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Ctown 5

Interpreting data emergencies long term descriptive trends for causality and intervention decisions. Ctown 5. Access to Food. Source: http://www.nso.malawi.net/data_on_line/economics/prices/urban_cpi.htm. Area level survey results, Kenya: GAM % by season.

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Ctown 5

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  1. Interpreting data • emergencies • long term descriptive trends • for causality and intervention decisions Ctown 5

  2. Access to Food Source: http://www.nso.malawi.net/data_on_line/economics/prices/urban_cpi.htm

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

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

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

  6. Figure 2. Trends in Underweight and HIV prevalence by region in Ethiopia

  7. Figure 6. Trends in Underweight and HIV prevalences by region in Uganda

  8. Child underweight prevalences are higher in lower HIV prevalence areas

  9. Figure 8. Kenya, Ethiopia, Uganda: Scatterplot of Underweight and HIV prevalences by country

  10. Under 5 mortality with HIV prevalence by area

  11. Decreases – SES Malnutrition Increases + HIV +

  12. Table 4. Associations of underweight with HIV and SES variables Ethiopia, Kenya, and Uganda, pooled. Dependent variable =underweight prevalence (%).

  13. Interaction between drought and HIV on changes in child underweight.

  14. Figures and Tables Figure 1. Kenya, Ethiopia, and Uganda: Drought (negative y-values) plotted over time 1989-2006.

  15. Figure 9. Scatterplot of Underweight and Drought with HIV results for all countries pooled (hivcat07=1 refers to high prevalence HIV).

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

  17. Differential growth patterns in Uganda and Somalia

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

  19. Conclude • Horn: effect of drought > HIV • Srn: drought and HIV interact – both together give rapid deterioration • Both: HIV still associated with lower malnutrition, because of assocn with SES.

  20. Size of effects: • Season: about 4 ppts wasting (GAM) • Drought: >= 10 ppts wasting (GAM)

  21. Wasting in different populations. • Similar mortality risk (e.g. U5MR of 2.0) at 5% GAM in (e.g. Uganda), 25% N Kenya • Hence interpret for specific population, and use trends more • About 10 ppts change in GAM suggests 0.5—1.0 increase in U5MR

  22. Policies and programmes to improve nutrition • Long-term: high priority for community-based (CBHNPs) with CHNWs • Reduce vulnerability, esp to drought • Mitigate emergencies

  23. Levels of food aid

  24. Estimating food aid levels: per need Denominator changes can give large fluctuations

  25. Estimating food aid levels: per population For descriptive, easier to see what’s happening

  26. Food aid levels per population: Lesotho and Mozambique

  27. Food aid levels per population: Malawi and Swaziland

  28. Food aid levels per population: Zambia and Zimbabwe

  29. Effects of HIV by area on underweight, controlling for SES

  30. Underweight with SES

  31. HIV with SES

  32. Country Coefficient (B) P-value R sq N All -0.543 0.000 0.43 55 Lesotho -0.317 0.12 0.78 4 Malawi -0.275 0.15 0.14 17 Mozambique -0.415 0.35 0.10 11 Swaziland -0.294 0.08 0.76 4 Zambia -0.635 0.08 0.29 9 Zimbabwe -0.126 0.22 0.18 10 Underweight with HIV

  33. Decreases – SES Malnutrition Increases + HIV +

  34. Variable \ Models 1 2 3 4 Prevalence of HIV (%) (hivprev4) -0.543 -6.358 0.000 - -0.292 -2.789 0.007 -0.198 -1.804 0.078 % head of hh with more than primary education (eduprim2) - -0.314 -7.790 0.000 -0.190 -3.170 0.003 -0.218 -2.760 0.009 % urban population (urban) - - -0.025 -0.839 0.405 -0.04358 -0.932 0.357 % hhs with electricity (electric) - - - 0.06598 0.658 0.514 % children >= 12 mo immunized for measles (measles) - - - 0.06633 1.126 0.267 % hhs with safe water (safewatr) - - - -0.01724 -0.415 0.680 % hhs with safe excreta disposal (safexcrt) - - - 0.05414 1.295 0.202 Constant 35.786 30.830 35.243 25.719 N 55 61 55 50 Adj R squ 0.422 0.499 0.551 0.563 Dep = underweight HIV is less associated with underweight controlling for SES In cells B T P

  35. Country Coefficient P-value R sq N All -0.127 0.100 0.050 55 * Lesotho -0.129 0.379 0.079 4 Malawi -0.241 0.118 0.155 17 Mozambique -0.366 0.340 0.102 11 * Swaziland -0.178 0.636 0.133 4 Zambia -0.102 0.734 0.018 9 Zimbabwe 0.0622 0.378 0.098 10 Removing SES from underweight, association with HIV becomes insignificant Coefficient smaller and less significant

  36. To recap … Significant overall

  37. Effects of HIV by area on change in underweight, controlling for SES

  38. Decreases – SES Malnutrition Increases + HIV To recap … +

  39. HIV with change underweight: no clear relation

  40. Variable \ Models 1 2 3 4 Dhivcat3 6.646 1.807 0.077 8.215 2.285 0.027 8.911 2.849 0.007 9.141 2.521 0.015 Eduprim2 -0.206 -2.276 0.027 -0.406 -4.373 0.000 -0.172 -1.808 0.077 Uwt -1.257 -4.036 0.000 - Safeexctr 0.151 1.583 0.120 urban -0.0704 -0.990 0.328 Constant -2.606 1.365 33.758 -8.721 N 50 50 50 50 Adj R squ 0.044 0.121 0.337 0.131 Change in underweight deteriorated more in higher HIV areas when SES is controlled. Dependent = change in underweight, ppts/yr Means adjusted for SES (education) In cells B T P F = 4.463, n = 50, p = 0.018

  41. Effects of food aid

  42. Targeting: actual food aid coverage vs VAC need Higher need Higher coverage Most targeting worked, to high need areas Lower need Higher coverage Some areas had high coverage with low need: these did worst in terms of child nutrition Lower need Lower coverage Higher need Lower coverage A few high need areas had low coverage

  43. Change in underweight (ppts/yr) by need and food aid coverage Low need areas do OK anyway Need to look into … More coverage improves nutrition in high need areas

  44. Change in underweight by RDA met by food aid Findings as before when stratified by VAC need

  45. Effect of food aid in high need group, by HIV (areas) Report CHUWY Change in underweight per year (percentage points) (+=deter; -=improv) HIVCAT3 HIV category FA1CATUS Food aid 1 Mean N Std. Deviation (relative to the country) category (unstandardized) 1 Low 1 Low (<=11.50) (-1.1902) (1) . (low & high) (low & high) 2 High (>11.50) -3.6520 6 4.98730 Total -3.3003 7 4.64688 2 High 1 Low (<=11.50) 7.3983 5 5.14885 2 High (>11.50) 2.0388 10 7.09411 Total 3.8253 15 6.83859 Total 1 Low (<=11.50) 5.9669 6 5.78813 2 High (>11.50) -.0953 16 6.82520 Total 1.5581 22 6.99192 High need, low food aid, high HIV, deteriorates fast; high food aid helps

  46. Need <50% >50% Coverage > 50% need 100% Coverage Coverage < 50% need 80% Coverage 50% Coverage How coverage of food aid met assessed need in drought -0.5 Change in Underweight ppts/yr +3.4 +4.5 +1.3

  47. BVACAT (beneficiaries / need category) Need Lo (Vaccat2=0) Need Hi (Vaccat2=1) Low (<0.5) 1.30 (9) 4.49 (9) High (>=0.5) 3.44 (10) -0.47 (13) Change in underweight prevalence by beneficiary/need coverage

  48. Effects of food assistance on child nutrition (24-59 mo) in Zimbabwe, 2002-3: regressions (HLM) of wt/age, relating changes between May 2002 to Feb 2003 to levels of supplementary feeding (SF) and food distribution; in cells – coefficient (B, unstandardized), t value, p, n. Low Supplementary Feeding WAZ Z-score Test if slopes are different High Supplementary Feeding May 2002 Feb 2003

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