Monitoring dietary change in populations and the need for specific food targets; lessons from the...

Journal of Human Nutrition a n d Dietetics (1995), 8, 119-128

Monitoring dietary change in populations and the need for specific food targets; lessons from the North West Thames Regional Health Survey

M. Davenport, P. Roderick, L. Elliott,* C. Victort and C. Geissler* North West Thames Regional Health Authority, Paddington, London, UK

Dietary goals are usually expressed in the form of consumption of absolute quantities of specific nutrients. Translating such information into food patterns is difficult. Consequently it is not easy to convey these goals in the promotion of a healthy diet or to monitor dietary change in populations. This paper explores the difficulties and advantages of a food frequency questionnaire approach as used in a large regional health survey to describe food patterns and hence to monitor change in relation to dietary variables. There was considerable variation in the frequency of consumption of individual foods between different sub-groups in the population and these were as expected. To assess the overall diet a composite dietary index was proposed. The index score was significantly associated with various socio-demographic and lifestyle variables. Unhealthy lifestyles were associated with low scores (i.e. an ‘unhealthy’ diet) on the index indicating a measure of validity. However there were several limitations in the food frequency questionnaire method used: neither the questionnaire nor simple global indices of a ‘healthy’ diet derived from the questions used had been validated against a more robust quantifiable measure of diet; some of the questions were not specific for population groups of interest; some questions grouped foods in categories that masked differences in consumption and several did not correspond to recent food targets; and finally there was no energy adjustment, which meant that the obese appeared to have a healthy dietary pattern. The use of a valid set of food frequency questions as an efficient and practical way of monitoring a population’s diet by survey is suggested. The questions must be validated against reliable measures of nutrients, and be compatible with food targets based on nutritional recommendations.

Key words: dietary surveys, food frequency questionnaire, food targets, monitoring diet

Introduction

The importance of the Western diet in the aetiology of many adult chronic diseases, such

Correspondence: Prof. C. Geissler, Department of Nutrition & Dietetics, King’s College London, University of London, Campden Hill Road, Lon- don Wa 7AH, UK.

*Dacorum and St Albans Community NHS Trust, Hertfordshire, UK.

‘Kensington, Chelsea & Westminster Health Agency, London, UK.

*Kings College, London, UK.

as coronary heart disease, is well recognized [WHO, 1990). Several reports have set target intakes for the major nutrients, such as dietary fat and non-starch polysaccharides (fibre) with the aim of reducing this chronic disease burden [WHO, 1990; DOH, 1991). In the UK, coronary heart disease (CHD) is now a priority area in the Health of the Nation [HOTN) strategy. National targets have been set for reductions in CHD mortality (Secretary of State for Health, 1991), in the percentage of total and saturated fat energy consumption and in body mass index (BMI). Possible ways in which average diets

119

120 M. Davenport et al.

Table 1. Faculty of Public Health Medicine: UK levels of health nutritional targets

An increase in the proportion of the adult population consuming fat-reduced

A reduction in the proportion of households using lard for cooking to less

An increase in the proportion of people eating fish at least twice a week to

milks [skimmed or semi-skimmed) to more than 60%.*

than lo%.*

more than 70% [NWT Survey data is based on frequency of consumption of at least once a week).t An increase in the proportion of people using spreads low in saturates to more than So%.* A reduction in the proportion of people consuming sweets on most days to less than 20% (not in NWT Regional Health Survey).t An increase in the proportion of people eating some fruit daily to more than 60% (preferably fresh or frozen fruit) (NWT Survey data! is based on fresh fruit or pure fruit juice).t

vegetables daily [including salads, excluding potatoes) to more than 60% (NWT Survey data includes potatoes).t

per day to more than 50% [NWT Survey data includes chapattis).t

or a cereal-based staple [pasta, rice) at least daily to more than SO%.*

An increase in the proportion of people having at least tone portion of

An increase in the proportion of people eating bread on at least two occasions

An increase in the proportion of people eating potatoes or other starchy root

*Target directly comparable to health survey question. tTarget is not exactly comparable to health survey question.

could be altered to meet the dietary goals have been suggested in terms of quantifiable changes in food categories (Bingham, 1991).

Health agencies are responsible for imple- menting the Health of the Nation Strategy at local level, one component of which is the setting of appropriate local targets to achieve the nutritional goals. Information about the dietary intake of local populations is required to monitor progress and ideally these data should be comparable with information collected at regional and national levels.

Most dietary goals are expressed as absolute quantities of specific nutrients or as the percentage of energy intake derived from these nutrients (DOH, 1991; Secretary of State for Health, 1991; HealthEducation Council, 1983). Translation of this information into food pat-

Medicine’s targets (1992) (Table 1) ,are a recent example. However, these and other frequency targets have not been fully validated against quantitative measures. At a local level the following questions arise: what type of targets should be set--nutrient or food groups; at what level should targets be set; and how can the dietary information be reliably colllected?

This paper explores the issue of monitoring diet in populations, by considering the analysis of the dietary component of a recent regional health survey, which was obtained by the food frequency method. Recommendations are made for target setting and for future dietary surveys.

Methods terns is difficult both for consumers and health educators. Surveying diet and monitoring change on this basis is also problematic. An The North ‘West Thames (NWT) Regional alternative approach is to set goals which Health Surve:y was undertaken in 1989-1991 in include the frequency of consumption of order to define the health status of the Region’s specific types of foods (WHO, 1990; FPHM, population and thereby facilitate more 1992). This is more immediately understand- effective targeting of limited health care re- able although less clearly quantifiable than sources. The questionnaire was based largely nutrient targets. The Faculty of Public Health on the General Household Survey (GHS), an

design

Monitoring dietary change 121

Table 2. Scoring system for dietary index ~~

Food frequency

3-61 1-21 > llday llday week week < 1 Never

Processed meat Cheeseleggs Rice Beans Fruit Vegetables Biscuits/cake Fish Poultry Wholemeal cereals Fried foods

Bread

- 5 -4 - 5 - 3

5 4 5 4 5 4 5 4

- 5 -4 5 4 5 4 5 4

- 5 -4

2/day or more llday or more 4 3

- 3 - 2 -1 1

3 2 3 2 3 2 3 2

- 3 - 2 3 2 3 2 3 2

- 3 - 2

34week 2

-1 0 3 - 2 1 0 1 0 1 0 1 0

-1 0 1 0 1 0 1 0

-1 0

1-2lweek or less 1

Food types Type of milk extra cream full cream semi-skimmed whitener none

- 5 - 3 5 -4 -2

Spreading fats butter hard margarine soft margarine PU* half fat none -4 -3 0 5 4 - 2

*Poly-unsaturated fats.

annual survey of approximately 18 000 people in private households in Great Britain aged 16 or over (Smyth & Browne, 1990). The NW Thames Survey included sections on socio-demographic details, utilization and perceptions of health services, self-reported illness, and lifestyles (smoking, exercise; alcohol and diet). The dietary section included questions on the frequency of consumption (see Table 2 for the range of frequencies) and type of common foods [R = 31); the format was based on The Health and Lifestyle Survey, a national survey of 9000 individuals undertaken in 1984-1985 (Cox et al., 1987). The NWT Regional Health Survey questionnaire was administered by trained interviewers and self-reported height and weight were also collected.

Survey sample

A sample of enumeration districts was selected from the 13 districts comprisingthe North West Thames Region, with probability proportionate to size and stratified to take health and social

differences into account. Individual households were then randomly selected from within the enumeration districts and one individual, aged 16 or over, was randomly selected from each address. From a possible sample of 12 808, 8251 adults were interviewed, a response rate of 64%. A separate survey was made of randomly selected homeless households living in bed and breakfast accommodation; the sam- pling method is described elsewhere (Victor, 1992). There were 319 respondents, a response rate of 61%. The separate study was required to obtain sufficient numbers for a meaningful analysis.

Dietary analysis

The frequency of consumption of certain foods was compared with those selected for the Faculty of Public Health Medicine’s targets (Table 1) (FPHM, 1992). The NW Thames data on type of milk and spreading fats were directly comparable with the FPHM targets, whilst


some food types and their frequencies of consumption were not specified in exactly the same manner. The NW Thames survey did not ask specifically about the intake of lard, sweets or potatoeslpasta, all separate FPHM targets.

A dietary ‘index’ measure was constructed by selecting a limited range of 14 foods, chosen to represent ‘healthy’ choices. Consumption of these foods was scored so that a ‘healthy’ eating pattern had a high score (Table 21. Foods regarded as ‘healthy’ were scored 0 to 5 and ‘unhealthy’ foods were scored - 5 to 0. Type of milk and spreading fats were scored to reflect their saturated fat content, from - 5 to 5. Cheeseleggs were scored so that non-consumption was similar to over-consumption. The index was the sum of the individual food scores.

A second index was constructed with a wider range of foods high in saturated fat or sugar (e.g. chocolate, soft drinks, sugar in tea or coffee, and beef, lamb, pork, ham and bacon), scored inversely to frequency of eating. However, this did not materially alter the conclusions drawn from the first index and so only the data from the first are presented.

The percentage of the population consuming individual foods were analysed by age and sex, ethnic group socio-economic group (SEG), educational attainment, alcohol consumption, smoking, frequency of exercise, and BMI. The homeless sample was analysed separately (Table 3).

The percentages in the top and bottom quartiles of the dietary index score and the mean score for each independent variable are presented (Table 4).

Definitions of non-dietary variables

The ethnic group category was self-reported using the 1991 Census question (GSS, 1992). The category ‘Other’ included Chinese, Japanese, Arabs and Whites (non-UK). Alcohol consumption was categorized as in the General Household Survey (Smyth & Browne, 1990) (Table 5).

‘Current’ and ‘Ex’ smoking categories refer to cigarettes only. Body Mass Index was categorized according to the Royal College of Physi- cians classification (1983) (Table 6).

‘Exercise’ was classified by this number of times respondents reported undertaking a range of activities (e.g. swimming, aerobics) in the 2 weeks prior to the interview.

Statistics

The proportion of the population categorized by various socio-demographic and lifestyle factors who achieve food targets and the mean diet score of such groups are shown with the standard error. The Analysis of Variance (ANOVA) was used to test for differences between mean diet index scores in sub-groups of the socio-demographic and lifestyle variables (Table 4).

Results

Individual food fkequencies

The survey population failed to meet the two FPHM targets that were directly comparable (Table 3). Only 47% and 54% consumed low fat milk and low saturated spreads respectively, compared to FPHM targets of SO% for each. This was also the case where modified criteria were used, despite the less stringent values for fruit, fish, bread and vegetables, with the exception of vegetable consumption, probably because potatoes were included within this category in i he NWT Regional Health Survey.

There wa,s considerable variatiion between population sub-groups in the percentages meeting the criteria for fruit, type of milk, fish and spreading fats. For example, 63% of the professional/managerial socio-economic group reported eating fruit or having fruit juice daily compared to 39% of the unskilled manual socio-economic group; the respeictive figures for current and non-smokers were 44% and 62%. In contrast, the questions on vegetables and bread did not discriminate between groups. Better discrimination would probably have been obtained if potatoes had been classified separately and bread reported as wholemeal or white.

The groups that came consistently closest to the targets were the professiona:l/managerial and the obese groups. Young males, the lower socio-economic groups, and those with lower educational ,attainment were less likely to make

Tab

le 3.

The

per

cent

age

of t

he N

W T

ham

es p

opul

atio

n co

nsum

ing

spec

ific

food

gro

ups

by v

ario

us s

ocio

-dem

ogra

phic

and

lif

esty

le f

acto

rs i

n co

mpa

riso

n w

ith

FPH

M ta

rget

s (s

tand

ard

erro

r in

par

enth

eses

)

Skim

med

lsem

i-

Low

sat

urat

e (n

) sk

imm

ed m

ilk

spre

ads

Fish

Fr

uit

Vee

etab

les

Bre

ad

FPH

M c

rite

ria

FPH

M ta

rget

(56

) T

otal

pop

M

ale

(age

, yea

rs)

16-4

4 4

54

4

65 +

Tot

al

Fem

ale

(age

, yea

rs)

16-4

4 45

-64

65 +

Tot

al

Whi

te

Afr

o-C

arib

bean

A

sian

O

ther

So

cio-

econ

omic

gro

up

Prof

essi

onal

lman

ual

Oth

er n

on-m

anua

l Sk

illed

man

ual

Sem

i-sk

illed

man

ual

Uns

kille

d m

anua

l M

isce

llane

ous

A-1

evel

lDeg

ree

Oth

er

Non

e A

lcoh

ol

Abs

tain

er

Occ

asio

nalll

ight

M

oder

atel

heav

y

Cur

rent

Ex

N

ever

Non

e 1-

7 ti

mes

8

+ t

imes

B

ody

Mas

s In

dex

Und

erw

eigh

t A

ccep

tabl

e 0

v e rw e

i g h t

O

bese

Eth

nic

grou

p

Edu

catio

n le

vel

Smok

ing

Exe

rcis

e in

last

2 w

eeks

(625

1)

(217

1)

(104

4)

(623

) (3

638)

(251

4)

(114

4)

(753

) (4

413)

(632

5)

(323

) (638)

(933

)

(231

5)

(191

3)

(215

3)

(964

1 12

091

(697

)

(179

8)

(303

9)

(336

5)

(126

3)

(557

0)

(139

4)

(246

8)

(310

0)

(265

4)

(443

9)

(258

2)

(122

6)

(117

3)

(449

7)

(205

0)

(460

)

Con

sum

e 6

0.0

47

.3 (

0.5)

40.4

(1

.1)

46.3

(1.

5)

41.5

(0

.8)

55.0

(1.0

) 54

.4 (

1.5)

40

.6 (

1.8)

52

.4 (

0.8)

49.4

(0.

6)

35.6

(2.

7)

35.6

(1.

9)

45.3

(1.

6)

56.5

(1.

0)

51.9

(1.1

)

34.0

(1.

5)

27.9

(3.

1)

40.5

(1.

9)

37.3

(1.

9)

43.4

(1.1

)

54.9

(1.

2)

49.2

(0.

9)

41.6

(0.

6)

39.9

(1.

4)

50.1

(0.

7)

43.2

(1.

3)

39.7

(1.0

) 54

.7 (

0.9)

46

.0 (

1.0)

42.2

(0.

7)

52.6

(1.0

) 54

.6 (

1.4)

41.1

(1.

4)

46.3

(0.

7)

49.2

(1.1

) 46

.2 (

2.3)

Con

sum

e 60

.0

53.7

(0.

5)

51.0

(1.1

) 55

.6 (

1.5)

47

.9 (

2.0)

51

.8 (

0.8

)

57.2

(1.0

) 56

.8 (

1.5)

47

.0 (

1.8

) 55

.3 (

0.6)

55.0

(0.

6)

49.2

(2.

6)

48.3

(2.

0)

49.3

(1.

6)

58.3

(1.

0)

56.3

(1.1

) 52

.8 (1.1

) 48

.1 (

1.6)

44.1

(1.

9)

58.2

(1.

2)

50.5

(0.

9)

47.1

(1.

4)

56.0

(0.

7)

50.2

(1.

3)

46.4

(1.0

)

43.4

(3.

5)

54.5

(0.

9)

57.9

(0.

9)

55.5

(1.

0)

50.6

(0.

7)

56.7

(1.

0)

56.0

(1.1

)

Z/w

eek*

70

.0

63.9

(0.

5)

54.2

(1.1

) 72

.6 (

1.4)

79

.1 (

1.6)

63

.3 (

0.8

)

55.3

(1.

0)

73.6

(1.3

) 80

.7 (

1.4)

64

.3 (

0.7)

67.1

(0

.6)

62.6

(2.

7)

35.3

(1.

9)

62.0

(1.

6)

68.5

(1.

0)

63.3

(1.1

) 6

1.1

(1.1

) 63

.1 (1

.6)

61.7

(3.

4)

60.1

(1.

9)

66.5

(1.1

) 59

.1 (

0.9)

66

.7 (

0.8

)

57.3

(1.

4)

66.0

(0.

6)

61.3

(1.

3)

55.4

(1.0)

71.9

(0.

8)

62.4

(0.

9)

65.1

(0.

7)

62.2

(1.

0)

62.8

(1.

4)

46.0

(1.

4)

59.9

(1.

4)

53.4

(0.

7)

62.4

(0.

7)

57.4

(1.1)

68.4

(1.0

) 61

.3 (

2.31

67

.6 1

2.21

Dai

lyt

60.0

56

.1 (0

.5)

43.6

(1.1

)

54.1

(2.

0)

59.0

(1.

5)

49.5

(0

.8)

57.4

(1.0

) 66

.4 (

1.4)

67

.1 (1

.7)

62.0

(0.

7)

54.9

(0

.6)

51.9

(2.

8)

63.5

(1.

9)

61.0

(1

.6)

63.4

(1.0

) 58

.0 (

1.1

) 52

.8 (

1.1

) 48

.8 (

1.6)

52.3

(1.

9)

61.3 (1

.1)

53.7

(0.

9)

64.9

(1.

3)

57.0

(0.

7)

44.5

(1.

3)

39.3

(3.

4)

55.9

(0.

9)

43.5

(1.

0)

61.1

(0.

9)

62.2

(0.

9)

55.2

(0.

7)

62.1

(1.

4)

54.9

(1.

0)

52.2

(1.

5)

55.2

(0.

7)

67.6

(2.

31

59.5

(1.1

)

tlda

yt

60.0

64

.6 (

0.5)

55.8

(1.1

) 70

.1 (

1.4)

72

.7 (

1.8

) 62

.5 (

0.8)

63.4

(1.

0)

75.0

(1.

3)

75.6

(1.

6)

68.4

(0.

7)

65.9

(0

.6)

44.8

(2.

8)

65.9

(1.

9)

71.2

(1.

5)

69.2

(1.0

) 63

.9 (1.1

) 65

.6 (

1.0

) 64

.8 (

1.5)

65

.3 (

3.3)

60

.1 (

1.9)

66.7

(1.1

) 63

.7 (

0.9)

66

.8 (

0.6)

71.2

(1.

3)

66.2

(0

.6)

58.6

(1.3

)

60.3

(1.0

) 69

.6 (

0.8

) 66

.3 (

0.9)

68.0

(0.

7)

61.4

(1.0

) 66

.2 (

1.4)

Z/da

y§

50.0

37

.0 (

0.5)

35.3 (

1.0

) 36

.0 (

1.5)

42

.0 (

2.0)

36

.6 (

0.8

)

36.1

(1.0

) 36

.6 (

1.4)

43

.1 (

1.8

) 37

.4 (

0.7)

36.0

(0

.6)

35.9

(2.

7)

52.6

(2.

0)

34.3

(1.

6)

36.2

(1.

0)

34.5

(1.1

) 38

.7 (

1.0)

39

.9 (

1.6)

35

.3 (

3.3)

37

.9 (

1.8

)

33.0

(1.1)

36.8

(0.

9)

39.3

(0

.8)

% 0 42

.3 (

1.4)

37

.2 1

0.61

!Y L

. i; 35

.3 (

1.0)

35

.5 (

0.9)

O

U

31.4

(1.

2)

g. 40

.7 (

1.0

)

38.7

(0.

7)

35.1

(0.

9)

35.1

(1.4

) %

6

38.6

(1.

4)

h

9% 62.3 (

1.4)

65

.1 (0

.7)

36.3

(0.

7)

68.3

(1.0

) 36

.9 (

1.1

) 67

.8 m

i

39.7

mi

H

omel

ess

(319)

33.2

(2.

6)

42.8

(2.

71

51.6

i2.

8)

41.3

iz.sj

44

.5 i2

.8j

36.7

{G

j

tNW

T R

egio

nal

Hea

lth S

urve

y in

clud

ed f

ruit

juic

e.

SNW

T R

qion

al H

ealth

Sur

vey

incl

uded

pot

atoe

s.

+

N

W

'NW

T R

egio

nal

Hea

lth S

urve

y w

as f

ish

mor

e th

an I

lwee

k.

fNW

T R

egio

nal

Hea

lth S

urve

y in

clud

ed c

hapa

ttis

.


Table 4. The association of socio-demographic and life style factor!; with the dietary index score in the NWT regional survey population

Variable

Per cent in Per cent in Results of

of index of index dietary index I' statistic* lowest quartile highest quartile Score on ANOVA

(least healthy) (most healthy) mean ( P value)

Total population Male (age, years) 16-44 45-64 65 + Total

16-44

65 +

Female (age, years)

45-64

Total

White Afro-Caribbean Asian Other

Socio-economic group Professional/manual Other non-manual Skilled manual Semi-skilled manual Unskilled manual Miscellaneous

A-level/Degree Other None

Alcohol Abstainer Occasional/light Moderate/heavy

Current Ex Never

None 1-7 times 8 + times

Body Mass Index Underweight Acceptable Overweight Obese

Homeless Total Males Females

Ethnic group

Education level

Smoking

Exercise in last 2 weeks

24.1

30.1 23.5 35.3 29.1

17.0 19.3 29.5 19.7

24.7 22.1 18.8 24.4

16.6 20.6 28.6 32.0 40.1 29.2

15.9 22.0 30.4

24.8 22.0 31.6

34.3 19.2 20.2

28.0 20.5 17.6

28.1 24.0 22.0 21.0

22.2 27.1 19.0

26.3

19.3 28.0 20.1 21.8

30.0 36.4 20.9 30.1

27.2 22.9 21.9 24.9

34.5 29.2 22.9 15.8 14.4 18.9

33.0 27.2 21.8

22.7 28.6 20.1

17.3 32.1 27.8

22.5 28.5 34.8

21.3 26.8 27.6 29.7

25.2 15.3 31.5

2.53 (0.01)

2.31 (0.02) 2.57 (0.04) 2.25 (0.05) 2.37 (0.02)

2.72 (0.02) 2.77 (0.03) 2.35 (0.04) 2.67 (0.02)

2.54 (0.01) 2.54 (0.06) 2.53 (0.04) 2.51 (0.04)

2.79 (0.02) 2.63 (0.03) 2.41 (0.02) 2.24 (0.03) 2.11 (0.08) 2.32 (0.04)

2.79 (0.03) 2.58 (0.02) 2.35 (0.02)

2.46 (0.03) 2.60 (0.02) 2.33 (0.03)

2.22 (0.02) 2.70 (0.02) 2.63 (0.02)

2.40 (0.02) 2.63 (0.02) 2.79 (0.03)

2.38 (0.03)

2.60 (0.02) 2.65 (0.05)

2.53 (0.06) 2.28 (0.09) 2.68 (0.08)

2.53 (0.02)

F = 22.79

(,P< 0.001)

F = 38.44

(,P< 0.001)

F= 0.141

(P = 0.935)

F = 56.99

( P < 0.001)

F = 93.18

( P < 0.001)

F = 34.21

(P<O.OOl)

F = 145.03

(P<O.OOl)

F = 70.25

(P < 0.001)

F = 10.93

( P < O . O O l )

F = 10.13

( P = 0.002)

*The F statistic was significant (P< 0.01) for all variables excepi. ethnic group, where there were no significant differences between sub-groups.


Dietary index score There were significant differences between the mean diet index scores of the sub-groups of all the socio-demographic and lifestyle variables, with the exception of ethnic group (Table 4). Similar associations were found for the combined dietary index as for the individual targets. Young males and the old of both sexes had a less ‘healthy’ distribution of score. There were trends with level of education and socio- economic group; the professional/managerial group and those with the highest educational level had the ‘healthiest’ scores. There were no significant differences in diet between ethnic groups.

There was clustering of unhealthy be- haviours. For example, current smokers and heavy drinkers had a poorer diet score. In contrast, the overweight and obese, paradoxi- cally, scored well on the index. Males over 65 years of age had the lowest index score and, therefore, the least ‘healthy’ diet. Homeless males scored considerably worse than homeless females.

Table 5. Alcohol consumption categories, after Smyth & Brown (1990), in units per week

Male Female

Abstainer None None Occasional/Light < 21 < 14 Moderate 21-51 14-36 Heavy > 51 > 36

‘healthy’ eating choices. There was no consistent pattern for ethnic groups. Indians, Bangladeshis and Pakistanis, for example, had the highest percentage meeting the fruit criteria but the lowest for milk and fish. The homeless had the lowest percentage meeting the targets; only a third reported drinking low fat milk, and 40% having fruit or fruit juice daily.

Smokers and heavy drinkers generally had lower percentages meeting the criteria than those who had never smoked or were occasional or light drinkers. More ex-smokers met the consumption criteria than non or current smokers, apparently espousing more vigor- ously an overall ‘healthier’ lifestyle. In contrast, fewer complete abstainers from alcohol met the milk, spreading fats and fish criteria than light or heavy drinkers, yet more met the fruit, vegetable and bread criteria. The overweight and obese had higher percentages meeting the milk, fish, fruit and vegetable criteria. This apparent paradox possibly reflects a conscious effort to reduce weight through a ‘healthy’ diet. The underweight had the lowest percentages meeting all the criteria. Non-exercisers were distinguished bom exercisers by fewer meeting the milk and spreading fats criteria but more meeting the fish, vegetable and bread criteria.

Table 6. Weight description by Body Mass Index

Description Level of index

Underweight 20 or less Acceptable 20 to 25 Overweight 25 to 30 Obese 30 or more

Discussion

The monitoring of dietary change at local level is essential to evaluate progress towards nutritional goals (Secretary of State for Health, 1991). National targets are largely of nutrient intakes, based on the rationale that food influences health by its composition and not by its nature, although the relationship of some foods with health is not readily explained by the nutrient composition (e.g. vegetables and cancer) (FPHM, 1992).

Dietary intake can be assessed by several methods: duplicate food samples combined with laboratory analysis of nutrient content; weighed dietary intake over a period of several days combined with the use of food tables to estimate the nutrient values; 24 h food records or recalls with estimates of portion size and the use of food tables; and food frequency questionnaires (Willett, 1990). These are listed in descending order of complexity and, therefore, expense. Only the latter two are realistically feasible for most large scale population surveys, particularly if they are to be conducted at frequent intervals. However, each has its limitations which must be kept in mind. Twenty-


Table 7. Blaxter’s (1990) ‘good’ dietary habits

Eating predominantly wholemeallbrown bread. Eating low fat or polyunsaturated spreads. Eating fresh fruit at least once a day in surruner.

Eating chips not more than twice a week. Eating other fried food not more than twice a week. Not eating sweets/biscuits every day.

Eating saladdraw vegetables at least oncelweek in winter.

four hour recalls tend to under-estimate intakes, and food frequency questionnaires need to be validated against quantitative measures as giving reliable estimates of the specific nutrients of interest. The validity of the frequency questions depends on whether the particular foods included in the questionnaire comprise the main sources of the nutrients of interest, or otherwise act as a proxy indicator for that nutrient.

Food frequency methods are therefore the most practical for use at local level and in large health surveys which permit analysis by sub- groups. This suggests that dietary targets should be based on foods rather than nutrients. Food group targets are more easily understood than nutrient targets and are more in line with dietary advice and with local campaigns, which are usually about food selection to effect changes in fat and fibre intake.

The analysis of the NW Thames Regional Health Survey food frequency questionnaire provided valuable information about the variation in dietary intake between different groups and associations with other lifestyle factors. These have important implications for food policy and targeted health education, as does the poor dietary pattern observed in the separate study of the homeless. Certain foods such as fruit, type of milk, and fish consistently differ- entiated between socio-demographic sub- groups and could be usefully included in future dietary surveys to detect differences in dietary habits amongst such groups in the population. Identification of other such key indicators could simplify the questionnaires necessary to monitor specific dietary change.

As it is the overall diet that is important to health rather than the intake of specific foods it was felt that a composite indicator of diet would distinguish more readily healthy and less healthy eating patterns. On this basis, as a methodological exercise, we selected specific

food groups from the survey questions that could be scored according to current concepts with respect to the effects on health of saturated fats, complex carbohydrates and anti-oxidants. Our initial selection included 14 items and as subsequent analysis with a greater range of food items did not improve the discrimination between population groups, this initial selection was retained.

Few authors have used frequency questionnaires to assess the quality of the diet as a whole despite the usefulness of a global assessment. This may be related to the problem of validating food frequency scores against measured nutrient intake. However, an indication of validity for the scoring method used in this study was the differentiation between both socio-economic group and lifestyle factors which was consistent with the expected variations in diet. A ‘healthy’ diet was more common in women, non-manual groups, those with higher educational achievement, non smokers and those taking vigorous exercise. Anderson and Hunt (1992) found similar associations with a healthy diet based on separate composite scores for fat and fibre, derived from six questions in each category. In her dietary index, Blaxter (1990) had seven food items with food frequencies which were arbitrarily assigned as healthy or nlDt (Table 7). Individuals achieving five or more (of these were (arbitrarj ly) judged to have a ‘healthy’ diet.

The NW ‘Thames food frequency questionnaire only permitted limited dietary analysis. It was not possible to directly compare food intakes with all the national FPHM targets, as some of the survey questions were not worded in the same way or they were not included at all. Althoughthe NW Thames Survey was designed before the publication of the FPHM document, this problem does raise the important issue of having nationally agreed food targets which relate to standard questions. Correct phrasing


so that they remain valid. The definition of national food frequency targets must be based on examination of the relationship between frequency of consumption of specific food groups and levels of intake of key nutrients or constituents, such as saturated fat.

At a national level, food consumption is currently monitored in the UK on an annual basis through the National Food Survey, at a household rather than individual level (MAFF, 1990). The survey records all food expenditure of the househoId but, until 1992, none of food consumed outside the home. Until recently therefore, total household consumption of food was under-estimated. In addition, regular surveys of individual intakes for specific age groups in the population are conducted in rotation every few years. The latest adult survey was conducted in 1986-1987 (Gregory et al., 1990). However, neither of these types of survey included food frequency questions to enable the validation and comparison of such questions at a national level. We suggest that further studies of individual intakes should incorporate food frequency questions so that a validation against quantifiable data can be undertaken. Limited analysis may be possible on the existing survey data as it was based on 7-day weighed intakes.

To maximize the usefulness of food frequency questionnaires for geographical and secular comparisons they should comprise core questions in an agreed standard format, supplemented with additional questions as appropriate, to reflect dietary diversity, e.g. of ethnic groups. This permits reliable comparisons at local, regional and national levels, yet allows for flexibility.

In conclusion, the food frequency method is relatively simple and cheap to undertake and could be used for monitoring dietary change by health agencies. However, there need to be nationally agreed standard questions for the whole population, with ethnic variants, incor- porated into local as well as regional and national surveys. The choice of questions needs to be validated against reliable methodologies, and targets based on these chosen food questions set in context of nutritional goals.

of such questions is essential to ensure valid responses. The NW Thames Survey, for example, combined h i t and fruit juice, it had no seasonal element and it excluded dried and tinned fruit. The questions on vegetables and bread were not sensitive to socio-economic or lifestyle factors, because they were non- specific. The vegetable question, for example, included potatoes, a staple food, and the ‘bread’ question included all types.

The survey dietary questions did attempt to cover ethnic diversity, given the large ethnic minority populations in the NW Thames Re- gion. This is clearly important but in conse- quence the bread question was different from the FPHM target because of the inclusion of chapattis. In addition, the use of fats was not well specified for Asians as they use ghee for frying although they may not regard it as a source of fat or even as frying. A more appropriate question to determine the amount of fried food consumption of Asians would be the quantity of cooking oil or ghee used in a certain period. An alternative approach to examining diet within ethnic groups would be to undertake ethnic-specific surveys,

The food frequency format did not contain any quantification of portion size. Correction for energy intake is therefore not possible, with the result that there could be misclassification of the ‘healthiness’ of the diet compared to the true nutrient intake. This may partly explain why the overweight and obese had a ‘healthy’ diet in this study. It is possible that the obese had adopted a healthy pattern of food intake to lose or maintain weight, or that their responses reflect what they feel they should eat rather than their true intake. It has been established that the reporting of consumption is less accu- rate in the overweight and obese (Prentice et a]., 1986).

What are the implications of OUT study? We suggest that there is a need for nationally agreed food targets based on selected food groups with a specified frequency of consumption and indications of portion size. These would pro- vide unambiguous and clearly understood messages for health education and allow for monitoring of changes in consumption by food frequency questionnaires.

The food groups included should be valid indicators of key nutrient intake and minimally affected by the changing nature of food retailing

Acknowledgements

We would like to thank Social and Community


Planning Research, its field workers and the respondents for their involvement i n the North West Thames Regional Health Survey.

References

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Blaxter, M. (1990) Health and Lifestyles. London: Tavistock Routledge.

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MS accepted December 1994

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