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Public Nutrition: Assessment and Advanced Analysis INHL 709 Spring 2010 Tues Thurs: 9.00—10.30

Public Nutrition: Assessment and Advanced Analysis INHL 709 Spring 2010 Tues Thurs: 9.00—10.30 + troubleshooting 1.30-3.00 Fridays in 2200-23. Note: Trouble shooting on Friday afternoons, 1.30-3.00.

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Public Nutrition: Assessment and Advanced Analysis INHL 709 Spring 2010 Tues Thurs: 9.00—10.30

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  1. Public Nutrition: Assessment and Advanced Analysis INHL 709 Spring 2010 Tues Thurs: 9.00—10.30 + troubleshooting 1.30-3.00 Fridays in 2200-23

  2. Note: Trouble shooting on Friday afternoons, 1.30-3.00. PANDA (Practical Analysis of Nutritional Data) is main material for course, available on web Tulane.edu/~panda3; also can be got on CD if needed..

  3. Readings. Beaton, G., Kelly, A., Kevany, J., Martorell, R. & Mason, J. (1990) Appropriate Uses of Anthropometric Indices in Children. ACC/SCN State‑of‑the‑Art Series, Nutrition Policy Discussion Paper No. 7. ACC/SCN, Geneva. http://www.unscn.org/archives/npp07/index.htm UNICEF Survey (MICS) Manual. http://www.childinfo.org/files/Multiple_Indicator_Cluster_Survey_Manual_2005.pdf SMART nutrition survey methodology manual http://www.smartindicators.org/SMART_Methodology_08-07-2006.pdf Public Health Surveillance: A Tool for Targeting and Monitoring Interventions. Nsubuga et al. 2006. DCP2 Ch 53 p997 http://files.dcp2.org/pdf/DCP/DCP53.pdf Developing Nutrition Information Systems In Eastern And Southern Africa. Report of Regional Technical Working Group Meetings Nairobi, 1-3 February and 19-21 April 2007. By: UNICEF Eastern and Southern Africa Regional Office (ESARO) and Tulane University, Department of International Health and Development (p:\niaer\FNB publn\Wshops report.doc) Nutritional surveillance in relation to the food price and economic crises. J Mason. Workshop Summary, Institute of Medicine, July 2009, pp 67-72. http://books.nap.edu/openbook.php?record_id=12698&page=67

  4. Introduction (lectures 1 & 2) • ‘Assessment and Analysis’ • Planning framework: questions to address • Research questions and dummy tables • Language, variables, indicators • Data sources • Data transformations, units of analysis.

  5. ASSESSMENT AND ANALYSIS Practical aspects analytical approach data handling pragmatic analysis interpretation Concepts what is data? what is its relation to reality? levels and units of analysis preserve information types of variables outcome classifying determining -- causal, interactive, confounding process Uses program design policy advocacy monitoring evaluation

  6. 2. Planning framework: questions to address (and dummy tables)

  7. For programme planning … You need to decide: Coverage: how many people? Targeting: who? Intensity: resources/head Content: what activities (components)?

  8. Research questions… Specify … keep going till you answer them … refer back to them when you get lost

  9. Answer questions Produce results (tables, models, graphics) Work up dataset Collect data Research questions Dummy tables Define variables Design questionnaire

  10. Research questions on malnutrition (examples): How serious/extensive is it? (Compare to norms) Is it worse in some places/for some populations? (Compare between groups at one time) Is it getting better or worse, for whom? (Compare between times, for groups: norm 0.5 – 1 ppt/yr) What is cause of current situation, or changes? (Analyze associations; includes evaluation) You could also ask: what problems are we trying to solve, and what resources do we have … this would come in at question 1, but then continue to ask how the resources address the problems ...

  11. How serious/extensive is malnutrition? • E.g. prevalences of underweight, wasting, GAM etc. • Note: interpretation may need to differ by population group, e.g. pastoralists vs agriculturalists; mortality risk varies in relation to GAM. 10% cut-point for agriculturalists may be equivalent to 20% for pastoralists E.G of dummy table E.g. of cut-points: 10% warning, 20% emergency

  12. Is malnutrition worse in some places/for some populations? Example of dummy table: compare districts A and B Don’t forget precise title! Prevalences of wasting and stunting in children < 110 cms in Northern province, January 2007

  13. 3. Is malnutrition getting better or worse, for whom? Example of dummy table Prevalences of wasting in children 6-59 months in January and July 2007 in Northern province

  14. or Prevalences of underweight children (6-59 mo) in 2001 (May-July) and 2005 (June-Nov) Sources: DHS, 2001; MICS, 2005

  15. 4A. What are possible causes of the current levels of malnutrition? Prevalence of underweight in children (6-59 mo) by food security and district, controlling for education level

  16. 4B. What are possible causes of changes in malnutrition? Changes in prevalences of malnutrition Jan – July 2007 in children (6-59 months) with receipt of food aid, for food insecure and secure households.

  17. 3. Language, variables, indicators

  18. 4. Data sources

  19. 5. Data transformations, units of analysis

  20. Units of analysis (file structure) • Preserve information • Decide early • Usually most disaggregated, repeating if needed (e.g. individual, household) • Beware confounding, ecological fallacies if aggregated (e.g. district) data • Care with hierarchical data, clusters, design effects.

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