1 / 36

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

Download Presentation

Overcoming the challenges of interpreting nutritional status data

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  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

More Related