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Regional Technical Working Group Meeting on Developing Nutrition Information Systems

Regional Technical Working Group Meeting on Developing Nutrition Information Systems Nairobi, 1-3 Feb 2007 Aims of Nutrition Information John B Mason, Tulane SPHTM. Area level survey results, Kenya: GAM % by season. Area level survey results, pooled & smoothed: Kenya, GAM%, by season.

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Regional Technical Working Group Meeting on Developing Nutrition Information Systems

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  1. Regional Technical Working Group Meeting on Developing Nutrition Information Systems Nairobi, 1-3 Feb 2007 Aims of Nutrition Information John B Mason, Tulane SPHTM

  2. Area level survey results, Kenya: GAM % by season

  3. Area level survey results, pooled & smoothed: Kenya, GAM%, by season

  4. Area level survey results, pooled & smoothed: Ethiopia, GAM%, by season

  5. Uses of information can be distinguished into: • long-term planning and policy-making • programme monitoring and evaluation • timely warning to pre-empt and mitigate crises

  6. Usual sources of data are: • repeated large-scale surveys (e.g. natl, DHS, MICS) • area level surveys (e.g. 30 by 30) • reporting systems using data from clinics, screeening, programmes • sentinels systems, from sites (e.g. clinics) or special surveys

  7. Trends give the most useful information • Note: evaluation only really works with before-after + with-without data

  8. Technical issues for surveys • sampling procedures • mortality estimates • indicators

  9. Sampling procedures • PPS, then issue is at last stage • Options: spin-the-bottle; segmentation-full enumeration; segmentation then sample • Consider bias; re-estimating pop numbers (for weights) • Depends on feasibility (incl. habitation features: e.g. along road/river vs round); and use • Compare results with norms, use for targeting, get trends

  10. Child mortality estimates from area level surveys • Issues are • Estimate and state confidence intervals, which will be wide with 30*30 design, and calculate design effect • Care in interpreting these -- e.g. what does 0.5-2.0/10,000/day over last 3 months mean?

  11. Indicators • Malnutrition can worsen without showing in wasting -- see Srn Africa 02-3: use wt/age if possible • Evidence suggests: interpret wasting WITHIN populations, not across, because of different growth patterns and relations to mortality • More focus on getting ages?

  12. Differences in stunting and wasting in two regions of Kenya

  13. Differential growth patterns in Uganda and Somalia

  14. Maternal and new born data Birth weight relates to maternal BMI, but that is not directly related to MMR as indicator of health care ...

  15. Different relations between GAM% and child mortality in different populations Hence interpret GAM within populations, not across ...

  16. Fitting systems together and deciding priorities

  17. Capacity • Enough people? • Is this useful for them? • Needed skills? • Resources and priorities? • How to meet these needs…?

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