Investigating Dietary Patterns. by Dr Ann Kirby* & Dr Pat McGregor

Transcription

1 Investigating Dietary Patterns by Dr Ann Kirby* & Dr Pat McGregor

2 AIMS Develop an operational definition of the concept of a dietary pattern Employ this to explain the relationship between household characteristics and the nutritional status of its food consumption Identify potential policy levers to improve national diet.

3 Introduction A synthesis of the economist s and nutritionist s approach. The conceptual link is dietary preferences where dietary choice (nutritional content (i.e the healthiness of a households diet) reflects the preferences of the household together with its income constraints.

4 Rationale Policy Implications Many nutritionists make blanket recommendations to cut down on fats ignoring the constraint of income Health consequences - risky behaviour such as consuming an unhealthy diet can affect consumer welfare.

5 Literature Review Dietary Patterns (Villegas et al., 2004, Pryer et al, 2001) Economic - (Friel et al., 2004, Drewnowski, 2003, Dowler, 1998) Demographic - (Stewart et al. 2004, Blaylock et al., 1999) Socio-economic - (Fuchs,2004, Chou et al., 2004) Access - (Nolan et al., 1998, Hendy et al., 1998, Nayga, 1998)

6 DATA Household Budget Survey Sample size 7,219 and 138 food items DaFNe classification (Trichopulou et al.1999) 2 food categories Fruit & Veg (FV) and Total Added Lipids (TAL )(INDA, 2007, DOHC, 2001, Drewnowski, 2003, Yen et al., 2002, Fuhrer et al., 2002)

7 Method of Analysis Latent Cluster Analysis- is defined as the classification of similar objects into groups, where the number of groups, as well as their forms, is unknown (Vermunt and Magidson, 2000a). Main Advantage - Groups hh into dietary patterns according to their dietary preferences while giving the Engel curve a central role.

8 Methods of Analysis Consider the LCA model with 2 indicators, FV and TAL, and 3 classes, X 1, X 2 and X 3. The indicators are assumed to be a linear function of income y i, and group membership is determined by a vector of covariates, z i. By dividing the observations into classes they generate an expanded density for the i th observation f FVi TAL i z i y i 3 = PX z j i f j1 FVi TAL i X j y i (1)

9 Methods of Analysis Next consider the determination of assumed to be a multinomial logit P X 1 z i z i P X 1 = 1 exp( z exp( z 1 i 1 i ) ) exp( z 2 i ) (2) Dietary patterns are defined as the food expenditure ass. with a subgroup of the population that statistically share the same Engle curve. Latent Class Regression- one dependent variable (i.e. either FV or TAL) and the slope is allowed to differ between the classes

10 Results Table 1 Summary of Results: Fruit and Vegetables (A Healthy diet) Estimation techniques :FV constant Differential intercepts Cluster 1 Cluster 2 Cluster 3 OLS 4.67*** (0.45) LCA LCA 5.82*** -1.90*** 3.95*** -2.05*** (0.117) (0.095) (0.158) (0.1208) LCR Class *** (0.09) Class *** (0.22) Class *** (0.50) slope R *** (0.14) 1.43*** (0.069).55*** (0.09) 2.48*** (0.15) 6.55*** (0.54).18.31

11 Results Table 2 Summary of Results: Total Added Lipids (An Unhealthy Diet) Estimation techniques :TAL constant Differential intercepts slope R 2 Cluster1 Cluster 2 Cluster 3 OLS 2.57*** 1.65***.12 (0.40) (0.11) LCA LCA 3.72*** -2.32*** -1.07*** 3.39*** 0.20***.43 (0.071) (0.065) (0.075) (0.112) (0.031) LCR Intercept slope R 2 Class *** (0.07) 1.15*** (0.07) Class *** (0.04) (0.03) Class *** (0.43) 4.25*** (0.36)

12 Expenditure Results Figure 1 Expenditure on a Healthy diet Log of equivalised Income OLS:FV LCA2:FV LCA2:TAL LCR2:FV LCR1:TAL

13 Expenditure Results Figure 2 Expenditure on an Unhealthy diet Log of equivalised Income OLS:TAL LCA1:FV LCA1:TAL LCR1:FV LCR2:TAL

14 Results Table 3 Correlation of the probabilities of class membership for the Latent Regression Models FV Class1 (FV1) FV Class2 (FV2) FV Class3 (FV3) TAL Class 1 (TAL1) TAL Class 2 (TAL2) TAL Class 3 (TAL3) Note: Significance is in parenthesis

15 Results Table 4 Correlation of the probabilities for the LCA and LRA:FV FV Class 1 FV Class 2 FV Class 3 cluster cluster cluster Note: Significance is in parenthesis

16 Results Table 5 Correlation of the probabilities for the LCA and LRA:TAL TAL Class 1 TAL Class 2 TAL Class 3 cluster cluster cluster Note: Significance is in parenthesis

17 Results Covariates Cluster1 Cluster2 Cluster3 Prop. of males under 5yrs -0.88* Prop. of males 14-20yrs -0.87*** 0.54* Prop. of males yrs -0.37** Prop. of males 45-64yrs 0.56*** -0.59*** Prop. of males 65yrsplus 0.46** -0.66*** Prop. of females 14-20yrs -0.69*** Prop. of females 45-64yrs 1.13*** -1.05*** Prop. of females 65yrsplus 0.68*** -0.68*** Single 0.12*** -0.07* Married with children 0.44*** -0.95*** 0.50*** Single adult+children 0.37*** -0.60*** 0.23** 2+unmarried adults+children -0.43*** 0.42*** Smoker 0.08*** -0.20*** 0.12*** Transport -0.16*** 0.17*** Region -0.15*** -0.15*** 0.31*** Housing tenure 0.22*** -0.23*** Working members -0.27*** 0.40*** Summer 0.13*** -0.07* Hhsize -0.07* Social class -0.05* 0.10***

18 Results Marginal effects - Many studies just examined the direction and statistical significance of the variable but changing a household characteristic and examining the magnitude of the effect is of interest as they may be used as policy levers (Villegas et al., Pryer et al.).

19 Results Marginal effect of Smoking and Education Summary The magnitude of the smoking effect on expenditure on TAL is not has high as for expenditure on FV. In terms of policy levers, a government s antismoking campaign could have spillover effects on an individual s diet. If an individual becomes a non-smoker the results would suggest that this will also have dietary consequences.

20 Conclusions Underestimated the effect that income can have on dietary patterns Dietary patterns are the result of the interaction of preferences and constraints: some households simply cannot afford a healthy diet but will adopt this should their income increase. Such households do not require any additional information to be provided by the government the standard policies to improve incomes such as educational opportunity, employment or benefits will secure the dietary improvement. On the other hand there are households where an increase in income yields no dietary benefits. Notwithstanding the data limitations, this first attempt to explain dietary behaviour will facilitate targeted intervention and is necessary as part of an integrated nutrition surveillance mechanism.

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