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Time Preferences and the Development of Obesity

Time Preferences and the Development of Obesity. Ewan Gray University of Aberdeen Health Economics Research Unit (HERU). Time Preferences. Time Perspective. Time Perspective is an equivalent psychological concept.

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Time Preferences and the Development of Obesity

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  1. Time Preferences and the Development of Obesity Ewan Gray University of Aberdeen Health Economics Research Unit (HERU)

  2. Time Preferences

  3. Time Perspective • Time Perspective is an equivalent psychological concept. • Consideration of Future Consequences Scale (CFCS) is a survey instrument designed to measure time perspective/time preference. High correlation with time preference rate.

  4. CFCS • Examples (1-7 scale): • “I am only concerned about the present, because I trust that things will work themselves out in the future.” • “With everything I do, I am only concerned about the immediate consequences (say a period of a couple of days or weeks). ”

  5. Time Preferences Incredibly simple model Health Behaviours Intentions Other factors influencing intentions

  6. DHS • DnB Household Survey (DHS) • Data from 1993-2009. Use 1996-2009. • 2,000 (1660 by 2009) households on CentERpanel (representative of Netherlands population). Online, arrangements for access with no computer. Self-report. • Includes: Basic demographic, basic health (BMI, limiting health problem, smoking, alcohol consumption), detailed income, assets, liabilities and some interesting psychological variables (time preferences, risk preferences, personality). • Includes CFCS, height and weight. • Previous cross-sectional study found weak evidence of association of high TP and increased BMI (Borghans and Golsteyn, 2006).

  7. Aim • Do time preferences (CFCS score) effect the development of obesity? • Previous studies have not obtained a conclusive answer. • Five previous studies (4 cross-sectional, 1 ecological) have found mixed evidence of a weak effect of time preference. • Statistical significance only achieved for sub-groups or in some models in each cross-sectional study. Studies used moderately large data-sets from USA, Netherlands, England and Japan.

  8. Methods • Non-parametric • Plot Kaplan-Meier survival functions for quartiles of CFCS distribution. Log-rank test. • Semi-parametric • Cox regression • CFCS score is independent. • Controlling for age, gender, education and initial BMI.

  9. Results Log-rank test:χ2 = 14.16, p 0.0027

  10. Results 2 Coef. (s.e.), *P<0.1, **P<0.05, ***P<0.01

  11. Results 3

  12. Sensitivity to Obesity BMI cut-off value

  13. Conclusions • CFCS is significantly associated with hazard of obesity. • A high CFCS predicts greater hazard of obesity. Hazard ratio (for normalised CFCS): 1.151 (1.07, 1.238). • This estimate is robust to different specifications of the control variables.

  14. Challenges/Limitations • Data: • Attrition/censoring is high and may be non-random • Missing and implausible values • Models: • Other BMI dynamics than occurrence of BMI>30 are of interest. • Other response variables may be more appropriate such as BMI or a binary dependent with a probit or logit link function.

  15. Questions?

  16. Summary Statistics

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