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A REVIEW OF THE SCIENCE UNDERLYING STUDIES OF OBESITY

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Title: A REVIEW OF THE SCIENCE UNDERLYING STUDIES OF OBESITY


1
A REVIEW OF THE SCIENCE UNDERLYING STUDIES OF
OBESITY
  • By
  • Sencer Ecer
  • and
  • Richard Higgins
  • LECG,LLC
  • 1725 Eye Street, NW, Suite 800
  • Washington, DC 20006
  • E-Mail Rhiggins_at_Lecg.com

2
WHO AND THE BUSH ADMINISTRATION
  • WHO Report of April 03
  • Recommendations
  • HHS Letter
  • Quality and objectivity of science are inadequate
  • Policy recommendations not justified by
    underlying science

3
CONTENTS
  • The Definition of Obesity
  • Proximate Causes of Obesity
  • Causes of Overeating and an Unbalanced Diet
  • Consequences of Obesity

4
THE DEFINITION OF OBESITY
  • In Terms of Body Fat or BMI
  • BMI Weight in LBS X .704.5/Height in
    inches-squared
  • You Know It When You See It
  • Top Percentiles
  • BMI that Minimizes the Risk of
  • Mortality?
  • Morbidity?
  • The role of multivariate analysis
  • Style of life
  • Key Reference Robert Fogel, AER, 1994.

5
Economic Growth, Population Theory, and
Physiology The Bearing of Long-Term Processes on
the Making of Economic Policy
  • By
  • Robert Fogel
  • American Economic Review, 1994

6
WAALER CURVES
  • Mortality/BMI Waaler curves for Norwegian men and
  • women between the ages of 50 and 54 as of 1963
  • show that a BMI of 23 maximizes longevity
  • Relative risk of mortality is related to BMI
  • U-shaped with minima at 23
  • The minima vary marginally by age category

7
FOGEL ISO-MORTALITY CURVES
  • Combinations of weight and height are associated
  • with the same relative risk of mortality
  • Can be defined in terms of morbidity too
  • Obesity is typically defined as BMI gt 30, where
    the ideal, 30, is associated with best
    performance measured in terms of mortality (or
    morbidity)
  • Optimal BMI is derived from the weight and height
    that minimizes mortality (or morbidity), here,
    based on the incidence of death in Waalers
    sample of 1.7 million Norwegian adult males (ages
    50 to 64)
  • optimal BMI for 6 male is 24 (mortality), 26
    (morbidity)
  • optimal BMI for 56 male is 25.7 (mortality),
    26.7 (morbidity)

8
PROXIMATE CAUSES OF OBESITY
  • Overeating
  • Unbalanced Diet
  • energy-dense, added sugar
  • excessive salt
  • Inadequate Exercise
  • Reference Cutler et al, J. Economic
    Perspectives, 2003.

9
Why Have Americans Become More Obese?
  • By
  • D. Cutler, E. Glaeser and J. Shapiro
  • Journal of Economic Perspectives
  • Summer 2003

10
FINDINGS (I)
  • Daily distribution of food intake by occasion
    Continuing Survey of Food Intake, 1977-78 and
    1994-1996
  • Comparisons
  • calorie intake for men rose by 268 per day for
    women, 143 per day
  • most of the increases are explained by increased
    calorie consumption through snacks 241 for men,
    160 for women

11
FINDINGS (I) (CONT.)
  • Implications
  • Obesity does not result from increased portion
    sizes in restaurants
  • Consumption of fattening foods at fast food
    restaurants has not caused increased obesity

12
FINDINGS (II)
  • Energy expenditure from time-use data (Robinson
  • and Godbey, Time for Life, Penn State University
  • Press, 1997).
  • Data Time-use diaries self reporting.
  • Overall daily activity time has not declined
    between 1965 and 1995
  • Time used in recreation increased over this
    period from 27 minutes/day to 47 minutes/day

13
FINDINGS (II) (cont.)
  • Hypotheses
  • Increased obesity results from greater food
    consumption, not less exercise
  • Principal cause is a reduction in the time-cost
    of food, which has led to food consumption on
    more occasions
  • Several Implications are tested (but with very
    limited data)
  • Cross-country regression percent of adult pop
    that is obese as a function of (1) the price of a
    Big Mac (2) National income per capita and (3)
    Percent of females in the labor force. Price of
    Big Mac is negative and significant.

14
FINDINGS (II) (cont.)
  • Authors also suggest that processing technology
    has not only
  • succeeded in the lowering the full price of food
    which
  • encourages consumption but that it has reduced
    the time
  • between the decision to consume and the act of
    consumption.
  • They hypothesize that many individuals lack self
    control (they
  • are not responsible individuals) they are less
    willing to trade
  • near-term consumption for somewhat more remote
  • consumption when the choice is imminent than when
    it is
  • remote.
  • How prevalent is such Irrationality? Is it
    present in children more than in adults?

15
CAUSES OF OVEREATING AND AN UNBALANCED DIET
  • Reduced Time-Cost of Food
  • Technology
  • Government subsidies
  • Media Influence
  • Advertising
  • Ignorance
  • Self-Control Problems

16
ADVERTISING AND OBESITY REVIEWS AND REVIEWS OF
REVIEWS
17
KAISER FAMILY FOUNDATION
  • The Role of Media in Childhood Obesity (2004)
  • Conclusions
  • Majority of research supports the proposition
    that more media time is positively related to
    being overweight
  • How media contribute to childhood obesity is
    inconclusive
  • Nonetheless, it appears likely that food
    advertising may well be the principal
    contributor

18
STRATHCLYDE REVIEW (2003)
  • Conclusions
  • Advertising to children has an adverse effect on
    food preferences, purchasing behavior and
    consumption

19
AMERICAN ACADEMY OF PEDIATRICS
  • Policy Statement Children, Adolescents, and
    Advertising, Pediatrics (1995) Conclusions
  • Advertising directed toward children is
    inherently deceptive

20
A REVIEW OF THE STRATHCLYDE REVIEW
  • David Ashton, Food Advertising and Childhood
  • Obesity, Journal of the Royal Society of
    Medicine,
  • Feb. 2004 Conclusions
  • The two most highly touted studies in the
    Strathclyde Review do not support the alleged
    positive relation between TV advertising or food
    promotion and food consumption behavior of
    children
  • There is no good evidence that advertising has
    substantial influence on childrens food
    consumption

21
A REVIEW OF THE STRATHCLYDE REVIEW (cont.)
  • Although food advertising to children has been
    banned in Quebec since 1980, childhood obesity
    prevalence does not differ across Canadian
    provinces

22
CONCLUSIONS
  • The magnitude of the effects of food advertising
    on
  • the demand for food is not known at this time.
    The
  • problem is made difficult for two reasons
  • The profitability of advertising depends
    principally on the competitive effects of
    advertising, not its effects on total demand.
    (Does advertising principally sell food brands or
    types of food?)
  • It is difficult to measure the total, as opposed
    to marginal effects of advertising in the absence
    of good natural experiments (the effects of bans,
    certain interventions, and so on)

23
ADVERTISING AND OVERWEIGHT IN CHILDREN
  • Effect of Television Advertisements for Foods on
    Food Consumption in Children by Jason, et al,
    Appetite (2003)
  • 42 children (aged 9-11) were divided into Lean,
    Overweight and Obese based on BMI (BMI gt 30,
    obese BMI lt 25, lean), they were shown cartoons
    with and without food advertising on two
    occasions two weeks apart after each showing
    they were presented with four types of snack
    foods and allowed to eat as much as desired.

24
ANALYSIS AND CONCLUSIONS
  • Obese and overweight children recognized
    significantly more food ads than the lean
    children
  • The heavier the child the more food was eaten
    after seeing the food ads, and the heavier the
    child the more food was eaten after seeing the
    non-food ads
  • correlation coefficient between food ads
    recognized and food consumed was 0.47 (not a
    partial correlation coefficient)
  • the correlation coefficient between non-food ads
    recognized and food consumed NOT reported

25
ANALYSIS AND CONCLUSIONS
  • Regardless of weight the average food intake with
    food ad was significantly greater than that w/o
    food ad
  • Why not regression?
  • The reason why obese and overweight children
    selectively recognized more food adverts and also
    ate the most food requires further
    investigation.
  • The observed association between remembering
    food ads and eating more indicates that a
    susceptibility to food cues contributes to this
    overeating and promotes weight gain in children.

26
Television Viewing as a Cause of Increasing
Obesity Among Children in the United States,
1986-1990
  • By
  • Steven Gortmaker, Aviva Must, Arthur Sobol,
  • Karen Peterson, Graham Colditz and William Dietz,
  • Archives of Pediatrics Adolescent Medicine, 1996

27
DATA
  • Baseline (1986) and follow-up (1990) interviews
    of 746 children 6 to 11 years old in 1986 with
    complete height and weight data from a nationally
    representative sample of youth (National
    Longitudinal Survey of Labor Market Experience,
    Youth Cohort (NLSY)
  • TV viewing time in 1990 0-2 hrs/day, (2-3
    hrs/day, (3-4 hrs/day, (4-5 hrs/day and (5-24
    hrs/day
  • Overweight, 1986, 1990 BMI gt 85th percentile
    adjusted for age and gender (using national
    standards)
  • Demographic and socioeconomic data

28
MODEL
  • Logistics model log odds overweight-not
    overweight regressed on (1) TV viewing time, (2)
    child overweight in 1986, (3) race, (4) mothers
    education, (5) gender, (6) intelligence of child,
    mother, and so on
  • Also looked at odds of being overweight in 1990
    if not overweight in 1986 related to similar
    variables, including TV viewing

29
RESULTS
  • Odds of being overweight are 5.3 times higher for
    children with more than 5 hours TV viewing a day
    than for those with at most 2 hours of TV viewing
    per day odds ratio of 5.3 and difference is
    statistically significant.
  • Becoming overweight at least 5 hrs./day vs. at
    most 2 hrs/day Odds ratio is 8.3, and is
    statistically significant.

30
COMMENTS
  • Duh!
  • Authors are not clear as to whether childs BMI
    in 1986 is a regressor, or whether mothers BMI
    is a regressor in general, more transparency
    would be helpful.
  • difficult to determine how robust findings are
  • difficult to evaluate methodology
  • Endogeneity obesity causes TV watching
  • bias
  • authors claim to test for this by looking at how
    TV watching in 1990 relates to overweight in
    1986 they find no statistically significant
    relation
  • inadequate test for causality (Granger, Hausman,
    and others)

31
CONCLUSIONS
  • TV viewing is only a proximate cause
  • TV viewing (studying?, computer game play?)
    substitutes for more energy-intense activities
  • increased snacking while watching TV ?
  • TV advertising increases the demand for food
    consumption ?
  • Concern that there is a cumulative effect of
    weight gain that will increase prevalence of
    chronic diseases adipocytes created by
    overeating ratchet effect
  • Solution TVs rigged to generators powered by
    riding bicycles

32
CONSEQUENCES OF BEING OVERWEIGHT
  • Effects on Earnings
  • Effects on Disability
  • Increased Morbidity and Mortality
  • Social costs
  • Reference J. Cawley, J. Human Resources, 2004

33
The Impact of Obesity on Wages
  • By
  • John Cawley,
  • Forthcoming in the Journal of Human Resources
    (2004)

34
DATA
  • NLSY longitudinal survey evidence with annual
  • interviews, 1979-1994, and bi-annual interviews,
  • 1996-2002

35
STATISTICAL MODEL
  • Log Wages a b BMI c X (demographic and
    socioeconomic variables) e
  • Estimated separately for Black, White and
    Hispanic women and men

36
MEASUREMENT AND ESTIMATION ISSUES
  • When e is correlated with BMI, the regression
    estimate of b is biased
  • Wages determine BMI (simultaneity bias)
  • Unobservable genetic and non-genetic factors that
    are correlated with BMI and with wage potential
    (errors in variables bias)

37
PREVIOUS FIXES
  • Lagged BMI (removes contemporaneous
    interdependence, only)
  • In absence of simultaneity, differencing using
    twins data
  • Instrumental Variables
  • Cawley deals with each, including removal of
    individual fixed effects to deal with
    time-invariant correlation between unobservable
    characteristics that affect both weight and wages

38
RESULTS
  • Cannot reject lack of simultaneity bias (Hausman
    test) OLS estimates best
  • There is a negative relation between wages and
    weight only for white females (an increase in
    weight of 64 pounds from the average of 148 to
    212, reduces wages by 9, the equivalent of 1.5
    years of education)

39
Body-Mass Index and Mortality in a Prospective
Cohort of U.S. Adults
  • By
  • E.E. Calle, M.J. Thun, J. Petrelli,C. Rodriguez,
    C. Heath,
  • The New England Joural of Medicine, 1999.

40
DATA
  • Participants in the Cancer Prevention Study II
    (that was begun by the American Cancer Society in
    1982).
  • Over 450,000 men and over 580,000 women were
    included (ages at least 30 years old, with at
    least one household member over 45.

41
STATISTICAL MODEL
  • Relative risks of death for four subgroups
  • Smokers (if ever smoked) v. non-smokers
  • People with history of disease v. no history of
    disease
  • History of disease cancer, heart disease,
    stroke, respiratory disease, current illness or a
    weight loss of at least 10 pounds in the previous
    year, current illness (of any type).
  • Controls for age, education, other demographics,
  • exercise level,
  • aspirin use,
  • index of fat consumption, vegetable consumption
  • (for women) estrogen replacement therapy

42
MEASUREMENT AND ESTIMATION ISSUES
  • History of disease variable includes any type of
    current illness. Vague?
  • Signs/magnitudes of coefficients are not
    reported. Expected directions? No statistical
    specification tests reported. Correct
    specification?

43
FINDINGS
  • Obesity is most strongly associated with
    increased death rate among nonsmokers and those
    with history of disease (Stronger in whites than
    blacks, weakest in black women).
  • High BMI most predictive of death from
    cardiovascular disease, especially in men.

44
AUTHORS OWN CRITICISMS
  • Antecedent disease bias pre-existing,
    unrecognized disease processes causing death.
  • Unable to control for long-term weight loss,
    which might increase relative risk
  • Self-reported index overestimates height,
    underestimates weight, thus overestimates BMI
  • No direct measure of central adiposity. Where
    fat is located in the body matters in relative
    risk of death. E.g., waist hip ratio predicts
    death better than BMI in Iowa elderly women

45
CONCLUSIONS
  • Risk of death from all causes increases
    throughout the range of overweight for all.
  • Risk associated with high BMI is higher for
    whites than blacks.
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