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Urbanization, Wealth and Overweight in Sub-Saharan Africa

Urbanization, Wealth and Overweight in Sub-Saharan Africa. Nyovani Madise & Gobopamang Letamo* *University of Botswana 12 th June 2014. Population Health Conference, Southampton. Rationale. Increasing prevalence of overweight and obesity globally

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Urbanization, Wealth and Overweight in Sub-Saharan Africa

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  1. Urbanization, Wealth and Overweight in Sub-Saharan Africa Nyovani Madise & Gobopamang Letamo* *University of Botswana 12th June 2014. Population Health Conference, Southampton

  2. Rationale • Increasing prevalence of overweight and obesity globally • Africa is no exception- levels particularly high among women ages 20+ years (31%) compared with 23% among males. • Overweight/obesity – risk factor for many non-communicable diseases • Estimated 3 million premature deaths per year dues to NCDs • Urbanization is rising. By 2030, 50% will be urban • Growing middle-class

  3. The Nutrition Transition Popkin and Gordon Larsen (2004) describe 5 patterns Collecting Food Famine Receding famine Nutrition-related NCDs Behavioural Change Patterns not restricted to time periods Links with epidemiological & demographic transitions

  4. Our Assumptions • Household wealth strongly associated with the risk of being overweight or obese; • Risk is higher among urban than rural women, except where national prevalence is very high • The most affluent women in urban areas may be the first to adopt behaviour change towards healthier diets and exercise.

  5. By far the largest problem is under-nutrition among young childrenPercentageof under 5 children who were stunted

  6. Data Nationally representative surveys from 30 countries in sub-Saharan Africa conducted between 2006 and 2012 [Demographic and Health Survey programme] • In total 208,656 women 15-49 years • Individual country sample sizes range 2,960-26,500 • Mean age 28.8 years (SD=9.8) • Mean children ever born 2.84 • 33% had no formal education; 4% had tertiary education

  7. Methods • Individual country-level logistic regression analyses • Dependent variable: BMI >=25 • Key variables: urban/rural residence, wealth status and their interaction • Also controlled for age, highest education, religion, region of residence, marital status • Excluded those pregnant and given birth in the 4 months before surveys • Identified three groups of countries based on interaction between wealth status and urban/rural residence

  8. Methods • Pooled data according to three groups • Used macro-level variables to explain patterns • Gross National Income per capita (PPP constant 2005, international $) • Life Expectancy at birth • Population living in urban areas • Percent under-five who are stunted. • Overall percent of all adult women overweight/obese

  9. Results

  10. Source of data: WHO Global Health Observatory.

  11. Group 1: Traditional African PatternLow national income (median=$1149), Medium Urbanization (36%), Low life expectancy (57 years); Lower female overweight/obesity (25%) Benin, Burkina Faso, Burundi, Cameroon, Ethiopia, Democratic Republic of Congo, Congo Brazzaville, Guinea, Senegal, Sierra Leone, Uganda

  12. Group 2: Urban/Rural Cross-OverLower-middle income (median $1416); High urbanization (43%); Medium life expectancy (62 years); High female overweight/obesity (32%) Botswana, Namibia, Tanzania, Zambia, Kenya, Gabon, Ghana, Madagascar, Niger, Liberia

  13. Group 3: Universally High Overweight/ObesityMiddle income (median $1881); Low urbanization (28%); Low level of life expectancy 56 years- HIV effectVery high levels of female overweight/obesity (43%) SADC countries: Lesotho, Swaziland, Mozambique, Zimbabwe. Plus: Rwanda

  14. Prevalence of overweight/obesity by age of woman and Group Type

  15. Concluding Remarks • Prevalence of overweight and obesity in Africa – 31% [over 70% in North Africa (e.g. Egypt) and South Africa] • Urban residents and wealthiest most at risk BUT cross-over towards overweight/obesity among rural residents Some regions e.g. southern Africa affected by HIV • Body size preferences linked to HIV fears; Still high child malnutrition levels

  16. Acknowledgment STARND –EDULINK partners (Universities of Botswana,, Malawi, Namibia, the Witwatersrand, Zambia, Eduardo Mondlane, Lesotho) ACP EDULINK Grant Contract # 2008/197619.

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