An examination of income-related disparities in the nutritional quality of food selections among Canadian households from 1986–2001

ARTICLE IN PRESS

Social Science & Medicine 64 (2007) 186–198 www.elsevier.com/locate/socscimed

An examination of income-related disparities in the nutritional quality of food selections among Canadian households from 1986–2001 Laurie E. Ricciuto, Valerie S. Tarasuk Nutritional Sciences, University of Toronto, Fitzgerald Building, 150 College Street, Toronto, Ont., Canada M5S 3E2 Available online 6 October 2006

Abstract Socio-economic disparities in nutrition have been documented in numerous countries, and have been linked to health inequalities. Social and economic policy changes occurring over the last several years have resulted in growing levels of income inequality in many countries. However, the extent to which these temporal changes have affected nutrition disparities is largely unknown. Our research examined income-related disparities in the nutritional quality of food selections among Canadian households from 1986 to 2001. Data from the 1986, 1992, 1996 and 2001 Family Food Expenditure surveys were pooled together (n ¼ 35 048). The relationships between household income and the nutritional quality of food purchases (considering nutrients both as absolute amounts and adjusted for energy, and total energy density) were estimated using general linear models, including tests of significance for differences across the survey years. Results revealed significant positive relationships between income and most nutrients, which persisted over time, and for some nutrients grew stronger. One exception was folate, where the positive relationship between income and folate (independent of energy) was no longer apparent in 2001; this could be attributed to the mandatory fortification of some cereal grain products with folic acid, which came into effect in 1998, resulting in greater availability of folate from grain products. There was also a significant negative relationship between income and total energy density (ratio of food energy to food weight), which persisted across the survey years. At a time of growing income inequality and worsening problems of poverty, food policy makers need to pay attention to the potential for policy interventions to exacerbate or improve nutrition disparities. r 2006 Elsevier Ltd. All rights reserved. Keywords: Canada; Nutrition; Inequalities; Socio-economic status; Time trends

Background Socio-economic differentials in health have been well-documented in several industrialized countries, with higher socio-economic status (SES) associated Corresponding author. Tel.: +1 416 9780618.

E-mail addresses: [email protected] (L.E. Ricciuto), [email protected] (V.S. Tarasuk). 0277-9536/$ - see front matter r 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.socscimed.2006.08.020

with better health, whether SES was measured by income, education, occupation or a composite of all three (Adler et al., 1994; Auger, Raynault, Lessard, & Choiniere, 2004; CIHI, 2004; Ecob & Smith, 1999; Evans, Barer, & Marmor, 1994; Lantz et al., 1998; Martikainen, Makela, Kockinen, & Vlakonen, 2001; Reeder, Liu, & Horlick, 1996; Veugelers, Yip, & Kephart, 2001). Health inequalities have persisted over time, and, in some cases have

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worsened (Davey Smith & Brunner, 1997; Kunst et al., 2005; Turrell & Mathers, 2005). Socio-economic disparities in nutrition have also been observed. Dietary intake studies have revealed that individuals with higher SES consume more nutritious foods (i.e. fruit and vegetables, low fat dairy products, lean meats, whole grains) (Billson, Pryer, & Nichols, 1999; Dowler, 2001; Dubois & Girard, 2001; Giskes, Turrell, Patterson, & Newman, 2002; Groth, Fagt, & Brondsted, 2001; Irala-Estevez et al., 2000; Mancino, Biing-Hwan, & Ballenger, 2004; Martikainen, Brunner, & Marmot, 2003; Perez, 2002; Smith & Baghurst, 1992). Similarly, examinations of household food expenditure data have revealed socio-economic disparities in food purchasing patterns (Horton & Campbell, 1991; Huang & Lin, 2000; James, Nelson, Ralph, & Leather, 1997; Kirkpatrick & Tarasuk, 2003; Ricciuto, Tarasuk, & Yatchew, 2006; Trichopoulou, Naska, & Costacou, 2002). These patterns may be linked to inequalities in health, given the important role of nutrition in promoting health (Davey Smith & Brunner, 1997; Dowler, 2001; James et al., 1997; Leather, 1996; Robertson, 2001). Furthermore, like health inequalities, nutrition disparities tend to be persistent (James et al., 1997; Paulin, 1998; Popkin, Siega-Ric, & Haines, 1996; Wrieden, Connaghan, Morrison, & Tunstall-Pedoe, 2004), with some indications of widening disparities over time (Leather, 1996). Levels of income inequality in Canada have widened over recent years (Sharpe, 2003; Dunn, 2002), similar to other countries (Leather, 1996; Wilkinson, 1996). At the same time, expansions in global trade, along with advances in food technology and nutritional science, have resulted in an increasingly diverse food supply (Lang & Heasman, 2004). Modern food policies, which take a marketbased approach to intervention, emphasizing consumer choice, have to some extent facilitated these food supply changes. Taken together, the changes provide greater opportunity for consumers to differentiate themselves on the basis of their food selection patterns (Lang & Heasman, 2004; Leather, 1996). However, the extent to which temporal changes in income inequality and the food marketplace affect nutrition disparities is largely unknown. Our understanding of Canadian food consumption patterns has been limited by the lack of nationally representative data on food intakes. However, the routine monitoring of household food purchasing patterns provides an opportunity to

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examine associations between food selection and socio-economic characteristics at different points in time. Our research objective was to examine income-related disparities in the nutritional quality of food selections among Canadian households from 1986 to 2001, using data from the Family Food Expenditure Survey. Methods Family food expenditure survey In Canada, household food expenditures are monitored through the family food expenditure (FOODEX) survey. The survey sample is selected from the Canadian Labor Force Survey sampling frame through stratified multistage sampling, and it is representative of the non-institutionalized Canadian population, excluding persons living on native reserves (Statistics Canada, 1999). The sample is drawn for the whole year and then divided into monthly sub-samples to allow for seasonal variation and other changes throughout the year that may affect food expenditures. Socio-demographic data are collected through an interview with the person mainly responsible for the household’s financial maintenance. This person maintains a diary of household food expenditures over a 2-week period, recording the type and quantity of food and beverages purchased from stores, price paid and the type of store where food was purchased. Information on food expenditures at restaurants is also recorded, but details on the types of foods and the composition of meals obtained in restaurants are not recorded. The FOODEX surveys have been conducted periodically since 1953, but the data only became available for public use in 1984 (Statistics Canada, 1987, 2003). We used data from the 1986, 1992, 1996 and 2001 surveys, since these were all conducted on a nationally representative sample of households. (The 1984 and 1990 FOODEX surveys sampled only urban households.) (Statistics Canada, 1987, 1988, 1993, 1994, 1997, 2003). Analytical samples Households were excluded if they did not report income or if they reported zero income; if they did not record any purchases in their diary; or, if they reported purchases for only 1 week. A greater proportion of households were excluded from the

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quality, in order to characterize both quantitative and qualitative differences in food selection. We chose the nutrient residual method for energy adjustment (Willett, 1990), which removes the variation in nutrient amounts due to differences in the total amount of food (energy) purchased; observed differences can then be attributed solely to differences in the nutritional quality of foods selected. Nutrient residuals are obtained from the regression of absolute nutrient amounts on total energy (e.g., protein ¼ benergy+e, where e represents the residuals or the variance unexplained by energy differences). Nutrients examined included protein, fat, carbohydrate, calcium, magnesium, iron, sodium, potassium, zinc, thiamin, riboflavin, niacin, folate, and vitamins A, C, B6, and B12. Due to the relatively large number of missing values for fibre, vitamin D, and vitamin E in the nutrient file, these nutrients were not included in our analyses. Energy density, the ratio of available food energy to food weight, was used as another measure of nutritional quality. Other research has linked diets higher in energy density to an increased risk of developing obesity (Drewnowski & Specter, 2004; Kant & Graubard, 2005). Total energy density for a household was calculated excluding all beverage purchases, recognizing that beverages can disproportionately influence total energy density values due to their dilution effect (Cox & Mela, 2000; Ledikwe et al., 2005). Infant and junior foods were also excluded from the energy density calculation. For each household, total energy density was calculated by dividing the total energy amount (kcal) by the total edible quantity of food purchased (kg). In order to better understand observed differences in the nutritional quality of food selections, we also examined food-purchasing patterns. For each survey year, we classified the food codes into five groups (i.e., grain products, vegetables and fruit, milk products, meat and alternatives, and ‘other’ foods), based on the categories in Canada’s

2001 sample than the other three (Table 1), primarily because more households failed to report their incomes in 2001. Only a very small proportion of households reported zero income in the 1986, 1992 and 1996 samples (0.09%, 0.07% and 0.14%, respectively), but households reporting zero income could not be identified in the 2001 sample, since they were included in the lowest income category (o$10 000). Therefore, households reporting zero income remained in the analytical sample for 2001. Measures For each survey, food purchased in stores was categorized by Statistics Canada using 195–200 different food codes, with the number varying by year (Statistics Canada, 1988, 1994, 1997, 2003). For each household within a survey year, the average weekly quantity purchased was calculated for each food code. These quantities were converted to ‘edible quantities’ to account for trim and cooking losses, and subsequently converted to nutrient amounts. Conversion factors and nutrient amounts specific to each survey year, obtained from Agriculture and Agri-Food Canada, were applied to ensure accurate representation of the nutrient composition of the food supply at the time of the survey (Robbins L., personal communication, 2005). To assign nutrient amounts to each food code, Agriculture and Agri-Food Canada chooses one representative food, based on market share (i.e., the most commonly purchased food). This means that the nutrient amount assigned to a household for a given food code may not correspond to what was actually purchased. This error will likely lead to an under-estimate of income-related disparities in nutritional quality, since we would expect higher income households to purchase ‘‘better’’ than the average, and lower income households ‘‘worse’’ than the average. We used absolute nutrient amounts and amounts adjusted for energy as indicators of nutritional

Table 1 Number of households sampled, excluded and included for each survey year

Number of households sampled Number of households excluded (%) Final analytical sample size

1986

1992

1996

2001

Total

10 919 871 (8%) 10 048

10 848 631 (6%) 10 217

10 924 924 (8%) 10 000

5643 860 (15%) 4783

38 334 3286 (9%) 35 048

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Food Guide to Healthy Eating (CFGHE) (Health Canada, 1992, Table 2). Food codes that did not fit into any group (e.g., prepared foods, soups, infant foods, herbs and condiments) were omitted from the analysis. Together, these foods comprised 6–10% of food expenditures at stores, depending on the survey year. We subdivided the five food groups into 17 smaller food categories to further differentiate foods in terms of nutritional quality. The 17 categories were based on a detailed examination of food codes used in the 1996 FOODEX survey (Ricciuto et al., 2006, Table 2). We applied the same category definitions to the other survey years, recognizing that there may be some variation in the foods present in each category due to changes in the food supply from 1986 to 2001. Average weekly quantities purchased were calculated for each food group and subgroup, and subsequently converted to edible quantities, as described above.

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In the 1986, 1992 and 1996 surveys, information on total household income (before taxes) was collected in terms of absolute dollar amounts. In 2001, information on total household income (before taxes) was reported as one of 11 predefined income categories. For consistency across survey years, households in the 1986, 1992, and 1996 samples were categorized into 11 income categories, designed to preserve the frequency distribution in the 2001 sample (see Table 3). Thus, while the range of incomes in each category varies from one survey year to the next, the proportion of the sample in each of the 11 categories remains constant. Statistical analyses All statistical analyses were performed using SAS/PC Version 8.02 (SAS Institute, Cary, NC, USA). Each household in the FOODEX surveys

Table 2 Description of food groups used in analysesa Food group and food group subdivisions

Types of foods included

Grain products

Breads, pasta, rice, grains, breakfast cereals

Grains Breakfast cereals

Breads, pasta, rice, grains Ready-to-eat breakfast cereals

Vegetables and fruit

Fresh, frozen, and canned, including juices

ABC-rich Other

Containing amounts of vitamin A, C and folate at or above the 75th percentile level Containing amounts of vitamin A, C and folate below the 75th percentile level

Milk products

Milk, cheese, yoghurt, ice cream

Lower fat milk Higher fat milk Cheese/Yoghurt Ice cream/Other

Skim and 1% fluid milks 2% and 3.2% fluid milks Regular and processed cheeses, yogourt Ice cream and ice milk novelties

Meat and alternatives

Beef, pork, poultry, fish, eggs, beans, nuts

Lower fat meat Higher fat meat Poultry/fish Eggs Nuts/beans

Fat content less than or equal to the 50th percentile level Fat content more than the 50th percentile level Chicken, turkey, fish

‘Other’ foods

Fats and oils, sugars/sweeteners, desserts and savory snacks, non-alcoholic beverages

Oils Sugars Desserts/snacks Non-alcoholic beverages (excluding milk)

Butter, margarine, cooking/salad oils Sugars, syrups, jams/jellies, candies Pies, cakes, cookies, chips Coffee, tea, fruit drinks, carbonated beverages

a

Adapted from Ricciuto et al. (2006).

Variety of nuts, peanut butter, legumes

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Table 3 Selected socio-demographic characteristics of each analytical sample Characteristic

Year % Householdsa

1992 (n ¼ 10 217)

1996 (n ¼ 10 000)

2001 (n ¼ 4783)

p6099 6100–9499 9500–12 399 12 400–18 799 18 800–26 199 26 200–32 399 32 400–38 899 38 900–44 099 44 100–49 999 50 000–60 099 X60 100

p8599 8600–12 599 12 600–16 699 16 700–25 499 25 500–34 999 35 000–43 799 43 800–52 599 52 600–61 099 61 100–69 199 69 200–83 299 X83 300

p9299 9300–12 999 13 000–17 799 17 800–26 099 26 100–36 399 36 400–45 999 46 000–55 999 56 000–64 899 64 900–72 999 73 000–88 599 X88 600

p9999 10 000–14 999 15 000–19 999 20 000–29 999 30 000–39 999 40 000–49 999 50 000–59 999 60 000–69 999 70 000–79 999 80 000–99 999 X100 000

Household size (% households) 1 2 3 4 5 or more

23.1 30.3 17.8 18.2 10.6

22.9 32.3 17.4 17.7 9.7

23.4 32.2 16.5 18.3 9.6

25.1 32.3 15.2 17.3 10.1

Proportion children (o15 years) (mean7SD)

0.1670.22

0.1570.23

0.1270.19

0.1170.19

Proportion older adults (X65 years) (mean7SD)

0.1670.34

0.1770.35

0.1970.37

0.1970.37

Income groups

1986 (n ¼ 10 048) Income range ($)

1 2 3 4 5 6 7 8 9 10 11

4.7 7.5 7.2 13.4 13.6 11.9 10.2 7.8 5.9 7.7 10.2

a

Intervals were defined on the basis of the percentage of households as seen in the 2001 FOODEX; income ranges were not fixed across surveys.

was assigned a weight by Statistics Canada to account for unequal probabilities of selection, nonresponse bias and population demographics (Statistics Canada, 1999). All analyses were weighted using standardized weights, obtained by dividing the originally assigned weight by the average of the original weights for those households included in each analytical sample. The weighted sample is designed to be nationally representative. The data from all four analytical samples were pooled (n ¼ 35 048), and the relationships between energy, nutrients, nutrient residuals, energy density and income were estimated using a general linear model procedure (proc GLM). Energy, nutrient, nutrient residual, or energy density was the dependent variable. Energy and nutrient amounts were log-transformed to improve model fit; prior to transformation a constant was added to avoid taking the logarithm of zero. The independent variables included income, as an ordinal variable, and year, defined as a classification variable. An interaction term, income  year, was included to test

whether the associations between income and nutritional quality varied according to survey year. Household size (log-transformed) and composition (two variables: one representing the proportion of householdo15 years old and the other the proportion of household X65 years old) were also included in the model to control for their confounding effects (Ricciuto et al., 2006). In cases where there was a significant interaction between income and year (i.e., po0.05), the relationship between income and nutritional quality for each survey year was derived from the parameter estimates in the linear model. Pair-wise comparisons of income/nutritional quality relationships in each survey year were conducted using ‘estimate’ statements in proc GLM, in order to determine the nature and extent of differences in these relationships over time. In cases where the interaction between income and year was not significant, models were re-run without the interaction term to estimate the relationship between income and nutritional quality.

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The same procedure was applied to examine food purchasing in relation to income, with quantity purchased as the dependent variable. Quantities of food were log-transformed to improve model fit (as with the nutrients, a constant was added prior to transformation). Income associations were estimated for each of the five major food groups, and each of the 17 smaller food groups. The results from the nutrient and food purchasing analyses were subsequently reviewed to explore congruence.

Table 4 Relationships between household income and log (nutrient amounts), where relationships were similar across survey years, n ¼ 35 048 Nutrients

Income betaa (SE)

P

Carbohydrate (g) Fat (g) Protein (g) Iron (mg) Magnesium (mg) Sodium (mg) Riboflavin (mg) Niacin (NE) Thiamin (mg) Folate (mg) Vitamin A (RE)

0.022 (0.002)

o0.0001

0.024 0.027 0.030 0.030

(0.002) (0.002) (0.002) (0.002)

o0.0001 o0.0001 o0.0001 o0.0001

0.026 (0.002) 0.023 (0.002)

o0.0001 o0.0001

0.028 0.025 0.032 0.041

o0.0001 o0.0001 o0.0001 o0.0001

(0.002) (0.002) (0.002) (0.003)

a

From the general linear model procedure. Slope indicates the average percent change in the amount of nutrient purchased associated with moving up one category of income, adjusted for the effects of household size and composition.

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Results Socio-demographic characteristics and food expenditure patterns Household composition appeared to have changed somewhat across the survey years (Table 3), consistent with demographic changes occurring in the Canadian population over this time period (Statistics Canada, 2002). Across the years, food expenditures in stores ranged from 76% to 73% of total food expenditures, with the lowest percentage occurring in 2001. The remainder (24–27%) was spent at restaurants. Relationships between income, energy, nutrients and energy density The amount of energy purchased was positively related to income across all survey years (b ¼ 0.021, SE ¼ 0.002, po0.0001). Similarly, all nutrients showed a positive relationship with income, which persisted across the survey years (Tables 4 and 5). The strongest relationships were observed for vitamins A and C (i.e., an average increase of 4% and 6–8%, respectively, in the amount purchased with a move up one category of income). There was some fluctuation in the relationship with income for six nutrients, but no consistent trends (Table 5). For many nutrients, the positive relationship with income persisted even after adjusting for energy differences (Tables 6 and 7). The exceptions were fat and sodium, which no longer showed an association with income, and carbohydrate, which showed a negative association with income (Table 6). For magnesium, thiamin, folate, vitamins A, C and B12

Table 5 Relationships between household income and log (nutrient amounts), where relationships varied across survey years, n ¼ 35 048 Nutrients

Calcium (mg) Potassium (mg) Zinc (mg) Vitamin C (mg) Vitamin B6 (mg) Vitamin B12 (mg)

Income betaa (SE) 1986 (n ¼ 10 048)

1992 (n ¼ 10 217)

1996 (n ¼ 10 000)

2001 (n ¼ 4783)

0.031abc 0.033a 0.033a 0.077a 0.036a 0.055a

0.024a 0.023b 0.020b 0.058b 0.025b 0.029b

0.032bc 0.030ab 0.026ba 0.067ab 0.028ab 0.029b

0.037c 0.035ac 0.030a 0.074ab 0.033ab 0.037b

(0.002) (0.002) (0.003) (0.005) (0.003) (0.004)

(0.002) (0.002) (0.003) (0.005) (0.003) (0.004)

(0.002) (0.002) (0.003) (0.005) (0.003) (0.004)

(0.004) (0.004) (0.004) (0.007) (0.004) (0.007)

a Derived from the general linear model procedure. Slope indicates the average percent change in the amount of nutrient purchased associated with moving up one category of income, adjusted for the effects of household size and composition. Betas with different superscripts are significantly different (po0.05), based on pair-wise comparisons of slopes, using ‘estimate’ statements in proc GLM.

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residuals, associations with income varied across survey years (Table 7). The positive relationships between income, thiamin and vitamin A residuals were only apparent in 1996 and 2001, and the positive relationship between income and vitamin C residuals appeared much stronger in 2001. In stark contrast to the rest of the nutrients, the positive relationship between folate residuals and income remained fairly constant until 2001, where no relationship was detected. Table 6 Relationships between household income and nutrient residualsa, where relationships were similar across survey years, n ¼ 35 048 Nutrients

Income betab (SE)

Carbohydrate (g) Fat (g) Protein (g) Calcium (mg) Iron (mg) Sodium (mg) Potassium (mg) Zinc (mg) 1.34 Riboflavin (mg) Niacin (NE) Vitamin B6 (mg)

10.1 0.511 12.6 259 1.11 185 557 1.34 0.273 4.78 0.174

(3.11) (1.30) (1.30) (16.2) (0.225) (97.4) (43.2) (0.259) (0.026) (0.517) (0.025)

p 0.0012 0.6933 o0.0001 o0.0001 o0.0001 0.0570 o0.0001 o0.0001 o0.0001 o0.0001 o0.0001

a Nutrient residuals obtained from the regression of absolute nutrient amounts on total energy. b Slope indicates the change in the amount of nutrient purchased, independent of energy, associated with moving up one category of income, and adjusted for the effects of household size and composition.

There was a negative association between income and energy density (b ¼ 22.0, SE ¼ 1.26, po0.0001), which persisted across the survey years. Relationships between income and quantities of food purchased Food selection, as it relates to nutritional quality, also tended to vary systematically with income, with relationships persisting over time. Quantities purchased from each of the five major food groups were positively associated with income, with the strongest association found for vegetables and fruit (Table 8). Positive relationships remained similar across the survey years, with the exception of milk products; the positive association between quantities purchased and income was stronger in 2001 relative to 1992 and 1986, consistent with the trends found for calcium (Table 5). The strongest positive relationship with income was observed for the ‘ABC-rich’ vegetables and fruit group (Table 9), consistent with the trends found for vitamins A and C (Tables 4 and 5). The positive relationship between income and purchasing of breakfast cereals was stronger in 1996 than in 1992 and 1986 (Table 9), consistent with the trends found for the thiamin residuals (Table 7). Breakfast cereals are significant sources of thiamin since they can be fortified with thiamin (Cheney, 1993), and thus will contain higher levels than those occurring

Table 7 Relationships between household income and nutrient residualse by survey year, where relationships varied across survey years, n ¼ 35 048 Nutrients

Income betaf (SE) 1986 (n ¼ 10 048)

Magnesium (mg) Vitamin C (mg) Vitamin A (RE) Thiamin (mg) Folate (mg) Vitamin B12 (mg) e

1992 (n ¼ 10 217)

1996 (n ¼ 10 000)

2001 (n ¼ 4783)

66.6ac (6.27)

39.0b (6.21)

38.5b (6.28)

70.5c (9.94)

42.3ab (3.92)

41.3a (3.88)

53.2bc (3.92)

62.3cd (6.22)

78.7ab (51.1)

37.3b (50.6)

144a (51.1)

235ac (81.1)

0.046a (0.035) 41.1a (5.63) 1.41a (0.315)

0.075a (0.035) 32.6ab (5.59) 0.280b (0.311)

0.243b (0.035) 16.4b (5.65) 0.055b (0.315)

0.130ab (0.056) 11.1c (8.96) 0.611ab (0.499)

Nutrient residuals obtained from the regression of absolute nutrient amounts on total energy. Derived from the general linear model procedure. Slope indicates the change in the amount of nutrient purchased, independent of energy, associated with moving up one category of income, and adjusted for the effects of household size and composition. Betas with different superscripts are significantly different (po0.05), based on pair-wise comparisons of slopes, using ‘estimate’ statements in proc GLM. f

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Table 8 Relationships between income and log quantities purchased from each of the five major food groups, n ¼ 35 048 Major food groups

Income betad (SE) p-value

Vegetables and fruit

0.039 (0.002) po0.0001 0.006 (0.001) po0.0001 0.011 (0.001) po0.0001 0.017 (0.002) po0.0001

Grain products Meat and alternates ‘Other’ foods

Milk products

1986

1992

1996

2001

0.022a (0.002)

0.012b (0.002)

0.023a (0.002)

0.036c (0.003)

d

Derived from the general linear model procedure. Slope indicates the percent change in the amount of food purchased associated with moving up one category of income, adjusted for the effects of household size and composition. Betas with different superscripts are significantly different (po0.05), based on pair-wise comparisons of slopes, using ‘estimate’ statements in proc GLM.

Table 9 Relationships between income and log quantities purchased from each of the 17 smaller food groups, n ¼ 35 048

Food subgroups

Income betae (SE) P All years

‘ABC-rich’ veg and fruit ‘Other’ veg and fruit Grains Breakfast cereals Fluid milk Cheese/yoghurt Other milk products Lower fat meat Highet fat meat Poultry/fish Eggs Nuts/beans Fats Sugars Snacks/desserts Beverages

1986

1992

1996

2001

0.043 (0.001) po0.0001 0.022 (0.001) po0.0001 0.004 (0.001) p ¼ 0.0041 0.003a (0.001) 0.017a (0.002) 0.014ab (0.001)

0.006a (0.001) 0.006b (0.002) 0.012b (0.001)

0.011b (0.001) 0.017ab (0.002) 0.017ac (0.001)

0.006ac (0.001) 0.012ab (0.004) 0.073d (0.002)

0.016a (0.001) 0.006a (0.001)

0.007b (0.001) 0.002ab (0.001)

0.004b (0.001) 0.001b (0.001)

0.003b (0.002) 0.004b (0.002)

0.001a (0.001)

0.005b (0.001)

0.006b (0.001)

0.005b (0.002)

0.001a (0.001) 0.001a (0.001) 0.010a (0.001)

0.004bd (0.001) 0.001a (0.001) 0.009a (0.001)

0.003ad (0.001) 0.011b (0.001) 0.027b (0.001)

0.009c (0.002) 0.003a (0.002) 0.019c (0.002)

0.014 (0.001) po0.0001

0.009 (0.001) po0.0001 0.005 (0.001) po0.0001

0.019 (0.002) po0.0001

e Derived from the general linear model procedure. Slope indicates the percent change in the amount of food purchased associated with moving up one category of income, adjusted for the effects of household size and composition. Betas with different superscripts are significantly different (po0.05), based on pair-wise comparisons of slopes, using ‘estimate’ statements in proc GLM.

naturally in whole grain products and those added to enriched grain products. Therefore, a stronger relationship between income and breakfast

cereal purchasing could translate into a stronger relationship between income and thiamin-rich food selections.

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Within the ‘other’ foods group, there was a negative association between income and purchasing of fats (Table 9). Fats, specifically butter and margarine, are significant sources of vitamin A, as are vegetables and milk (Agriculture and Agri-Food Canada, 1998). Greater purchasing of vitamin Arich fats among lower income households may have compensated for the lower amounts of vitamin Arich vegetables and milk purchased in the earlier survey years, possibly accounting for the lack of a relationship between vitamin A-rich foods and income in those years (Table 7). Discussion Our analyses revealed that higher income is associated with purchasing foods of higher nutritional quality, an association that persisted over time (1986–2001), and in some cases (i.e., vitamin A, C and thiamin) grew stronger. By modeling linear relationships between income and several indices of nutritional quality and comparing these statistically over time, our study provides a novel contribution to the literature on nutrition disparities, and importantly adds to the limited research in Canada. The positive income gradients observed for energy and all nutrients, when expressed in absolute amounts, should not be interpreted to indicate increasing food consumption with increasing income. Our measure of in-store food purchasing is not a measure of consumption. Among other things, it fails to take into account the amount of food wasted in households, but this too is likely positively related to income. Furthermore, while the use of linear regression to model the relationship between income and energy facilitated comparisons of relationships between survey years, this approach masks any threshold effects. Income thresholds, below which purchasing declines, have been observed for several food groups (Ricciuto et al., 2006). It is likely that the positive relationships found between income and energy, or income and many nutrients, level off at a certain level of income. While it is impossible to draw inferences about income-related disparities in individuals’ nutrient intakes from the household food expenditure data we analyzed, our documentation of disparities in the nutritional quality of foods purchased is supported by the results one Canadian study examining the relationship between adults’ nutrient intakes and several SES indicators, using data from the 1990 Quebec Nutrition Survey (Dubois & Girard, 2001).

Using nutrient residuals to estimate diet quality, the authors reported positive income gradients for several macro- and micronutrients, although the specific findings varied with gender. More Canadian research is needed to understand how the incomerelated disparities in the nutritional quality of household food selections documented here translate into disparities at the level of individuals’ intakes. Although there has been little research examining trends in nutritional disparities using food expenditure data, studies examining dietary intake trends over time in the US, Netherlands and Scotland indicate that socio-economic disparities in diet quality have tended to persist over time, and in some cases have widened (Hulshof, Brussaard, Kruizinga, Telman, & Lowik, 2003; Popkin et al., 1996; Wrieden et al., 2004). A positive relationship between SES and fruit and vegetable intake was found to persist over a 26-year time span (1965–1991) in the US, while SES disparities in calcium widened (Popkin et al., 1996). In the Netherlands, a positive relationship between SES and the intake of most vitamins and minerals (per unit of food energy) was found to persist over a 10year time period (1988–1998) (Hulshof et al., 2003). Despite the different time periods examined, the different populations and the use of different SES indicators and dietary measures, these studies indicate persistent inequalities, with higher SES groups consuming more nutritious foods. Similar inferences can be drawn from our analyses; higher income was associated with purchasing foods of higher nutritional quality, a pattern that persisted over time. Higher SES groups are likely more responsive to dietary recommendations, due to a greater awareness of diet-disease relationships, greater belief that their food choices can influence their health (Aldrich, 1999; NIN, 2002), less resistance to change (Kearney & McElhone, 1999), greater ability to take action on such recommendations (Cade & Booth, 1990; Dowler, 2001; James et al., 1997; Jetter & Cassady, 2005; Kearney & McElhone, 1999; Morton & Guthrie, 1997; Travers, 1996), or a combination of these factors. It is also conceivable that health messaging is more geared towards higher SES groups than to lower SES groups, and this may inadvertently contribute to inequalities. The fact that some income-related disparities have widened over time may be related to changes in the level of income inequality. Canadian statistics

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indicate that income inequality, as measured by the Gini coefficient, remained stable between 1986 and 1996, and then increased steadily from 1996 to 2000 (Dunn, 2002; Sharpe, 2003). These trends are based on disposable income (i.e., income earnings+government transfersincome taxes), a better indicator of purchasing power than total income. The time period during which income inequality increased coincides with the time during which the Canadian government dramatically reduced their spending on social assistance and employment insurance programs (Sharpe, 2003). The situation for low-income individuals, in particular, has worsened. Rates of poverty were higher and the gap between income levels of the poor and others was greater in 1996 and 1999 relative to 1986 and 1989 (Picot & Morissette, 2003). It is conceivable that these economic trends may have partly contributed to widening nutrient disparities. Changes in the food marketplace may also contribute to the widening nutrient disparities. There are some indications that the diversity of foods available in the marketplace has expanded over the years, particularly with respect to ‘nutritionally improved’ foods (Frazao & Allshouse, 1996; Kantor et al., 2001; Lang & Heasman, 2004). While data for Canada are lacking, studies of the US food market have shown a greater availability of many types of products with improved nutrient profiles in 1995 relative to 1988 (Frazao & Allshouse, 1996), and increased volume sales for many whole grain products between 1995 and 1999 due to the introduction of new products (Kantor et al., 2001). These nutritionally improved foods tended to cost more than their regular counterparts (Frazao & Allshouse, 1996; Kantor et al., 2001), and became relatively more expensive in 1993 than in 1989 for 57% of the food categories studied (Frazao & Allshouse, 1996). Though research in Canada is limited, two Canadian studies have also shown that foods of higher nutritional quality tend to cost more (Ricciuto, Ip, & Tarasuk, 2005; Travers et al., 1997). These trends suggest that over time, consumers have gained a greater opportunity to differentially allocate their food dollars according to income and price levels, which can then translate into widening income disparities in food selection patterns. The effects of food fortification on income-related disparities in nutritional quality are particularly noteworthy. Although a consistent, positive income gradient was observed for folate across the study

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period, when folate was expressed as a residual (i.e., ‘removing’ the effect of energy), the income gradient ceased to be detected in 2001. This is most likely due to changes in the food supply resulting from the mandatory fortification of some cereal grain products with folic acid (i.e., flour, pasta, and cornmeal), which came into effect in 1998 (Public Health Agency of Canada, 2003). A study in the US, where a similar fortification policy is in effect, showed that the food category ‘breads, rolls and crackers’ became the single largest contributor to folate in the American diet, surpassing vegetables, which were the primary folate source prior to fortification (Dietrich, Brown, & Block, 2005). Our analysis of data from the 1996 FOODEX survey indicated that grain products were ‘staple’ foods, commonly purchased by all households, regardless of income level (Ricciuto et al., 2006). By making these ‘staple’ foods richer in folate, mandatory fortification can diminish income-related differences in folate attributable to differences in food selection. These findings may help inform the policy debates occurring in several countries, including the UK, Australia and the EU, where the implementation of mandatory folic acid is still under consideration and remains a controversial issue (Finglas et al., 2006; Food Standards Agency, 2002; Maberly & Stanley, 2005). In contrast, voluntary fortification (i.e., the addition of vitamins and minerals to foods at the manufacturers’ discretion) of breakfast cereals with thiamin may be related to greater incomerelated disparities in thiamin (independent of energy) since purchasing of breakfast cereals is positively related to income. These results are of concern, given the current directions in food policy in Canada, which will expand voluntary fortification practices (Health Canada, 2005). In addition, given the globalization of our food supply and the ongoing debates about the health and safety of nutrient additions to foods, which are driven largely by the competition for consumer dollars (Lang & Heasman, 2004), these findings have international relevance. Socio-economic disparities in the nutritional quality of food selections may have important implications for health, insofar as nutrition is related to the development of certain chronic diseases. The contribution of diet to socio-economic inequalities in health has been the subject of some inquiry (Martikainen et al., 2003; Stallone, Brunner, Bingham, & Marmot, 1997), with some researchers

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identifying micronutrient intakes (vitamins A, C, magnesium and potassium) as significant contributors to socio-economic inequalities in cardiovascular disease (Stallone et al., 1997). Whether or not the observed differences in nutrient amounts at the household level are large enough to generate health disparities still remains a question. Since the consumption of food outside the home and the distribution of food among household members are not known, inferences about the potential health implications of observed nutrient differences are limited. Nevertheless, the patterns documented in our study (less micronutrients available to lower income households) are consistent with current theory linking health inequalities to inequalities in micronutrient intakes. High energy density diets have been implicated in the development of obesity (Cox & Mela, 2000; Drewnowski, 2003; Drewnowski & BarrattFornell, 2004; Drewnowski, Darmon, & Briend, 2004; Drewnowski & Specter, 2004; Kant & Graubard, 2005; Ledikwe et al., 2005). Some authors have argued that the food selection patterns of low-income groups are driven by the fact that energy-dense foods, such as fats and sweets, provide dietary energy at a lower cost than do lean meats, fish, fresh vegetables and fruit (Drewnowski, 2003; Drewnowski et al., 2004; Drewnowski & BarrattFornell, 2004). The inverse relationship between income and energy density documented in our study, along with the income-related disparities in food selection, lend support to this argument; however, an examination of energy density/price relationships was beyond the scope of our study. Drewnowski has suggested that the association between poverty and obesity in the US is mediated in part by the low cost of energy-dense foods (Drewnowski, 2003; Drewnowski et al., 2004; Drewnowski & Barratt-Fornell, 2004; Drewnowski & Specter, 2004). More research is needed to determine how energy density relates to price and obesity rates in Canada. One limitation of our study was the exclusion of ‘food away from home’, due to the lack of detailed information available in the FOODEX surveys. However, since food spending in restaurants declines with decreases in income (data not shown), it is unlikely that lower levels of nutrients from instore purchases among lower income households would be compensated for by food consumption outside the home. Thus, it is unlikely that inclusion of foods purchased in restaurants would reverse or

weaken the trends found in our study. Given the slight increase in restaurant spending from 1986 to 2001, estimates from 2001 may be more affected by the exclusion of food away from home, but it is unlikely that these spending trends could fully account for changes in nutrient disparities over time. In conclusion, we have documented persistent income-related disparities in many nutrients and growing disparities in three key nutrients. Where differences were observed, higher income was associated with more nutritious food selections. The documented impact of mandatory fortification in diminishing disparities related to food selection points to the positive effect such regulatory interventions can have. Indications that disparities may also be influenced by discretionary fortification practices raise concerns about the impact changes in the global food supply may have on nutrition disparities. At a time of growing income inequality and worsening problems of poverty, food policy makers need to pay attention to the potential for policy interventions to either exacerbate or improve nutrition disparities. Health professionals have an important role to play as advocates for policy interventions that can benefit all socio-economic groups.

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