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Original Investigation |

Sociodemographic Differences in Fast Food Price Sensitivity

Katie A. Meyer, ScD1; David K. Guilkey, PhD2,3; Shu Wen Ng, PhD1,3; Kiyah J. Duffey, PhD1,4; Barry M. Popkin, PhD1,3; Catarina I. Kiefe, MD5; Lyn M. Steffen, PhD6; James M. Shikany, DrPH7; Penny Gordon-Larsen, PhD1,3
[+] Author Affiliations
1Department of Nutrition, Gillings School of Global Public Health, University of North Carolina, Chapel Hill
2Department of Economics, University of North Carolina, Chapel Hill
3Carolina Population Center, University of North Carolina, Chapel Hill
4Department of Human Nutrition, Foods, and Exercise, Virginia Polytechnic Institute and State University, Blacksburg
5Department of Quantitative Health Sciences, University of Massachusetts Medical School, Worcester
6Division of Epidemiology and Community Health, University of Minnesota, Minneapolis
7Division of Preventive Medicine, University of Alabama at Birmingham, Birmingham
JAMA Intern Med. 2014;174(3):434-442. doi:10.1001/jamainternmed.2013.13922.
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Importance  Fiscal food policies (eg, taxation) are increasingly proposed to improve population-level health, but their impact on health disparities is unknown.

Objective  To estimate subgroup-specific effects of fast food price changes on fast food consumption and cardiometabolic outcomes.

Design, Setting, and Participants  Twenty-year follow-up (5 examinations) in a biracial US prospective cohort: Coronary Artery Risk Development in Young Adults (CARDIA) (1985/1986-2005/2006, baseline N = 5115). Participants were aged 18 to 30 years at baseline; design indicated equal recruitment by race (black vs white), educational attainment, age, and sex. Community-level price data from the Council for Community and Economic Research were temporally and geographically linked to study participants’ home address at each examination.

Main Outcomes and Measures  Participant-reported number of fast food eating occasions per week, body mass index (BMI), and homeostasis model assessment insulin resistance (HOMA-IR) from fasting glucose and insulin concentrations. Covariates included individual-level and community-level social and demographic factors.

Results  In repeated measures regression analysis, multivariable-adjusted associations between fast food price and consumption were nonlinear (quadratic, P < .001), with significant inverse estimated effects on consumption at higher prices; estimates varied according to race (interaction P = .04), income (P = .07), and education (P = .03). At the 10th percentile of price ($1.25/serving), blacks and whites had mean fast food consumption frequency of 2.20 (95% CI, 2.07-2.33) and 1.55 (1.45-1.65) times/wk, respectively, whereas at the 90th percentile of price ($1.53/serving), respective mean consumption estimates were 1.86 (1.75-1.97) and 1.50 (1.41-1.59) times/wk. We observed differential price effects on HOMA-IR (inverse for lower educational status only [interaction P = .005] and at middle income only [interaction P = .02]) and BMI (inverse for blacks, less education, and middle income; positive for whites, more education, and high income [all interaction P < .001]).

Conclusions and Relevance  We found greater fast food price sensitivity on fast food consumption and insulin resistance among sociodemographic groups that have a disproportionate burden of chronic disease. Our findings have implications for fiscal policy, particularly with respect to possible effects of fast food taxes among populations with diet-related health disparities.

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Figure 1.
Slopes for the Estimated Effect of Fast Food Price on Fast Food Consumption Frequency According to Sociodemographic Subgroups: Coronary Artery Risk Development in Young Adults (CARDIA) 1985/1986 to 2005/2006

A, Race; B, income; C, educational attainment. Slopes (marginal effects) from repeated-measures negative binomial regression models of fast food consumption on fast food price, adjusted for age, race, sex, examination year, study center, maximum educational attainment, highest reported income, population density, cost of living, and neighborhood deprivation. Effect estimates reflect change in fast food consumption per 2-SD unit ($0.20) change in fast food price. Random effects were used to account for within-person clustering over examination periods. Fast food prices were deflated to 1982 to 1984 values. Prices are shown at decile cut points (10th to 90th percentiles: $1.25, $1.30, $1.32, $1.36, $1.38, $1.40, $1.47, $1.49, $1.53). The mean (SD) frequency of fast food consumption was 1.80 (2.34) times per week. Interaction term P values: race, P = .04; income, P = .07; educational attainment, P = .03. P values for trend (by group): P < .001 (blacks), P = .42 (whites), P < .001 (income, <$40 000), P = .04 (income, $40 000-$75 000), P = .09 (income, >$75 000), P = .003 (education, <16 years), and P = .03 (education, ≥16 years).

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Figure 2.
Slopes (95% CI) for the Estimated Effect of Fast Food Price on ln Homeostasis Model Assessment Insulin Resistance (HOMA-IR) and ln Body Mass Index (BMI) According to Sociodemographic Subgroups: Coronary Artery Risk Development in Young Adults 1985/1986 to 2005/2006

Slopes (marginal effects) (symbols) and 95% CIs (vertical lines) from repeated-measures linear regression models of ln-HOMA-IR (A) and ln-BMI (B) on fast food price, adjusted for age, race, sex, examination year, study center, maximum educational attainment, highest reported income, population density, cost of living, and neighborhood deprivation. Effect estimates reflect change in outcome per 2-SD unit ($0.20) change in fast food price. Random effects were used to account for within-person clustering over examination periods. Fast food prices were deflated to 1982 to 1984 values. The mean (SD) frequency of ln-HOMA-IR was 1.31 (0.47). The mean (SD) frequency of ln-BMI was 3.27 (0.22). A, ln-HOMA-IR interaction term P values: race, P = .09; income, P = .02; educational attainment, P = .005. P values for trend (by group): P = .06 (blacks), P = .86 (whites), P = .79 (income, <$40 000), P = .009 (income, $40 000-$75 000), P = .95 (income, >$75 000), P = .02 (education, <16 years), and P = .69 (education, ≥16 years). B, ln-BMI interaction term P values: race, P < .001; income, P < .001; educational attainment, P < .001. P values for trend (by group): P < .001 (blacks), P < .001 (whites), P = .98 (income, <$40 000), P = .007 (income, $40 000-$75 000), P = .02 (income, >$75 000), P = .02 (education, <16 years), and P = .01 (education, ≥16 years).

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Submit a Comment
Restrict food stamps to the purchase of healthy foods only
Posted on March 8, 2014
David L. Keller, MD, MS, FACP
none
Conflict of Interest: None Declared
Obesity rates are high among the poor of our country, and the Food Stamp program could do more to encourage good health among recipients. Food stamps should be restricted to the purchase of foods deemed healthy by a panel of nutritionists, dieticians and physician experts. Fresh vegetables, fruits, whole grains, fish, low fat meats and dairy products, nuts, legumes and so on could become the core diet of America's poor. With such nutritional support, they might lose weight, feel more confident, and perhaps be thus enabled to work their way up the socio-economic ladder. It can't hurt, might help and is certainly worth a try.
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