Understanding Bias in Relationships between the Food Environment and Diet Quality: The Coronary Artery Risk Development in Young Adults (CARDIA) Study

Rummo, Pasquale E.; Guilkey, David K.; Ng, Shu Wen; Meyer, Katie A.; Popkin, Barry M.; Reis, Jared P.; Shikany, James M.; & Gordon-Larsen, Penny. (2017). Understanding Bias in Relationships between the Food Environment and Diet Quality: The Coronary Artery Risk Development in Young Adults (CARDIA) Study. Journal of Epidemiology and Community Health, 71(12), 185-90. PMCID: PMC5713903

Rummo, Pasquale E.; Guilkey, David K.; Ng, Shu Wen; Meyer, Katie A.; Popkin, Barry M.; Reis, Jared P.; Shikany, James M.; & Gordon-Larsen, Penny. (2017). Understanding Bias in Relationships between the Food Environment and Diet Quality: The Coronary Artery Risk Development in Young Adults (CARDIA) Study. Journal of Epidemiology and Community Health, 71(12), 185-90. PMCID: PMC5713903

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BACKGROUND: The relationship between food environment exposures and diet behaviours is unclear, possibly because the majority of studies ignore potential residual confounding. METHODS: We used 20 years (1985-1986, 1992-1993 2005-2006) of data from the Coronary Artery Risk Development in Young Adults (CARDIA) study across four US cities (Birmingham, Alabama; Chicago, Illinois; Minneapolis, Minnesota; Oakland, California) and instrumental variables (IV) regression to obtain causal estimates of longitudinal associations between the percentage of neighbourhood food outlets (per total food outlets within 1 km network distance of respondent residence) and an a priori diet quality score, with higher scores indicating higher diet quality. To assess the presence and magnitude of bias related to residual confounding, we compared results from causal models (IV regression) to non-causal models, including ordinary least squares regression, which does not account for residual confounding at all and fixed-effects regression, which only controls for time-invariant unmeasured characteristics. RESULTS: The mean diet quality score across follow-up was 63.4 (SD=12.7). A 10% increase in fast food restaurants (relative to full-service restaurants) was associated with a lower diet quality score over time using IV regression (beta=-1.01, 95% CI -1.99 to -0.04); estimates were attenuated using non-causal models. The percentage of neighbourhood convenience and grocery stores (relative to supermarkets) was not associated with diet quality in any model, but estimates from non-causal models were similarly attenuated compared with causal models. CONCLUSION: Ignoring residual confounding may generate biased estimated effects of neighbourhood food outlets on diet outcomes and may have contributed to weak findings in the food environment literature.




JOUR



Rummo, Pasquale E.
Guilkey, David K.
Ng, Shu Wen
Meyer, Katie A.
Popkin, Barry M.
Reis, Jared P.
Shikany, James M.
Gordon-Larsen, Penny



2017


Journal of Epidemiology and Community Health

71

12

185-90








PMC5713903


10630

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