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Overcoming the challenges of interpreting nutritional status data

Overcoming the challenges of interpreting nutritional status data. Examples from field experience. Objectives. Prevention: To describe how FS data can be used to predict nutritional decline

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Overcoming the challenges of interpreting nutritional status data

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  1. Overcoming the challenges of interpreting nutritional status data Examples from field experience.

  2. Objectives • Prevention: To describe how FS data can be used to predict nutritional decline • Survey design, analysis and interpretation: To discuss the importance of understanding the context in which a nutrition survey is conducted • Programming: To discuss the importance of understanding the causes and risks of malnutrition at all stages of the programme cycle

  3. Predicting and preventing:how FS data can be used to predict nutritional decline • Malnutrition prevalence has limited predictive capacity • Changes in prevalence of acute malnutrition are often a late indicator of a crisis • Sphere humanitarian charter affirms the importance of the right to life with dignity for emergency affected populations

  4. Food information system: North Darfur, Sudan

  5. Post harvest assessment and estimation of food deficit and recommendations Nutritional surveys in relevant FEZs if gross deficits predicted. cf baseline Oct-Nov-Dec-Jan-Feb-Mar-Apr-May-June-July-Aug-Sept-Oct Verification of post harvest assessment Annual collection of price data from 6 sites

  6. Baseline, predicted (Oct 1997) and actual food sources (April 1998), poor pastoral households, N. Kutum, Darfur. SC UK

  7. Case 1: Darfur, Sudan 2001-2 • some areas affected by two years of drought • millet prices high and rising in areas with poor harvests • terms of trade poor and only temporarily alleviated by lift on livestock export ban Dec 01-Feb 02 • water shortages in hafirs and dams in some areas

  8. October 01 January 02 April 02

  9. Case 2: Binga, Zimbabwe 2001-2 • 2001 harvest (may) 30% less than average: knock on effect on prices of livestock and wage labour rates • Dec 2001 sharp decline in food available on the market: prices increase x5 may 2001-march 2002 • fuel crisis affecting access to health care

  10. Free food aid (75% ration) for 50% population (4 rounds) supplementary feeding for preschoolers

  11. Coping? • Destocking of livestock and heavy reliance on other coping strategies such as migration to fishing camps, gathering of wild foods and reduced expenditure on non-food items to enable households to remain food secure. • 2002-03 is set to be much worse than 2001-02 and even 1992. • 80-100% loss of food crop harvests, and 70% loss of cash crops compared to a normal year.

  12. Case 3: Kirundo, Burundi • 1998-2000 poor rainfall in the north • poor recovery of livestock holdings following displacement in 1993-5 • poor people have very poor access to land and rely on the richer households for labour

  13. (5% oedema) HEA predicted increased reliance in labour markets & price increases. Poor HH facing 30-60% deficit in last 4 months HEA showed food and cash income came from own prod. in first 7-8 months Only half recommended food aid distributed Epidemics begin

  14. Coping? • prevention of migration • prevention of nutritional crisis (cf Karuzi) • widespread sale of assets. Proportion of households with no livestock doubled

  15. Key lessons which SC has learnt • Systems can be set up which provide timely information according to the anticipated cycle of the emergency. • Nutrition and mortality survey data are useful for verifying predictions of food security. • Food security information allows determination of the appropriate timing of a nutritional survey • We should be advocating against an over-reliance on nutrition data to initiate response. • Food security information allows a judgement on the affects of the crisis on the sustainability of livelihoods and the vulnerability to future crises

  16. Nutrition SurveysUnderstanding the context for survey design • Two stage cluster sample surveys assume uniform prevalence both across the geographical area of the survey and within the population

  17. Case 4: Darfur, Sudan. April, 2001Save the Children UK surveys

  18. Another NGO assessment at the same time in N Darfur • 4 people, 27 locations, 21 days • 424 children measured with MUAC in convenience samples • focus on those perceived at risk: the displaced • Results • 1% had a MUAC <110mm, • 5% was between 110 - 125mm and • 12.5% between 126 – 135mm.

  19. The displaced were actually those who had moved to wadis with cattle and were often from the richest groups

  20. Interpreting the data:understanding the “normal” nutrition situation • populations with assumed high baseline rates • seasonal variation • understanding the consequences of speed of change in nutritional status: relationship with mortality

  21. Case 5: Wolayita, Ethiopia

  22. Interpreting the data: information on the causes of malnutrition • Household food security information is essential (questions as part of nut survey often difficult to interpret) • Health surveillance data / reports of outbreaks • Infant feeding information: difficult to get adequate precision on rates; separated children; population demography; • the importance of complete information

  23. Interpreting the data:making the right recommendations Case 6: displaced people in El Laeit and Tweisha rural councils, N Darfur Sudan, April 2000 • Global malnutrition 22.7 % (CI 18.2 – 27.2 )Severe malnutrition 3.2% • Crude mortality rate 3.73/10000/day • Under five mortality rate 8.49 /10000/day

  24. food deficit predicted 10-15% among the poor households hitting in June- Oct • poor cereal production, high grain prices and low groundnut prices

  25. Morbidity in the previous 2 weeks • Diarrhoea 73 cases 19.7% • Fever 23 ,, 6.25 • Measles 198 ,, 53.5% • ARI 127 ,, 34.3% • Night Blindness 11 ,, 3.0% • 26% measles immunisation coverage

  26. Case 7:North Shewa, Ethiopia

  27. Making the right recommendationsDeciding who needs assistance • Targeting households according to nutritional status: you cannot prevent malnutrition among those at risk • Understanding of households at risk allows agencies to work withcommunities to design programmes appropriately

  28. Within community variationCase 8: Wollo, Ethiopia

  29. Key lessons which SC has learnt • It is possible to identify geographical areas for nutrition surveys which take into account the food security variations over space • interpretation of cross sectional surveys is greatly improved if baseline or previous repeated surveys are available • understanding of seasonality is essential to interpret a nutrition prevalence rate • data interpretation should not be attempted unless information is available on food security, health and care factors • data on who is at risk of malnutrition is veryimportant for making sound recommendations

  30. Monitoring & Evaluation • As with mortality, malnutrition is an outcome of multiple complex processes • malnutrition rates may be slower to change than other indicators • limited value for readjusting your programme to make it more effective • survey data need to be carefully used in evaluation

  31. 8% of malnourished children were not malnourished in August; only 4% were malnourished in August

  32. While the rates of malnutrition declined overall many new children became malnourished • Those at risk of nutritional decline were not targeted including those reliant on loans from mohajon and who lost everything in the floods • Those vulnerable to nutritional decline may vary at different stages of the emergency

  33. Case 10: Guinea, Sierra Leone, Liberia • RNIS reports do not indicate high rates of malnutrition among accessible IDP/ refugee populations • extremely poor donor support of CAP • inadequate rations and assumptions over self reliance: extremely limited livelihood options • sexual exploitation of women and girls has become widespread

  34. Key lessons which SC has learnt • Risk of malnutrition is not identified in nutrition surveys • Malnutrition rates have to be used with caution for evaluation purposes: Sphere standards provide a useful guide

  35. Sphere standards and indicators Minimum standard The nutritional needs of the population are met Key indicators • rates of moderate malnutrition are stable at or declining to acceptable levels • no cases of scurvy pellagra or beri beri • access to a range of foods • infants under the age of 6 months have access to breastmilk (or appropriate substitute)

  36. Overcoming the challenges of interpreting nutritional status data what needs to be in place? • A sound understanding of food access to predict a crisis, decide when to survey, decide the survey population, aid interpretation of results and inform programme design • Causal analysis of malnutrition and nutritional risk is essential to make sound recommendations for programmes to address malnutrition

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