|Type||Journal Article - Food & Nutrition Bulletin|
|Title||Identifying priorities for emergency intervention from child wasting and mortality estimates in vulnerable areas of the Horn of Africa|
Background. The relation between anthropometric measures and mortality risk in different populations can provide a basis for deciding how malnutrition prevalences should be interpreted.
Objective. To assess criteria for deciding on needs for emergency interventions in the Horn of Africa based on associations between child wasting and mortality from 2000 to 2005.
Methods. Data were analyzed on child global acute malnutrition (GAM) prevalences and mortality estimates from about 900 area-level nutrition surveys from Ethiopia, Kenya, Somalia, Sudan, and Uganda; data on drought, floods, and food insecurity were added for Kenya (Rift Valley) and Ethiopia, from Food and Agriculture Organization (FAO) reports at the time.
Results. Higher rates of GAM were associated with increased mortality of children under 5 years of age (U5MR), more strongly among populations with pastoral livelihoods than with agricultural livelihoods. In all groups spikes of GAM and U5MR corresponded with drought (and floods). Different GAM cutoff points are needed for different populations. For example, to identify 75% of U5MRs above 2/10,000/day, the GAM cutoff point ranged from 20% GAM in the Rift Valley (Kenya) to 8% in Oromia or SNNPR (Ethiopia).
Conclusions. Survey results should be displayed as time series within geographic areas. Variable GAM cutoff points should be used, depending on livelihood or location. For example, a GAM cutoff point of 15% may be appropriate for pastoral groups and 10% for agricultural livelihood groups. This gives a basis for reexamining the guidelines currently used for interpreting wasting (or GAM) prevalences in terms of implications for intervention.
|»||Kenya - Multiple Indicator Cluster Survey 2000|
|»||Kingdom of Eswatini - Multiple Indicator Cluster Survey 2000|
|»||Lesotho - Multiple Indicator Cluster Survey 2000|
|»||Malawi - Multiple Indicator Cluster Survey 1995|
|»||Sudan - Multiple Indicator Cluster Survey 2000|
|»||Zambia - Multiple Indicator Cluster Survey 1999|