows: ‘Blue
Collar Communities’ – typified by living in terraced accommo-
dation, presence of young children and routine or semi-routine
employments along with those working in manual labour type
roles; ‘City Living’ – typically including high proportions of
adults aged 25-44, large numbers of individuals born outside
of the UK, single person rented houses or flats and many in or
holding higher education qualifications; ‘Countryside’ – this
groups contains high proportions of adults aged over 45 years
old, living in detached housing with two or more cars in the
household. Many work from home, provide unpaid care or work
in agriculture; ‘Prospering Suburbs’ – many aged 45-64 living
as two adults and no children reside in these areas. There are
non-dependent children living in these areas in mostly detached
housing with two or more cars per household; ‘Constrained by
circumstance’ – this Supergroup is typified by individuals living
in care homes, or public provided accommodation, as such there
are many divorced or separated, single pensioners, or lone par-
ent households and those who are unemployed or with limiting
long-term illness; ‘Typical Traits’ – this groups contains those
with most average characteristics; and ‘Multicultural’ – this
group has high proportions of those born outside the UK with
a range of different ethnicities. Many will use public transport
or be unemployed (see Appendix B – Summary of OAC Super-
groups for more information).
GOR and OAC were assigned via the postcode unit for
each woman in the cohort, using the geoconvert tool.21
Metabolic Equivalent of Tasks (METs) were used as
a measure of physical activity, calculated by assigning a value
from the Compendium of Physical Activities22 to the results of
questions asked at baseline where the women reported hours per
typical week spent in various common activities. Smoking is re-
ported as a binary value which indicates if the woman was a cur-
rent smoker at baseline. Total calorie intake, including calories
from alcohol, is derived from the FFQ. Age, social class and
education were collected in the UKWCS baseline lifestyle ques-
tionnaire.
STATA IC 12.1 statistical software has been used for
the analysis.23 Chi squared statistics are used to detect differ-
ences across categories for tabulated data.
Multinomial logit regression was carried out for the
categorical data. The regression analysis looked at the likeli-
hood of consuming a particular dietary pattern compared to the
“Traditional Meat Chips and Pudding Eater” pattern (the most
commonly consumed in the UKWCS) accounting for place of
residence. In the regional analysis the region of residence was
compared to living in the South East, (where the majority of the
UKWCS women reside). The geodemographic analysis com-
pared the Supergroup which the women live in with women liv-
ing in the Typical Traits Supergroup (named since those residing
in such areas are average with respect to the demographic vari-
ables used to create the classification). Results are presented as
Relative Risk Ratios (RRRs) with 95% confidence intervals and
p values.
Regression models are adjusted to account for potential
confounders which were identified using a causal diagram. The
adjusted regression model includes physical activity, smoking
and total calorie intake including alcohol, age, social class and
education. The composition characteristics – age, social class
and education – were not adjusted for in the analysis with OAC
(Table 2) as this would have over adjusted these characteristics
as they are included in the OAC already.
The UKWCS mean age was 52.2 years (SD 9.3) with
means ranging between 51.4 to 53.0 years by region and 50.1
to 53.4 years by OAC Supergroup. Physical activity, measured
using METS calculated from a physical activity questions for a
24 hour period had a mean value of 16.0 METS (SD 11.7); mean
regional range 16.4 to 17.8; OAC Supergroup range 15.0 to 18.2.
Sixty-five percent of the cohort had a professional/managerial
occupation with a regional range of 58-67% and OAC Super-
group range of 53-73%, with highest numbers in the City Living
Supergroup and lowest in the Blue Collar Communities. Fifty-
two percent of the cohort were educated to A-level (school year
13) or above with a regional range of 46-65% and OAC Super-
group range 37-70% with lowest values in the ‘Constrained by
Circumstance’ and ‘Blue Collar Communities’ and highest again
in ‘City Living’.
A wider variation in the mean characteristics of the
women is evident when grouped by OAC Supergroup compared
to grouping by region. This supports the expectation that incor-
porating demographic variables into a geographical classifica-
tion enhances understanding of specific populations, rather than
seeing averaged out characteristics of a much larger geographi-
cal region.
The distribution of the UKWCS as a percentage of the
total UK population of women, by region, ranges from 0.08%
for the North East, North West and Scotland and 0.17% for
the South East and South West (with other regions falling in-
between). So whilst the lowest number of cohort women reside
in the North East, this is also the region which has the lowest
population in the whole UK. The dietary pattern consumption
vary significantly across regions (p=0.002), with the exception
of the Health Conscious dietary pattern (Figure 1), however, dif-
ferences are small.
Results from a multinomial regression analysis (Table
1), for dietary pattern consumption by region, identify that some
regions are more likely to consume certain dietary patterns over
others. However, the regression results show that no region has
overall better dietary habits than another. Greater London high-
lights how one region can exhibit statistical significance in rela-
tive risk ratio (RRR) of being more likely to consume “Health
Conscious” (most healthy) and also more likely to consume a
“Monotonous Low Quantity Omnivore” (least healthy)19 diet
compared to the “Traditional Meat, Chips and Pudding Eater”
pattern, which was the reference group, showing that both ex-
tremes of dietary patterns reside in the same region. While some
regions show a significant increased likelihood of consuming a
poor “Monotonous Low Quantity Omnivore” dietary pattern; for
example, Wales, Yorkshire and the Humber, North West; these
regions are, however, not significantly less likely to consume
a “Health Conscious” dietary pattern which would make it dif-
ficult to know where to precisely target healthy dietary pattern
promoting interventions.
These observations remain true when the model is ad-
justed for physical activity and energy intake and smoking and
for demographic characteristics; age, social class and education
and show much the same results, in some cases slightly accen-
RRR (95% CI) RRR (95% CI) RRR (95% CI) RRR (95% CI) RRR (95% CI) RRR (95% CI)
0.87(0.70 to 1.08) 0.82(0.61 to 1.12) 0.79(0.63 to 0.99) 0.62(0.49 to 0.77) 0.64(0.51 to 0.81) 0.84(0.67 to 1.06) North East 1.00 p=0.211 p=0.218
1.20(1.04 to 1.38) 0.91(0.75 to 1.12) 0.81(0.69 to 0.94) 0.85(0.73 to 0.98) 0.84(0.73 to 0.98) 0.83(0.71 to 0.98) North West 1.00 p=0.013 p=0.368
Yorkshire and 1.11(0.95 to 1.30) 0.97(0.78 to 1.20) 1.07(0.92 to 1.25) 0.96(0.83 to 1.12) 0.90(0.77 1.06) 0.99(0.84 to 1.17)
the Humber p=0.179 p=0.776
1.01(0.85 to 1.18) 1.15(0.93 to 1.42) 1.07(0.91 to 1.25) 1.13(0.97 to 1.32) 0.92(0.78 to 1.09) 1.00(0.84 to 1.19) East Midlands 1.00 p=0.944 p=0.209
1.07(0.92 to 1.25) 1.05(0.85 to 1.30) 0.88(0.75 to 1.04) 1.03(0.89 to 1.19) 0.82(0.70 to 0.97) 0.96(0.81 to 1.13) West Midlands 1.00 p=0.384 p=0.652
East of 0.89(0.76 to 1.03) 0.92(0.75 to 1.12) 0.94(0.81 to 1.09) 0.98(0.85 to 1.12) 0.89(0.77 to 1.03) 0.89(0.76 to 1.05)
England p=0.115 p=0.407
Greater 1.42(1.23 to 1.64) 1.38(1.14 to 1.67) 0.89(0.76 to 1.05) 1.28(1.12 to 1.48) 1.77(1.54 to 2.04) 1.64(1.41 to 1.90)
London p<0.001 p=0.001
South East 1.00 1.00 1.00
1.01(0.89 to 1.16) 1.15(0.96 to 1.38) 1.11(0.97 to 1.27) 1.04(0.92 to 1.19) 0.99(0.86 to 1.13) 1.20(1.04 to 1.38) South West 1.00 p=0.837 p=0.120
0.84(0.71 to 0.99) 1.11(0.90 to 1.37) 0.97(0.82 to 1.14) 0.77(0.65 to 0.90) 0.66(0.56 to 0.78) 0.84(0.70 to 1.00) Scotland 1.00 p=0.039 p=0.329
1.25(1.03 to 1.51) 1.19(0.92 to 1.55) 0.89(0.72 to 1.10) 1.08(0.89 to 1.31) 0.98(0.80 to 1.20) 1.11(0.90 to 1.37) Wales 1.00 p=0.027 p=0.187
Adjusted model (adjusting for smoking, total calorie intake including alcohol, typical daily physical activity (METs), age, social class, education) (pseudo R = 0.12)
1.14(0.89 to 1.47) 0.77(0.56 to 1.06) 0.71(0.56 to 91) 0.71(0.55 to 0.91) 0.67(0.53 to 0.86) 0.74(0.58 to 0.95) North East 1.00 p=0.303 p=0.107
1.48(1.25 to 1.74) 0.91(0.73 to 1.12) 0.76(0.64 to 0.90) 0.95(0.81 to 1.11) 0.90(0.77 to 1.06) 0.81(0.68 to 0.96) North West 1.00 p<0.001 p=0.368
Yorkshire and 1.27(1.06 to 1.53) 0.95(0.75 to 1.20) 1.03(0.87 to 1.23) 1.05(0.89 to 1.25) 0.92(0.77 to 1.09) 0.96(0.80 to 1.15)
the Humber p=0.009 p=0.676
1.09(0.90 to 1.32) 1.10(0.87 to 1.38) 1.07(0.90 to 1.28) 1.19(1.00 to 1.41) 0.95(0.80 to 1.14) 0.96(0.80 to 1.15) East Midlands 1.00 p=0.363 p=0.429
1.16(0.97 to 1.38) 1.02(0.81 to 1.27) 0.86(0.72 to 1.03) 1.01(0.85 to 1.19) 0.79(0.66 to 0.94) 0.91(0.76 to 1.09) West Midlands 1.00 p=0.105 p=0.879
East of 0.90(0.76 to 1.07) 0.96(0.77 to 1.20) 1.00(0.85 to 1.17) 0.98(0.84 to 1.14) 0.84(0.07 to 0.99) 0.91(0.77 to 1.08)
England p=0.229 p=0.721
Greater 1.23(1.04 to 1.45) 1.37(1.11 to 1.68) 0.89(0.75 to 1.05) 1.14(0.97 to 1.33) 1.46(1.26 to 1.70) 1.45(1.23 to 1.70)
London p=0.015 p=0.003
South East 1.00 1.00 1.00
1.07(0.92 to 1.25) 1.09(0.90 to 1.32) 1.11(0.96 to 1.29) 1.01(0.87 to 1.17) 0.99(0.85 to 1.14) 1.18(1.01 to 1.37) South West 1.00 p=0.392 p=0.381
1.06(0.88 to 1.28) 0.89(0.71 to 1.11) 0.80(0.67 to 0.96) 0.83(0.70 to 0.99) 0.63(0.53 to 0.76) 0.68(0.56 to 0.81) Scotland 1.00 p=0.550 p=0.305
1.49(1.31 to 1.69) 1.06(0.80 to 1.41) 0.77(0.61 to 0.97) 1.15(0.93 to 1.43) 1.00(0.81 to 1.25) 0.97(0.78 to 1.22) Wales 1.00 p=0.002 p=0.670
Table 1. Regression models investigating whether Government Office Region predicts dietary patterns displaying Relative Risk Ratio (95% Confidence Interval) and p value.
Unadjusted model (pseudo R =0.003)
p=0.043 p<0.001 p<0.001 p=0.149
p=0.006 p=0.023 p=0.025 p=0.024
1.00 p=0.392 p=0.646 p=0.199 p=0.873
p=0.444 p=0.123 p=0.340 p=0.997
p=0.135 p=0.713 p=0.019 p=0.610
1.00 p=0.400 p=0.736 p=0.124 p=0.169
1.00 p=0.165 p<0.001 p<0.001 p<0.001
1.00 1.00 1.00 1.00
p=0.142 p=0.528 p=0.847 p=0.011
p=0.688 p=0.001 p<0.001 p=0.046
p=0.300 p=0.417 p=0.817 p=0.327
p=0.006 p=0.007 p=0.002 p=0.019
p=0.001 p=0.525 p=0.206 p=0.016
1.00 p=0.712 p=0.571 p=0.323 p=0.636
p=0.433 p=0.046 p=0.590 p=0.658
p=0.094 p=0.949 p=0.008 p=0.316
1.00 p=0.987 p=0.764 p=0.038 p=0.270
1.00 p=0.178 p=0.107 p<0.001 p<0.001
1.00 1.00 1.00 1.00
p=0.157 p=0.909 p=0.860 p=0.032
p=0.014 p=0.044 p<0.001 p<0.001
p=0.026 p=0.194 p=0.988 p=0.823
tuated. The pseudo R2 value shows that the adjusted model ex-
plains 12% of variation in dietary pattern, compared to less than
1% in the unadjusted model. Most of this variation is explained
by the energy intake adjustment.
The UKWCS comprises women from each of the seven
OAC Supergroups, with some over-representation in the ‘Pros-
pering Suburbs’ and ‘Countryside’ and under-representation in
the ‘Constrained by Circumstance’, ‘Blue Collar Communities’
and ‘Multicultural’ Supergroups (Figure 2). This is as expected
in a predominantly middle class cohort of women. That said,
there are still large numbers in each of the Supergroups.
Significant variation for consumption of all dietary pat-
terns by OAC Supergroup is observed (Figure 3). In general,
variation is wider when considering differences by geodemo-
graphic Supergroup compared to by region, suggesting that the
inclusion of demographic variables with geography could tell us
something more interesting about dietary patterns.
The multinomial regression model (Table 2) shows that
OAC Supergroup has a significant relationship with dietary pat-
tern consumption. The unadjusted model shows that the ‘Con-
strained by Circumstance’ group have a significantly elevated
RRR of 1.33 (95% CI 0.11 to 1.59) for consuming the least
healthy, “Monotonous Low Quantity Omnivore” dietary pat-
tern whilst also having a significantly lower RRR for consuming
the two most healthy dietary patterns, the “Health Conscious”
(RRR=0.69 95% CI 0.51 to 0.93) and the “Higher Diversity Tra-
ditional Omnivore” (RRR=0.74 95% CI 0.59 to 0.92). The oppo-
site is observed for the ‘Countryside’ Supergroups who have an
increased likelihood of consuming the healthy patterns (“Health
Conscious” RRR=1.33 95% CI 1.14 to 1.55 and “High Diversity
Traditional Omnivore” RRR=1.37 95% CI 1.21 to 1.54) and de-
creased likelihood of consuming the unhealthiest, “Monotonous
Low Quantity Omnivore” pattern (RRR=0.76 95% CI 0.68 to
0.86).
The adjusted model accounts for total energy intake
Blue Collar 1.16(1.00 to 1.36) 0.87(0.69 to 1.10) 0.81(0.68 to 0.97) 0.87(0.74 to 1.02) 0.65(0.55 to 0.78) 0.57(0.47 to 0.69)
Communities p=0.054 p=0.206
1.05(0.88 to 1.26) 1.55(1.23 to 1.95) 1.05(0.87 to 1.28) 1.36(1.14 to 1.61) 1.56(1.32 to 1.84) 1.59(1.33 to 1.89) City Living 1.00 p=0.587 p<0.001
0.76(0.68 to 0.86) 1.33(1.14 to 1.55) 1.37(1.21 to 1.54) 1.15(1.03 to 1.29) 0.79(0.71 to 0.89) 0.97(0.86 to 1.09) Countryside 1.00 p<0.001 p<0.001
Prospering 0.76(0.69 to 0.84) 0.90(0.78 to 1.04) 1.10(0.99 to 1.23) 0.96(0.87 to 1.06) 0.59(0.53 to 0.65) 0.66(0.59 to 0.73)
Suburbs p<0.001 p=0.144
Constrained 1.33(0.11 to 1.59) 0.69(0.51 to 0.93) 0.74(0.59 to 0.92) 0.91(0.75 to 1.10) 0.71(0.58 to 0.86) 0.67(0.54 to 0.83) by 1.00 p=0.002 p=0.015 Circumstance
Typical Traits 1.00 1.00 1.00
1.23(1.02 to 1.47) 1.16(0.90 to 1.49) 0.93(0.76 to 1.15) 1.09(0.91 to 1.32) 1.70(1.43 to 2.01) 1.55(1.29 to 1.86) Multicultural 1.00 p=0.027 p=0.260
Adjusted model(adjusting for smoking, total calorie intake including
Blue Collar 1.25(1.06 to 1.48) 0.79(0.62 to 1.00) 0.72(0.60 to 0.87) 0.95(0.80 to 1.12) 0.70(0.59 to 0.83)
Communities p=0.009 p=0.050
0.88(0.73 to 1.07) 1.66(1.32 to 2.10) 1.10(0.90 to 1.34) 1.24(1.04 to 1.48) 1.48(1.25 to 1.75) City Living 1.00 p=0.208 p<0.001
0.79(0.69 to 0.89) 1.3(1.12 to 1.53) 1.35(1.20 to 1.53) 1.14(1.01 to 1.28) 0.79(0.70 to 0.89) Countryside 1.00 p<0.001 p=0.001
Prospering 0.77(0.69 to 0.86) 0.91(0.79 to 1.06) 1.11(1.00 to 1.25) 0.94(0.85 to 1.05) 0.58(0.52 to 0.64)
Suburbs p<0.001 p=0.215
Constrained 1.40(1.16 to 1.71) 0.66(0.49 to 0.90) 0.69(0.55 to 0.86) 0.98(0.80 to 1.20) 0.75(0.61 to 0.92) by 1.00 p=0.001 p=0.008 Circumstance
Typical Traits 1.00 1.00 1.00
0.99(0.82 to 1.21) 1.24(0.96 to 1.60) 0.98(0.79 to 1.21) 0.99(0.82 to 1.20) 1.62(1.36 to 1.92) Multicultural 1.00 p=0.951 p=0.107
Table 2. Regression models investigating whether OAC Supergroup predicts dietary patterns displaying Relative Risk Ratio (95% Confidence Interval) and p value.
and physical activity in order that observed effects can be as-
sumed to be dietary pattern related and not due to the volume of
energy intake or expenditure. Other variables, such as age, edu-
cation and social class, which could be considered as confound-
ers have not been adjusted for, as these variables are included in
assignment of the OAC.
In the adjusted model, the relationships observed in the
unadjusted models are unaffected when additionally adjusting
for smoking, total calorie intake including alcohol, typical dai-
ly physical activity (METs), age, social class, education. They
are in fact accentuated for the “Constrained by Circumstance”
group (in comparison to the reference group “Traditional Meat,
Chips and Pudding Eaters”: “Monotonous Low Quantity Omni-
vore” diet RRR=1.40 95% CI 1.16 to 1.71, “Health Conscious”
Unadjusted model(pseudo R =0.007)
1.00 p=0.022 p=0.095 p<0.001 p<0.001
p=0.611 p=0.001 p<0.001 p<0.001
p<0.001 p=0.016 p<0.001 p=0.582
1.00 p=0.083 p=0.444 p<0.001 p<0.001
p=0.008 p=0.330 p=0.001 p<0.001
1.00 1.00 1.00 1.00
p=0.524 p=0.360 p<0.001 p<0.001
alcohol and typical daily physical activity(METs)(pseudo R =0.10)
1.00 0.55(0.05) p<0.001 p=0.001 p=0.526 p<0.001
1.64(0.15) p<0.001 p=0.349 p=0.019 p<0.001
0.95(0.06) p=0.386 p<0.001 p=0.029 p<0.001
1.00 0.65(0.04) p<0.001 p=0.052 p=0.259 p<0.001
0.66(0.08) p<0.001 p=0.001 p=0.859 p=0.005
1.00 1.00 1.00 1.00
1.65(0.15) p<0.001 p=0.839 p=0.938 p<0.001
RRR=0.66 95% CI 0.49 to 0.90, “Higher Diversity Traditional
Omnivore” RRR=0.69 95% CI 0.55 to 0.86). The ‘Blue Col-
lar Communities’ also show the same convincing pattern (in
comparison to the reference group “Traditional Meat, Chips and
Pudding Eaters”: “Monotonous Low Quantity Omnivore” diet
RRR=1.25 95% CI 1.06 to 1.48, “Health Conscious” RRR=0.79
95% CI 0.62 to 1.0, “Higher Diversity Traditional Omnivore”
RRR=0.72 95% CI 0.60 to 0.87).
Interestingly, in both the unadjusted and adjusted mod-
els, the ‘City Living’ and ‘Multicultural’ Supergroups are ap-
proximately 50% more likely to consume a vegetarian diet, with
other Supergroups less likely, compared to the reference ‘Typi-
cal Traits’ Supergroup.
The pseudo R2 value indicates 10% of variation in di-
etary pattern is explained by the model. This is similar to that
shown in the adjusted model using GOR.
Variations in dietary pattern consumption are observed
both regionally and according to the geodemographic Super-
group in which the women live. These variations occur both
within a dietary pattern across the Regions and Supergroups and
also between dietary patterns within a Region or Supergroup.
Analysis using a geodemographic classification provides more
insight into spatial variations in dietary pattern in the UKWCS
than analysis using GOR whilst controlling for certain demo-
graphic characteristics.
Results suggest an association between a healthy diet –
illustrated by the “Health Conscious” dietary pattern and dietary
diversity, illustrated by the “Higher Diversity Traditional Omni-
vore” – and being a member of the more affluent OAC groups.
This is supportive of other research suggesting that a healthy diet
is more expensive and therefore restricted by financial ability.24
The Family Spending report from the Living Costs and Food
Survey25 indicates that the OAC Supergroups ‘Countryside’ and
‘Prospering Suburbs’ spend the most on food per week, which
is also in line with our findings based on the assumption that a
healthier diet is a more expensive one.
Unlike earlier studies using fruit and vegetable intake
as a proxy measure of a healthy diet,12 in this study the “Health
Conscious” dietary pattern incorporates more dietary compo-
nents than just fruit and vegetable intake to represent dietary
healthiness.A full spectrum of dietary diversity is also represent-
ed by the dietary patterns (as indicated by the pattern names).
High dietary diversity appears to be associated with living in
more affluent OAC Groups, and conversely dietary monotony
correlates with the poorer areas. A conservative dietary pattern
and the traditional dietary pattern have slightly higher concen-
trations in the deprived areas.
Summary characteristics for the UKWCS vary little by
er, when the same characteristics are presented by OAC Super-
group variation is greater. The OAC uses variables such as age,
occupation and education, as collected in the census. No infor-
mation relating to diet is included in the census or the OAC, yet
increased variation in dietary patterns by OAC rather than GOR
is observed supporting evidence that socio-demographic charac-
teristics influence diet,26 but also that small area geography – the
immediate local environment – influences diet.27 Using the small
geographic unit of Output Area (containing approximately 250
individuals) compared to the large geographical unit of a GOR
(containing millions of individuals) produces results which will
be much more relevant at a local area level.
The UKWCS, specifically designed to investigate the
effect of dietary patterns on health outcomes, provides quality
dietary data for analysis. Vegetarians were deliberately over
sampled and as such make up a higher proportion in this co-
hort than in the general population. This means that this study
is powered sufficiently to detect differences between different
types of diet which is a strength of the design. Measurement of
diet is subject to a range of potential bias including under or over
reporting. Collecting information from a sample which is large
enough to be generalisable to a given population is also chal-
lenging due to temporal and financial constraints, making this
study a valuable resource.
Geographic location of participants was not a design
factor for the UKWCS, so despite large numbers in each of
the nine GORs of the England and Scotland and Wales these
regions are not equally represented across the cohort. The low-
est numbers (n=957) were observed residing in the North East.
However, when considering this in the context of other dietary
survey information, this is a large number of women on which
to base robust analysis. The National Diet and Nutrition Survey
(NDNS), for example, whilst it was designed to be geographi-
cally representative, only includes 3073 individuals in total
(from the three year rollout) of which only about one quarter are
women. The UKWCS sample is approximately 40 times the size
of that national sample of women.
Recruitment of the UKWCS was on a volunteer basis
from a WCRF mailing list of previous questionnaire partici-
pants so it may be expected that there is some volunteer bias.
The women are predominantly middle age, middle class and
white. This may account for the over representation observed
when comparing the UKWCS to the whole UK population, in
the ‘Countryside’ and ‘Prospering Suburbs’ Supergroups which
are characterised by middle age, more middle class, white indi-
viduals with larger detached houses. Under representation is ob-
served in ‘Blue Collar Communities’, ‘Constrained by Circum-
stance’ and ‘Multicultural’ Supergroups. Despite this there are
large numbers of women in each of the seven OAC Supergroups,
sufficient to provide confidence in the associations observed in
this study.
The dietary patterns used, whilst data driven, are not
necessarily comparable to dietary patterns used in other surveys.
That said, they provide a comprehensive illustration of dietary
patterns consumed in the UK. Comparing these to another data
driven but not international classification, such as the OAC could
be considered subjective, but this paper illustrates how different
spatial measures can be useful in public health and specifically
dietary research, rather than critique dietary patterns or geode-
mographic classifications.
For some geodemographic classifications, created by
market research companies, the methods used to generate the
classification are not transparent as these are the intellectual
property of the company. This can be a limitation for use in re-
search as it can make adjustment for confounding a guessing
game. However, the methods used to generate the OAC are re-
ported in full, allowing for researchers to have clear insight into
the classification data they are using.
The OAC groups include all of the UK population who
completed the Census questionnaire, so incorporate men and
children in addition to women. Whilst the NDNS reports statisti-
cal differences between the food consumed by men and women
(stratified both by region and whether or not the individuals are
in receipt of benefits)28 it has not specifically reported whether
there was a difference in the diets of men and women within the
same household in the UK. With this in mind, the results of this
study can only reliably be applied to women.
The dietary data used in this study were collected in the
late 1990s. It is feasible that dietary habits could have changed
since this time. However it is rare that dietary information of this
quality is collected in such a large sample in the UK. Prospective
dietary data collection is essential when considering influence
on diseases with a latent development period, such as cancer.
Collection dietary records for cancer cases can impact on subject
recall of their diet.2 Therefore the application of these results
with respect to the effect of diet on health is relevant, despite
possible dietary change since the data was collected.
This study uses the UK as a case study. However, the
methods are transferable elsewhere and it would be possible to
carry out the same sort of analysis for other countries for which
geodemographic classifications have been generated. An exam-
ple, Callcredit group have generated geodemographic classifica-
tions for 40 countries worldwide.29
Dietary pattern variation between regions, with the ex-
ception of the “Health Conscious” dietary pattern (p=0.186), is
statistically significant (p<0.01). Such variation at a large geo-
graphical scale is suggestive that there are regional influences on
eating habits, not just that of the local surrounding area. Further
investigation could be carried out into this regional variation;
however, it may be erroneous to ascribe specific factors to a par-
nating influence on the dietary pattern. The regression analysis
using region, including demographic variables, shows that some
regions have significant RRRs of consuming a particular dietary
pattern. For example Greater London has significantly increased
risk, compared to those living in the South East, of consuming
both the “Health Conscious” (most healthy) and the “Monoto-
nous Low Quantity Omnivore” (least healthy) diet (compared
to consuming the traditional diet). Observations like this mean
that implementing a cost effective nutritional intervention at the
regional level would be extremely difficult.
However, when we consider dietary pattern variation
by geodemographic Supergroup the results present a clearer pic-
ture. Those living in a ‘Blue Collar Community’, for example
have significantly increased risk of consuming the “Monotonous
Low Quantity Omnivore” (least healthy) dietary pattern and a
significantly reduced risk of consuming the “Health Conscious”
and “High Diversity Traditional Omnivore” (two most healthy)
dietary patterns, suggesting that it would be worthwhile imple-
menting a healthy diet promotion in these types of areas. The re-
gional regression model, controlling for age and education does
not produce such clear results and the regression models using
geodemographic Supergroups, indicating that the small scale ge-
ography combined with a number of demographic variables is a
powerful tool. Combination of both the regional and Supergroup
results could help to target interventions to certain types of areas
within the region most at risk of consuming a poor dietary pat-
tern.
Being able to estimate dietary patterns at a small area
level using a classification such as OAC, allows for smarter tar-
geting of public health interventions, to improve diet and subse-
quent health. For example, to provide a specific intervention to in-
dividuals to living in ‘Constrained by Circumstances’Supergroup
(who consume the highest percentage of “Monotonous Low
Quantity Omnivore” diets) which would encourage them to in-
troduce more variety into their diets with the best addition being
fruit and vegetables. This could be done through social services
interventions in specific communities, or at a GP practice level
in local communities.
Future studies could investigate how consuming each
of these dietary patterns could influence long term health, which
would strengthen the relevance of this research to public health
interventions. Another step would be to incorporate the cost of
these dietary patterns in order to assess the economic influence
of food price, compared to usage by geodemographic type. It
would be key to profile the dietary patterns of this cohort for
cities in the UK using a geodemographic classification and in-
vestigate patterns observed. Additional case studies from other
countries would allow for international comparisons to be made.
individuals reside. The type of area, using a small scale geo-
graphical unit, combined with demographic characteristics pro-
vides richer prediction of dietary consumption than the large
regional unit. Healthy or diverse dietary patterns are more com-
mon in geodemographic groups in the ‘Countryside’ or ‘Pros-
pering Suburbs’ with less healthy patterns in areas such as ‘Con-
strained by Circumstance’ and ‘Blue Collar Communities’. With
this in mind it may be beneficial to use such classifications in
the application of dietary advice to encourage healthy eating in
order to promote long term health. Geodemographic classifica-
tions are a useful tool to better understand spatial variations in
diet in the UK.
Further information on the UK Women’s Cohort Study Data is
available at www.ukwcs.leeds.ac.uk
The main author was funded through and ESRC/MRC PhD stu-
dentship. The UKWCS was initially established using funding
from the World Cancer Research Fund. Thanks to Dr. Lorna Fra-
ser for advice with the regression analysis.
The study in the article has 35,000 participants who consented at
the time of recruitment to the cohort in the 1990s, as described
in the methods. In line with this consent, no personally identified
information is included in this article.