Article Text

other Versions

PDF
Understanding bias in relationships between the food environment and diet quality: the Coronary Artery Risk Development in Young Adults (CARDIA) study
  1. Pasquale E Rummo1,2,
  2. David K Guilkey3,4,
  3. Shu Wen Ng1,4,
  4. Katie A Meyer1,4,
  5. Barry M Popkin1,4,
  6. Jared P Reis5,
  7. James M Shikany6,
  8. Penny Gordon-Larsen1,4
  1. 1Department of Nutrition, Gillings School of Public Health, University of North Carolina, Chapel Hill, North Carolina, USA
  2. 2Department of Population Health, NYU School of Medicine, New York, NY, USA
  3. 3Department of Economics, University of North Carolina, Chapel Hill, North Carolina, USA
  4. 4Carolina Population Center, Chapel Hill, North Carolina, USA
  5. 5Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, Bethesda, Maryland, USA
  6. 6Division of Preventive Medicine, University of Alabama at Birmingham, Birmingham, Alabama, USA
  1. Correspondence to Dr Penny Gordon-Larsen, Department of Nutrition, University of North Carolina, Chapel Hill, NC 27514, USA; pglarsen{at}email.unc.edu

Abstract

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 (β=−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.

  • diet
  • epidemiological methods
  • health behaviour
  • neighborhood/place
  • nutrition

Statistics from Altmetric.com

Footnotes

  • Contributors PER had full access to the data and takes responsibility for the integrity of the data and the accuracy of the data analysis. PER performed the statistical analysis and analysed and interpreted the data. PER and PG-L drafted the article. DKG, SWN, KAM, BMP, JMS, JPR and PG-L critically revised the article for important intellectual content. PG-L acquired the data, obtained the funding, approved the final draft of the article and supervised the study. All authors contributed to the study concept and design.

  • Funding This work was supported by the National Heart, Lung, and Blood Institute(grants R01HL104580 and R01HL114091). The Coronary Artery Risk Development in Young Adults Study is supported by contracts from the National Heart, Lung, and Blood Institute, the Intramural Research Program of the National Institute on Aging, and an intra-agency agreement between NIA and NHLBI (grants HHSN268201300025C, HHSN268201300026C, HHSN268201300027C, HHSN268201300028C, HHSN268201300029C, HHSN268200900041C, and AG0005). The authors are grateful to the Carolina Population Center, University of North Carolina at Chapel Hill, for general support from the Eunice Kennedy Shriver National Institute of Child Health and Human Development(grant P2C HD050924), to the Nutrition Obesity Research Center,University of North Carolina for support from the National Institute for Diabetes and Digestive and Kidney Diseases (grant P30DK56350),and to the Center for Environmental Health Sciences, University of North Carolina for support from the National Institute for Environmental Health Sciences (grant P30ES010126).

  • Competing interests None declared.

  • Ethics approval Ethics approval was obtained by the University of North Carolina at Chapel Hill Institutional Review Board (IRB: #11-1393).

  • Provenance and peer review Not commissioned; externally peer reviewed.

Request permissions

If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.