higher unprocessed red meat, chicken and fish intake is associated with a higher vegetable intake in...

ORIGINAL RESEARCH

Higher unprocessed red meat, chicken and fishintake is associated with a higher vegetable intakein mid-age non-vegetarian womenndi_1599 1..7

Laura JENKINS,1 Mark MCEVOY,2 Amanda PATTERSON1 and David SIBBRITT2

1Discipline of Nutrition and Dietetics, School of Health Sciences and 2The Centre for Clinical Epidemiology andBiostatistics, School of Medicine and Public Health, Faculty of Health, The University of Newcastle, Newcastle, NewSouth Wales, Australia

AbstractAim: To investigate whether higher intakes of unprocessed red meat, chicken and fish are associated with higherintakes of vegetables in middle-aged, non-vegetarian Australian women.Methods: Food intake data was collected from a nationally representative sample of 10 530 middle-aged Australianwomen (50–55 years) who completed the third survey of the Australian Longitudinal Study on Women’s Health. Thevalidated Dietary Questionnaire for Epidemiological Studies (Version 2) was used. Multivariate regression analyseswere used to determine the association between vegetable intake and four variables: total meat, red meat, chickenand fish intake in grams per day.Results: Total meat (regression coefficient (RC) = 0.32, 95% CI: 0.30–0.34; P < 0.001), red meat (RC = 0.45, 95% CI:0.42–0.48; P < 0.001), chicken (RC = 0.78, 95% CI: 0.70–0.85; P < 0.001) and fish intake (RC = 0.48, 95% CI:0.42–0.53; P < 0.001) were significantly associated with higher vegetable intakes after adjusting for confounders.The adjusted R2 values for each of the regression models were relatively small (0.1590, 0.1394, 0.0932, 0.0802),indicating that the included predictors did not account for much of the variation in vegetable intake.Conclusion: These results provide some evidence that higher intakes of unprocessed red meat, chicken and fishare associated with higher intakes of vegetables. This supports the notion that many Australians who are serving upunprocessed red meat, chicken or fish for their meals are also consuming a number of vegetable serves.

Key words: Dietary intake, food pattern.

Introduction

Vegetables are rich in vitamins, minerals and antioxidants.1–4

There is evidence that higher intakes of vegetables are asso-ciated with a reduced risk of cancer5–8 and cardiovasculardisease.9,10 Studies have shown that many Australians do notconsume the recommended five serves of vegetables eachday.11,12 In recent years, nutritional epidemiology has shownthe importance of focusing on dietary habits and patterns,rather than examining the impact of single nutrients or food

items.13–16 A study commissioned by Meat and LivestockAustralia (MLA) in 2009 showed that many Australians werefollowing a dietary pattern of traditional, familiar meals atdinner time, typically consisting of unprocessed red meat,chicken or fish, and vegetables.17 This dietary pattern couldhave a number of health benefits; the nutrients provided bythe red meat, chicken or fish portion of the meal are supple-mented by the nutrients provided by the vegetable serves.

While the nutritional benefits of consuming fish areoften reported,18,19 red meat has typically been associatedwith dietary patterns of poor nutritional quality.20 Increasingintakes of red meat have been associated with an increasedrisk of cardiovascular disease,21,22 type 2 diabetes23 andcertain types of cancers;24,25 however, the most conclusiveevidence is often for processed red meats, which are higher inkilojoules, saturated fat and sodium.

Red meat is a source of haem iron, zinc, selenium, vitaminB12, omega 3 fatty acids and high biological value protein.26

Suboptimal intakes of any of these nutrients can result inimpaired body functioning, with iron deficiency being themost common nutrient disorder in the world.27 White meat

L. Jenkins, B(Nutr.&Diet), APD, Honours candidateM. McEvoy, MMedSc [Clinical Epidemiology], Lecturer inEpidemiologyA. Patterson, PhD, APD, Lecturer in Nutrition and DieteticsD. Sibbritt, PhD, Associate Professor in BiostatisticsCorrespondence: A. Patterson, Discipline of Nutrition and Dietetics,School of Health Sciences, The University of Newcastle, Callaghan,Newcastle, NSW 2308, Australia. Email:[email protected]

Accepted December 2011

Nutrition & Dietetics 2012; ••: ••–•• DOI: 10.1111/j.1747-0080.2012.01599.x

© 2012 The AuthorsNutrition & Dietetics © 2012 Dietitians Association of Australia

1

also offers protein and a number of vitamins and mineralsessential to good health; however, it has a lower ironcontent.28

If Australians are generally eating dinner meals consist-ing of vegetables and unprocessed red meat, chicken orfish, it is expected that there may be a positive correlationbetween vegetable intake and the intake of unprocessed redmeat, chicken or fish. Few previous studies have looked atthis association. A study in Ireland in 2005 used intake datacollected from seven-day food diaries as part of a nation-wide cross-sectional study.29 Meat was divided into red,white and processed categories then compared with overalldiet quality. After adjusting for confounders, there was asignificant increase in potato (P < 0.01) and vegetableintake (P < 0.01) with increasing red meat intake in men,and a significant increase in potato intake (P < 0.01) withincreasing red meat intake in women.29 In both men andwomen, higher intakes of processed meat were associatedwith significantly lower intakes of vegetables (P < 0.001).29

There were no significant associations between white meatand vegetables.29 An Australian study in 2000 used the1995–1996 National Nutrition Survey (NNS) 24-hourrecall data to examine red meat consumption in the popu-lation, and reported a trend of increasing vegetable andpotato intake with increasing red meat intake in both menand women.30 However, this study included processed meatproducts such as hamburgers, pies, hotdogs and sausagesin the red meat category.30 There appears to be no studiesthat have looked specifically at fish in relation to vegetableintake, though the ‘Mediterranean’ dietary pattern generallyincorporates one to three servings of fresh fish a week andhas a high diet quality score.13

This study aimed to investigate whether there wasan increase in vegetable intake with increasing intakes ofunprocessed red meat, chicken and fish in middle-aged,non-vegetarian Australian women.

Methods

Survey sample and design: The data for this research wasobtained from the Australian Longitudinal Study of Women’sHealth (ALSWH), which commenced in 1996 and isexpected to run for 20 years. The design of this study hasbeen explained in detail elsewhere.31–33 In brief, women fromthree age cohorts (young, 18–23; mid-age, 45–50; older,70–75 years) were randomly selected from the HealthInsurance Commission Database (Medicare). Over 41 000women responded to the baseline survey, which collecteddemographic, social, physical, psychological and behav-ioural information. The study sample was compared to 1996census data, which showed it was reasonably representativeof the general population of women in the same age groups,although there was some underrepresentation of immigrantgroups.32 Each age cohort is surveyed once every three years,via surveys sent out in the mail. The focus of the currentinvestigation was the data obtained from the third survey ofthe mid-age cohort in 2001, five years into the study. Thissurvey was the most recent of the mid-age cohort that

contained the required portion size and food frequencyquestions to allow calculation of average daily food intake.

Data collection: The Dietary Questionnaire for Epidemio-logical Studies (Version 2)34 was used to collect usual foodintake data from the participants. This food frequency ques-tionnaire (FFQ) has previously been validated in mid-ageAustralian women,35 and information related to the ques-tionnaire can be found in the user information guide.36 Par-ticipants were asked how often on average they consumed avariety of different food items over the 12 months prior, withper day, per week or per month answer options. Participantswere also presented with pictures of different serving sizes offood items, and asked how much of each item they usuallyate at one sitting. Portion size factors were then calculatedthat allowed the standard portion size of each item to bescaled up or down according to the serving size the partici-pant usually consumed. From this, it was determined howmany grams of each food item participants ate per day onaverage. Demographic, lifestyle and morbidity variablessuch as existing health conditions were collected by usingstandardised questions sourced from the census and othernational surveys.33

Meat and vegetable definitions: Red meat was classified asbeef, veal, lamb and pork, which was based on the definitioncurrently used in epidemiological studies.37 The white meatcategory included only chicken, as turkey was not includedin the FFQ, and fish included steamed, grilled, baked andtinned varieties. The total meat category included the sumof all red meat, chicken and fish in grams per day. Totalvegetable intake in grams per day included the sum of allfresh, frozen and tinned varieties, including but not limitedto potatoes, tomatoes, capsicum, lettuce, beetroot, carrots,broccoli, baked beans, chick peas, pumpkin and mush-rooms. Meat products such as meat pies, pasties, hamburg-ers, ham, corned beef, luncheon meats, salami, sausages andfrankfurts were not included in the analysis, as the focus ofthis study was on unprocessed meat. Unprocessed meat ismost commonly defined as that which is fresh, minced orfrozen.37 The omitted meat products are likely to have under-gone processing by being combined with foodstuffs that arehigh in kilojoules, saturated fat and sodium, or preservedby methods other than freezing such as salting or smoking.Similarly, fried fish (including takeaway), and potatoes thatwere baked or fried in fat (including hot chips) were notincluded, as these encompassed takeaway products such as‘fish and chips’, which are often battered and deep-fried.

Statistical analysis: Subjects with a total meat intake ofzero (n = 79) were dropped from the data set to excludevegetarians from the analysis. Outliers in the data that wereexcluded from the analysis were defined as total meat intakesgreater than an average of 800 g per day, vegetable intakesgreater than an average of 500 g per day and individual redmeat, white meat and fish intakes greater than an average of350, 160 and 250 g per day, respectively. Vegetable intakewas divided into quartiles, from lowest to highest consump-tion, and the demographic, lifestyle and morbidity charac-teristics of the consumers were compared across groupsusing chi-squared and one-way analysis of variance tests.

L. Jenkins et al.


2

Univariate linear regression analyses were used to determinewhether total meat, red meat, white meat and fish intakewere significantly associated with vegetable intake. Multi-variate linear regression models were then used to allowfor inclusion of potential confounding variables to obtainadjusted regression coefficients (RCs). Determination of theconfounders involved two steps. Firstly, a list of plausibledemographic, lifestyle and morbidity covariates was estab-lished and univariate analyses were used to test for theirassociation with vegetable intake. Next, all variables thathad a univariate P value less than 0.25 were included inthe multivariate regression model. A backward stepwiseapproach was then used to eliminate insignificant variablesone by one using the adjusted R2 value. Because of the largesample size, statistical significance was set at a = 0.005.STATA/IC Version 10 (StataCorp LP; College Station, Texas,USA) for Windows was used to conduct the analysis.

Results

There were 10 530 participants included in the analysis,and they were divided into quartiles of vegetable gramsconsumed per day (Table 1). Quartile 1 had the lowestconsumption of vegetables, and quartile 4 had the highest.Characteristics found to be significantly different acrossquartiles included education level, Accessibility/RemotenessIndex of Australia classification,38 menopause status, maritalstatus, ability to manage on income, occupation, smokingstatus, body mass index,39 alcohol intake and depressivesymptoms score (CESD-10)40 (P < 0.005). There were fewerwomen with university education in the highest quartile ofvegetable intake compared to the lowest. In addition, therewere fewer women from major cities and more from regionalareas in the highest quartile compared to the lowest. Womenwho were married or living with a partner were also morelikely to be in the highest quartile of vegetable intake.Women with a higher CESD-10 score were more likely to bein the lowest vegetable intake quartile. The univariate regres-sion analysis (Table 2) showed that total meat intake wassignificantly associated with vegetable intake (RC = 0.30,95% CI: 0.28–0.32; P < 0.001). Individually, red meat (RC =0.45, 95% CI: 0.43–0.48; P < 0.001), chicken (RC = 0.80,95% CI: 0.74–0.87; P < 0.001) and fish intake (RC = 0.44,95% CI: 0.39–0.49; P < 0.001) were also significantlyassociated with vegetable intake. Covariates found to beassociated with vegetable intake in the univariate regressionanalyses were included in the multivariate regressionmodels. Total meat intake remained significantly associ-ated with vegetable intake (RC = 0.32, 95% CI: 0.30–0.34;P < 0.001) after the inclusion of the confounding variables(Table 3). The RC for total meat indicated that for each 100 gof meat consumed, there was a corresponding 32 g increasein vegetable intake. Based on the adjusted R2 value (0.1590),this model was able to account for 15.90% of the variabilityin vegetable intake. Red meat (RC = 0.45, 95% CI: 0.42–0.48; P < 0.001), chicken (RC = 0.78, 95% CI: 0.70–0.85;P < 0.001) and fish intake (RC = 0.48, 95% CI: 0.42–0.53;P < 0.001) also remained significantly associated with

vegetable intake, and these variables accounted for 13.94%,9.32% and 8.02% of the variability in vegetable intake,respectively (adjusted R2 values = 0.1394, 0.0932, 0.0802;results not shown).

Discussion

This study provides some evidence that higher intakesof unprocessed red meat, chicken and fish are associatedwith higher intakes of vegetables among middle-aged, non-vegetarian Australian women. This supports earlier findingsfrom the 2009 MLA report investigating Australian dinnermeals.17 The strongest positive association with vegetableintake was seen in chicken, with associations for red meatand fish slightly weaker. Chicken consumption has beensteadily rising in Australia since the 1960s, and chicken hasrecently overtaken beef as the favourite meal time meat,41

which may account for its stronger association with veg-etable intake. The increasing popularity of chicken may bedue to its steadily declining price, which has fallen in bothabsolute terms and relative to other meat.42

The Australian study in 2000 reported similar findings tothe current investigation; increasing red meat intakes wereassociated with higher vegetable intakes in women.30 TheIrish study in 2005 found that neither red nor white meatwas significantly associated with a higher vegetable intake inwomen; however, their study did not include any potato inthe vegetable category.29 Given the Irish propensity towardseating an abundance of potato,43 it is possible that theirpotato serves were taking the place of other vegetables in thediet. However, given that potatoes are a source of carbohy-drate, vitamin C, vitamin B6, potassium, zinc, magnesium,fibre and folic acid,44 and are included in the vegetable groupin the Australian Guide to Healthy Eating (AGHE),45 thereseemed to be no justification for excluding all potatoes fromthe vegetable category in this study.

The adjusted R2 values that resulted from the final multi-variate regression models were relatively small. This indi-cated that there were additional vegetable intake predictorsnot included in the models. While there is literature regard-ing vegetable intake predictors for children and adoles-cents,46,47 and also for a population of people recoveringfrom myocardial infarction,48 the research into predictors forthe general adult population has been limited to psycho-social factors such as ideological beliefs, attitudes andperceived social norms49,50 and supermarket availability.51–53

While the ALSWH survey was extensive, there was the pos-sibility that vegetable intake predictors were not covered inthe questions asked of the subjects. However, comparingthe univariate and multivariate adjusted R2 values of thedifferent meat and fish categories showed that theyaccounted for more of the variation in vegetable intake thanall of the included social and demographic covariates com-bined, indicating that unprocessed red meat, chicken andfish are important predictors of vegetable intake.

Most nutritional epidemiology studies adjust for totalenergy or total food intake when examining associa-tions between dietary patterns and health outcomes, as the

Meat, fish and vegetable intake


3

Tab

le1

Part

icip

ant

dem

ogra

phic

sfo

rth

em

id-a

geco

hort

ofth

eA

ustr

alia

nLo

ngit

udin

alSt

udy

onW

omen

’sH

ealt

hac

cord

ing

tove

geta

ble

inta

kequ

arti

le

Cha

ract

eris

ticSu

bgro

upor

mea

n( S

D)

Qua

rtile

sof

vege

tabl

ein

take

P-va

lue

Q1

(low

)Q

2Q

3Q

4(h

igh)

(n=

2633

)(n

=26

32)

(n=

2639

)(n

=26

26)

Edu

cati

onle

vel

No

form

al41

1(2

4.3%

)38

6(2

2.8%

)40

0(2

3.6%

)49

5(2

9.3%

)<0

.001

*SC

/HSC

1183

(23.

2%)

1309

(25.

7%)

1311

(25.

7)12

95(2

5.4%

)Tr

ade,

dipl

oma

524

(24.

9%)

521

(24.

7%)

545

(25.

9%)

518

(24.

6%)

Uni

vers

ity

494

(31.

8%)

389

(25.

0%)

365

(23.

5%)

307

(19.

7%)

AR

IAa

Maj

orci

ty11

39(3

1.9%

)95

9(2

6.9%

)80

5(2

2.5%

)66

8(1

8.7%

)<0

.001

*In

ner

regi

onal

955

(22.

3%)

1047

(24.

5%)

1122

(26.

2%)

1156

(27.

0%)

Out

erre

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al43

0(1

9.8%

)51

9(2

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)57

5(2

6.4%

)65

2(3

0.0%

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emot

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mot

e96

(21.

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98(2

1.4%

)13

0(2

8.4%

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4(2

9.3%

)M

enop

ause

stat

usSu

rgic

alm

enop

ause

656

(21.

6%)

754

(24.

8%)

752

(24.

7%)

882

(29.

0%)

<0.0

01*

HRT

use

464

(25.

9%)

455

(25.

4%)

454

(25.

3%)

421

(23.

5%)

OC

Pus

e66

(28.

8%)

50(2

1.8%

)62

(27.

1%)

51(2

2.8%

)Pr

e-m

enop

ause

239

(25.

0%)

238

(24.

9%)

257

(26.

9%)

223

(23.

3%)

Peri

-men

opau

se47

5(2

5.2%

)49

9(2

6.4%

)47

3(2

5.0%

)44

2(2

3.4%

)Po

st-m

enop

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707

(27.

7%)

615

(24.

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631

(24.

7%)

600

(23.

5%)

Labo

urfo

rce

part

icip

atio

nN

otin

labo

urfo

rce

527

(23.

7%)

519

(23.

3%)

566

(25.

4%)

615

(27.

6%)

0.00

7La

bour

forc

eem

ploy

ed20

49(2

5.3%

)20

74(2

5.6%

)20

23(2

5.0%

)19

61(2

4.2%

)La

bour

forc

eun

empl

oyed

42(3

0.9%

)26

(19.

1%)

34(2

5.0%

)34

(25.

0%)

Mar

ital

stat

usM

arri

ed/d

efa

cto

2015

(23.

1%)

2179

(24.

9%)

2266

(25.

9%)

2276

(26.

1%)

<0.0

01*

Not

mar

ried

537

(33.

7%)

401

(25.

1%)

337

(21.

2%)

319

(20.

0%)

Abi

lity

tom

anag

eon

inco

me

Impo

ssib

le/a

lway

sdi

fficu

lt35

0(3

0.3%

)26

6(2

3.0%

)26

5(2

2.9%

)27

5(2

3.8%

)<0

.001

*So

met

imes

diffi

cult

692

(24.

4%)

681

(24.

0%)

704

(24.

8%)

765

(26.

9%)

Not

too

bad

1091

(24.

1%)

1210

(26.

7%)

1152

(25.

4%)

1082

(23.

9%)

Eas

y47

1(2

5.0%

)44

7(2

3.7%

)48

8(2

5.9%

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9(2

5.4%

)O

ccup

atio

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rofe

ssio

nal/a

ssoc

iate

1102

(26.

3%)

1084

(25.

9%)

1003

(24.

0%)

996

(23.

8%)

<0.0

01*

Trad

e/pr

oduc

tion

/labo

ur96

(25.

8%)

79(2

1.2%

)10

0(2

6.9%

)97

(26.

1%)

Cle

rica

l/sal

es/s

ervi

ce64

3(2

4.4%

)69

5(2

6.4%

)70

0(2

6.6%

)59

8(2

2.7%

)N

opa

idjo

b57

0(2

2.8%

)58

0(2

3.2%

)64

0(2

5.6%

)70

9(2

8.4%

)V

itam

inus

eYe

s14

60(2

5.3%

)14

54(2

5.2%

)14

42(2

5.0%

)14

09(2

4.4%

)0.

512

No

1156

(24.

5%)

1162

(24.

7%)

1188

(25.

2%)

1204

(25.

6%)

Exe

rcis

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oup

Sede

ntar

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w14

36(2

5.8%

)13

67(2

4.6%

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66(2

4.6%

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88(2

5.0%

)0.

299

Mod

erat

e48

2(2

3.6%

)52

2(2

5.6%

)53

4(2

6.2%

)50

1(2

4.6%

)H

igh

593

(23.

8%)

627

(25.

2%)

639

(25.

7%)

631

(25.

3%)

Smok

ing

stat

usYe

s46

2(3

0.7%

)36

5(2

4.3%

)34

3(2

2.8%

)33

4(2

2.2%

)<0

.001

*N

o21

62(2

4.1%

)22

55(2

5.1%

)22

83(2

5.4%

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78(2

5.4%

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IM

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( SD)

26.1

(5.3

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26.9

(5.4

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df=

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L. Jenkins et al.


4

emphasis is often on the relative amount of the nutrientconsumed in the context of the total diet. Given that theAGHE recommendation for vegetable intake is reported asan absolute (five serves per day)45 rather than a relativevalue, analysing vegetable intake in total grams rather thanas an adjusted proportion of total energy/food consumedwas considered legitimate for this investigation—thoughdoes not give an indication as to the variety of vegetablesbeing consumed.

Other results observed during this study include the asso-ciation of demographic, lifestyle and morbidity variableswith vegetable intake. Subjects who lived in regional orremote areas tended to eat more vegetables, a finding sup-ported by results from the 1995 NNS.11 Also supported by

previous research was the finding that women who reporteda higher level of depressive symptoms were less inclined toeat vegetables.54 These findings may be explained by previ-ous research, which shows that women with depressivesymptoms are less inclined to take part in health-promotingactivities generally.55 Given the age of the subjects, it wasunsurprising that the characteristics more common amongwomen in a traditional family unit—living with a partnerand not working—were associated with greater vegetableintakes. Results may have been different had the young orolder cohorts of women been included in the analysis. Thesegroups may be less likely to be living in a home environmentthat facilitates the consumption of traditional meals consist-ing of unprocessed meat, fish and vegetables.

Table 2 Univariate regression analysis of vegetable intake and vegetable intake predictors for the mid-age cohort of theAustralian Longitudinal Study on Women’s Health

Variable RC P-value Adjusted R2 value

Total meat intake 0.3009 <0.001 0.1142Red meat intake 0.4525 <0.001 0.1009Chicken intake 0.8028 <0.001 0.0525Fish intake 0.4407 <0.001 0.0302SC/HSC -2.9446 0.165* 0.0048Trade/diploma -6.2205 0.012*University qualifications -17.3487 <0.001*ARIAa: Inner regional 20.0205 <0.001* 0.0192ARIAa: Outer regional 25.2108 <0.001*ARIAa: Remote/very remote 23.6190 <0.001*HRT use -9.0253 <0.001* 0.0034OCP use -13.6987 0.008*Pre-menopausal -7.4925 0.007*Peri-menopausal -9.5478 <0.001*Post-menopausal -9.8485 <0.001*Menopause: ‘unclassifiable’ -32.6178 0.001*Labour force employed -5.3536 0.003* 0.0007Labour force unemployed -6.6236 0.321Not married/de facto -19.0894 <0.001* 0.0082Some difficulty managing on income 9.5029 <0.001* 0.0010Managing on income ‘not too bad’ 5.7302 0.022*Managing on income ‘easy’ 6.7498 0.017*Trade/production/labour worker 6.0736 0.135* 0.0024Clerical/sales/service worker 0.1895 0.919No paid job 8.8712 <0.001*No vitamin use -1.8374 0.217* 0.0001Moderate level exercise 2.2268 0.256 <0.0001High level exercise 2.4204 0.185*Smoker -10.3274 <0.001* 0.0022BMI 1.1973 <0.001* 0.0073Alcohol intake -0.9913 0.074* 0.0002Age 1.0879 0.032* 0.0003CESD-10 scoreb -0.9132 <0.001* 0.0040Number of comorbidities 1.2177 0.046* 0.0003a Accessibility/Remoteness Index of Australia, Commonwealth Department of Health and Aged Care.38

b Center for Epidemiologic Studies Depression Scale.40

*Statistically significant covariates (P < 0.25) for inclusion in multivariate regression model.BMI, body mass index; HRT, hormone replacement therapy; OCP, oral contraceptive pill; RC, regression coefficient; SC/HSC, schoolcertificate/high school certificate.



5

A weakness of this study arose when trying to separateunprocessed from processed products. While the FFQ sepa-rated out processed products such as salami, corned beef andmeat pies, it grouped the remaining foods into broad catego-ries, for example, ‘chicken’ or ‘beef’. This left no room for thevastly different types of products that could be included ineach category; for example, respondents would have includeda grilled skinless chicken breast in the same category as acrumbed and deep-fried chicken schnitzel. Similarly, hotchips were grouped in with potatoes baked in fat, despitemost likely having vastly different nutrient profiles.

Future research into the nutrient intakes and diet qualityscores of consumers of unprocessed red meat, chickenand fish may uncover further benefits associated with thisdietary pattern, and may aid in future health campaigns thatpromote vegetable consumption.

Acknowledgements

The authors report no conflicts of interest. This researchreceived no specific grant from any funding agency in thepublic, commercial or not-for-profit sectors. The AustralianLongitudinal Study on Women’s Health is funded by a grantfrom the Australian Department of Health and Ageing.

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Table 3 Multivariate regression analysis of vegetable intake,total meat intake and other vegetable intake predictors forthe mid-age cohort of the Australian Longitudinal Study onWomen’s Health

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Total meat intake 0.3166 <0.001*SC/HSC -0.3879 0.869Trade/diploma -2.7167 0.316University qualifications -6.8815 0.023ARIAa: Inner regional 17.1346 <0.001*ARIAa: Outer regional 21.1769 <0.001*ARIAa: Remote/very remote 16.4171 <0.001*HRT use -3.5184 0.138OCP use -11.1940 0.032Pre-menopause -4.2694 0.154Peri-menopause -6.3152 0.008Post-menopause -5.8381 0.007Menopause: ‘unclassifiable’ -19.7411 0.089Not married -9.4709 <0.001*Some difficulty managing on income 5.2636 0.070Managing on income ‘not too bad’ 4.5155 0.113Managing on income ‘easy’ 7.4908 0.019Trade/labour/production worker 3.4751 0.417Clerical/sales/service worker 0.7556 0.707No paid job 7.8948 <0.001*Moderate exercise 4.6822 0.020High exercise 1.5363 0.414Smoker -12.0093 <0.001*BMI 0.2899 0.055Alcohol intake -0.1237 0.035Age 1.0282 0.066CESD-10 scoreb -0.9704 <0.001*Number of comorbidities 1.4074 0.049a Accessibility/Remoteness Index of Australia, CommonwealthDepartment of Health and Aged Care.38

b Center for Epidemiologic Studies Depression Scale.40

*Statistically significant (P < 0.005).BMI, body mass index; HRT, hormone replacement therapy; OCP,oral contraceptive pill; RC, regression coefficient; SC/HSC, schoolcertificate/high school certificate.

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