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Revisiting the Socioeconomic Gradient in Obesity

Marcel Bilger 1 , Eliza Kruger 1 , Eric Finkelstein 1, 2. 1 Signature Program in Health Services & Systems Research Duke-NUS Graduate Medical School, Singapore 2 Lien Centre for Palliative Care, Duke-NUS, Singapore. Revisiting the Socioeconomic Gradient in Obesity.

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Revisiting the Socioeconomic Gradient in Obesity

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  1. Marcel Bilger1, Eliza Kruger1, Eric Finkelstein1, 2 1 Signature Program in Health Services & Systems Research Duke-NUS Graduate Medical School, Singapore 2 Lien Centre for Palliative Care, Duke-NUS, Singapore Revisiting the Socioeconomic Gradient in Obesity Inaugural Conference of the Singapore Health Economics Association Looking Beyond the Obesity Threshold April 17th, 2013

  2. Outline • Why study socioeconomic inequality in obesity • Our proposed method to measure socioeconomic inequality • Trends in socioeconomic inequality in obesity in the US • Decomposition of socioeconomic inequality according to gender, race and other factors • Discussion

  3. Obesity Rates • One of the most prominent health concerns facing the US is the growing prevalence of obesity • Rates for adults have more than doubled in the past 30 years (CDC 2010) • 66% of adults are overweight • 34% are obese • Singapore is also affected as obesity rates have increased from 6% in 1998 to 11% in 2010 (MOH Singapore)

  4. Distribution of Obesity • Whether the rich or the poor are disproportionally affected by obesity matters too • If the poor are disproportionally affected: • This will contribute to increase health inequality • The poor will not be able to financially cope with the increased health risks they face • Ultimately, this will affect the public health care sector, through increased subsidies and/or increased social insurance premiums

  5. Concentration Index (CI) • Measures degree of socioeconomic inequality in health (e.g. obesity) • CI<0: the poor are more affected by obesity • CI>0: the rich are more affected by obesity • CI is normalized between -1 and +1 Source: Zhang Q, Wang Y (2007)

  6. Foster-Greer-Thorbecke (FGT) measures • c is the obesity threshold (i.e. BMI>30) • αis the parameter that sets the sensitivity of the measure above the threshold • α = 0: Y measures obesity status • α = 1: Y measures depth of obesity • α = 2: Y measures severity of obesity

  7. Data • National Health and Nutrition Examination Survey (NHANES) • Inclusion criteria – age 20 to 65, non-pregnant, data on income and BMI available • We separately analyze data from 5 time periods (sample size in brackets); • NHANES 1 1971-1974 (2,986) • NHANES 2 1976-1980 (9,101) • NHANES 3 1988-1994 (12,275) • Continuous NHANES 1999-2004 (8,533) • Continuous NHANES 2005-2010 (7,969)

  8. Variable Definitions • Poverty income ratio • calculated by dividing family income by the poverty lines established by the federal Register • Education (5 levels) • Gender • Race (Mexican American, Non-Hispanic White, Non-Hispanic Black) • Immigration status (born outside the US or not) • Marital status (5 levels)

  9. Concentration Index of Status, Depth and Severity of Obesity (BMI>30)

  10. Decomposition of Overall Socioeconomic Inequality in Obesity (2005-2010)

  11. Decomposition of the CI by race

  12. Decomposition of the CI by Gender

  13. We propose measures of socioeconomic inequality in obesity that account for excess weight above the obesity threshold In the US, there is no socioeconomic inequality in the prevalence of obesity anymore But the poor are still disproportionally more severely obese than the rich Socioeconomic inequality in obesity greatly varies according to race, gender, and many other factors Decomposition analysis permits finer analysis Summary

  14. Poor obese individuals will not be able to financially cope with the consequences of their increased health risks Socioeconomic inequality thus increases the burden on the public health sector Given the increase in obesity prevalence in Singapore, a similar analysis would be useful The measures we propose are not limited to obesity but could be applied to any quantitative variables with a threshold or ceiling e.g. hypertension, diabetes Discussion

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