COMCEC

Malnutrition in the OIC Member

Countries: A Trap for Poverty

Brief Description of Over- and Undernutrition Profiles of the Selected Countries

For this briefing, the most recent Global Nutrition Scorecard was used for each country.

Senegal: Some evidence of progress. Prevalence of under-five stunting went from 29% in 2011

to 19% in 2014. Some evidence of growing over-nutrition: in 2008, 28% of people were

overweight and

% were obese. The country is off-course for all under-nutrition targets of the

WHA (World Health Assembly Indicators).

Indonesia: Very high prevalence of under-nutrition and limited signs of progress: prevalence of

under-five stunting only went down from 40% in 2007 to 36% in 2013. Prevalence of over

nutrition: 21% were overweight and 5% were obese in 2008. The country is off-course for all

WHA targets (World Health Assembly Indicators).

Egypt: Signs of progress: prevalence of under-five stunting went down from 31% in 2008 to

22% in 2014. The country is on course to meet the WHA indicator of under-five stunting. High

prevalence of over-nutrition: proportion of adolescent overweight is 33% in 2011; 62% of the

population was overweight and 29% was obese in 2014. The country is off-course on under-

five wasting and general overweight indicators.

Bangladesh: High rates of under-nutrition but signs of progress (the country is on course for

meeting the WHA under-five stunting target). Prevalence of under-five stunting went down

from 42% in 2012 to 36% in 2014. Limited prevalence of overweight (with compared to the

countries in the sample): 18% of the population was overweight and 4% was obese in 2014.

The country is on-course for meeting the under-five overweight target.

Tajikistan: High prevalence of under-five stunting but some signs of progress (it went down

from 39% in 2007 to 27% in 2012), but the country is off-course for meeting this under-five

stunting target. Very high prevalence of overweight: 45% of the population was overweight

and 14% was obese in 2014; however, the country is on-course to meet the under-five

overweight target.

Quantitative Analysis

For each selected country, two sets of quantitative data analysis were undertaken. The first

one intends to shed light on the relationships between poverty and malnutrition. The second

one relates to the intergenerational transmission of malnutrition.

Poverty and malnutrition

DHS reports routinely feature cross-tabulations of malnutrition against meaningful categories

such as age, rural-urban residence, wealth groups or education levels. While these are

suggestive of the relationships between poverty and malnutrition, such an approach does not

inform us on the most significant factors of malnutrition. The reason is that many of these

categories are correlated, such as education and wealth. If both turn out to be significantly

associated with malnutrition in cross-tabulations, it does not guarantee that the same is true

when one accounts for this correlation. It may be, for instance, that education matters, but not

wealth (or the other way around). To account for these correlations, multivariate regressions

of malnutrition are estimated. These multivariate regressions will rely on the latest available

cross-section from DHS (this is true for all countries bar Indonesia for which DHS does not

have anthropometry data; the Indonesia Family Life Survey is used instead) and will focus on

the last-born child in each household (to enable the inclusion in the analysis of detailed

breastfeeding practices).