1 Public Nutrition: Assessment and Advanced Analysis INHL 709 Spring 2010 Tues Thurs: 9.00— troubleshooting Fridays in

2 M/CDates 2010TopicRefAssignment 1 2 Tues 12 Jan Thur 14 Jan IntroductionPANDA Ch 1Go through PANDA Ch Tues 19 an Thur 21 Jan Data cleaning (intro) Data cleaning (progress) PANDA Ch 2Clean bdeshd1.sav Due Tue 26 Jan Tues 26 Jan Thur 28 Jan One way analyses: district aggregated data (intro) Review cleaning results. One way (progress) PANDA Ch 3, pp 1-2Situation analysis and ranking for B’desh district data (bdeshc.sav). Due Tues 2 Feb Tue 2 Feb Thur 4 Feb Tues 9 Feb Thur 11 Feb Review one-way results. Associations in aggregated data (Bdesh). Associations (progress, Bdesh). Intro Indonesia assignment. Indonesia assignment (progress) Review Indonesia results Child level, intro. PANDA Ch 3, p3Examine associations in B’desh dataset. Due Fri 5 Feb. Set up and analyse Indonesia provincial dataset. Due Wed 10 Feb Thur 18 Feb Tues 23 Feb Child-level data Test. PANDA Ch 3Use Kenya data for ranking and associations, due Thurs 25 Feb Thu 25 Feb Tue 3 Mar Thu 4Mar Two-way analyses by tabulation and regression; causal factors. PANDA Ch 4Use Kenya data for 2- way analyses, due Fri 5 Mar Tue 9 Mar Thu 11 Mar Multi-way analysis, confounding and interactions. Targetting and coverage evaluation. PANDA Ch 5 PANDA Ch 3 p2; Chs 4&5. Use Kenya data for associations controlling for confounders, assemble all results, due Fri 12 Mar

Tue 16 Mar - Thurs 27 April Analyse different Kenya dataset for: (a) situation analysis, (b) targetting priorities, (c) program design (d) program coverage and targetting. All PANDA, incl. Ch 7Analysis of provincial Kenya datasets. Assignments and class discussion: Due dates to be discussed Note: Trouble shooting on Friday afternoons, 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..

4 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. UNICEF Survey (MICS) Manual. SMART nutrition survey methodology manual Public Health Surveillance: A Tool for Targeting and Monitoring Interventions. Nsubuga et al DCP2 Ch 53 p997 Developing Nutrition Information Systems In Eastern And Southern Africa. Report of Regional Technical Working Group Meetings Nairobi, 1-3 February and April 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

5 Introduction (lectures 1 & 2) 1.‘Assessment and Analysis’ 2.Planning framework: questions to address 3.Research questions and dummy tables 4.Language, variables, indicators 5.Data sources 6.Data transformations, units of analysis.

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9 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

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

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

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

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

17 Research questions on malnutrition (examples): 1.How serious/extensive is it? (Compare to norms) 2.Is it worse in some places/for some populations? (Compare between groups at one time) 3.Is it getting better or worse, for whom? (Compare between times, for groups: norm 0.5 – 1 ppt/yr) 4.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...

18 1.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 Wasting % (Cis) Stunting % (Cis) Oedema % District A E.g. of cut-points: 10% warning, 20% emergency E.G of dummy table

19 2.Is malnutrition worse in some places/for some populations? GroupWasting %Stunting % IDPs District A District B Total 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

20 3.Is malnutrition getting better or worse, for whom? Example of dummy table GroupWasting: Jan 2007 Wasting: July 2007 District A District B Total Prevalences of wasting in children 6-59 months in January and July 2007 in Northern province

21 or Group Under- weight 2001 Under- Weight 2005 U5MR 2001 U5MR 2005 Province AUrban Rural Province BUrban Rural TotalU+R Prevalences of underweight children (6-59 mo) in 2001 (May- July) and 2005 (June-Nov) Sources: DHS, 2001; MICS, 2005

22 4A.What are possible causes of the current levels of malnutrition? Food security Education high Education low Total District AInsecure OK District BInsecure OK Prevalence of underweight in children (6-59 mo) by food security and district, controlling for education level

23 4B.What are possible causes of changes in malnutrition? District A1/07 – food insecure 7/07 – food insecure 1/07 – food secure 7/07 – food secure With food aid No food aid Total Changes in prevalences of malnutrition Jan – July 2007 in children (6-59 months) with receipt of food aid, for food insecure and secure households.

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44 5. Data transformations, units of analysis

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46 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.