Sociodemographic Differences in Fast Food Price Sensitivity

Meyer, Katie A.; Guilkey, David K.; Ng, Shu Wen; Duffey, Kiyah J.; Kiefe, Catarina I.; Steffen, Lyn M.; Shikany, James M.; Gordon‐Larsen, Penny

doi:10.1001/jamainternmed.2013.13922

Cited by 21 publications

(22 citation statements)

References 36 publications

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“…Although more research is needed to support causality, results may have relevance for policies affecting the built food environment nutritional landscape that attempt to increase the convenience of healthy choices relative to less healthy choices via land use, taxation, and point-of-sale environmental changes. 16,43,44 …”

Section: Discussionmentioning

confidence: 99%

“…26,27 In addition, other factors affecting relations between the built food environment and dietary intake should be examined for their relative and potentially explanatory or moderating influence in future studies, including food prices, shopping frequency, and perceptions of the shopping environment. 1,9-16 The self-reported estimates of dietary intake reported in this study may be over- or underinflated, although variance adjustment was employed to mitigate this issue in part. Future studies in this area might also minimize error from self-reported dietary data, as well as better understand how the environment translates into purchasing behaviors, by supplementing it with observational methods or the provision of food purchase receipts.…”

Section: Discussionmentioning

confidence: 99%

“…9-16 The mixed results found in the built food environment literature also may be at least partially attributable to a failure to examine multiple food vendor types and multiple aspects of dietary intake within the same sample. This seems particularly important to improved understanding of these potentially complex associations, as healthy and unhealthy food sources may spatially co-occur (ie, fast food restaurants may be located around supermarkets).…”

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confidence: 99%

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The Built Food Environment and Dietary Intake among African-American Adults

et al. 2016

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Objectives The built food environment surrounding people's homes may influence their dietary intake. This exploratory study examined how the density of different sources of food in the residential environment was associated with dietary consumption among 77 African-American adults in Houston, Texas. Methods The number of fast-food-type restaurants, large grocery stores, and convenience-type stores within 2- and 5-mile residential buffers were divided by the respective areas to obtain food environment density variables. Intake of fruit and vegetables [FV], fiber [FI], and percent energy from fat [PEF] was assessed using National Health Interview Survey items. Covariate-adjusted regressions were used to assess relations of interest. Results Greater density of fast-food-type restaurants within 2 miles was associated with greater FV, FI, and PEF (ps ≤ .012); and for FV and FI within 5 miles (ps < .004). Density of large grocery stores was unrelated to intake. Greater density of convenience-type stores within 2 miles was negatively associated with FV and FI (ps ≤ .03); results became marginal at 5 miles for FV (p = .10) but not FI (p = .03). Conclusion Maximizing healthy offerings in venue-rich metropolitan areas might provide direction for policies to reduce obesity.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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confidence: 99%

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The Built Food Environment and Dietary Intake among African-American Adults

et al. 2016

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“…We based our causal model on substantive considerations. In particular, the selection of IVs was guided by published findings on the role of the built environment in health behaviors (30)(31)(32)(33)(34). We included an assessment of the strength of our full set of IVs to predict endogenous health behaviors with F tests of first-stage models.…”

Section: Discussionmentioning

confidence: 99%

Instrumental-Variables Simultaneous Equations Model of Physical Activity and Body Mass Index

Meyer

Guilkey²,

Tien³

et al. 2016

Am. J. Epidemiol.

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We used full-system-estimation instrumental-variables simultaneous equations modeling (IV-SEM) to examine physical activity relative to body mass index (BMI; weight (kg)/height (m)2 ) using 25 years of data (1985/1986 to 2010/2011) from the Coronary Artery Risk Development in Young Adults (CARDIA) Study (n = 5,115; ages 18-30 years at enrollment). Neighborhood environment and sociodemographic instruments were used to characterize physical activity, fast-food consumption, smoking, alcohol consumption, marriage, and childbearing (women) and to predict BMI using semiparametric full-information maximum likelihood estimation to control for unobserved timeinvariant and time-varying residual confounding and differential measurement error through model-derived discrete random effects. Comparing robust-variance ordinary least squares, random-effects regression, fixed-effects regression, single-equation-estimation IV-SEM, and full-system-estimation IV-SEM, estimates from random-and fixed-effects models and the full-system-estimation IV-SEM were unexpectedly similar, despite the lack of control for residual confounding with the random-effects estimator. Ordinary least squares tended to overstate the significance of health behaviors in BMI, while results from single-equation-estimation IV-SEM were notably different, revealing the impact of weak instruments in standard instrumental-variable methods. Our robust findings for fixed effects (which does not require instruments but has a high cost in lost degrees of freedom) and full-system-estimation IV-SEM (vs. standard IV-SEM) demonstrate potential for a full-system-estimation IV-SEM method even with weak instruments. body mass index; endogeneity; epidemiologic methods; fixed effects; health behaviors; instrumental variables; semiparametric methods; simultaneous equations Abbreviations: BMI, body mass index; CARDIA, Coronary Artery Risk Development in Young Adults; CI, confidence interval; FIML, full-information maximum likelihood; GMM, generalized method of moments; IV, instrumental variable; IV-SEM, instrumentalvariables simultaneous equations model/modeling; OLS, ordinary least squares.

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“…In response to these changes in diet behavior and cardiometabolic risk, there is consistent evidence that higher fast food prices are associated with lower fast food consumption,[12-14] lower body mass index (BMI),[11, 15] and higher insulin resistance,[14, 16] with greater price sensitivity in low SES sub-populations. [14, 15, 17] Additionally, observational studies have demonstrated that taxes on fast food are associated with meaningful decreases in fast food consumption,[16, 18] indicating that higher fast food prices may be useful in reducing cardiometabolic disease risk.…”

Section: Introductionmentioning

confidence: 99%

Fast food price, diet behavior, and cardiometabolic health: Differential associations by neighborhood SES and neighborhood fast food restaurant availability in the CARDIA study

et al. 2015

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Little research has addressed whether neighborhood context influences associations between fast food price, diet, and cardiometabolic health. We investigated these associations using 25 years of Coronary Artery Risk Development in Young Adults (CARDIA) study data (n=4,469, observations=21,134). We found a negative association between fast food price and consumption, with stronger inverse associations in more (vs. less) deprived neighborhoods [3rd tertile: β=−0.68 (95% CI: (−0.85, −0.51); 1st tertile: β=−0.22 (95% CI: −0.42, −0.02) ; p-interaction-0.002], and a similar association for BMI [3rd tertile: β=−1.34 (95% CI: −1.54, −1.14); 1st tertile: β=−0.45 (95% CI: −0.66, −0.25); p-interaction<0.001], but not insulin resistance [3rd tertile: β=− 0.07 (95% CI: −0.24, 0.09); 1st tertile: β=0.09 (95% CI: −0.08, 0.26); p-interaction=0.40]. We observed no modification of fast food price by fast food availability. Future research on obesity disparities should consider potential differences in the association between fast food prices and health outcomes across neighborhood socioeconomic levels.

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Sociodemographic Differences in Fast Food Price Sensitivity

Cited by 21 publications

References 36 publications

The Built Food Environment and Dietary Intake among African-American Adults

The Built Food Environment and Dietary Intake among African-American Adults

Instrumental-Variables Simultaneous Equations Model of Physical Activity and Body Mass Index

Fast food price, diet behavior, and cardiometabolic health: Differential associations by neighborhood SES and neighborhood fast food restaurant availability in the CARDIA study

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