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Hemoglobin Status Is The Best Nutritional Variable That Predicts Achievement Among Schoolchildren In Rural Uganda Hedwig Acham, Joyce K. Kikafunda , Thorkild Tylleskar & Marian K. Malde. The school-age group. Background.

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  1. Hemoglobin Status Is The Best Nutritional Variable That Predicts Achievement Among Schoolchildren In Rural UgandaHedwig Acham, Joyce K. Kikafunda, ThorkildTylleskar & Marian K. Malde

  2. The school-age group Background • Corresponds to a period from primary one through lower secondary school • Lumping them together, makes them a neglected group compared to pre-school children, yet at primary one (6 years), children are transitioning from pre-school age (a period of high mortality). School children

  3. Enrolment figures 1997-2003 for Uganda • Due to child survival programs • Most children are surviving to school age today • Increased numbers enroll into primary school • More marginal populations access education

  4. Unfortunately, in the health sector the major focus of health programmes - reducing mortality in infants and very young children. • While in the education sector; • Teaching and learning resources • School infrastructure • Institutional management • Levinger (1994)- Active Learning Capacity (ALC Model) – Mitigates

  5. Active Learning Capacity Framework • Primary variables • Health and nutritional status • Psychosocial support • Hunger level • Secondary variables • Prior learning experience • Learning receptiveness • Aptitude

  6. School child spends 75 % of his total time in the school environment where; • Inadequate safe water supply • Poor latrine coverage • Absence of medical and dental care • Absence of feeding programmes. Uganda

  7. No study had assessed health and nutrition among school children to relate it to academic achievement in Uganda. • Kumi particularly- poor education outcomes (2002-2005). • Evidence: Quality enhancement initiative project (MOES) Problem Statement

  8. To determine the association between nutritional status with academic achievement Aim/Objective

  9. Study Design, Materials And Methods Cross-sectional study design Schools involved- public/ gov’t, mixed day schools. School sampling- modification of cluster sampling (30x30). Class selected – P.4. Child selection- randomized sampling Number involved for this objective (n=145).

  10. Figure 2. A profile of sampling procedure used. The children being discussed in this paper are those highlighted (n=145).

  11. Variables of study 1. Variables of Anthropometry Height Weight

  12. 2. Micronutrient variables • Iron status • hemoglobin (characterized anemia) • serum ferritin (characterized iron dfeiciency) Anemia was defined as Hb<12.0 mg/dl, Iron deficiency as sef <15.0µg/L. Blood collection and preparation

  13. serum preparation

  14. Iodine status semi qualitative method (UROJOD test-kit, 3.01299.0001 UROJOD, Germany).

  15. Vitamin A status • modification of high perfomance liquid chromatography (LC-MS assay principal ), on dried blood spots. VAD defined as SeR<0.70µmol/L 4.Learning Assessments (non-standardized) • Teaching/assessing (English and Mathematics) • Life skills Assessments • Oral comprehension

  16. Control of variables/ modifiers of learning • Teacher quality • Methods of measurement and testing • Time of the day • Hunger level • Days of the week • Interschool variability • Age & sex

  17. Mathematics Teaching

  18. English Teaching

  19. Breakfast

  20. Scale of measurement of academic achievement LevelsMark range (%) V. Good/ Excellent 80-100 Good 60-79 Fair 31-59 Poor/ weak 0-30 • ______ • Source: MOES

  21. Socio-demographic factors • Household wealth • School attendance rates Other Factors Studied

  22. Data analysis Use of Epiinfo (derive nutritional indices) SPSS (multivariate regression tests level at which results were considered significant (<0.05)

  23. Results • Prevalence rates indicated; • Thinness (11.8%) • Stunting (8.9%) • Underweight (14.4%) • Anemia (24.1%) • Iron deficiency (16.6%) • VAD (30.3%) • Iodine deficiency (3.4%) • More boys were significantly thinner, underweight and stunted than girls (p<0.05). • The reverse was true for anemia, VAD and iodine status.

  24. Normal 120.0 40 30 FEMALE Frequency 20 10 0 SEX 120.0 40 30 MALE 20 10 0 0.00 50.00 100.00 150.00 200.00 250.00 300.00 GRAND TOTAL Figure 4. Gender differences in achievement (n=645). Results of academic achievement revealed that; • The best performed subject was oral comprehension (47.66±31.03), worst mathematics (12.53±7.80). • A strong positive correlation between all subjects tested (p<0.01). • T-test showed no gender differences in achievement (p>0.05). • Over all, 68.4% scored below cut off for good achievement (66.7% boys, 69.8% girls).

  25. ***. P<0.001; **. p<0.01, *. P<0.05, R2= .17 (Cox & Snell), .23 (Nagelkerke). Model X2 (6) = 87.56 Dependent variable –total score (1 if < 120.0 points). Variables entered on step 1: Mothers education; Variables entered on step 2: Attendance rate; Variables entered on step 3: Feeding Table 2. Stepwise logistic regression model of association between anthropometric variables with academic achievement, with other variables included (n=645)

  26. Dependent variable: Total score (1 if <120.0 points). Variables entered on final step: Household size, age category, mother’s education, wealth, feeding, land quantity, attendance rates, and household head. • R2= .20 (Cox & Snell), .27 (Nagelkerke). Model X2 (14) = 23.10 Table 3. Model between micronutrient variables and achievement, with anthropometric and other variables included (n=145)

  27. Although not significant, there is an association between nutritional status and achievement among children in rural Uganda (Kumi district). • Hemoglobin status seems to be a better predictor of achievement compared to the other nutritional variables. • Recommendations: • More comprehensive research (Ltd data) • Efforts to fight under nutrition (SHN programmes) should be strengthened (include malaria prevention) • Time to focus on achievement (regularly testing them). • A time to re-think school feeding • Thematic curriculum a big snag!!!! Conclusions and recommendations

  28. Carnegie cooperation of New York Norwegian support (NUFU) Fanta-2 Thank you all. Acknowledgement:

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