METHODS

The data were collected as part of the 2000 Brisbane food study (BFS). Details of the study’s scope and coverage, its research design, sampling procedures, data collection methods, and representativeness have been published elsewhere.³⁹ Only a brief overview is provided here.

Sample design

The BFS was conducted in the Brisbane City Statistical Sub-Division (SSD). The sample comprised 1000 households and 50 census collectors districts (CCD), and was selected using a stratified two stage cluster design. A CCD is the smallest administrative unit used by the Australian Bureau of Statistics (ABS) to collect census data. As at 1996, the Brisbane SSD consisted of 1517 contiguous CCD, each containing an average of 200 occupied private dwellings. Stratification consisted of ranking the CCD on the basis of each area’s index of relative socioeconomic disadvantage (IRSD) score. A CCD’s IRSD score is derived by the ABS using principal components analysis, and it reflects the overall level of socioeconomic disadvantage of each area measured on the basis of attributes such as low income, low educational attainment, high levels of public sector housing, high unemployment, and jobs in relatively unskilled occupations.⁴⁰ The IRSD scores used in this study were calculated from data collected in the 1996 Australian census. The distribution of IRSD scores was subsequently divided into 10 strata (deciles) and five CCD were selected from each of the strata using systematic without replacement probability proportional to size sampling. The spatial and socioeconomic characteristics of the 50 CCD are presented in figure 1. As would be predicted from the stratification process, the sampled CCD differed markedly on all key socioeconomic indicators.

Stage 2 involved selecting 1000 private dwellings from the 50 CCD (20 dwellings on average per CCD), and this was undertaken using simple random sampling. Given the focus of the study, we interviewed the person within each dwelling who was primarily responsible for most of the food shopping. A final response rate of 66.4% was achieved.³⁹

Data collection

The individual level data collection within each CCD occurred between September and December 2000, and was conducted on the basis of face to face interviews. Interviews lasted an average of one hour, and respondents were offered a small financial gratuity (AUS$10). The interview sought information on food purchasing choices, factors influencing choice, shopping practices, subjective perceptions of food availability and food prices, food expenditure, food and nutrition knowledge, and the sociodemographic characteristics of the respondent and other household head (if a couple household). Although the data were collected from a single individual, the interview questions elicited information about food purchasing patterns for the household as a whole.

Measures

Area level SES for each CCD was measured using its IRSD score (see above).

Individual level SES was measured by the study participant’s estimate of total household income (including pensions, allowances, and investments) collected as a 14 category variable and subsequently re-coded into four categories for analysis: (1) less than AUS$20 799, (2) $20 800–36 399, (3) $36 400–51 999, and (4) $52, 000 or more. Households in categories 1 and 2 received incomes at or below the Australian average as at 2000, and those in categories 3 and 4, above the average.⁴¹ Household income was used as the socioeconomic indicator for three reasons. Firstly, income is a well established and important determinant of dietary quality, and affects directly a family’s ability to afford and procure food.⁴² Secondly, household income was likely to capture the socioeconomic characteristics of all people living in the household (reflecting individual level incomes, and to some extent education and occupation) and therefore presumably embodied most of the within household socioeconomic processes influencing food choice. Thirdly, it seemed appropriate (substantively and analytically) to examine the relation between SES and food purchasing using variables that were each measured at the same level (that is, household), thus improving model specificity and fit.

Foods purchased for each household were classified into two broad groups: grocery items (including meat and chicken), and fruit and vegetables. Grocery purchasing was examined on the basis of 16 questions, each of which had two or more response categories. For example, respondents were asked: “When you go shopping, what type of bread do you usually buy?” The response options included: I do not buy bread, white, wholemeal, multigrain, white high in fibre, rye, soy and linseed, plus others. Multiple responses were permitted for each question. The other 15 questions were structured in an identical manner and pertained to rice, pasta, baked beans, fruit juice, tinned fruit, milk, cheese, yoghurt, beef mince, chicken, tinned fish, vegetable oil, margarine, butter, and solid cooking fat. In Australia, health promotion and education campaigns⁴³ directed at disseminating dietary guideline messages⁴⁴ recommend that people purchase and consume a variety of nutritious foods that are comparatively high in fibre, and low in fat, salt, and sugar. In keeping with these campaigns, we classified respondents’ food purchasing choices into a “recommended” and “regular” category (table 1).

Purchasing patterns for each grocery food type were then scored as follows. Respondents were categorised as never purchasing the food (scored 0), as purchasing the regular option exclusively (scored 1), as purchasing a variety of food that included both the recommended and regular options (scored 2), or as purchasing the recommended option exclusively (scored 3). The food types were then summed to form a purchasing index, and using an approach described elsewhere,^8,45 the index scores were adjusted to account for the fact that some people did not purchase particular foods. This index was then scaled to range from range from 0 to 100, with high scores being indicative of greater compliance with dietary guideline recommendations.

Fruit purchasing information was elicited using a question that asked “When shopping for fresh fruit, how often do you buy these types”? The respondent was instructed to include seasonal fruits, but exclude fruit juice, tinned fruit, and dried fruit. The question item set consisted of 19 fruits selected from the food frequency questionnaire used in the 1995 Australian National Nutrition Survey.⁴⁶ Respondents were asked to indicate their usual fruit purchasing pattern on the basis of five point scales that ranged from never buy (scored 0) to always buy (scored 4). A fruit purchasing index was created by summing the items, and scoring the measure to range from 0 to 100. Higher scores indicated that respondents regularly purchased many different types of fruits when shopping for their household (that is, a high score was obtained by reporting “Always” or “Nearly always” for most of the fruits listed). In addition, high scores were consistent with two of the Australian Dietary Guideline recommendations, namely, “Eat a wide variety of nutritious foods”, and “Eat plenty of... vegetables (including legumes) and fruits”.⁴⁴

Vegetable purchasing behaviour was measured using an identical format and method to that used for fruit. Respondents were asked to indicate how often they purchased 21 vegetables (including fresh and frozen, but excluding tinned or dried) using five point items. These were subsequently summed to form an index and re-scored to range from 0 to 100, with higher scores being interpreted in the same way that was outlined for fruit purchase.

Analysis

Table 2 presents descriptive statistics for each of the measures used in this analysis. Of the 1000 households interviewed for the BFS, 24 declined to answer the income question, four did not know the income of other people in their household, and two provided insufficient information for their food purchasing behaviours to be reliably assessed. Each of these respondents was excluded, resulting in a final useable sample of 970.

The data were analysed as a two level random intercept variance components model, using MLwiN version 2.1c.⁴⁷ Three models were specified for each food purchasing behaviour. Firstly, a null model, comprising individuals (level 1) nested in CCDs (level 2) with no predictor variables in the fixed part of the model. Substantive interest for the null model focuses on the CCD level random term, which if significant (indicated using χ²), suggests between area variation in food purchasing behaviour. For the null (and all other) models the intraclass correlation was calculated to estimate the percentage of total variance in food purchasing behaviour that was between the CCD (the remaining percentage is between individual variation). The null model was subsequently extended to include fixed effects for age, sex, and household income (model 2) and then the exposure of interest in this study: area socioeconomic disadvantage (model 3). The effect size for the area SES variable was expressed as a two standard deviation increase in area SES, which is equivalent to the difference in area SES score between the median values for the top and bottom quartiles of the area SES index. Improvements in the fit of the three nested models due to the successive inclusion of the fixed effect variables were assessed using the deviance statistic.

RESULTS

Table 3 presents the multilevel results for fruit purchasing behaviour. The CCD level random term was significant (χ² = 4.96, p = 0.025) indicating that the average purchasing score was not constant across the 50 small areas. Of the total variability, 4.1% occurred between CCD and 95.9% between individuals. Thus while the vast majority of the variance in fruit purchasing behaviour was accounted for by individual level factors, the null model tentatively suggested that area characteristics might also independently contribute to this behaviour. Model 2 adds the fixed effect terms for age, sex, and household income. The coefficients and 95% CI indicate that older persons, women, and high income households had significantly higher fruit purchasing scores. The inclusion of these fixed effect terms significantly increased the overall fit of this model relative to the null model (χ² = 153.9, p<0.0001). The variance of the CCD level random term, however, was reduced to non-significance after adjustments for compositional variations based on age, sex, and household income (χ² = 2.09, p = 0.147). Model 3 adds the fixed term for area socioeconomic disadvantage, and this was only moderately related with fruit purchasing. A two standard deviation increase on the area based SES measure was associated with a 2.01 unit increase on the fruit purchasing index, and the confidence intervals included null (95% CI −0.49 to 4.50). The inclusion of area SES made a negligible contribution to the fit of the fruit purchasing model (χ² = 2.43, p = 0.119). For a model that included area SES but not household income (results not shown), a two standard deviation increase in area SES was associated with a 3.97 unit increase on the fruit purchasing index (95% CI 1.55 to 6.40).

Tables 4 and 5 present the equivalent results for vegetable and grocery purchasing respectively. The null models for both vegetable and grocery purchase showed that no statistically significant variation was evident at the CCD level: vegetables (χ² = 0.613, p = 0.433), grocery foods (χ² = 0.581, p = 0.445). In other words, apart from non-systematic sampling fluctuations, there were no differences in the purchasing scores among the 50 areas. The inclusion of the fixed terms for age, sex, and household income (model 2) showed that these factors significantly improved the fit of each model (results for deviance tests not reported). For both vegetable and grocery purchasing, average index scores were significantly higher for older persons, women, and residents of high income households. Area SES was only weakly related with the purchase of vegetables and grocery foods (model 3). A two standard deviation increase on the area SES measure was associated with a 0.60 unit increase on the vegetable purchasing index (95% CI −1.36 to 2.56) and a 0.94 unit increase on the grocery index (95% CI −1.35 to 3.23). The inclusion of area SES produced no statistically significant improvement in the fit of the models for vegetable and grocery purchasing. For models that included area SES but not household income, a two standard deviation increase in area SES was associated with a 1.86 unit increase on the vegetable index (95% CI 0.00 to 3.73) and a 3.22 unit increase on the grocery purchasing index (95% CI 1.04 to 5.39).

DISCUSSION

Multilevel studies conducted in the US and Britain have found evidence in support of contextual or neighbourhood socioeconomic effects on diet independent of individual level factors.^19,23,25 Typically, residents of socioeconomically disadvantaged areas have poorer diets than those in more advantaged areas. Our study in the Brisbane metropolitan region suggests that small area variation in the purchase of fruit, vegetables, and grocery foods mainly reflect spatial differences in the socioeconomic composition of the people living in the areas. Much of the apparent association of area SES with food purchasing was attributable to confounding by household income. After controlling for household income, and the age and sex of respondents, a two standard deviation increase on the area SES measure produced a modest increase of 2.01 units on the fruit purchasing index, with the 95% confidence limits including zero (−0.49 to 4.50), and very small unit increases for the vegetable (0.60, 95% CI −1.36 to 2.56) and grocery indices (0.94, 95% CI −1.35 to 3.23). A two standard deviation change in area SES was equivalent to the difference in score between the median values for the top and bottom quartiles of the area SES measure, enabling an approximate comparison with the effect sizes between the high and low categories of household income. This comparison shows that the area SES effect for fruit purchasing was only about 25% of the household income association, and about 10% of the income association for vegetable and grocery purchase.

Our findings of a null, or at best modest association of area SES with food purchasing behaviour suggest that urban areas in Brisbane are not highly differentiated on the basis of food availability, accessibility, or affordability—unlike the US and Britain, where living in a socioeconomically disadvantaged area seems to act as a hindrance to the procurement and consumption of healthy food. US research has shown that socioeconomically disadvantaged neighbourhoods are underserved by supermarkets relative to more advantaged areas^{24,26,29,35,49} and that intakes of fruits and vegetables are linked with the number of supermarkets in a neighbourhood.²⁵ This work is consistent with results reported by ecological studies conducted in Britain.^12,27,28,31 There is little Australian research that can be used to help interpret the essentially negative findings of this multilevel study, however, some research does exist, and it provides evidence (albeit indirect) supporting the probable limited impact of contextual or neighbourhood effects on food purchasing in Brisbane. Two studies published in the early 1990s reported that foods being recommended in the Australian Dietary Guidelines were affordable by low income families; indeed, some diets based on the guidelines were actually cheaper than a more traditional diet.^50,51 A later representative study of the Brisbane population in 1993, found that although socioeconomic groups differed significantly in terms of their food purchasing choices, most respondents from all socioeconomic groups shopped at large supermarkets where dietary guideline food was readily available, few reported difficulties accessing these shops, and the price difference between recommended and regular foods was, in most cases, small or non-existent.³⁸ Furthermore, as part of the BFS, we collected information on the number and types of food shops, and their location and distance vis a vis the sampled households, and preliminary (unpublished) results are suggesting that socioeconomically advantaged and disadvantaged areas of Brisbane are similarly served by food shops.

Importantly, our results and conclusions about the likely limited effect of area SES on food purchasing behaviour in Brisbane needs to be considered against a number of study limitations. Firstly, (and with the benefit of hindsight), our study was seemingly under-powered to detect statistically significant contextual effects. This notwithstanding however, the association of area SES with each outcome variable was in the expected direction, thus while a larger study may have found statistically significant area effects due to increased precision, it is unlikely that a larger sample would have found a substantially increased strength of association between area SES and food purchasing behaviour. Secondly, we only controlled for one individual level socioeconomic factor as a potential confounder (that is, income), which argues against there being any true contextual effect. If we had controlled for other (potentially confounding) individual level socioeconomic factors such as occupation or education, then it is likely that the already weak to moderate area SES effect would have further reduced to the null. Thirdly, it is possible that our study was adversely influenced by selection or information bias, although we are uncertain of the probable magnitude and direction of this bias. As with most multilevel studies^52,53 our areal units were selected for reasons of sampling and analytical convenience rather than for reasons that were hypothesised to influence food purchasing behaviour, and this would probably underestimate area SES associations. Furthermore, non-differential misclassification bias of food purchasing would probably result in an underestimate of the area SES association and the (confounding) income association. In short, the net effect of measurement error in our multilevel study (and multivariable models generally) is unclear.^54–56 Fourthly, the inclusion of individual level covariates in multilevel analyses may result in over-control, which argues for the possibility of a true contextual effect on food purchasing behaviour in Brisbane. Household income, for example, may in part depend on where you live or on cumulative small area effects over the lifecourse. Given each of these limitations, the finding of no significant area SES effect needs to be viewed circumspectly, and further research in a variety of settings is required before more definitive conclusions can be reached.

There is now a large body of Australian and international research that has examined the relation between individual level SES and diet, with diet most often being measured on the basis of food and nutrient intake.⁸ These studies usually find that socioeconomically disadvantaged groups have intakes that are least in accord with minimal risk for the onset of chronic disease.^10,11,57,58 The individual level results of the BFS adds to this research, and shows that low income households were less likely to purchase foods consistent with recommendations promulgated in diet related promotion messages. For each food type, purchasing score was graded across the income categories, suggesting a high degree of income sensitivity to the purchase of healthy food.

In summary, this first known Australian multilevel study of diet found little evidence that food purchasing behaviour in Brisbane was influenced by area level socioeconomic disadvantage. Thus despite the fact that major urban areas in this country are differentiated on the basis of their social and economic characteristics³⁷ this does not seem to be sufficient to shape and circumscribe the procurement of food. It seems that what matters most in Brisbane City in terms of food purchasing behaviour is the socioeconomic characteristics of individuals and their households, rather than the socioeconomic characteristics of the areas in which they live. This Australian finding seems to be in contrast with countries like the US and Britain, where the nature and extent of spatial segregation along social and economic lines is large enough to be detectable in people’s dietary behaviour.