3. • 1st described by Jamaican pediatrician, Cecily Williams (1933)
• The origin isn’t clear; a number of theories have been fronted
Etiology of Kwashiorkor
Free radical damage theory (Golden, 1988)
• Kwashiorkor outcome is determined by extrinsic factors leading to
free radical formation & intrinsic factors which may impair the
bodies ability to safely dispose/scavenge free radicals.
• The net result is damage of the membrane and leakage of the fluids
in the cells, leading to edema
4. • Socioeconomic factors: mother’s education, wealth standing,
child birth weight etc.
• Disease burden >social capital
• Political factors
• Environmental factors
Factors influencing Kwashiorkor prevalence
5. Objectives
• To assess the prevalence and distribution of severe acute
malnutrition; annually, by age group and severity
• To establish the epidemiological characteristics of Kwashiorkor
• To compare annual case fatality ratios by MUAC, WHZ,
kwashiorkor vs. overall fatality ratio
• To assess the predictability of death in Inpatients by MUAC, age,
sex, kwashiorkor, residence, and year
6. Males, 55
Out, 35
Malnut, 23.4
death, 5.4 Yes, 6.4
Yes, 9.3
0
25
50
75
100
Sex DSS Malnut Outcome Oedema Wasting
percentage General characteristics of Inpatients
8. 0.0%
10.0%
20.0%
30.0%
6 12 18 24 30 36 42 48 54
prevalence
age(months)
Severe Acute Malnutrition prevalence by age and severity
severe moderate
9. 0.0%
2.0%
4.0%
6.0%
8.0%
6 12 18 24 30 36 42 48 54
prevalence
age group(m)
Kwashiorkor and Wasting prevalence by age
Kwash Wasting
10. Inpatient Kwashiorkor proportions by residence and sex of patient
DSS Sex
In Out Female Male
Proportion (%) 4.33 9.97 7.12 5.87
Diff. proportion -5.6 1.26
p-value(diff) <0.001 <0.001
• Kwashiorkor prevalence decreases with increase in age
• Proportion of Kwashiorkor in DSS is significantly lower than outside the DSS
• Females have a higher Kwashiorkor proportion than male inpatients
13. 0.0%
10.0%
20.0%
30.0%
1999 2001 2003 2005 2007 2009 2011
Case fatality ratios by Severe Acute Malnutrition factor
Overall Kwash Muac<11.5
ttest, p=0.85
R2=0.3231, p=0.031
14. Severe Moderate At risk
Kilifi(MUAC) 10.1 (8.9-11.4) 3.1 (2.6-3.6) 1.5 (1.3-1.8)
Overall(WHZ)* 9.4 (5.3-16.8) 3.0 (2.0-4.5) 1.5 (1.2-1.9)
* Ghana, Senegal, Guinea Bissau, the Philippines, India, Nepal, Bangladesh, Pakistan,
*Robert et al- Maternal and child undernutrition: global and regional exposures and
health consequences, the Lancet 2008
Comparison of Mortality odds by S.A.M severity in Kilifi and
8 other countries
15. Odds ratios of Inpatient deaths by Multivariable analysis cont’n
Model 1 Model 2
Factor Category 95% Conf. P-value 95% Conf. P-value
interval interval
12.5-13.5 1.7 (1.4-2.0) <0.01 1.5 (1.3-1.8) <0.01
MUAC 11.5-12.5 3.4 (2.9-4.0) <0.01 2.9 (2.4-3.4) <0.01
<11.5 10.3 (9.0-11.7) <0.01 7.8 (6.8-9.0) <0.01
Sex Male 1.1 (1.0-1.2) 0.211 1.1 (1.0-1.2) 0.140
Age <2yrs (base)
2-5yrs 1.5 (1.3-1.7) <0.01 1.5(1.3-1.7) <0.01
Residence Out-DSS 1.8 (1.6-2.0) <0.01 1.7 (1.6-1.9) <0.01
Kwashiorkor 2.3 (2.0-2.6) <0.01
Year 0.98 (0.96-0.99) 0.002
16. Conclusions
• Kilifi is typical of other severe acute malnutrition case areas in
the developing countries; by prevalence and rate of reduction in
severe acute malnutrition
• Kwashiorkor, and by extension severe acute malnutrition, is
highly prevalent in early stages of life- before 2yrs.
• Kwashiorkor is higher in females and Out -DSS than males and
In-DSS respectively
• MUAC, residence, age, year of admission, and presence of
edema all significantly predict death in children 6-60m
17. Acknowledgement
The J.A.B Group
• Dr Jay Berkley- Supervisor
• Dr Abdisalan Noor
• Dr Greg Fegan
• Martha Mwangome
• Naomi Waithira (in absentia)
• Ken Mwai
• Moses Ngari
• Robert Mutai
• CTF + Archives family
• John Ojal
• Caesar Olima
• James Mburu
• ICT and KEMRI wazee groups
Training department
• Dr Sam Kinyanjui
• Liz Murabu
• Edna Pendo
• 2013 Interns
