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By how much would limiting TV food advertising reduce childhood obesity?

Veerman JL, Van Beeck EF, Barendregt JJ, Mackenbach JP - Eur J Public Health (2009)

Bottom Line: The strength of this effect is unclear.In an additional analysis we use a Delphi study to obtain experts' estimates of the effect of advertising on consumption.Based on literature findings, the model predicts that reducing the exposure to zero would decrease the average BMI by 0.38 kg/m(-2) and lower the prevalence of obesity from 17.8 to 15.2% (95% uncertainty interval 14.8-15.6) for boys and from 15.9% to 13.5% (13.1-13.8) for girls.

View Article: PubMed Central - PubMed

Affiliation: Department of Public Health, Erasmus University Rotterdam, Rotterdam, The Netherlands. l.veerman@uq.edu.au

ABSTRACT

Background: There is evidence suggesting that food advertising causes childhood obesity. The strength of this effect is unclear. To inform decisions on whether to restrict advertising opportunities, we estimate how much of the childhood obesity prevalence is attributable to food advertising on television (TV).

Methods: We constructed a mathematical simulation model to estimate the potential effects of reducing the exposure of 6- to 12-year-old US children to TV advertising for food on the prevalence of overweight and obesity. Model input was based on body measurements from NHANES 2003-04, the CDC-2000 cut-offs for weight categories, and literature that relates advertising to consumption levels and consumption to body mass. In an additional analysis we use a Delphi study to obtain experts' estimates of the effect of advertising on consumption.

Results: Based on literature findings, the model predicts that reducing the exposure to zero would decrease the average BMI by 0.38 kg/m(-2) and lower the prevalence of obesity from 17.8 to 15.2% (95% uncertainty interval 14.8-15.6) for boys and from 15.9% to 13.5% (13.1-13.8) for girls. When estimates are based on expert opinion, these values are 11.0% (7.7-14.0) and 9.9% (7.2-12.4), respectively.

Conclusion: This study suggests that from one in seven up to one in three obese children in the USA might not have been obese in the absence of advertising for unhealthy food on TV. Limiting the exposure of children to marketing of energy-dense food could be part of a broader effort to make children's diets healthier.

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Modelled BMI distribution of 10-year old US boys. The solid line represents the distribution in 2003–04 based on NHANES data, the dashed line is based on the NHES data from 1963 to 1970.15 The area under the curve represents the total population of 10-year old boys. In 2003–04 a greater proportion of the distribution lies above the age-specific cut-off points for overweight (19.76) and obesity (22.64), reflecting the rising prevalence of overweight and obesity
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Figure 1: Modelled BMI distribution of 10-year old US boys. The solid line represents the distribution in 2003–04 based on NHANES data, the dashed line is based on the NHES data from 1963 to 1970.15 The area under the curve represents the total population of 10-year old boys. In 2003–04 a greater proportion of the distribution lies above the age-specific cut-off points for overweight (19.76) and obesity (22.64), reflecting the rising prevalence of overweight and obesity

Mentions: Finally, we applied Rose's theorem that the mean predicts the number of deviant individuals14 and assumed that the average BMI predicts the number of overweight and obese. The development of the obesity epidemic can be conceptualized as a shifting population distribution of BMI.11 Over the years, the BMI-curve has shifted to higher values with increased skew to the right (figure 1).15 This is reflected in the mean BMI, which has also steadily increased.16 We fitted the measured BMI-data of the NHANES 2003–04 study, using the proper sample weights, to a lognormal curve using the least squares method.17 To mimic the historic changes observed in the data, we fixed the lower end of the BMI-distribution in the intervention population. This is consistent with the data and theoretically plausible: below a certain BMI level, no life is possible. We modelled boys and girls separately in 1-year age categories. Children with a BMI between the 85th and 95th percentiles of US reference populations are categorized as overweight, and those above the 95th percentile as obese.18,19 Because these definitions are based on age by month, we used the mid-year values. The entire BMI distribution can be manipulated by changing its mean value. Shifting the average upwards increases the variance and the rightward skew, which pushes a higher proportion of the population above the 85th and 95th percentile thresholds. The model was implemented in a spreadsheet (MS Excel).Figure 1


By how much would limiting TV food advertising reduce childhood obesity?

Veerman JL, Van Beeck EF, Barendregt JJ, Mackenbach JP - Eur J Public Health (2009)

Modelled BMI distribution of 10-year old US boys. The solid line represents the distribution in 2003–04 based on NHANES data, the dashed line is based on the NHES data from 1963 to 1970.15 The area under the curve represents the total population of 10-year old boys. In 2003–04 a greater proportion of the distribution lies above the age-specific cut-off points for overweight (19.76) and obesity (22.64), reflecting the rising prevalence of overweight and obesity
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2712920&req=5

Figure 1: Modelled BMI distribution of 10-year old US boys. The solid line represents the distribution in 2003–04 based on NHANES data, the dashed line is based on the NHES data from 1963 to 1970.15 The area under the curve represents the total population of 10-year old boys. In 2003–04 a greater proportion of the distribution lies above the age-specific cut-off points for overweight (19.76) and obesity (22.64), reflecting the rising prevalence of overweight and obesity
Mentions: Finally, we applied Rose's theorem that the mean predicts the number of deviant individuals14 and assumed that the average BMI predicts the number of overweight and obese. The development of the obesity epidemic can be conceptualized as a shifting population distribution of BMI.11 Over the years, the BMI-curve has shifted to higher values with increased skew to the right (figure 1).15 This is reflected in the mean BMI, which has also steadily increased.16 We fitted the measured BMI-data of the NHANES 2003–04 study, using the proper sample weights, to a lognormal curve using the least squares method.17 To mimic the historic changes observed in the data, we fixed the lower end of the BMI-distribution in the intervention population. This is consistent with the data and theoretically plausible: below a certain BMI level, no life is possible. We modelled boys and girls separately in 1-year age categories. Children with a BMI between the 85th and 95th percentiles of US reference populations are categorized as overweight, and those above the 95th percentile as obese.18,19 Because these definitions are based on age by month, we used the mid-year values. The entire BMI distribution can be manipulated by changing its mean value. Shifting the average upwards increases the variance and the rightward skew, which pushes a higher proportion of the population above the 85th and 95th percentile thresholds. The model was implemented in a spreadsheet (MS Excel).Figure 1

Bottom Line: The strength of this effect is unclear.In an additional analysis we use a Delphi study to obtain experts' estimates of the effect of advertising on consumption.Based on literature findings, the model predicts that reducing the exposure to zero would decrease the average BMI by 0.38 kg/m(-2) and lower the prevalence of obesity from 17.8 to 15.2% (95% uncertainty interval 14.8-15.6) for boys and from 15.9% to 13.5% (13.1-13.8) for girls.

View Article: PubMed Central - PubMed

Affiliation: Department of Public Health, Erasmus University Rotterdam, Rotterdam, The Netherlands. l.veerman@uq.edu.au

ABSTRACT

Background: There is evidence suggesting that food advertising causes childhood obesity. The strength of this effect is unclear. To inform decisions on whether to restrict advertising opportunities, we estimate how much of the childhood obesity prevalence is attributable to food advertising on television (TV).

Methods: We constructed a mathematical simulation model to estimate the potential effects of reducing the exposure of 6- to 12-year-old US children to TV advertising for food on the prevalence of overweight and obesity. Model input was based on body measurements from NHANES 2003-04, the CDC-2000 cut-offs for weight categories, and literature that relates advertising to consumption levels and consumption to body mass. In an additional analysis we use a Delphi study to obtain experts' estimates of the effect of advertising on consumption.

Results: Based on literature findings, the model predicts that reducing the exposure to zero would decrease the average BMI by 0.38 kg/m(-2) and lower the prevalence of obesity from 17.8 to 15.2% (95% uncertainty interval 14.8-15.6) for boys and from 15.9% to 13.5% (13.1-13.8) for girls. When estimates are based on expert opinion, these values are 11.0% (7.7-14.0) and 9.9% (7.2-12.4), respectively.

Conclusion: This study suggests that from one in seven up to one in three obese children in the USA might not have been obese in the absence of advertising for unhealthy food on TV. Limiting the exposure of children to marketing of energy-dense food could be part of a broader effort to make children's diets healthier.

Show MeSH
Related in: MedlinePlus