FROM CAUSE TO IMPACT: MODELLING DETERMINANTS AND EFFECTS OF OBESITY

Transcription

1 FROM CAUSE TO IMPACT: MODELLING DETERMINANTS AND EFFECTS OF OBESITY May 2, 2017 Sahara Graf Andrea Feigl OECD FCAN meeting

2 Rate of obesity Obesity rates will continue to rise 50% 45% 40% 35% 30% 25% 20% 15% 10% 5% Obesity projections in the OECD France USA Mexico England Canada Spain Switzerland Italy Korea 0% Year Note: Obesity defined as BMI 30kg/m². OECD projections assume that BMI will continue to rise as a linear function of time. Source: OECD analysis of national health survey data. Obesity will continue to rise in the OECD, with especially strong rates of increase in the US, England, Canada and Switzerland

3 Source: Department of Health (UK). Tackling obesities: future choices (2007)

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5 - The causal chains leading to obesity are complex and interlinked - Complex problems warrant complex solutions and analysis

6 Relative index of inequality Inequalities of fruit and vegetable consumption are significant in most countries Prevalence of low consumption is higher in those with a lower level of education Australia: prevalence is nearly 40% higher in women with a low level of education Inequalities are higher for women than for men overall Relative index of inequality by level of education Australia Canada Chile England France Hungary Italy Korea Mexico Spain USA Men Women Notes: Data refer to ages except in Canada (15-64). Upper confidence interval truncated for Korea (2.2) Source: OECD analysis of health survey data.

7 Individuals with lower socio-economic characteristics are at highest risk Populations at highest risk of low quality diet Low quality diet Chile Korea Mexico Spain USA Aged under 20 Women with with medium medium SES education and education Aged with medium SES Women aged over 65 with medium education Men aged 20-34, Mexican- American, with low education and normal weight Source: OECD analysis of health survey data. Latent class analysis sorted individuals into classes based on health behaviours: diet quality (national guidelines), physical activity levels (WHO recommendations), Sedentarism ( 7 hours/day) Multinomial regressions characteristics of those at highest risk

8 Modelling Scenarios and Policies Price policies Regulation/ enforcement Education Healthcare Diet, Physical activity, Obesity Self-regul. advertisement Compulsory food labelling Mass media campaigns Physician counselling Physic/diet counselling Either Risk (PA, Diet, Alcohol) Fiscal measures to change price (tax/subsid) Regulation advertisement School-based programmes Worksite interventions Alcohol consumption Minimum pricing Drink drive enforcement Limit opening hours Brief Intervention Drug / psychos therapy

9 Policies impact on the market for specific products (revenues, cost for industry The New Multi-Risk Factors OECD Platform Open source, developed in C++ Modular and upgradable Modelling timeframe: Geographical scope: Europe, (soon OECD; and beyond) Major risk factors and NCDs Healthcare costs Impact on labour outcomes / economy (by 2018) Absenteeism & presentism Welfare benefits & transfer payments

10 Modelling Framework: from birth to death Birth/Entry Risk Factors Diseases Death/Exit Birth Inward migration Behavioural Physiolog. Environm. SES Incidence Remission Prevalence QoL (Costs) Death by disease Residual mortality Outward migration Risk factors: Physical activity, alcohol, tobacco salt, BMI, blood pressure, cholesterol, [pollution]. Dietary dimensions to be added Diseases: Stroke (3 types), IHD, Cancers (stomach, colorectal, lung, liver, breast), diabetes, COPD, cirrhosis, depression, Alzheimer /dementia, Injuries (2 types), MSDs (4 types) More diseases to be added (e.g. AUDs)

11 Considerations for food reformulation: labelling and content BMI reduction: - Food reformulation - Salt reduction - Portion size changes - Nutrition labeling - Vending machine content replacement - Product diversification (i.e. diet sodas, flavored waters) - Added Sugar reduction Weaker evidence on: - Revenue impact for higher alcohol taxes 11

12 Next steps/ collaboration Adding to the knowledge information for OECD countries How consumers react behavioural economics Bridging the gap with the private sector

13 Additional Slides

14 Health outcomes SSB taxation impact in South Africa Health impact Impact on health expenditure life years (thousands) DALYs (thousands) costs (million $PPP) Age group Source: OECD analysis, forthcoming

15 The interventions applies the SSB tax as designed by the South African National Treasury, an increase of R2.21 per litre or R per gram of sugar on a litre over 4g/100ml, equivalent to a 12% effective tax rate at current prices and sugar content of soft drinks and concentrates (National Treasury, 2017). The tax is assumed to pass through to customers as in the baseline scenario of Manyema et al (2014). It is also assumed that the SSB industry does not put in place any intervention to reformulate SSBs. Reformulation by the industry would lead to smaller consumption changes but greater mean BMI changes. The own-price elasticity for SSB consumption is meaning the 12% effective tax rate reduces SSB consumption by approximately 9%. It is assumed that a tax increase translates almost immediately into an increase in the price of SSBs. The price increase on SSBs increases fruit juice consumption by 6% (cross-price elasticity = 0.53) and diet drink consumption by 5% (cross-price elasticity = 0.44) (National Treasury, 2017).

16 Effectiveness of various nutrition interventions

17 The Economic Impact of Tackling Obesity: Effectiveness and Cost-Effectiveness Effect of food labels in selecting and healthier option GDA Other food labels Traffic light Overall Source: Cecchini & Warin, Ob Rev Increase (%) in people selecting a healthier option