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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. - PowerPoint PPT Presentation
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Ewan Gray
University of Aberdeen
Health Economics Research Unit (HERU)
Time Preferences and the Development of Obesity
Time Preferences
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.
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). ”
05
1015
Percent
0 20 40 60 80CFCS
Incredibly simple model
Other factors influencing intentions
Time Preferences
IntentionsHealth Behaviours
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).
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.
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.
– ).exp()(
)exp()()|(
543210
0
CFCSSecondaryUUniversityGenderageth
xthxth
Results
Log-rank test: χ2 = 14.16, p 0.0027
Results 2
Variable Model 1 2 – Quadratic Age 3 – Interactions
CFCS 0.01**(0.004) 0.011**(0.004) 0.011*** (0.004)
Age -0.01***(0.003) 0.061***(0.018) 0.067***(0.018)
Gender (Male) -0.386***(0.067) -0.385***(0.075) -1.474***(0.289)
University -1.18***(0.225) -1.104***(0.226) -1.113***(0.226)
U. Secondary -0.544***(0.097) -0.486***(0.096) -0.502***(0.099)
Age2 -0.0007***(0.0002) -0.0009***(0.0002)
Gender*Age 0.023***(0.006)
Coef. (s.e.), *P<0.1, **P<0.05, ***P<0.01
Results 3Variable 5- InitialBMI
CFCS 0.015***(0.004)
Age 0.063***(0.022)
Gender (Male) -0.221(0.333)
University 0.369(0.284)
U. Secondary 0.346***(0.126)
Age2 -0.0007***(0.0002)
Gender*Age 0.003(0.006)
Initial BMI 0.288***(0.006)
Sensitivity to Obesity BMI cut-off value
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.
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.
Questions?
Summary Statistics
Variable 1996 Mean (s.d.) or % 2009 Mean (s.d.) or %
Age 47.0(14.2) 54.9(14.4)
Female 46.3% 44.4%
BMI 24.3(3.46) 26.0(3.99)
CFCS score 41.63 (11.1) 42.95 (8.17)
University Education (2002) 2.0% 8.89%
U. Secondary Education(2002)
7.9% 26.9%