Content uploaded by Katherine Younger
Author content
All content in this area was uploaded by Katherine Younger on Jul 21, 2014
Content may be subject to copyright.
Socio-economic differences in food group and nutrient intakes among young
women in Ireland
Daniel M. A. McCartney*, Katherine M. Younger, Joanne Walsh, Marie O’Neill, Claire Sheridan
and John M. Kearney
School of Biological Sciences, Dublin Institute of Technology, Kevin Street, Dublin 8, Republic of Ireland
(Submitted 14 September 2012 – Final revision received 27 March 2013 – Accepted 8 April 2013 – First published online 31 May 2013)
Abstract
The present study aimed to investigate socio-economic disparities in food and nutrient intakes among young Irish women. A total of 221
disadvantaged and seventy-four non-disadvantaged women aged 18–35 years were recruited. Diet was assessed using a diet history pro-
tocol. Of the total population, 153 disadvantaged and sixty-three non-disadvantaged women were classified as plausible dietary reporters.
Food group intakes, nutrient intakes and dietary vitamin and mineral concentrations per MJ of energy consumed were compared between
the disadvantaged and non-disadvantaged populations, as was compliance with dietary fibre, macronutrient and micronutrient intake
guidelines. The disadvantaged women had lower intakes than the non-disadvantaged women of fruit, vegetables, fish, breakfast cereals,
low-fat milk and wholemeal bread (all P,0·001), yogurt (P¼0·001), low-fat spread (P¼0·002) and fresh meat (P¼0·003). They also had
higher intakes of butter, processed red meats, white bread, sugar-sweetened beverages, fried potatoes and potato-based snacks (all
P,0·001) and full-fat milk (P¼0·014). Nutritionally, the disadvantaged women had higher fat, saturated fat and refined sugar intakes;
lower dietary fibre, vitamin and mineral intakes; and lower dietary vitamin and mineral densities per MJ than their more advantaged
peers. Non-achievement of carbohydrate (P¼0·017), fat (P,0·001), saturated fat (P,0·001), refined sugar (P,0·001), folate (P¼0·050),
vitamin C (P,0·001), vitamin D (P¼0·047) and Ca (P¼0·019) recommendations was more prevalent among the disadvantaged women.
Both groups showed poor compliance with Fe and Na guidelines. We conclude that the nutritional deficits present among these socially
disadvantaged women are significant, but may be potentially ameliorated by targeted food-based interventions.
Key words: Nutrients: Dietary intakes: Low socio-economic status: Irish women
There is a substantial body of research demonstrating both
increased morbidity
(1,2)
and premature mortality
(3)
among
Irish adults of low socio-economic status (SES), in comparison
with their more advantaged peers. International research has
suggested that such health inequalities may be at least partially
attributable to differences in food and nutrient intakes across
the socio-economic spectrum
(4)
. However, while literature
describing dietary and nutritional inequalities among Irish
adults has been published
(5 – 7)
, it has been a challenge for
the larger national nutrition surveys conducted in recent
years
(8 – 11)
to capture the food and nutrient intake patterns
of the very poorest members of Irish society. The methodo-
logical constraints that limit the recruitment of such subjects
in national nutrition surveys have highlighted the need for
focused, dedicated research in this area. For example, the
Low Income Diet and Nutrition Survey (LIDNS) in the UK
(12)
was commissioned specifically to examine food and nutrient
intake patterns among a carefully selected population of
low-SES consumers in that country.
Research in many countries has highlighted profound
differences in food group and nutrient intakes across the
socio-economic spectrum. For example, lower consumption
of fruit and vegetables has been consistently demonstrated
among low-SES groups in the UK
(13)
, Europe
(14)
, Norway
(15)
,
the Netherlands
(16)
, Denmark
(17)
, Australia
(18)
, New Zealand
(19)
and the USA
(20)
. Similarly, lower consumption of ready-to-eat
breakfast cereals has been reported among low-SES groups in
the UK
(21)
, France
(22)
, Spain
(23)
, the USA
(24)
and Australia
(25)
,
while lower wholemeal bread intakes have been observed
among low-SES men and women in the UK
(26)
. Lower fish
intakes have also been observed among low-SES respondents
in Switzerland
(27)
, Italy
(28)
and Spain
(23)
, while higher intakes
of processed red meats
(29 – 31)
, chips and fried potatoes
(32,33)
,
potato-based snacks
(32,34)
, white bread
(32,35)
, and sugar-
sweetened foods and drinks
(26,36)
have also been widely
reported among these socially disadvantaged groups.
These dietary differences have been shown to lead to
unfavourable macronutrient
(4,35)
and micronutrient
(18)
intakes
*Corresponding author: D. M. A. McCartney, fax þ353 1 402 4995, email Daniel.McCartney@dit.ie
Abbreviations: EAR, estimated average requirement; ED, electoral district; EI, energy intake; NMES, non-milk extrinsic sugar; SES, socio-economic status.
British Journal of Nutrition (2013), 110, 2084–2097 doi:10.1017/S0007114513001463
qThe Authors 2013
British Journal of Nutrition
among low-SES groups. The present study aimed to determine
whether similar socio-economic differences in diet occur
among young Irish women and, if so, whether they are
associated with poorer nutritional intakes among these
low-SES women.
Methods
Data collection
A total of 295 women aged 18–35 years were recruited
from areas around Dublin using a de novo socio-economic
sampling frame that ranked electoral districts (ED) based
on a composite score for six socio-economic indicators. In
order to construct this sampling frame, small-area population
statistics were derived from national census data, enabling
prevalence estimates for unemployment, low occupational
social class, low socio-economic group, low formal education,
single-parent household structure and local authority accom-
modation to be calculated for each of the 353 ED in Greater
Dublin. Each of the ED was then ranked from 1 to 353 for
each of these socio-economic parameters. For each ED, the
ranking scores for each of these six socio-economic para-
meters were multiplied together. The product of the ranking
scores for each ED was finally used to score each ED from 1
to 353, with larger numbers indicating a greater level of overall
disadvantage. Using this system, the most disadvantaged quin-
tile of ED (i.e. the seventy-one ED with the largest scores) was
identified. From these seventy-one ED, areas were identified
in North, South, West and City Centre Dublin. Within these
areas (four in North Dublin, three in South Dublin, four in
West Dublin and four in the City Centre), fifteen recruitment
sites (e.g. local community development schemes, training
centres, cre
`ches) were selected. Ultimately, 221 disadvantaged
women were recruited from these sites. A reference popu-
lation of seventy-four women was recruited from areas
in the top four quintiles to establish a non-disadvantaged
comparison group that would be broadly representative of
the wider ‘non-poor’ female population in this age group.
Several indices were subsequently used to assess the SES
of each respondent. These included educational attainment
and occupational social class, in addition to material indices
of disadvantage including ‘at risk of poverty’ status, relative
deprivation and consistent poverty. At risk of poverty (relative
income poverty) was calculated by comparing equivalised
household income against the 60 % median income
threshold
(37)
. Relative deprivation was assessed by determin-
ing whether the respondents had experienced the enforced
absence (due to financial constraint) of one or more basic
necessities from a list of eight
(37)
, while consistent poverty
was identified if a respondent reported being ‘at risk of
poverty’ in addition to experiencing enforced absence of
one or more of the eight basic markers of deprivation
(37)
.
The derivation of relative deprivation is now defined as the
enforced absence of two or more basic necessities from a
revised list of eleven
(38)
; however, this itinerary had not
been finalised or implemented during the design or early
data collection phases of the present study.
Habitual dietary intake was assessed by an interviewer-
assisted questionnaire in a group setting, using a semi-
structured weekly diet history according to a previously
described protocol
(39)
. A facilitator outlined each mealtime
and inter-meal period to the group in turn, with each respon-
dent recording each component of her typical weekly menu at
the appropriate location on her diet history questionnaire.
Thereafter, three fieldworkers (one dietitian and two final-
year nutrition and dietetics students) conducted face-to-face
interviews with each respondent in the group to elicit the
types of foods consumed at these meal- and snack times
and the frequency with which these foods were consumed.
Portion sizes for each food were quantified using verbal
descriptions from the respondents expressed in terms of typi-
cal household measures and were further explicated using
an atlas of food portion sizes
(40)
and an itinerary of average
portion sizes and typical weights of common foods as
required
(41)
. Information regarding the type, brand, frequency
and dosage of any dietary supplements taken was also sought.
A new food code was created for each of these supplements in
a nutrient analysis package (Weighed Intake analysis Software
Package (WISP) version 3.0; qTinuviel Software Limited,
2005) using nutritional composition data derived from the
manufacturer or taken directly from the nutrition label on
the product. Where the brand was unknown to the respon-
dent, the food code for the most commonly cited brand of
that supplement type after all dietary records had been
entered was used as the default.
Quantitative demographic, ecological, socio-economic,
health behavioural and attitudinal data were simultaneously
collected by a questionnaire at this time. Body weight was
also measured to the nearest 0·2 kg using a Seca Compact
Digital Floor Scale III, model 888 (Seca Limited), while
height was measured to the nearest 0·5 cm using a collapsible
‘Leicester Height Measure’ stadiometer (CMS Weighing Equip-
ment). Waist circumference was measured on the left hand
side around the umbilicus, at the mid-point between the
lower rib margin and the supra-iliac crest on the mid-axillary
line. These measurements were taken to the nearest 0·5 cm
with a Seca Circumference Measuring Tape model 200
(Seca Limited), held snugly against the skin as described in
the North/South Ireland Food Consumption Survey
(NSIFCS)
(42)
. Overall, anthropometric indices (weight, height
and waist circumference) were measured for 292 of the 295
respondents.
Data management
For each respondent, the total weekly intake for each food in
the diet history (i.e. the g per portion multiplied by the
number of servings per week) and each dietary supplement
was entered into a MS Excel
w
spreadsheet. Using the formula
function, the weekly intake data were then divided by 7 to
yield an average daily intake (i.e. amount per d) for all of
the foods and supplements recorded in the respondent’s diet
history. These average daily intakes for each food and sup-
plement were subsequently entered into WISP version 3.0,
which generated food group and nutrient intake estimates
Social variation in Irish women’s diets 2085
British Journal of Nutrition
based on McCance and Widdowson’s The Composition of
Foods 6th Edition
(43)
and Supplements.
WISP version 3.0 generated seventeen default food groups,
from which ten broad food groups were initially created.
In order to assess differences in the intakes of foods within
these groups, six of the ten food groups (milk and dairy
foods, starchy carbohydrates, meat and meat products,
beverages, potatoes and fish) were disaggregated manually.
This was done by examining all of the 295 diet records from
WISP, highlighting the foods in each of these six broad food
groups, and then separating the foods in each group into
their constituent subgroups. This created twenty-three new
subgroups from the six original larger food groups, including
categories such as low-fat milk and full-fat milk; white,
brown and wholemeal breads; and sugar-sweetened and
non-sugar-sweetened beverages, the latter of which included
all teas, coffees, squashes and waters. These disaggregated
food group data were added to the original food and nutrient
intake data from WISP, and these were then merged with
the demographic, ecological, socio-economic, health beha-
vioural and attitudinal data, creating a final relational database
that covered all of these parameters for all the 295 res-
pondents. The full database was exported to a statistical
software package (SPSS version 16.0; SPSS, Inc., 2007) for
subsequent analyses.
To address the issue of dietary misreporting, the 292
respondents for whom body weight data were available
were stratified into one of four relative physical activity
categories, based on habitual vigorous activity levels and esti-
mated daily sitting times. In order to do this, the respondents
were asked questions adapted from the short version of the
International Physical Activity Questionnaire (IPAQ)
(44)
about
how long they were seated for on a typical weekday and a
typical weekend day, and about their duration of vigorous
physical activity on a typical weekday and a typical weekend
day. Weighted average daily durations spent at each of these
activity levels were calculated from these data, permitting
the respondents to be classified into one of three tertiles for
sitting time (low, moderate or high) and as ‘exercisers’ or
‘non-exercisers’ based on their participation or non-
participation in vigorous activity. Those in the highest tertile
for sitting time (i.e. the least active) were attributed a ‘sedentar-
ism’ score of 1, with those in the moderate category receiving a
score of 2 and those in the lowest category (i.e. the most active)
receiving a score of 3. Non-exercisers who did not participate in
vigorous activity were similarly given a score of 1, while the
‘exercisers’ were allocated a score of 2. The respondents then
had their sedentarism (sitting) scores and their vigorous activity
scores multiplied together, generating overall relative physical
activity scores from 1 (least active) to 6 (most active). These
overall scores were finally used to classify the 292 respondents
into four relative activity categories: low (n64); low to
moderate (n96); moderate to high (n66) and high (n66).
Based on published estimates of typical physical activity
levels among women
(45 – 47)
and considering the demonstrably
low overall levels of vigorous activity in our study population
(two-thirds of participants performed no vigorous activity at
all), this cohort was deemed to have habitual levels that
lay at the lower reaches of the documented physical activity
spectrum for young women. Accordingly, the four relative
physical activity categories were ‘mapped’ to a series of physi-
cal activity level estimates (1·40, 1·48, 1·56 and 1·64) according
to previously described protocols
(48)
. Lower physical
activity level ‘cut-off’ thresholds for the respondents in each
physical activity level category were then calculated
(48)
.
Those whose energy intake (EI) divided by their calculated
BMR (EI/BMR) fell below the calculated cut-off threshold
for their category were classified as dietary ‘under-reporters’
(n53), while in all categories, those with an EI/BMR greater
than 2·5 were classified as dietary ‘over-reporters’ (n23).
These under-reporters and over-reporters were excluded
from further dietary and nutrient analyses.
Statistical analysis
Compliance with dietary fibre, macronutrient, vitamin and
mineral intake guidelines was compared between the
disadvantaged and non-disadvantaged groups, using cross-
tabulation with
x
2
analyses and reporting Yates’ continuity
correction for all 2 £2 analyses. For the macronutrients, this
first necessitated that their percentage contribution to food EI
(i.e. to EI after the contribution from alcohol had been excluded)
be derived. Notwithstanding the fact that these are population
guidelines, the respondents were categorised as ‘compliers’ if
their intake exceeded the population guideline (in the case of
carbohydrate and protein) or fell below the population guide-
line (in the case of total fat, saturated fat and non-milk extrinsic
sugars (NMES)). ‘Non-compliers’ for the various macronutrients
were those whose percentage of food energy fell below the
population guideline in the case of carbohydrate and protein
and those whose percentage of food energy fell above the popu-
lation guideline in the case of total fat, saturated fat and NMES. In
addition to macronutrient compliance, dichotomous categorical
variables were also created to compare compliance with dietary
fibre, NSP, alcohol, vitamin and mineral intake guidelines
between the groups, again by means of cross-tabulation with
x
2
analyses. Compliance thresholds for dietary fibre and NSP
were defined according to the WHO/Food and Agriculture
Organization
(49)
and UK Department of Health
(50)
guidelines,
respectively. Compliance thresholds for macronutrient intakes
were defined according to the UK percentage food energy
guidelines
(50)
and Irish alcohol unit intake guidelines
(51)
. For
each vitamin and mineral, the compliance threshold was
defined as the Irish estimated average requirement (EAR)
(52)
,
with the exception of Na, where in the absence of an EAR, the
compliance threshold was defined at the population maximum
recommended intake
(53)
.
All of the food group intakes were non-normally distri-
buted, and intake comparisons between the disadvantaged and
non-disadvantaged populations were made by non-parametric
Mann–Whitney Utests. The percentage of consumers for each
food group was also compared between the disadvantaged
and non-disadvantaged populations. This was done by first
classifying each respondent as a consumer (intake .0 g/d)
or a non-consumer (intake ¼0 g/d) for each of the diffe-
rent food groups of interest. These dichotomous categorical
D. M. A. McCartney et al.2086
British Journal of Nutrition
variables were then individually cross-tabulated against
disadvantaged/non-disadvantaged status, with
x
2
analyses and
Yates’ continuity correction being applied in each case to
determine the statistical significance of their associations.
Differences in energy, dietary fibre and macronutrient
intakes between the disadvantaged and non-disadvantaged
populations were analysed using parametric independent
samples ttests for those whose intakes were normally
distributed (energy, carbohydrate, total fat, saturated fat,
monounsaturated fat, polyunsaturated fat and protein) and
by non-parametric Mann–Whitney Utests for those whose
intakes were non-normally distributed (dietary fibre, NSP,
NMES, cholesterol and alcohol). Macronutrient intakes were
compared between the disadvantaged and non-disadvantaged
populations with the contribution of alcohol excluded
(i.e. comparison of percentages of energy from food only).
This was done to ensure that high alcohol intakes did not
artifactually reduce the calculated percentage of energy
derived from fat, saturated fat or NMES, leading to erroneous
conclusions about the overall quality of such diets.
Differences in vitamin and mineral intakes and dietary
micronutrient density between the disadvantaged and non-
disadvantaged groups were similarly assessed by parametric
independent samples ttests for normally distributed
parameters (Na, Zn and P) and by non-parametric Mann–
Whitney Utests for non-normally distributed parameters
(vitamins A, B
1
,B
2
,B
5
,B
6
,B
12
, C, D and E, niacin, folate, car-
otene, K, Fe, Ca, Mg and Cu).
Vitamin and mineral intake and compliance analyses
were performed with dietary supplements included to com-
pare total intakes and adequacy of these nutrients according
to SES. Vitamin and mineral density analyses, however,
were performed with supplements excluded to assess whether
there were differences in the dietary concentration of these
micronutrients between the two groups.
The two-sided significance of all results was assessed at the
P,0·05 level.
The present study was conducted according to the guide-
lines laid down in the Declaration of Helsinki, and all
procedures involving human subjects were approved by
the Dublin Institute of Technology Ethics Committee, 2005.
Written informed consent was obtained from all subjects.
Results
Sample population
The disadvantaged population differed significantly from the
non-disadvantaged population in terms of both the social
(occupational social class, education and single-adult family
structure) and material (income, deprivation and consistent
poverty) indices of disadvantage used by the Irish Central
Statistics Office
(37)
(Table 1). The disadvantaged women in
the present study also had rates of relative income poverty or
‘at risk of poverty’ (51·1 %), relative deprivation (40·5 %) and
consistent poverty (25 %) that were substantially greater than
those observed in the national population (16·5, 24·4 and
5·1 %, respectively). The disadvantaged respondents’ poverty
rates were also considerably higher than those of identifiably
vulnerable population groups in Irish national surveys, such
as those in lone-parent households (37·6 % relative income
poverty rate) and the unemployed (17·5 % consistent poverty
rate)
(54)
. While all the respondents were aged 18– 35 years,
the ‘disadvantaged’ sample was younger than the reference
‘non-disadvantaged’ peer group (25·1 (SD 5·7) v. 26·9 (SD 3·9)
years, P¼0·011). Overall, 90·7 % of the overall population was
Caucasian Irish, with 3·6 % from other EU member states,
3·4 % of Black African ethnicity, 1·7 % classified as travellers
and 0·6 % from Asia. This compared to the most recent National
Census data at the time (2006), which classified 87·4 % of the
population as Irish, 0·5 % as Irish Travellers, 6·9 % as ‘Any
Table 1. Socio-economic characteristics of the full study population (n295)
Definition
Percentage of disadvantaged
population (n221)
Percentage of advantaged
population (n74)
Disadvantage Recruited from a site within the lowest quintile of ED 100·0 0·0
Low social class Social class: (4) skilled manual, (5) semi-skilled or
(6) unskilled
63·3 0·0
Low socio-economic
group
Socio-economic group: (E) manual skilled,
(F) semi-skilled or (G) unskilled
43·4 0·0
Low education None, primary or intermediate education 54·8 0·0
Early school leaving Left school aged 16 years or under 46·6 2·7
Relative income
poverty*
Equivalised income less than 60 % of the median income
(i.e. ,e208·71/week)
51·1 2·7
Relative deprivation† Lacking one or more of the eight basic deprivation indicators 40·5 4·1
Consistent poverty Equivalised income ,e208·71/week and lack $1 of the
eight basic deprivation indicators
25·0 1·4
Benefit entitlement Entitled to social welfare payments 63·6 10·8
Medical card status Entitled to a medical card 69·2 1·4
Single-adult family unit Family unit comprising a single adult and one or more children 44·8 0·0
ED, electoral district.
* Equivalised income calculated on 1·0 (first adult), 0·5 (second and subsequent adults) and 0·3 (children under 14 years) scales used by the Central Statistics Office,
Ireland
(37)
.
† The eight ‘basic necessities’ selected by the Central Statistics Office
(37)
to describe relative deprivation in Ireland are not having new, but second-hand clothes, a meal with
meat, chicken or fish every second day, a warm, water-proof coat, two pairs of strong shoes, and a roast or its equivalent once per week or having debt problems arising
from normal living expenses (or availing of charity), a day in the last 2 weeks without a substantial meal and needing to go without eating during the last year through lack
of money.
Social variation in Irish women’s diets 2087
British Journal of Nutrition
other White background’, 1·0 % as African, 0·1 % as ‘Any other
Black background’, 0·4 % as Chinese, 0·9 % as ‘Any other
Asian background’ and 1·1 % as ‘Other including Mixed
background’
(55)
.
Food group intakes
Median intakes of fruit and fruit juices, vegetables, breakfast
cereals, low-fat milk, yogurt, low-fat spread, poultry, whole-
meal bread, non-sugar-sweetened beverages, fresh fish and
tinned fish were all significantly lower among the dis-
advantaged women than among their more affluent peers.
Conversely, median intakes of full-fat milk, butter, red
meats, processed red meats, white bread, sugar-sweetened
beverages, fried and roasted potatoes and potato-based
snacks were all significantly higher among the disadvantaged
women than among the non-disadvantaged women (Table 2).
For fruit, vegetables, breakfast cereals, low-fat milk, butter,
processed red meats, wholemeal bread, sugar-sweetened
beverages, fried and roasted potatoes, potato-based snack
foods and fish, two- to threefold disparities in intake
(and considerably more in some cases) were observed,
indicating profound dietary differences between the two
groups (Table 2).
Most of these discrepancies in food group intake were
mediated by differences in both ‘intake level’ (i.e. portion
size and frequency of consumption) and ‘prevalence of
consumption’ (i.e. the percentage of consumers within the
respective populations). For example, there were signifi-
cantly fewer consumers of fruit juices (69 v. 94 %), breakfast
cereals (58 v. 86 %), low-fat milk (24 v. 64 %), oily fish
(4 v. 36 %) and tinned fish (15 v. 47 %) (all P,0·001), yogurt
(36 v. 61 %, P¼0·001), low-fat spread (20 v. 39 %, P¼0·007),
poultry (87 v. 98 %, P¼0·018), wholemeal bread (49 v. 73 %,
P¼0·002), non-sugar-sweetened beverages (82 v. 95 %,
P¼0·020) and fresh fish (12 v. 31 %, P¼0·001) in the dis-
advantaged group than in the non-disadvantaged group. A
higher percentage of disadvantaged than non-disadvantaged
women consumed full-fat milk (80 v. 58 %, P¼0·001), butter
(80 v. 59 %, P¼0·002), white bread (88 v. 64 %, P,0·001),
sugar-sweetened beverages (82 v. 61 %, P¼0·002), fried and
roasted potatoes (88 v. 73 %, P¼0·020) and potato-based
snacks (71 v. 38 %, P,0·001).
However, when non-consumers were removed, consider-
able differences in intake level between the disadvantaged
and non-disadvantaged consumers persisted for several food
groups. Disadvantaged consumers had higher daily intakes
of butter (14 v. 7g, P¼0·002), red meats (47 v. 39 g,
P¼0·002), processed red meats (40 v. 20 g, P,0·001), white
bread (78 v. 31 g, P,0·001), sugar-sweetened beverages
(601 v. 200 g, P,0·001), fried and roasted potatoes (89 v.
30 g, P,0·001) and potato-based snacks (17 v. 9g,
P,0·001); and lower daily intakes of fruit and fruit juices
(145 v. 214 g, P¼0·006), breakfast cereals (17 v. 30 g,
Table 2. Differences in food group consumption between the disadvantaged and non-disadvantaged respondents* (n216)
Disadvantaged (n153) Non-disadvantaged (n63)
Food groups Disaggregated food groups Amount (g/d)† IQR (g) Amount (g/d)† IQR (g) P
Fruit and vegetables Fruit and fruit juices 74 196 200 219 ,0·001
Vegetables 72 75 194 116 ,0·001
Breakfast cereals All types of cereals 4 18 29 44 ,0·001
Dairy products Full-fat milk 96 140 49 150 0·014
Low-fat milk 0 0 63 154 ,0·001
Cheese 6 20 13 21 0·057
Yogurt 0 36 20 90 0·001
Butter 11 18 4 8 ,0·001
Non-butter spread 0 0 0 0 0·330
Low-fat spread 0 0 0 8 0·002
Meat and meat products Red meats 46 41 33 35 0·003
Poultry 40 48 63 52 ,0·001
Processed red meats 37 45 17 22 ,0·001
Processed poultry 0 0 0 0 0·164
Starchy carbohydrates White bread 71 60 12 48 ,0·001
Wholemeal bread 0 41 42·5 93 ,0·001
Sweet foods and drinks Sweet foods and confectionery 67 92 64 52 0·498
Sugar-sweetened beverages 428 790 71 264 ,0·001
Non-sugar-sweetened beverages 520 880 1061 877 ,0·001
Potatoes Plain 56 60 50 50 0·135
Fried/roasted 74 88 24 47 ,0·001
Potato-based snacks 11 28 0 6 ,0·001
Fish and fish products Overall fish 0 21 26 36 ,0·001
Fresh 0 0 0 14 0·001
Oily 0 0 0 17 0·960
Crumbed/battered 0 0 0 0 ,0·001
Tinned 0 0 0 24 ,0·001
IQR, interquartile range.
* All food group intakes were non-normally distributed, with intake comparisons between the disadvantaged and non-disadvantaged populations being performed
using non-parametric Mann–Whitney Utests.
† Median values used for comparison.
D. M. A. McCartney et al.2088
British Journal of Nutrition
P,0·001), poultry (43 v. 66 g, P¼0·004), wholemeal bread
(41 v. 71 g, P¼0·012) and non-sugar-sweetened beverages
(671 v. 1074 g, P¼0·001) than the non-disadvantaged women
who consumed these food groups.
Nutrient compliance
Energy, fibre and macronutrients. Non-compliance with
macronutrient intake guidelines occurred more often among
the disadvantaged women, who were significantly more
likely than their non-disadvantaged peers to fall short of
the recommended carbohydrate intake and to exceed the
recommended intake guidelines for fat, saturated fat and
NMES. Additionally, three times more disadvantaged than
non-disadvantaged respondents exceeded the recommended
300 mg of dietary cholesterol per d, while a considerable
majority of both groups failed to meet dietary fibre and n-3
fatty acid guidelines (Table 3).
Micronutrients. While differences in vitamin and mineral
compliance were less pronounced between the two groups,
the disadvantaged women were significantly more likely
to fall short of the EAR for folate, vitamin C, vitamin D
and Ca (Table 3). In the case of folate, while 35·3 % of the
disadvantaged women (and 20·6 % of the non-disadvantaged
women) failed to achieve the EAR of 230 mg/d
(52)
, just
0·5 % of the disadvantaged women and only 5·4 % of the
non-disadvantaged women took a 400 mg/d supplement of
folic acid as recommended for all women of child-bearing
potential
(52)
. A significant percentage of both populations
also failed to achieve the requisite intake of vitamin C,
vitamin A, Fe, Ca and especially vitamin D, while a majority
of both groups exceeded the recommended intake of Na.
Nutrient intakes
Energy, dietary fibre and macronutrients. Mean and median
energy consumption and dietary fibre and macronutrient
intakes differed considerably between the two groups, when
the contribution from alcohol was excluded (Table 4).
Mean daily EI was 1·25 MJ greater in the disadvantaged
group than in the non-disadvantaged group (1·46 MJ with
alcohol included). Dietary fibre, NSP, carbohydrate and pro-
tein intakes were lower, and fat, saturated fat, NMES and
cholesterol intakes were higher, among the disadvantaged
respondents in comparison with their more affluent peers.
Micronutrients. Mean and median vitamin and mineral
intakes differed substantially between the disadvantaged and
non-disadvantaged women, with the contribution from
supplements both included and excluded. With supplements
included, median intakes of vitamin B
2
, niacin, vitamin B
5
,
vitamin B
6
, folate, vitamin C, carotene, vitamin D and
vitamin E were all lower among the disadvantaged women in
comparison with the non-disadvantaged women (Table 5).
Regarding mineral consumption, median intakes of Mg were
Table 3. Differences in achievement of the recommended dietary fibre, macronutrient, cholesterol, alcohol, vitamin and mineral intakes between the
disadvantaged and non-disadvantaged respondents (n216)
Percentage of individuals falling
outside the recommended guidelines
Nutrients including supplements Population guideline* Disadvantaged (n153) Advantaged (n63) P
Dietary fibre (Southgate) (g/d) .25 g/d
(49)
99·3 98·4 1·000
% Food energy from carbohydrate .50% food energy
(50)
49·0 30·2 0·017
% Food energy from non-milk extrinsic sugars ,11 % food energy
(50)
59·5 30·2 ,0·001
% Food energy from fat ,35% food energy
(50)
73·9 34·9 ,0·001
% Food energy from saturated fat ,11 % food energy
(50)
88·9 65·1 ,0·001
Cholesterol (mg/d) ,300 mg/d 37·9 12·7 ,0·001
Alcohol (units/week) ,14 units (140 ml ethanol)/week
(51)
37·7 25·4 0·114
Vitamin B
1
(mg/d)† .0·6 mg/d
(52)
1·3 0·0 0·896
Vitamin B
2
(mg/d) .1·1 mg/d
(52)
15·7 7·9 0·194
Niacin (mg/d)‡ .1·3 mg/MJ per d
(52)
6·5 0·0 0·085
Vitamin B
6
(mg/g protein per d)§ .13 mg/g protein per d
(52)
0·7 0·0 1·000
Vitamin B
12
(mg/d) .1·0 mg/d
(52)
0·0 0·0 1·000
Folate (mg/d) .230 mg/d
(52)
35·3 20·6 0·050
Vitamin C (mg/d) .46 mg/d
(52)
30·7 6·3 ,0·001
Vitamin A (mg/d) .400 mg/d
(52)
54·2 65·1 0·190
Vitamin D (mg/d)k.5mg/d 80·4 66·7 0·047
n-3 PUFA (mg/d) .0·2 % dietary energy
(50)
85·0 76·2 0·179
Na (mg/d){,2400 mg/d
(53)
79·1 68·3 0·129
Fe (mg/d) .10·8mg/d
(52)
49·7 38·1 0·161
Ca (mg/d) .615 mg/d
(52)
24·8 9·5 0·019
Zn (mg/d) .5·5mg/d
(52)
8·5 3·2 0·270
Cu (mg/d) .0·8 mg/d
(52)
7·8 19·0 0·032
P (mg/d) .400 mg/d
(52)
0·0 0·0 1·000
* Population intake guidelines defined in terms of the WHO/Food and Agriculture Organization guidelines on dietary fibre
(49)
, UK Department of Health guidelines on
macronutrient intake
(50)
, Irish alcohol intake guidelines
(51)
and current Irish estimated average requirements (EAR)
(52)
.
† EAR for vitamin B
1
set at 72 mg/MJ per d and assumed at 0·6 mg/d for a daily energy intake of approximately 8·4 MJ.
‡ EAR for niacin set at 1·3 mg/MJ per d and assumed at 11mg/d for a daily energy intake of approximately 8·4 MJ.
§ EAR for vitamin B
6
set at 13 mg/g protein per d and assumed at 1·1 mg/d for a daily protein intake of approximately 85g.
kEAR for vitamin D assumed at 5 mg/d (i.e. half of the maximum of the current RDA).
{Target maximum recommended intake set at 2400 mg/d by the Food Safety Authority of Ireland
(53)
.
Social variation in Irish women’s diets 2089
British Journal of Nutrition
lower, while mean Na intakes were higher among the disad-
vantaged women.
The use of dietary supplements was more prevalent
among the non-disadvantaged women than among the disad-
vantaged women (48·4 v. 30·5 %, P¼0·011), with multivitamins
and multiminerals, cod-liver oil, vitamin C, fish oils and Fe the
most commonly used preparations. Even with the contribution
from these supplements excluded, however, several micro-
nutrient differences persisted between the two groups. For
example, mean niacin intakes (20·3 v. 23·9 mg/d, P¼0·001)
and median vitamin C (59 v. 112 mg/d, P,0·001) and carotene
(2528 v. 4482 mg/d, P,0·001) intakes remained significantly
lower among the disadvantaged women, while there was
also a tendency towards lower folate intakes (252 v. 273 mg/d,
P¼0·060) in this group. Notwithstanding the fact that
both group means fell below the 400 mg/d EAR, vitamin A
intakes excluding the contribution from supplements were
significantly higher in the disadvantaged group. For mineral
intakes, mean Fe (10·2 v. 11·4 mg/d, P¼0·011) and median
Mg (250 v. 259 mg/d, P¼0·035) intakes were significantly
lower among the disadvantaged women than among their
non-disadvantaged peers, while mean Na intakes (3178
v. 2716 mg/d, P,0·001) remained significantly higher among
the poorer women.
Nutrient density
Micronutrients. Further analyses examined the vitamin and
mineral densities of the diet per MJ of energy consumed to
elucidate differences in the micronutrient quality of the diet
between the disadvantaged and non-disadvantaged cohorts.
Here, with the contribution of supplements excluded, nutrient
densities for vitamin B
1
, vitamin B
2
, niacin, vitamin B
5
, vitamin
B
12
, folate, vitamin C, carotene, vitamin D and vitamin E were
all significantly lower among the disadvantaged women
(Table 6). For the minerals investigated, dietary K, Fe, Ca,
Mg and Zn densities per MJ of energy consumed were all sig-
nificantly lower among the disadvantaged women than among
the non-disadvantaged women (Table 6).
Discussion
Methodology
The present study aimed to establish how the food group
and nutrient intake patterns of young, low-SES Irish women
differed from those of their more affluent peers. The sampling
frame for the recruitment of our disadvantaged women was
developed de novo using small-area population statistics
data from the most recent national census, reflecting similar
approaches used previously for socio-economic health
research in Ireland
(56)
. The post hoc profile of our disadvan-
taged women confirms their low SES and supports the use
of such multi-component sampling tools. Notwithstanding
this fact, however, the data presented do raise the prospect
that the dietary and nutritional deficits identified here may
be still more pronounced in areas (and among groups)
where these markers of material disadvantage are even more
preponderant.
While the exclusive recruitment of our study population
from the Greater Dublin area is a limitation, there is no
reason to believe that the profound nutritional deficits
identified among these young women are not characteristic
of other young, urban women of similar SES across Ireland.
Although non-probability, purposive sampling was selected
for our study design, efforts were made to ensure that the
Table 4. Differences in energy, dietary fibre and macronutrient intakes (excluding alcohol) between the disadvantaged and non-disadvantaged
respondents*
(Mean values and standard deviations; medians and interquartile ranges (IQR), n216)
Food energy (excluding energy from alcohol)
Disadvantaged (n153) Non-disadvantaged (n63)
Macronutrients Recommended daily intake Mean SD Median IQR Mean SD Median IQR P
Energy (kcal) Approximately 2000 kcal/d 2208 560 2130 823 1906 374 1792 514 ,0·001
Energy (MJ) Approximately 8·4 MJ/d 9·28 2·35 8·97 3·43 8·03 1·57 7·56 2·17 ,0·001
Dietary fibre
(Southgate) (g/d)
.25 g/d
49)
10·1 3·9 9·8 4·9 12·6 4·5 12·5 5 ·8 ,0·001
NSP (Englyst) (g/d) .18 g/d
50)
11·7 3·8 11·4 4·7 15·0 5·0 14·5 7·2 ,0·001
Carbohydrate
(% energy)
.50 % Food energy
(50)
48·7 5·9 48·6 7·6 51·0 6·1 51·6 8·0 0·007
NMES (% energy) ,11% Food energy
(50)
13·7 8·6 11·6 10·2 9·4 6·0 8·8 6·4 ,0·001
Total fat (% energy) ,35 % Food energy
(50)
37·2 5·4 37·7 7·2 31·8 5·4 31·8 7·0 ,0·001
Saturated fat
(% energy)
,11 % Food energy
(50)
14·6 3·3 14·9 4·8 12·0 2·8 12·0 3·7 ,0·001
Monounsaturated fat
(% energy)
12·2 2·4 12·4 3·3 10·2 2·2 10·1 3·4 ,0·001
Polyunsaturated fat
(% energy)
6·0 2·0 5·9 2·7 5·9 1·9 5·7 2·7 0·892
Protein (% energy) 14·1 3·0 13·8 3·6 17·1 2·8 16·7 2·8 ,0·001
NMES, non-milk extrinsic sugars.
* Energy, carbohydrate, total fat, saturated fat, monounsaturated fat, polyunsaturated fat and protein intakes are normally distributed and socio-economic differences in mean
intakes (italicised values) between the disadvantaged and non-disadvantaged groups are assessed by parametric methods (independent samples ttests). Dietary fibre,
NSP and NMES intakes are non-normally distributed and socio-economic differences in median intakes (italicised values) between the disadvantaged and advantaged
groups are assessed by non-parametric methods (Mann– Whitney Utests).
D. M. A. McCartney et al.2090
British Journal of Nutrition
respondents were recruited from a geographically disperse
number of areas in North, South, West and City Centre
Dublin, with a roughly equal number of low-SES recruitment
sites being selected in each region. However, it must be recog-
nised that in the absence of a comprehensive, multi-tiered and
explicit sampling frame and the application of a robust prob-
abilistic algorithm to select participants within this frame,
our purposive sampling method lacks the rigour of a full prob-
ability-based sampling protocol. The propensity for sampling
bias is increased where ‘judgement’ or ‘assumption’ rather
than randomisation distribution is used for participant selec-
tion in this way. While use of an area-level sampling frame
to identify ED of low SES, along with the equal selection of
low-SES ED from the four geographic regions of Greater
Dublin, was undertaken in an effort to mitigate such bias
and enhance the representativeness of our low-SES sample,
the inherent limitations of this sampling protocol must be
acknowledged.
The small sample size of our study population also needs
to be addressed. By convention, a power of 80 % and a
significance level of 5 % were selected to limit the chance of
type 1 error (i.e. false positive findings) to less than
5 %. The minimum number of disadvantaged and non-
disadvantaged respondents required for the comparison of
mean macronutrient (fat, saturated fat, carbohydrate and
protein) intakes between these two independent samples
was calculated using standard errors from a similarly sized
population of young women in the NSIFCS (n269), according
to previously described protocols
(57)
. This yielded a minimum
sample size of sixty-three in each group. However, this does
not automatically infer that this sample size will suffice for
comparative analyses of other nutrient intakes between the
low-SES women and their reference group, and this limitation
needs to be acknowledged.
Because dietary misreporting constitutes a significant
problem in nutritional surveys
(58)
, unreliable dietary records
were identified and removed from our food group and
nutrient analyses
(59)
. This process, however, only excludes
unreliable dietary records on the basis of implausible EI v.
an individual’s calculated energy requirements. It does not
eradicate other sources of potential error such as inaccuracy
and imprecision of dietary reporting
(60)
and possible selective,
preferential misreporting of certain food groups
(61)
.
It could also be argued that the diet history method facili-
tates the omission of consumed foods, by failing to provide
‘cues’ to assist dietary recall in the way that a FFQ might.
Additionally, some studies have suggested a lower internal
consistency and a greater divergence of population EI with
Table 5. Differences in vitamin and mineral intakes (including supplements) between the disadvantaged and non-disadvantaged respondents
(Mean values and standard deviations; medians and interquartile ranges (IQR), n216)
Daily intake including supplements*
Disadvantaged (n153) Non-disadvantaged (n63)
Vitamins EAR
(52)
Mean SD Median IQR Mean SD Median IQR P
Vitamin B
1
(mg/d)† 0·6 mg/d 1·6 0·7 1·5 0·8 1·8 0·8 1·6 1·5 0·170
Vitamin B
2
(mg/d) 1·1mg/d 1·9 0·8 1·7 1·0 2·1 0·8 1·9 1·4 0·021
Niacin (mg/d)‡ Approximately 11 mg/d 23·0 9·5 20·8 12·1 29·0 10·2 26·7 17·4 ,0·001
Vitamin B
5
(mg/d) None defined 5·8 2·6 5·1 2·6 6·8 2·9 5·5 5·5 0·028
Vitamin B
6
(mg/d)§ 1·1 mg/d 2·5 1·2 2·2 1·3 3·2 2·2 2·8 2·2 0·007
Vitamin B
12
(mg/d) 1·0 mg/d 4·7 2·0 4·3 2·5 4·8 1·7 4·6 2 ·1 0·383
Folate (mg/d) 230 mg/d 286 115 258 141 365 162 324 224 0·001
Vitamin C (mg/d) 46 mg/d 89 73 71 77 184 210 149 118 ,0·001
Vitamin A (mg/d) 400 mg/d 517 416 379 355 549 501 316 801 0·336
Carotene (mg/d) None defined 3035 2288 2528 2665 5139 2943 4482 3806 ,0·001
Vitamin D (mg/d)k0–10mg/d 3·1 3·2 1·8 2·1 4·5 4·9 2·8 4 ·8 0·030
Vitamin E (mg/d){8 mg/d 8·7 4·9 7·4 6·1 11·9 7·5 8·4 11·5 0·008
Na (mg/d)** 2400 mg/d 3178 923 3056 1275 2716 615 2641 983 ,0·001
K (mg/d) None defined 2969 823 2858 1035 3010 714 2885 1081 0·687
Fe (mg/d) 10·8 mg/d 18·5 24·0 10·9 6·1 15·2 7·5 11·9 12·7 0·073
Ca (mg/d) 615 mg/d 840 320 799 369 874 250 830 326 0·219
Mg (mg/d) None defined 252 69 250 85 270 80 261 89 0·013
Zn (mg/d) 5·5 mg/d 8·9 2·5 8·8 3·2 8·8 1·7 8·7 2·0 0·915
Cu (mg/d) 0·8 mg/d 1·4 0·5 1·3 0·8 1·3 0·5 1·2 0 ·8 0·134
P (mg/d) 400 mg/d 1351 367 1328 441 1376 247 1347 317 0·621
EAR, estimated average requirement.
* Where mean intakes for the disadvantaged and non-disadvantaged groups are in italics (Na, Zn and P), intakes of that nutrient are normally distributed and comparison
between the two groups is by parametric independent samples ttests. Where median intakes for the disadvantaged and non-disadvantaged groups are in italics (vitamins A,
B
1
,B
2
,B
5
,B
6
,B
12
, C, D and E, niacin, folate, carotene, K, Fe, Ca, Mg and Cu), intakes of that nutrient are non-normally distributed and comparison is by means of
non-parametric Mann– Whitney Utests.
† EAR for vitamin B
1
set at 72 mg/MJ per d and assumed at 0·6 mg/d for a daily energy intake of approximately 8·4 MJ.
‡ EAR for niacin set at 1·3 mg/MJ per d and assumed at 11mg/d for a daily energy intake of approximately 8·4 MJ.
§ EAR for vitamin B
6
set at 13 mg/g protein per d and assumed at 1·1 mg/d for a daily protein intake of approximately 85g.
kEAR for vitamin D assumed at 5 mg/d (i.e. half of the maximum of the current RDA).
{RDA for vitamin E previously set at 8 mg/d for women aged 18 – 64 years (Irish RDA, 1983); no current Irish EAR.
** Target maximum recommended intake set at 2400 mg/d by the Food Safety Authority of Ireland
(53)
.
Social variation in Irish women’s diets 2091
British Journal of Nutrition
the diet history method compared with weighed intake
records
(39)
, while interviewer bias and social desirability bias
may be further concerns with this methodology. Nonetheless,
as a single-pass, non-presumptive and readily comprehensible
way of estimating habitual dietary intakes in an inaccessible
population whose intakes are thought to deviate from those
of the wider population, this diet history protocol was
deemed a more suitable instrument than either a 24 h recall
or a FFQ.
Finally, the inherent limitations of nutrient conversion from
dietary records must be stated, as the UK Food Composition
database upon which WISP version 3.0 is based, itself has
several deficits. For example, the dietary fibre content of
most foods as measured by the Southgate
(49)
and Englyst
(50)
methods is available on the database. However, the Associ-
ation of Organic and Analytic Chemists method
(62)
, which
measures not just NSP, but also resistant starches, lignins and
fructans and is arguably an analytically superior estimate of
dietary fibre in food, remains unavailable for many foods in
the UK food composition database. Similarly, the food com-
position databases upon which WISP version 3.0 is based
are significantly incomplete for Se, iodine and trans-fatty
acids, meaning that output results for these nutrients could
not be reliably reported.
Dietary intakes
There is significant epidemiological evidence from Ireland
(3,63)
and elsewhere
(64 – 66)
that those in the lower socio-economic
strata experience poorer health outcomes and have signifi-
cantly greater premature mortality than their more affluent
peers. There is also a wealth of data describing the con-
sumption of lower-quality diets among low-SES groups
(6,35,67)
.
Several researchers have linked these two phenomena, high-
lighting the role of poor diet in socio-economic health
inequalities
(4,68)
.
The present study clearly demonstrates the existence of less
favourable dietary habits among a group of young, socially
disadvantaged women from Dublin, in comparison with
those of their more affluent peers. Additionally, while 48·4 %
of the non-disadvantaged women reported taking dietary sup-
plements, this estimate fell to 30·5 % among the disadvantaged
women. This prevalence of usage among the disadvantaged
women is comparable to that reported for all women aged
18–64 years in the National Adult Nutrition Survey
(NANS)
(11)
. However, the discordant estimates between the
two groups also highlight the fact that those who have poten-
tially most to gain from using these products are relatively less
likely to use them than their better nourished, wealthier
counterparts.
Table 6. Differences in vitamin and mineral densities per MJ of energy consumed (excluding supplements) between the disadvantaged and non-disad-
vantaged respondents
(Mean values and standard deviations; medians and interquartile ranges (IQR), n216)
Daily intake per MJ excluding supplements*
Disadvantaged (n153) Non-disadvantaged (n63)
Vitamins EAR
(52)
Mean SD Median IQR Mean SD Median IQR P
Vitamin B
1
(mg/MJ per d)† 0·6 mg/d 150 40 140 50 170 40 170 70 ,0·001
Vitamin B
2
(mg/MJ per d) 1·1 mg/d 0·17 0·05 0·16 0·07 0·20 0·05 0·20 0·08 ,0·001
Niacin (mg/MJ per d)‡ Approximately 11 mg/d 2·12 0·78 19·7 0·87 2·93 0·85 2·84 0·96 ,0·001
Vitamin B
5
(mg/MJ per d) None defined 0·51 0·14 0·49 0·16 0·62 0·12 0·62 0·18 ,0·001
Vitamin B
6
(mg/g protein per d)§ 1·1 mg/d 27·3 6·8 26·7 6·8 26·5 5·6 26·5 9·4 0·414
Vitamin B
12
(mg/MJ per d) 1·0 0 mg/d 0·47 0·19 0·43 0·20 0·55 0·18 0·52 0·20 0·001
Folate (mg/MJ per d) 230 mg/d 26 ·3 7·7 25·6 11·6 33·2 8·1 33·1 12·4 ,0·001
Vitamin C (mg/MJ per d) 46 mg/d 8·2 6·2 6·3 5·8 15·3 7·5 12·8 13 ·7 ,0·001
Vitamin A (mg/MJ per d) 400 mg/d 35·4 16·7 32·5 18·3 33·0 12·4 32·1 14·5 0·484
Carotene (mg/MJ per d) None defined 319 245 248 294 623 364 581 458 ,0·001
Vitamin D (mg/MJ per d)k0–10mg/d 0·19 0·14 0·16 0·11 0·27 0·18 0·20 0 ·23 0·004
Vitamin E (mg/MJ per d){8 mg/d 0·71 0·26 0·69 0·34 0·91 0·27 0·89 0·41 ,0·001
Na (mg/MJ per d)** ,2400 mg/d 326 65 323 73 331 71 328 86 0·646
K (mg/MJ per d) None defined 307 65 305 71 363 66 357 93 ,0·001
Fe (mg/MJ per d) 10·8 mg/d 1·1 0·3 1·0 0·3 1·4 0·3 1·3 0·4 ,0·001
Ca (mg/MJ per d) 615 mg/d 85 27 81 30 104 25 100 33 ,0·001
Mg (mg/MJ per d) None defined 26 6 25 7 33 6 32 8 ,0·001
Zn (mg/MJ per d) 5·5 mg/d 0·9 0·2 0·9 0·3 1·1 0·2 1·1 0·2 ,0·001
Cu (mg/MJ per d) 0·8 mg/d 0·15 0·05 0·13 0·08 0·16 0·06 0·13 0·10 0·224
EAR, estimated average requirement.
* Where the mean intakes per MJ for the disadvantaged and non-disadvantaged groups are in italics (vitamins B
1
,B
2
,B
6
, E, folate and Zn), intakes of that nutrient per MJ
are normally distributed and comparison between the two groups is by means of parametric independent samples ttests. Where the median intakes per MJ for the
disadvantaged and non-disadvantaged groups are in italics (vitamins A, B
5
,B
12
, C and D, carotene, Na, K, Fe, Ca, Mg and Cu), intakes of that nutrient per MJ are
non-normally distributed and comparison between the two groups is by means of non-parametric Mann– Whitney Utests.
† EAR for vitamin B
1
set at 72 mg/MJ per d and assumed at 0·6 mg/d for a daily energy intake of approximately 8·4 MJ.
‡ EAR for niacin set at 1·3 mg/MJ per d and assumed at 11mg/d for a daily energy intake of approximately 8·4 MJ.
§ EAR for vitamin B
6
set at 13 mg/g protein per d and assumed at 1·1 mg/d for a daily protein intake of approximately 85 g.
kEAR for vitamin D assumed at 5 mg/d (i.e. half of the maximum of the current RDA).
{RDA for vitamin E previously set at 8 mg/d for women aged 18 – 64 years (Irish RDA, 1983); no current Irish EAR.
** Target maximum recommended intake set at 2400 mg/d by the Food Safety Authority of Ireland
(53)
.
D. M. A. McCartney et al.2092
British Journal of Nutrition
While the socio-economic differences in dietary intakes
highlighted by the present study are profound, the reasons
for these differences are more elusive. Putative barriers to
healthy diet among low-SES groups include poor nutritional
knowledge
(69)
, inadequate food preparation skills
(70)
, high
cost of healthy food
(71,72)
, poor local food environment
(7,73,74)
,
low perceived control and self-efficacy
(32,75,76)
and poorer
health-related and dietary attitudes
(75 – 77)
.
Nutritional consequences
Whatever the origin of these dietary inequalities, the present
study highlights the adverse impact that poor dietary habits
have on the nutritional intake of low-SES Irish women. The
lower dietary fibre and carbohydrate intakes and the higher
fat, saturated fat, NMES and Na intakes observed among
the low-SES women are wholly consistent with low-cost
diets, which are low in fruit, vegetables, wholemeal bread,
breakfast cereals and fish and high in processed meats,
butter, full-fat (rather than low-fat) milk, sugar-sweetened
beverages, fried potatoes and potato-based snacks
(4,78 – 81)
.
Similarly, the lower absolute and energy-adjusted intakes of
vitamin C, folate, carotene, vitamin D, vitamin E, K, Fe, Ca
and Mg observed among the low-SES women, along
with the high prevalence of micronutrient intake inadequacy
in this group, are reflective of lower fruit, vegetable,
breakfast cereal, unprocessed meat, fish and overall milk
intakes, foods that constitute the richest dietary sources of
these nutrients.
Health implications
The health consequences of these aberrant food group and
nutrient intake patterns are well established. Diets high in
red and processed meats, fat and saturated fat have been
associated with increased risk of overweight and obesity
(82)
,
elevated LDL-cholesterol levels
(83)
, increased risk of colorectal
cancer
(84)
and greater mortality
(85)
. Similarly, diets high in
refined, extrinsic sugars have been associated with overweight
and obesity
(86,87)
and the metabolic syndrome
(87)
, with high
intakes of sugar-sweetened beverages, and in particular
fructose-containing drinks, being strongly linked to multiple
metabolic risk factors
(88,89)
. In the present study, sugary
drinks contributed 6 % of the total energy among the disad-
vantaged respondents v. 2 % among the non-disadvantaged
women. High alcohol intakes have also been associated with
increased cardiometabolic risk
(90)
, increased likelihood of col-
orectal cancer
(91)
, lower bone mineral density and increased
fracture risk
(92)
and higher overall mortality among socially
disadvantaged women
(93)
.
The micronutrient deficits observed among the disadvan-
taged women also have significant health consequences. For
example, low folate intake and status have been associated
with increased serum homocysteine levels and cardiovascular
risk
(94)
, as well as elevated cancer risk
(91,95)
. Low intakes of
several antioxidants including vitamin C and vitamin E
have been inconsistently associated with increased cardio-
vascular
(96)
and cancer risks
(84)
, while high Na, low Ca and
low K intakes have been implicated in hypertension
(97,98)
and in poorer skeletal health
(99)
. Several micronutrient deficits
observed among the low-SES women including low Fe
(100)
,
Ca
(101)
and folate
(102,103)
intakes may also exert deleterious
effects on the health of their offspring.
Interventions
The depth and breadth of the nutritional deficits elicited by
their poor dietary patterns commend these low-SES women
as a primary target for diet-related public health interventions.
Fortunately, the candidate food groups for such interventions
have been largely established. For example, the significant
vitamin and mineral intakes achievable from fruit and vege-
tables
(104,105)
, breakfast cereals
(106,107)
, wholegrain cereals
(108)
,
milk and dairy products
(104,109)
and fish
(108,110,111)
are well
known. However, apart from their own valuable micronutrient
contributions, there is also considerable evidence that
increasing the intake of these foods would displace the
intake of other more energy-dense, nutrient-deplete foods
from the diet. For instance, a higher intake of breakfast cereals
has been consistently associated with lower overall fat
intakes
(106,107)
. Conversely, high sugar-sweetened beverage
intake has been associated with reduced milk intake
(112)
and higher processed meat consumption with lower fish and
poultry intakes
(30,31)
. In the case of high-fat, high-sugar
foods, there is clear evidence that their displacement effect
on micronutrient-dense foods exerts a deleterious impact on
overall nutrient intake and adequacy
(36)
. The interplay
between these competing low-energy, micronutrient-rich
foods and their high-energy, nutrient-dilute alternatives is,
therefore, a critically important consideration in optimising
food-based dietary guidelines for young women of low SES.
In the present study, a lower percentage of the dis-
advantaged women consumed fruit and fruit juices, breakfast
cereals, fish, wholemeal bread, low-fat milk and low-fat
spread, and a higher percentage of these disadvantaged
women consumed sugar-sweetened drinks and potato-based
snacks. Low-SES women should, therefore, be advised and
facilitated to introduce these foods de novo into their diets.
Indeed, there is a synergistic ‘displacement’ benefit to be
gained by explicitly recommending that fruit replace potato-
based snacks, that low-fat milk or fruit juices replace
sugar-sweetened beverages, that wholemeal bread replace
white bread, that fish replace processed red meats, that break-
fast cereals replace other breakfast foods such as processed
meats and that low-fat spread replace butter.
The fact that fruit and fruit juice, vegetable, breakfast cereal,
poultry and wholemeal bread intakes remain lower and that
red meat, processed red meat, white bread, sugar-sweetened
beverage, fried potato and potato-based snack intakes
remain higher in this low-SES group when non-consumers
are excluded indicates that frequency of consumption is also
a crucial component of these dietary inequalities. Therefore,
additional guidance should be given to low-SES women
who already consume these healthy foods to increase their
frequency of consumption, with the displacement of less
healthy alternatives again being a key objective.
Social variation in Irish women’s diets 2093
British Journal of Nutrition
Conclusion
The present study highlights the presence of endemic food
group and nutrient intake deficits among young women of
low SES in Ireland. Although a coincident biomarker analysis
to assess the nutritional status of these women would have
been illuminating, their food and nutrient intakes alone
suggest that many may experience deficiency of one or
more nutrients. While such nutritional inadequacies portend
obvious deleterious effects for these women themselves,
their public health impact is compounded by the critical
importance of nutrients such as Fe, folate, vitamin A,
vitamin D and Ca to the optimal growth of their offspring
in utero
(113,114)
. Our findings constitute an evidence base
for diet-related interventions in such groups and have enabled
us to suggest several explicit food-based dietary guidelines.
However, psychosocial, sociocultural and ecological impedi-
ments to the adoption of such guidelines abound among
low-SES women and remain critical barriers to be overcome
in the success of any such interventions.
Acknowledgements
The present study was supported by the Food Safety Pro-
motion Board (SafeFood) (grant no. 03CR/06). We gratefully
acknowledge Dr Clare Corish for assistance in the preparation
of the final manuscript. Finally, we acknowledge with thanks
the generosity of the community development workers and
young women who gave of their time to participate in our
study, and without whom this work would not have been
possible. The contributions of the authors are as follows:
D. M. A. M., researcher, was responsible for the fieldwork
and manuscript preparation; K. M. Y. was the project
supervisor and manuscript editor; J. W. and M. O. were the
fieldworkers; C. S. was responsible for the disaggregation of
food groups and data analysis; J. M. K. was the principal
investigator and grant recipient. None of the authors has any
conflict of interests.
References
1. Barrington R (2004) Poverty is Bad for your Health. Dublin:
Combat Poverty Agency.
2. Barry J, Sinclair H, Kelly A, et al. (2001) Inequalities in
Health in Ireland – Hard Facts. Dublin: Department of
Community Health, General Practice Trinity College.
3. Balanda K & Wilde J (2001) Inequalities in Mortality: A
Report on All-Ireland Mortality Data 1989 – 1998. Dublin:
Institute of Public Health in Ireland.
4. James WP, Nelson M, Ralph A, et al. (1997) Socioeconomic
determinants of health. The contribution of nutrition to
inequalities in health. BMJ 314, 1545–1549.
5. Friel S & Conlon C (2004) Food Poverty and Policy. Dublin:
Combat Poverty Agency in association with Crosscare and
the Society of St Vincent de Paul.
6. Friel S, Kelleher CC, Nolan G, et al. (2003) Social diversity
of Irish adults nutritional intake. Eur J Clin Nutr 57,
865–875.
7. Layte R, Harrington J, Sexton E, et al. (2011) Irish excep-
tionalism? Local food environments and dietary quality.
J Epidemiol Community Health 65, 881– 888.
8. Harrington KE, Robson PJ, Kiely M, et al. (2001) The North/
South Ireland Food Consumption Survey: survey design
and methodology. Public Health Nutr 4, 1037–1042.
9. National Nutrition Surveillance Centre (2003) Dietary
Habits of the Irish Population: Results from SLAN (Summary
Report). Dublin: National Nutrition Surveillance Centre and
Health Promotion Unit, Department of Health & Children.
10. Harrington J, Perry I, Lutomski J, et al. (2008) SLAN 2007:
Survey of Lifestyle, Attitudes and Nutrition in Ireland. Diet-
ary Habits of the Irish Population. Dublin: Department of
Health and Children and the Stationery Office.
11. Walton J (2011) National Adult Nutrition Survey – Sum-
mary Report on Food and Nutrient Intakes, Physical
Measurements, Physical Activity Patterns and Food
Choice Motives. Dublin: Irish Universities Nutrition Alliance.
12. Nelson M, Erens B, Bates B, et al. (2007) Low Income Diet
and Nutrition Survey Volume 1 – Background, Methods
and Sample Characteristics. London: TSO.
13. Shohaimi S, Welch A, Bingham S, et al. (2004) Residential
area deprivation predicts fruit and vegetable consumption
independently of individual educational level and occu-
pational social class: a cross sectional population study in
the Norfolk cohort of the European Prospective Investi-
gation into Cancer (EPIC-Norfolk). J Epidemiol Community
Health 58, 686–691.
14. Boylan S, Lallukka T, Lahelma E, et al. (2011) Socio-
economic circumstances and food habits in Eastern, Central
and Western European populations. Public Health Nutr 14,
678–687.
15. Wandel M (1995) Dietary intake of fruits and vegetables
in Norway: influence of life phase and socio-economic
factors. Int J Food Sci Nutr 46, 291– 301.
16. Kamphuis CB, van Lenthe FJ, Giskes K, et al. (2007)
Perceived environmental determinants of physical activity
and fruit and vegetable consumption among high and
low socioeconomic groups in the Netherlands. Health
Place 13, 493–503.
17. Groth MV, Fagt S & Brondsted L (2001) Social determinants
of dietary habits in Denmark. Eur J Clin Nutr 55, 959 – 966.
18. Giskes K, Turrell G, Patterson C, et al. (2002b) Socioeco-
nomic differences among Australian adults in consumption
of fruit and vegetables and intakes of vitamins A, C and
folate. J Hum Nutr Diet 15, 375–385.
19. Metcalf P, Scragg R & Davis P (2006) Dietary intakes by
different markers of socioeconomic status: results of a
New Zealand workforce survey. N Z Med J 119, U2127.
20. Dubowitz T, Heron M, Bird CE, et al. (2008) Neighborhood
socioeconomic status and fruit and vegetable intake among
whites, blacks, and Mexican Americans in the United States.
Am J Clin Nutr 87, 1883–1891.
21. Lang R, Thane CW, Bolton-Smith C, et al. (2003) Con-
sumption of whole-grain foods by British adults: findings
from further analysis of two national dietary surveys.
Public Health Nutr 6, 479–484.
22. Touvier M, Me
´jean C, Kesse-Guyot E, et al. (2010) Vari-
ations in compliance with starchy food recommendations
and consumption of types of starchy foods according
to sociodemographic and socioeconomic characteristics.
Br J Nutr 103, 1485–1492.
23. Aranceta J, Perez-Rodrigo C, Ribas L, et al. (2003) Sociode-
mographic and lifestyle determinants of food patterns in
Spanish children and adolescents: the enKid study. Eur J
Clin Nutr 57, Suppl. 1, S40– S44.
24. Siega-Riz AM, Popkin BM & Carson T (2000) Differences
in food patterns at breakfast by sociodemographic
D. M. A. McCartney et al.2094
British Journal of Nutrition
characteristics among a nationally representative sample of
adults in the United States. Prev Med 30, 415 – 424.
25. Mishra G, Ball K, Arbuckle J, et al. (2002) Dietary patterns
of Australian adults and their association with socioeco-
nomic status: results from the 1995 National Nutrition
Survey. Eur J Clin Nutr 56, 687–693.
26. Gray L & Leyland AH (2009) A multilevel analysis of diet
and socio-economic status in Scotland: investigating the
‘Glasgow effect’. Public Health Nutr 12, 1351–1358.
27. Galobardes B, Morabia A & Bernstein MS (2001) Diet and
socioeconomic position: does the use of different indicators
matter? Int J Epidemiol 30, 334–340.
28. Vannoni F, Spadea T, Frasca G, et al. (2003) Association
between social class and food consumption in the Italian
EPIC population. Tumori 89, 669–678.
29. Ministry of Agriculture Fisheries and Food (1996) National
Food Survey 1980, 1995. London: Her Majesty’s Stationery
Office.
30. Guenther PM, Jensen HH, Batres-Marquez SP, et al. (2005)
Sociodemographic, knowledge, and attitudinal factors
related to meat consumption in the United States. JAm
Diet Assoc 105, 1266–1274.
31. Cosgrove M, Flynn A & Kiely M (2005) Consumption of red
meat, white meat and processed meat in Irish adults in
relation to dietary quality. Br J Nutr 93, 933 – 942.
32. Barker M, Lawrence W, Crozier S, et al. (2009) Educational
attainment, perceived control and the quality of women’s
diets. Appetite 52, 631–636.
33. Deshmukh-Taskar PR, O’Neil CE, Nicklas TA, et al. (2009)
Dietary patterns associated with metabolic syndrome,
sociodemographic and lifestyle factors in young adults: the
Bogalusa Heart Study. Public Health Nutr 12, 2493– 2503.
34. Vinkeles Melchers NV, Gomez M & Colagiuri R (2009) Do
socio-economic factors influence supermarket content and
shoppers’ purchases? Health Promot J Austr 20, 241 – 246.
35. Darmon N & Drewnowski A (2008) Does social class pre-
dict diet quality? Am J Clin Nutr 87, 1107– 1117.
36. Bhargava A & Amialchuk A (2007) Added sugars displaced
the use of vital nutrients in the National Food Stamp
Program Survey. J Nutr 137, 453– 460.
37. Central Statistics Office (2007) EU Survey on Income and
Living Conditions (EU-SILC) 2006. Dublin: Central Statistics
Office.
38. Government of Ireland (2007) National Action Plan for
Social Inclusion 2007–2016. Dublin: The Stationery
Office. www.socialinclusion.ie/documents/NAPinclusion-
ReportPDF.pdf (accessed 8 February 2013).
39. Black AE, Welch AA & Bingham SA (2000) Validation of
dietary intakes measured by diet history against 24 h urinary
nitrogen excretion and energy expenditure measured by
the doubly-labelled water method in middle-aged women.
Br J Nutr 83, 341–354.
40. Nelson M, Atkinson M, Meyer J on behalf of Nutritional
Epidemiology Group UK (1997) Food Portion Sizes: A Photo-
graphic Atlas. London: Ministry of Agriculture Fisheries
and Food (MAFF).
41. Mills A & Patel S (1994) Food Portion Sizes, 2nd ed. London:
Her Majesty’s Stationery Office.
42. McCarthy SN, Harrington KE, Kiely M, et al. (2001)
Analyses of the anthropometric data from the North/South
Ireland Food Consumption Survey. Public Health Nutr 4,
1099–1106.
43. Food Standards Agency (2002) McCance & Widdowson’s
The Composition of Foods, 6th ed. Cambridge: Royal Society
of Chemistry.
44. Booth ML (2000) Assessment of physical activity: an inter-
national perspective. Res Quar Exerc Sport 71, s114 – s120.
45. Food & Agriculture Organization/World Health Organis-
ation/United Nations Universities (1985) Energy and
Protein Requirements. Report of a Joint Food & Agriculture
Organization/World Health Organization/United Nations
Universities Consultation. WHO Technical Report Series
no. 724. Geneva: World Health Organization.
46. Goldberg GR, Black AE, Jebb SA, et al. (1991) Critical evalu-
ation of energy intake data using fundamental principles of
energy physiology: 1. Derivation of cut-off limits to identify
under-recording. Eur J Clin Nutr 45, 569–581.
47. Black AE, Coward WA, Cole TJ, et al. (1996) Human energy
expenditure in affluent societies: an analysis of 574 doubly-
labelled water measurements. Eur J Clin Nutr 50, 72 – 92.
48. Black AE (2000) The sensitivity and specificity of the Gold-
berg cut-off for EI:BMR for identifying diet reports of poor
validity. Eur J Clin Nutr 54, 395–404.
49. World Health Organization/Food & Agriculture Organi-
zation (2003) Diet, Nutrition and the Prevention of Chronic
Diseases. Report of a Joint WHO/FAO Expert Consultation.
Geneva: World Health Organization.
50. Department of Health (UK) (1991) Dietary Reference Values
for Food Energy and Nutrients for the United Kingdom.
Report on Health and Social Subjects 41. London: Her
Majesty’s Stationery Office.
51. Strategic Task Force on Alcohol (2004) Strategic Task Force
on Alcohol – Second Report. Dublin: Department of Health
& Children.
52. Food Safety Authority of Ireland (1999) Recommended
Dietary Allowances for Ireland 1999. Dublin: Food Safety
Authority of Ireland.
53. Food Safety Authority of Ireland (2005) Salt and Health:
Review of the Scientific Evidence and Recommendations
for Public Policy in Ireland. Dublin: Food Safety Authority
of Ireland.
54. Central Statistics Office (2008) EU Survey on Income and
Living Conditions (EU-SILC) 2007. Dublin: Central Statistics
Office.
55. Central Statistics Office (2007) Census 2006 Volume 5 –
Ethnic or Cultural Background (Including the Irish Travel-
ler Community). Dublin: Central Statistics Office.
56. Kelly A (2006) Development and Updating the National
Deprivation Index for Health & Health Services Research.
Dublin: Small Area Health Research Unit (SAHRU), Depart-
ment of Public Health & Primary Care in Trinity College
Dublin.
57. Daly LE & Bourke GJ (2000) Sample size determination.
In Interpretation and Uses of Medical Statistics, 5th ed.,
pp. 276–281. Oxford: Blackwell Science.
58. Livingstone MB & Black AE (2003) Markers of the validity of
reported energy intake. J Nutr 133, Suppl. 3, S895 – S920.
59. Samaras K, Kelly PJ & Campbell LV (1999) Dietary under-
reporting is prevalent in middle-aged British women and
is not related to adiposity (percentage body fat). Int J
Obes Relat Metab Disord 23, 881–888.
60. Nelson M, Beresford SAA & Kearney JM (2004) Nutritional
epidemiology. In Public Health Nutrition [MJ Gibney,
BM Margetts, JM Kearney and L Arab, editors]. Cambridge:
The Nutrition Society & Blackwell Publishing.
61. Scagliusi FB, Polacow VO, Artioli GG, et al. (2003) Selective
underreporting of energy intake in women: magnitude,
determinants, and effect of training. J Am Diet Assoc 103,
1306–1313.
62. Prosky L, Asp NG, Schweizer TF, et al. (1992) Determi-
nation of insoluble and soluble dietary fibre in food and
Social variation in Irish women’s diets 2095
British Journal of Nutrition
food products: collaborative study. J Assoc Offic Anal Chem
75, 360–366.
63. Layte R, Nolan A & Nolan B (2007) Poor Prescriptions: Pov-
erty and Access to Community Health Services. Dublin:
Combat Poverty Agency.
64. Marmot M, Allen J, Goldblatt P, et al. (2010) Fair Society,
Healthy Lives – Strategic Review of Health Inequalities in
England Post-2010. London: The Marmot Review.
65. Thomas B, Dorling D & Davey-Smith G (2010) Inequalities
in premature mortality in Britain: observational study from
1921 to 2007. BMJ 341, c3639.
66. Singh GK & Siahpush M (2006) Widening socioeconomic
inequalities in US life expectancy, 1980 – 2000. Int J Epide-
miol 35, 969–979.
67. Mullie P, Clarys P, Hulens M, et al. (2010) Dietary patterns
and socioeconomic position. Eur J Clin Nutr 64, 231–238.
68. Anderson AS (2007) Nutrition interventions in women in
low-income groups in the UK. Proc Nutr Soc 66, 25 – 32.
69. McLeod ER, Campbell KJ & Hesketh KD (2011) Nutrition
knowledge: a mediator between socioeconomic position
and diet quality in Australian first-time mothers. J Am Diet
Assoc 111, 696–704.
70. Larson NI, Perry CL, Story M, et al. (2006) Food preparation
by young adults is associated with better diet quality. JAm
Diet Assoc 106, 2001–2007.
71. Monsivais P & Drewnowski A (2009) Lower-energy-density
diets are associated with higher monetary costs per kilocal-
orie and are consumed by women of higher socioeconomic
status. J Am Diet Assoc 109, 814–822.
72. Aggarwal A, Monsivais P, Cook AJ, et al. (2011) Does diet
cost mediate the relation between socioeconomic position
and diet quality? Eur J Clin Nutr 65, 1059– 1066.
73. Zenk SN, Lachance LL, Schulz AJ, et al. (2009) Neighbor-
hood retail food environment and fruit and vegetable
intake in a multiethnic urban population. Am J Health
Promot 23, 255– 264.
74. Inglis V, Ball K & Crawford D (2008) Socioeconomic vari-
ations in women’s diets: what is the role of perceptions
of the local food environment? J Epidemiol Community
Health 62, 191–197.
75. Lawrence W, Skinner C, Haslam C, et al. (2009) Why
women of lower educational attainment struggle to make
healthier food choices: the importance of psychological
and social factors. Psychol Health 24, 1003–1020.
76. Lawrence W & Barker M (2009) A review of factors affecting
the food choices of disadvantaged women. Proc Nutr Soc
68, 189–194.
77. Hearty AP, McCarthy SN, Kearney JM, et al. (2007) Relation-
ship between attitudes towards healthy eating and dietary
behaviour, lifestyle and demographic factors in a represen-
tative sample of Irish adults. Appetite 48, 1 – 11.
78. Andrieu E, Darmon N & Drewnowski A (2006) Low-cost
diets: more energy, fewer nutrients. Eur J Clin Nutr 60,
434–436.
79. Aggarwal A, Monsivais P & Drewnowski A (2012) Nutrient
intakes linked to better health outcomes are associated with
higher diet costs in the US. PLoS One 7, e37533.
80. Biltoft-Jensen A, Fagt S, Groth MV, et al. (2008) The intake
of saturated fat and dietary fibre: a possible indicator of diet
quality. Br J Nutr 100, 624–632.
81. van Lee L, Geelen A, van Huysduynen EJ, et al. (2012) The
Dutch Healthy Diet index (DHD-index): an instrument to
measure adherence to the Dutch Guidelines for a Healthy
Diet. Nutr J 11, 49.
82. Bray GA, Paeratakul S & Popkin BM (2004) Dietary fat and
obesity: a review of animal, clinical and epidemiological
studies. Physiol Behav 83, 549–555.
83. Jakobsen MU, O’Reilly EJ, Heitmann BL, et al. (2009) Major
types of dietary fat and risk of coronary heart disease: a
pooled analysis of 11 cohort studies. Am J Clin Nutr 89,
1425–1432.
84. World Cancer Research Fund/American Institute for Cancer
Research (2007) Food, Nutrition, Physical Activity and the
Prevention of Cancer: A Global Perspective. Washington,
DC: American Institute for Cancer Research.
85. Pan A, Sun Q, Bernstein AM, et al. (2012) Red meat con-
sumption and mortality: results from 2 prospective cohort
studies. Arch Intern Med 172, 555 – 563.
86. Fitch C & Keim KS (2012) Academy of Nutrition and Diete-
tics Position of the Academy of Nutrition and Dietetics: use
of nutritive and nonnutritive sweeteners. J Acad Nutr Diet
112, 739–758.
87. Livesey G, Taylor R, Hulshof T, et al. (2008) Glycemic
response and health – a systematic review and meta-anal-
ysis: relations between dietary glycemic properties and
health outcomes. Am J Clin Nutr 87, Suppl. 1, S258 – S268.
88. Stanhope KL, Schwarz JM, Keim NL, et al. (2009) Consum-
ing fructose-sweetened, not glucose-sweetened, beverages
increases visceral adiposity and lipids and decreases insulin
sensitivity in overweight/obese humans. J Clin Invest 119,
1322–1334.
89. Hu FB & Malik VS (2010) Sugar-sweetened beverages and
risk of obesity and type 2 diabetes: epidemiologic evidence.
Physiol Behav 100, 47–54.
90. Foerster M, Marques-Vidal P, Gmel G, et al. (2009) Alcohol
drinking and cardiovascular risk in a population with high
mean alcohol consumption. Am J Cardiol 103, 361 – 368.
91. Giovannucci E (2004) Alcohol, one-carbon metabolism,
and colorectal cancer: recent insights from molecular
studies. J Nutr 134, Suppl. 9, S2475–S2481.
92. Berg KM, Kunins HV, Jackson JL, et al. (2008) Association
between alcohol consumption and both osteoporotic frac-
ture and bone density. Am J Med 121, 406– 418.
93. Herttua K, Makela P & Martikainen P (2007) Differential
trends in alcohol-related mortality: a register-based
follow-up study in Finland in 1987–2003. Alcohol Alcohol
42, 456–464.
94. Wald DS, Wald NJ, Morris JK, et al. (2006) Folic acid, homo-
cysteine, and cardiovascular disease: judging causality in
the face of inconclusive trial evidence. BMJ 333,
1114–1117.
95. Figueiredo JC, Levine AJ, Grau MV, et al. (2008) Colorectal
adenomas in a randomized folate trial: the role of baseline
dietary and circulating folate levels. Cancer Epidemiol Bio-
markers Prev 17, 2625–2631.
96. Hamilton KL (2007) Antioxidants and cardioprotection. Med
Sci Sports Exerc 39, 1544– 1553.
97. McCarron DA & Reusser ME (2001) Are low intakes of
calcium and potassium important causes of cardiovascular
disease? Am J Hypertens 14, Suppl. 1, S206–S212.
98. He FJ & MacGregor GA (2009) A comprehensive review on
salt and health and current experience of worldwide salt
reduction programmes. J Hum Hypertens 23, 363–384.
99. Teucher B, Dainty JR, Spinks CA, et al. (2008) Sodium and
bone health: impact of moderately high and low salt intakes
on calcium metabolism in postmenopausal women. J Bone
Miner Res 23, 1477–1485.
100. Lee HS, Kim MS, Kim MH, et al. (2006) Iron status and its
association with pregnancy outcome in Korean pregnant
women. Eur J Clin Nutr 60, 1130–1135.
D. M. A. McCartney et al.2096
British Journal of Nutrition
101. Bergel E & Barros AJ (2007) Effect of maternal calcium
intake during pregnancy on children’s blood pressure: a
systematic review of the literature. BMC Pediatr 7, 15 – 23.
102. Medical Research Council Vitamin Study Research Group
(1991) Prevention of neural tube defects: results of the
Medical Research Council Vitamin Study. Lancet 338,
131–137.
103. Sinclair KD, Allegrucci C, Singh R, et al. (2007) DNA meth-
ylation, insulin resistance, and blood pressure in offspring
determined by maternal periconceptional B vitamin and
methionine status. Proc Natl Acad Sci U S A 104,
19351–19356.
104. Vincent E (2008) Fruit, vegetable, and dairy intake predicts
nutritional adequacy. J Am Diet Assoc 108, 659– 660.
105. Darmon N, Darmon M, Maillot M, et al. (2005) A nutrient
density standard for vegetables and fruits: nutrients per cal-
orie and nutrients per unit cost. J Am Diet Assoc 105,
1881–1887.
106. Galvin MA, Kiely M & Flynn A (2003) Impact of ready-to-eat
breakfast cereal (RTEBC) consumption on adequacy of
micronutrient intakes and compliance with dietary rec-
ommendations in Irish adults. Public Health Nutr 6,
351–363.
107. Deshmukh-Taskar PR, Radcliffe JD, Liu Y, et al. (2010) Do
breakfast skipping and breakfast type affect energy
intake, nutrient intake, nutrient adequacy, and diet quality
in young adults? NHANES 1999–2002. J Am Coll Nutr 29,
407–418.
108. Maillot M, Darmon N, Darmon M, et al. (2007) Nutrient-
dense food groups have high energy costs: an econometric
approach to nutrient profiling. J Nutr 137, 1815– 1820.
109. Nicklas TA, O’Neil CE & Fulgoni VL III (2009) The role of
dairy in meeting the recommendations for shortfall nutri-
ents in the American diet. J Am Coll Nutr 28, Suppl. 1,
S73–S81.
110. Gebauer SK, Psota TL, Harris WS, et al. (2006) n-3 Fatty acid
dietary recommendations and food sources to achieve
essentiality and cardiovascular benefits. Am J Clin Nutr
83, Suppl. 6, S1526–S1535.
111. Bonham MP, Duffy EM, Robson PJ, et al. (2009) Contri-
bution of fish to intakes of micronutrients important for
fetal development: a dietary survey of pregnant women
in the Republic of Seychelles. Public Health Nutr 12,
1312–1320.
112. Cavadini C, Siega-Riz AM & Popkin BM (2000) US adoles-
cent food intake trends from 1965 to 1996. Arch Dis Child
83, 18–24.
113. Rao KR, Padmavathi IJ & Raghunath M (2012) Maternal
micronutrient restriction programs the body adiposity, adi-
pocyte function and lipid metabolism in offspring: a review.
Rev Endocr Metab Disord 13, 103– 108.
114. Barker DJ, Lampl M, Roseboom T, et al. (2012) Resource
allocation in utero and health in later life. Placenta 33,
Suppl. 2, e30–e34.
Social variation in Irish women’s diets 2097
British Journal of Nutrition
