Nutrition and Health Status of Children in Internally Displaced Households in the Kosti Region of Sudan

Brian J. Bence, Jounghee Lee, Robert F. Houser, F. James Levinson

Gerald J and Dorothy R Friedman School of Nutrition

Science and Policy, Tufts University Boston, Massachusetts USA

September, 2004

Food Policy and Applied Nutrition Program Discussion Paper No. 26

Discussion papers provide a means for researchers, students and professionals to share thoughts and findings on a wide range of topics relating to food, hunger, agriculture and nutrition. They contain preliminary material and are circulated prior to a formal peer review in order to stimulate discussion and critical comment. Some working papers will eventually be published and their content may be revised based on feedback received. The views presented in these papers do not represent official views of the School. The discussion paper series is available on line at http://nutrition.tufts.edu/1184937206715/Nutrition-Page-nl2w_1184937205502.html.

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The Gerald J. and Dorothy R. Friedman School of Nutrition Science and Policy FOOD POLICY AND APPLIED NUTRITION PROGRAM

Nutrition and Health Status of Children in Internally Displaced Households in the Kosti Region of Sudan

Brian J. Bence, Jounghee Lee, Robert F. Houser, F. James Levinson

September 2004

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Nutrition and Health Status of Children in Households of Internally Displaced People in Kosti Region of Sudan

Brian J. Bence, Jounghee Lee, Robert F. Houser, F. James Levinson

EXECUTIVE SUMMARY The civil wars in Sudan have resulted in the displacement of roughly 60,000 people in the White Nile State. Internally displaced people (IDP) in Kosti, Sudan face food and nutrition insecurity, as well as poor health and environmental conditions, which are compounded by economic hardships. The data used in this study is from the ADRA/Sudan Kosti Food Security Initiative Baseline Survey. The Adventist Development and Relief Agency (ADRA) planned to carry out a nutrition and health intervention program targeted at the IDP in the Kosti region of Sudan. The aim of the program was to enhance health and nutritional conditions by improving primary health care, supplemental feeding, water security, and sanitation. The statistical analysis reported here focused on the health and nutrition conditions of children aged 6 to 24 months and the determinants of these conditions. Although the survey carried out was not recommended by Tufts University, and despite some serious problems in data collection, some important results have emerged which add to the still sparse literature presenting detailed data analysis of the nutritional wellbeing of children in IDP households. Of the children sampled, 21.6% were moderately or severely malnourished using WHZ. This compares with 19.5% from the Juba riverine livelihood area in Somalia (2002, FAO), 21.5% for IDPs from Malanje in Angola (1999, MSF-H), and 14.3% for refugees from Rakhine State, Myanmar in Bangladesh among children aged six and 59 months (1999, UNHCR) and is higher than any national average except for Afghanistan. Statistical analysis focused on predictors of wasting (WHZ), underweight (WAZ), and stunting (HAZ). The predictors include incidence of diarrhea, gender and age of the children, food consumption frequency of the family, immunization status, hygiene practices, literacy, and food expenditures. Age, gender (females fare better than males), and diarrhea were found to be significant predictors in the underweight model. Only age was a significant predictor in the wasting model. Age, and diarrhea were significant predictors in the stunting model.

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Although not statistically significant, children moderately or severely wasted in Kosti were less likely to have been immunized, had more diarrhea, were less likely to come from a household headed by a literate person, and were more likely to be male. The findings are not entirely conclusive. Age heaping was pronounced, suggesting considerable error in age assessment. The lack of significant findings from the WHZ analysis and the relatively more significant findings in the WAZ analysis makes any convincing deduction somewhat difficult. It is generally agreed that wasting is the most important anthropometric indicator for assessing nutritional status during complex emergencies. Furthermore, WHZ is unaffected by unreliable age data. The situation suggests it would be prudent to focus on the WHZ findings. Unfortunately, the WHZ findings are generally insignificant in the Kosti data. The findings from the WAZ analysis show considerably more significant results. However, WAZ is negatively affected by unreliable age data, and underweight tends to be an unreliable indicator on the whole. Careful consideration of these issues must be kept in mind when interpreting the results and devising an appropriate intervention strategy. INTRODUCTION When the civil war broke out in Sudan in 1983, there was much bloodshed between the Northern Khartoum-led government military and the Southern Sudan People’s Liberation Army (SPLA). The conflict resulted in aggression against civilians, triggering the displacement of many families. Recurring drought and famine, caused by the conflict, have added to the suffering of the Sudanese civilians, particularly the internally displaced people (IDP) (ADRA). Internally displaced people may be defined as “individuals or groups who have been forced to flee or to leave their homes or habitual residence, particularly as a result of or in order to avoid the effects of armed conflicts, situations of generalized violence, violations of human rights or natural or man-made disasters; and who have not crossed an internationally recognized state border” (Huag, 2002). Internally displaced people often face hunger and disease, among many other atrocities (Taha, 1998). Kosti is one of four provinces, and has the largest population, in the White Nile State of Sudan (Starbase, 2003). Kosti is a river port that joins the White Nile State with the southern part of Sudan (Starbase, 2003).

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The 2000 Needs Assessment of the United Nations Operation Lifeline Sudan (OLS) has estimated that there are 60,000 IDP. The IDP are staying in 2 official camps, Goz Al Salaam and Ellaya and 3 squatter communities (ADRA). According to a 1996 survey, 40,000 persons live in the Goz Al Salaam and Ellaya displaced camps (OCHA, 1998). The IDP have not been incorporated into the local communities (ADRA). Food and nutrition insecurity, as well as significant health problems plague the IDP. Insufficient food availability, limited access, and poor utilization are quite common. The provision of food aid has been the main vehicle for food security of the IDP (ADRA; Starbase, 2003). The IDP in Kosti copes with food insecurity by decreasing the number of meals and the portions of food and by increasing domestic labor. They may also obtain assistance from kinship (Starbase, 2003). Economic hardship and the challenge of livelihoods facing the IDP further exacerbate the situation (ADRA). Not only were the IDP forced from the places where they had lived, but many are now confronted with the burden of establishing new livelihoods (Huag, 2002). Land tenure for the IDP is unstable, forcing many to work as casual laborers for local inhabitants (ADRA; Starbase, 2003). Meanwhile, wage-based employment is scarce, compounded by the continued poverty and inflation. Steady work is hard to find. The agricultural labor engaged in by men is only seasonal. Rainfall was also inadequate in 2000. Female-headed households are particularly at risk (ADRA). Complex emergencies often result in the division of families and the devastation of social support systems, rendering children and women especially vulnerable (Hynes, et al., 2002). A 2000 survey conducted by Islamic African Relief Agency (IARA) indicated that the malnutrition rate in the White Nile State was 24%, with 7.2% acute malnutrition and 16.8% moderate malnutrition. The survey also found that 14.4% of children under five were found to be moderately malnourished and 7.8% of under-fives were severely malnourished (Starbase, 2003). “In the IDP camps, morbidity rates are high among children” largely as a result of malaria, acute respiratory infection (ARI), diarrhea, and skin diseases (Starbase, 2003). Water security and sanitation were major problems in the IDP camps. In the camps, sanitation was poor, open defecation was quite common and there was a critical lack of access to safe drinking water (OCHA, 1998). “The proportion of the population in the state with access to safe drinking water was 38.9% and the proportion of the population with access to improved sanitation was 54.1%” (Starbase, 2003). The use of unsafe water may considerably increase the prevalence of diarrhea (UNICEF, 2003). Environmental sanitation was quite inadequate. Latrines and garbage collection containers were rarely used, and hygiene practices were often problematic (Starbase, 2003).

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Although the IDP camps had health facilities, the majority of the IDP could not afford the costs of the healthcare offered (Starbase, 2003). As indicated by a 2000 Multiple Indicator Cluster, infant mortality rate per 1,000 live births in the White Nile State was 70 and the under-five mortality rate per 1,000 live births was 111 (Starbase, 2003). Most of the health workers in the locale were untrained and supplied inadequate treatment (OCHA, 1998). II. METHODOLOGY The data used in this analysis is from the ADRA/Sudan Kosti Food Security Initiative Baseline Survey. The Adventist Development and Relief Agency (ADRA)1 planned to carry out a nutrition and health intervention program targeted at the IDP in the Kosti region of Sudan. The aim of the program was to enhance health and nutritional conditions by improving primary health care, supplemental feeding, water security, and sanitation. ADRA listed four major objectives of the program: (1) enhance the health status of the IDP; (2) improve the nutritional status of children under 5 years old; (3) upgrade sanitation and environmental hygiene; (4) improve water access for vulnerable IDP households. The intention of this analysis was to focus on the second objective; thus our data analysis will focus on the causalities of malnutrition of children, and specifically among the critical 6-24 month age group In order to assess the situation, a baseline survey was conducted in 2001. A standard 30-cluster random selection method was employed, as there were 30 subjects in each of the 30 clusters. Data on 900 heads of household were collected. Investigation of the Kosti data, as well as review of the literature, indicated that 6 months to 24 months old children were most sensitive to the factors. In general, these factors included hygiene and sanitation practices, health status, and food and water security. Children under 6 months of age were excluded from analysis, as these children are more likely to be breastfed and less likely to consume complementary foods. The experiences of Action Contre le Faim (ACF) in Southern Sudan has shown that “prevalence of malnutrition is high amongst infants and children aged 6 months to 3 years, who account for the majority of admissions” to the therapeutic feeding centers. Only 5-10% of admissions consisted of children aged 3 to 5 years (ENN Online, 2004). Further, experience by ACF in IDP camps in Luuq Town, Somalia has shown Global Acute malnutrition to be significantly higher for children aged 6 to 29 months than for children over 29 months

1 As of this writing, there is no indication ADRA has initiated the nutrition and health program in Kosti. ADRA, however, has been involved in assisting the internally displaced nomadic pastoralists, the Hawaweer of Wadi Al Muggadam in northern Sudan (Haug, 2002).

Deleted:

Deleted:

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(Sautreau, 2000). As a result of age heaping,2 the data set contained 125 children who were recorded as being exactly 24 months old. Hence, all 24-months old children were included in the data analysis. Anthropometric indices are expressed using z-scores in order to assess the growth of a child against a reference population (de Onis and Blossner, 2003). WAZ, WHZ, and HAZ were calculated using ANTHRO software v 1.02. The final data set consisted of 440 cases of children aged 6 months to 24 months. However, several cases contained improbably high values, and were therefore excluded from some analyses.3 Three regression models were run using SPSS 11.5 for Windows software. In the regression analyses, each of the anthropometric indices, WAZ, HAZ, and WHZ, were used as continuous dependent variables. The independent variables used in each regression model are as follows: gender of the child, age of the child in months, incidence of diarrhea, an immunization score, a food frequency score, a sanitation/hygiene practices score, literacy of the head of household, and food expenses divided by household adult equivalency. The quadratic form of age of child in months was also included as an explanatory variable. The binary variables are as follows: gender, diarrhea, and literacy of the head of household. Diarrhea pertains to the survey question that asks whether the child experienced an episode of diarrhea during the two weeks prior to the interview. Adult equivalency ratios were calculated for each case. Monthly household food expenditure was divided by the respective adult equivalency ratio. By dividing monthly food expenditure by the adult equivalency ratio, one can adjust for the differing ages of the members in each household. Household members are likely to differ in their food consumption depending on their ages. For instance, children are likely to consume less than adults. Hence, a smaller portion of the monthly food expenditures is likely to be devoted to children. The distribution for food expenditure, considering adult equivalency ratio of household, included one extreme value equal to 240, which was set to missing. As mentioned, three scores were calculated: a food score, an immunization score, and a sanitation/hygiene practices score. The immunization score ranged from a minimum of 0 to a maximum of 4. For each of the 4 immunization variables, a

2 The problem of age heaping will be discussed more fully in the Discussion section. 3 A frequency distribution for HAZ showed three children with unlikely z-scores: -9.98, 9.44, and 9.98. These three cases were excluded from all HAZ analyses. The WHZ data contained two unlikely values, 9.98 and 9.99, which are excluded from all WHZ analyses.

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score of 1 was given if the child had been fully vaccinated, and a score of 0 was given if the child was only partially vaccinated or not vaccinated at all. The four immunization variables are as follows: BCG (Tuberculosis), OPV (Polio), DPT (Diphtheria, Pertussis, Tetanus), and measles. The food score ranged from 0 to 15, and was created based on information from the weekly frequency of consumption of the family. There were 5 food categories as follows: cereals, meat/fish, milk, vegetables, and legumes. For each food category, a score of 0 is assigned if the food is not consumed at all, 1 is assigned if the food is eaten once per week, 2 is assigned if the food is eaten 3 to 5 times per week, and 3 is given if the food is eaten every day of the week. This consumption data only refers to the food consumption of the family, not the child. This food score is used as a proxy variable for food availability of the family. Finally, the sanitation/hygiene score ranged from 0 to 4. The score was based on four criteria: keeping of cooking utensils, drainage of stagnant water within the house, water treatment, and proper storage of water. After performing frequency distributions, these four indicators appeared to show the most significant results and were hence compiled into a score. Conceptual framework: Causality of malnutrition of IDP in Kosti, Sudan4 A conceptual framework (Figure 1; Lamstein; UNICEF) was developed to examine potential predictors of malnutrition in children less than five years, based on the data collected in the Kosti Baseline Survey. Such a framework is useful in properly accounting for predictors when analyzing the data, and was particularly valuable in deciding which predictors to include in the regression analyses. Clearly, this conceptual framework does not cover all predictors of child malnutrition of IDP in Kosti. However, a vital feature of the conceptual framework is that it illustrates the multi-fold, interrelated importance of food security, water security, and health and environmental conditions in producing child malnutrition. Addressing malnutrition requires at least an awareness of each of these factors. For instance, unless cooking utensils are kept properly and/or water is treated adequately, severe and recurring diarrhea is likely to result, causing growth faltering in spite of sufficient food security (Shrimpton and Kachondham, 2003). The two immediate predictors of child nutritional status are food security and health and environmental conditions. The two underlying predictors of health and environmental conditions are immunization status, and sanitation and hygiene

4 The conceptual framework is located at the end of the report.

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practices. Health and environmental conditions, particularly sanitation and hygiene practices, is a predictor of diarrhea incidence. The components of immunization status are BCG vaccination, OPV vaccination, DPT vaccination, and measles vaccination. The basic predictors of sanitation predictors are drainage of stagnant water, maintenance of cooking utensils, water treatment, and storage of water. The two predictors of food security are food expenditures and food availability. Literacy of the head of household, the number of household members, as well as age and gender of the child, are also included as predictors of child nutritional status. Statistical Analysis Frequency distributions were performed on all key indicators to examine frequency of responses as well as to check for missing values. Independent samples T-tests were conducted to test for significant differences in nutritional status by gender. Spearman Cross Correlation Matrices were created to investigate significant correlations between key variables. Cross tabulations were performed to examine relationships between binary variables. Chi-square tests were then performed to observe significance of the relationships. Finally, multiple linear regression was conducted to test a conceptual model predicting causality of malnutrition.

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III. RESULTS Table 1: Socio-Demographic Characteristics of Sample Households

Total (N=440) % (N)

Total number of household members (Mean±SD) 6.10±2.3 Gender of the head of household Male

Female

91.8% (404) 8.2% (36)

Occupation of the head of household Laborer Agriculture Small Enterprise Employment Other

78.0% (343) 14.1% (62) 3.2% (14) 3.6% (16) 1.1% (5)

Literacy of the head of household Read and write Read only Illiterate

34.1 % (150)

2.7% (12) 63.2% (278)

Building material used Mud Red bricks & Mud Temporary shelters (Huts) Other

(N=439) 19.8% (87) 6.6% (29) 10.3% (45)

63.3% (278) Source of water

Artesian well/ Hand pump Shallow well River/Canal Other

0.7% (3) 0.7% (3)

97.7% (430) 0.9% (4)

Water Distance (minutes) 0-30 31-60 61-90 91-120

(N=422) 31.0% (131) 22.5% (95) 16.8% (71)

29.6% (125)

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Socio-demographic characteristics of the sample5 households are presented in Table 1. The large majority (91.8%) of the households were headed by men, with most (78%) of the heads of household working as laborers and not able to read (63.2%). Most households (97.7%) obtained their water from a river or canal. Anthropometry Table 2 Nutrition status of children by gender of child (≥ 6m, ≤2y)

Wasting (N=437, m=220, f=217) Stunting (N=437, m=216, f=221) Underweight (N=440, m=218, f=222) Nutrition status

Mod

erat

e +S

ever

e %

( N)

Seve

re

%( N

)

Mea

n±SD

Mod

erat

e +S

ever

e %

( N)

Seve

re

%( N

)

Mea

n±SD

Mod

erat

e +S

ever

e %

( N)

Seve

re

%( N

)

Mea

n±SD

Male 26.8% (59)

8.6% (19) -1.39±1.28 35.3%

(78) 13.1% (29) -1.25±1.81 48.2%

(107) 20.3% (45) -1.94±1.43**

Female 16.2% (35)

6.5% (14) -1.15±1.27 31.0%

(67) 13.9% (30) -.98±2.27 38.5%

(84) 15.1% (33) -1.55±1.49**

Total 21.6% (94)

7.6% (33) -1.27±1.28 33.2%

(145) 13.5% (59) -1.12±2.05 43.4%

(191) 17.7% (78) -1.74±1.48

Independent samples T-test, **p<0.01 Note: Severe malnutrition was defined as a z-score < -3 and moderate to severe malnutrition was defined as a z-score < -2.

5 The sample of households consists of 440 cases. Only cases of children 6 months through 24 months old were included in this sample.

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Table 2 summarizes information about moderate and severe malnutrition for WHZ, WAZ, HAZ, in male and female children. There were no significant differences between male and female children, except for underweight (p<0.01). While the differences are not significant, the mean wasting and stunting Z-scores are considerably lower for males than for females. Table 3 Nutrition status of children among age groups (≥ 6m, ≤2y)

Wasting (N=437, 6-12m=153, 13-18m=103, 19-24m=181)

Stunting (N=437, 6-12m=152, 13-18m=104, 19-24m=181)

Underweight (N=440, 6-12m=154, 13-18m=105, 19-24m=181)

Nutrition status

Mod

erat

e +S

ever

e %

( N)

Seve

re

%( N

)

Mea

n±SD

Mod

erat

e +S

ever

e %

( N)

Seve

re

%( N

)

Mea

n±SD

Mod

erat

e +S

ever

e %

( N)

Seve

re

%( N

)

Mea

n±SD

6-12 months 18.4% (28)

9.2% (14) -1.10±1.60 14.5%

(22) 3.3% (5) -.40±2.13** 25.9%

(40) 9.7% (15) -1.25±1.36**

13-18 months 29.1% (30)

9.7% (10) -1.47±1.07 45.2%

(47) 18.3% (19) -1.65±1.66** 57.2%

(60) 24.8% (26) -2.00±1.61**

19-24 months 19.9% (36)

5.0% (9) -1.31±1.05 42.0%

(86) 19.3% (35) -1.41±2.03** 50.2%

(91) 20.4% (37) -2.01±1.39**

Total 21.6% (94)

7.6% (33) -1.27±1.28 33.2%

(145) 13.5% (59) -1.12±2.05 43.4%

(191) 17.7% (78) -1.74±1.48

One-way ANOVA, **p<0.01 Table 3 compares degrees of malnutrition among age groups. The mean stunting and underweight Z-scores are statistically different among age groups (p<0.01). As age group increases, the mean underweight Z-score statistically decreases. However, the mean stunting Z-score is statistically lower for 13-18 month old children than for 6-12 month olds, but higher for 19-24 month old children compared to the 13-18 month olds.

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Table 4. Wasting: Comparison of acutely malnourished and non-malnourished children by selected indicators

WHZ BCG6 OPV7 DPT 8 Measles Diarrhea

Literacy of the head of

household Male

WHZ≥ -2 58.3%* (200/343)

41.2% (141/342)

40.5% (138/341)

34.4% (108/314)

53.5% (183/342)

35.6% (122/343)

46.9%* (161/343)

WHZ< -2 40.4%* (38/94)

33.0% (31/94)

31.2% (29/93)

24.1% (21/87)

61.7% (58/94)

28.7 (27/94)

62.8%* (59/94)

Total 54.5% (238/437)

39.4% (172/436)

38.5% (167/434)

32.2% (129/401)

55.3% (241/436)

34.1% (149/437)

50.3% (220/437)

Chi-square test, *p<0.05 Table 4 summarizes the differences in immunization status, incidence of diarrhea, literacy of the head of household, and gender between acutely malnourished and non-malnourished children, using WHZ as the indicator of malnutrition. The Chi-square test was used to test for significant differences between acutely malnourished and non-malnourished children. Significant differences were only found in the case of two indicators, BCG immunization and gender. The percentage of children fully vaccinated with BCG was significantly higher for the non-malnourished than for the acutely malnourished (p<0.05). The percentage of male children was significantly higher for the acutely malnourished than for the non-malnourished (p<0.05).

6 The Bacillus Calmette-Guérin (BCG), tuberculosis vaccine 7 Oral polio vaccine (OPV) 8 Diphtheria, pertussis, tetanus (DPT)

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Table 5. Underweight: Comparison of acutely malnourished and non-malnourished children by selected indicators

WAZ BCG OPV DPT Measles Diarrhea Literacy of the head of household Male

WAZ≥ -2 60.2%* (150/249)

41.1% (102/248)

40.1% (99/247)

33.3% (73/219)

50.0%* (124/248)

37.8% (94/249)

46.2%* (115/249)

WAZ< -2 46.6%* (89/191)

37.2% (71/191)

35.8% (68/190)

30.3% (56/185)

62.3%* (119/191)

29.3% (56/191)

56.0%* (107/191)

Total 54.3% (239/440)

39.4% (173/439)

38.2% (167/437)

31.9% (129/404)

55.4% (243/439)

34.1% (150/440)

50.5% (220/440)

Chi-square test, *p<0.05 Table 5 summarizes the differences in immunization status, incidence of diarrhea, literacy of the head of household, and gender between acutely malnourished and non-malnourished children, using WAZ as the indicator of malnutrition. The Chi-square test was used to test for significant differences between acutely malnourished and non-malnourished children. Significant differences were found in the case of three indicators, BCG, Diarrhea, and gender. The percentage of children fully vaccinated with BCG was significantly higher for the non-malnourished than for the acutely malnourished (p<0.05). The percentage of male children was significantly higher for the acutely malnourished than for the non-malnourished (p<0.05). The percentage of children not experiencing diarrhea is significantly higher for non-malnourished children than for acutely malnourished children (p<0.05).

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Table 6. Stunting: Comparison of acutely malnourished and non-malnourished children by selected indicators

HAZ BCG OPV DPT Measles Diarrhea Literacy of the head of household Male

HAZ≥ -2 57.2% (167/292)

40.5% (118/291)

39.4% (114/289)

32.8% (86/262)

51.9% (151/291)

37.3%* (109/292)

49.0% (143/292)

HAZ< -2 49.0% (71/145)

37.2% (54/145)

35.9% (52/145)

30.7% (43/140)

62.1% (90/145)

26.9%* (39/145)

53.8% (78/145)

Total 54.5% (238/437)

39.4% (172/436)

38.2% (166/434)

32.1% (129/402)

55.3% (241/436)

33.9% (148/437)

50.6% (221/437)

Chi-square test, *p<0.05 Table 6 summarizes the differences in immunization status, incidence of diarrhea, literacy of the head of household, and gender between acutely malnourished and non-malnourished children, using HAZ as the indicator of malnutrition. The Chi-square test was used to test for significant differences between acutely malnourished and non-malnourished children. Significant differences were only found in the case of literacy of the head of household. The percentage of children living in households with a literate head of household is significantly higher for non-malnourished children than for acutely-malnourished children (p<0.05).

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Correlation Matrices Table 7. Spearman Cross Correlation Matrix of Selected Predictors of Nutritional Status and Anthropometric Z-Scores

Gender (0=

Female, 1=male)

Age Diarr -hea Literacy

Food group

consum-ption

combin -ation

Immunization

Combin-ation

Hygiene Combin-

ation

Food expenditu

re (House-

hold adult

equivale-ncy)

HAZ WAZ WHZ

Correlation Coefficient Gender (0=female, 1=male)

1.00

Age .05 1.00 Diarrhea .01 -.02 1.00 Literacy -.01 -.07 .000 1.00 Food group consumption combination

.01 -.01 .03 .21** 1.00

Immunization Combination -01 .14** .01 .22** .19** 1.00

Hygiene Combination .00 -.07 -.01 .32** .25** .26** 1.00

Food expenditure (HH adult equivalency)

.00 .05 .02 .06 .18** .00 .06 1.00

HAZ -.06 -.18** -.11* .04 .07 .04 .02 -.04 1.00 WAZ -.12** -.26** -.10* .13** .14** .10 .12* -.04 .71** 1.00 WHZ -.10* -.16** -.05 .13** .13** .11* .09 -.01 .01 .61** 1.00 **Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed).

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Table 7 shows that significant correlations are found between WAZ and HAZ, as well as between WAZ and WHZ (both at the p<0.01 level). Gender and diarrhea are not significantly correlated with any of the selected indicators. However, gender is significantly correlated with underweight and wasting. Diarrhea is significantly correlated with stunting and underweight. It is also significantly correlated between age and immunization status as well as age and all 3 z-scores. Literacy was significantly correlated with the food score, the immunization score, the hygiene score, underweight and wasting. The food score indicator is significantly correlated with the immunization score, the hygiene score, the food expenditure (divided by household adult equivalency) variable, underweight and wasting. It is significantly correlated between the immunization score and hygiene score as well as immunization score and wasting. Finally, the hygiene score and underweight were significantly correlated.

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Regression Table 8. Regression Analysis Results

Stunting (N=394)

Wasting (N=393)

Underweight (N=396)

Coefficient B Std. Error P-value Coefficient B Std. Error P-value Coefficient

B Std. Error P-value

(Constant) 4.635 1.225 .000 .994 .760 .191 1.907 .893 .033 Age

(6-24) -.615 .134 .000*** -.316 .084 .000*** -.397 .098 .000***

Age2

(36-576) .017 .004 .000*** .009 .002 .000*** .010 .003 .000***

Gender (0-1) -.252 .191 .188 -.223 .119 .061 -.322 .140 .022*

Diarrhea (0-1) -.410 .192 .034* -.114 .119 .340 -.302 .140 .032*

Literacy of HH (0-1) -.038 .221 .865 .147 .137 .283 .201 .161 .211

Food combination (0-15) .062 .044 .163 .039 .027 .150 .050 .032 .123

Immunization combination

(0-4) .062 .059 .296 .061 .037 .098 .069 .043 .112

Hygiene combination (0-4) .057 .105 .584 .062 .065 .341 .096 .077 .209

Ln (Food expenditure/ HH adult equivalency)

(-.56-4.67) -.332 .159 .037* -.034 .098 .730 -.216 .116 .063

Multiple regression, *p<.05, **p<.01, and ***p<.001 Adjusted R²: stunting (.077), wasting (.061), underweight (.108). R: stunting (.312), wasting (.288), underweight (.358)

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All three regression models contain the variables mentioned in the methodology section. Regression coefficient B, standard errors and P-values from the multiple regression models are listed in Table 8. The underweight model had the most significant predictors, as well as the highest adjusted R2. Age, age2 (both at p<.001), gender, and diarrhea (both at p<.05) were significant predictors in the underweight model. Only age and age2 (both at p<.001) were significant predictors in the wasting model. Age, age2 (both at p<.001), and diarrhea (at p<.05) were significant predictors in the stunting model. General Equation: Nutritional Status = Constant + Age + Age2 + Gender + Diarrhea + Literacy of HH head + Food combination +Immunization Combination + Hygiene Combination + Ln(food expenditure/HH adult equivalency) IV. DISCUSSION AND CONCLUSIONS Reliability of the data This section will address problematical aspects regarding reliability of the data. Discrepancies in Data set and Coding Scheme During the midterm and end-line evaluation surveys, should these be carried out, more careful attention should be devoted to ensuring the quality of the data set and coding scheme. Proper training of the enumerators must be conducted, as well appropriate supervision of the data collection "with performance checks" (Karim, et al., 2002). It may be important to conduct key informant interviews with community leaders or health staff in order to validate the data (Hynes, et al., 2002). More careful attention should be devoted to the process of data entry and the construction of the decoding sheets for the questionnaires. Several of the variables on the decoding sheet in the baseline survey were labeled differently in the actual data set, making it difficult to locate variables (particularly age, height, and weight of the child) when performing the data analysis. This problem might result from poor training of staff or lack of supervision. In the ADRA questionnaire, there are many variables with a high proportion of ‘not applicable’ or ‘other’ responses. This suggests the possibility that the enumerators did not ask (or record the answers to) questions properly. Further, some of the questions on the baseline survey are inadequately constructed, rendering the data for these questions unreliable or not useful for analysis. For instance, 38.5% answered ‘N/A’ for Storing food properly, and 74.5% answered ‘N/A’ for Separate area for keeping animals. Considering the data concerns displaced persons, it is not very likely that many IDP households store

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food at all, nor is it likely they would possess any animals. A question such as, ‘Do you store food?’ should have preceded ‘Do you store food properly?’. Age Heaping in the Kosti Data Clearly, age data is essential information in order to gain an accurate understanding of child health and nutritional status before designing and targeting interventions. After running a frequency distribution for the age of child in months variable, the age of children tended to be aggregated in multiples of twelve. A histogram showed humps at 12-month intervals. A considerable degree of rounding occurred at 12-month intervals, and to a lesser extent, at 6-month intervals. For instance, 125 children were shown to be 24 months olds, 60 children were 12 months old, and 43 children were 18 months old. Such a frequency distribution demonstrates a certain lack of reliability in the age data. The Kosti age data exhibits substantial age heaping. Age data is commonly obtained by asking the mother or caretaker. Recall bias may lead the mother to round her child’s age to the nearest 12-month or 6-month interval. The effect of heaping tends to be more pronounced after one year of age. Mothers are more likely to be aware of the number of months of age when a child is under one year and then begin rounding when the child surpasses the first year. The "rounding" of age data may impose considerable "systematic bias" (Gorstein, 1994). Rounding up of the child’s age can cause the child to be misclassified as malnourished and rounding down can lead to missing cases of malnourished children. Clearly, during an intervention, this can result in children not receiving the proper treatment or not being treated at all. In sum, age heaping can lead to erroneous calculations of WAZ and HAZ, while unreliable age data does not affect the calculation of WHZ (Gorstein, 1994). It is unlikely that many of the households possessed birth certificates or baptismal certificates, especially considering their displacement. If they were not used during the employment of the baseline survey, it is strongly recommended that events calendars (Sautreau, 2000) be used during implementation of the midterm and end-line evaluations. Discussion Analysis of anthropometric indicators is essential in determining groups of children in danger of "poor functional outcomes", such as morbidity and mortality. Identification of these children is useful in distinguishing who should be the recipients of an intervention program (Gorstein, et al, 1994). Low weight-for-height exhibits a "failure to gain weight or a

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loss of weight" (Gorstein, et al., 1994), and tends to be considered the most crucial anthropometric indicator in complex emergencies. A benefit of weight-for-height is that it is not affected by age data, which is quite important considering the rather unreliable age data in the Kosti data set. As weight-for-height is especially susceptible to “acute growth disturbances” (Onis, et al), this anthropometric indicator is most often used for analysis in complex emergency situations. Although analysis of the Kosti data has demonstrated weight-for-age to show several significant findings, it must be noted that weight-for-age does not differentiate "tall, thin children from those who are short with adequate weight" (Gorstein, et al., 1994). Gorstein, et al. (1994) executed an ecological correlation analysis of WHO data on 22 African countries (de Onis, et al., 1993). Prevalence of underweight was found to be positively correlated with that of stunting and wasting (for children under 5, r=0.61 and 0.64, respectively). These findings are fairly consistent with the positive correlations found between underweight and stunting in the Kosti data (for children under 5 years, r = 0.72; for children 6-24 months, 0.71). The WHO findings are also fairly consistent with the positive correlations found between underweight and wasting in the Kosti data (for children under 5 years, r = .595; for children 6-24 months, r = 0.61). In the WHO analysis, very little correlation was demonstrated between wasting and stunting (r = 0.1). This finding is also consistent with the lack of correlation between wasting and stunting in the Kosti data (for children under 5 years, r = -0.02; for children 6-24 months, r = 0.01). The above findings have shown that older age groups tend to comprise the greater proportions of moderately and severely malnourished. This suggests that older children are more at-risk of becoming malnourished. Older children are less likely to be breastfed than younger children. Hence, complementary feeding tends to make up a larger portion of older children’s consumption than younger children’s consumption. Sanitary and environmental conditions tend to be rather poor in the Kosti IDP camps, much like most IDP camps. Water used for cooking may be unclean, or utensils may be unsanitary, putting children at risk for such ailments as diarrhea. Incidence of diarrhea has a direct impact on the nutritional status of a child. No information on seasonal effects, particularly periods or poor rainfall or drought, was collected in the Kosti baseline survey. Hence, seasonal effects would be a component of the unobserved effects in the regression models. If seasonal effects were strongly related to any of the independent variables, particularly the food group consumption combination and/or food expenditure/(Household adult equivalency), this could cause bias in the parameter estimates. Due to constraints in the data, only a narrow definition of sanitation and hygiene practices could be utilized.

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The data is not entirely conclusive. The lack of significant findings from the WHZ analysis and the relatively more significant findings in the WAZ analysis makes any convincing deduction somewhat difficult. It is generally agreed that wasting is the most important anthropometric indicator for assessing nutritional status during complex emergencies. Additionally, WHZ is unaffected by unreliable age data. Hence, precedent points to focusing on the WHZ findings. Unfortunately, the WHZ findings were generally insignificant in the Kosti data. The findings from the WAZ analysis show considerably more significant results. However, WAZ is negatively affected by unreliable age data, and underweight tends to be an unreliable indicator on the whole. Hence, careful consideration of these issues must be kept in mind when interpreting the results and devising an appropriate intervention strategy. Lessons Learned While there are undoubtedly limitations present when collecting data in IDP camps or squatter areas, it may be valuable to examine how the Kosti baseline Survey could have been improved. Initially, the Nutrition School of Tufts University had developed a baseline survey. However, the baseline survey that was eventually utilized by ADRA differed from the Tufts survey in several significant ways. With reference to the Tufts survey, recommendations will be given on how the ADRA survey could have been more complete. The ADRA survey neglected to survey whether the households lived in IDP camps or squatter areas. Moreover, data was not collected on the length of the stay in the IDP camps or squatter areas. The ADRA survey did not gather more detailed information on the source of monthly income. For example, data was not collected on monthly income derived from produce or fishing. Instead, ADRA only collected data on the occupation of the head of household. The occupation question includes ‘employment’ as a possible category. This is a rather general response, and no distinction was made between ‘self-employment’ and ‘employment’. One can only get an idea of household food consumption and food availability from the ADRA questionnaire. There are no direct questions dealing with consumption of the children. On the Tufts questionnaire, there is a question asking how many meals the children of the household eat per day. Another question measures the quantity of various food items, such as lentils, beans, and fruit, a household purchases per week and month.

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There are other questions on the Tufts questionnaire which were not included on the ADRA questionnaire, concerning: (1) receipt of food relief, including the amount of grain received and what the household does with the grain it receives; (2) information on periods when feeding was difficult, including coping mechanisms used. There is no information in the ADRA survey on participation in feeding programs. Food aid is likely to be a vital part of children’s diets in IDP camps. Further, food aid may be a significant component of cash income to households through sale of food (ACF, 2003). There is also an inadequate amount of information on birth spacing in the ADRA questionnaire. Value of Study Presently, there is a dearth of studies, descriptive or evaluative in nature, dealing with the health and nutrition status of displaced people (Hynes, et al, 2002). In Hovy’s (2002) article for the Migration Policy Institute (MPI), several explanations are discussed regarding the lack of data on IDP. Hovy compares data collection in terms of refugees and IDP. Although the conditions surrounding the displacement of IDP are often comparable to that of refugees, the quality of data for IDP tends to be rather inferior to that of refugees (Hovy, 2002). Hovy cites several reasons for this difference in quality. IDP tend not to receive the same degree of “international protection and assistance” as refugees. Further, “considering that they are still living in the country where they have been persecuted, the internally displaced may be less willing to register than those who enjoy the protection of their asylum country” (Hovy, 2002). While ADRA has not yet carried out the intervention program it had initially planned, such an extensive survey of IDP still has considerable value in terms of assessing the conditions of the IDP in Kosti, as well as being medium in which to improve similar such surveys in the future. Considering there are roughly 37 million displaced persons globally (Hynes, et. al., 2002), studies like the Kosti study are crucial for learning lessons. Further, findings from the Kosti study might be applied to other IDP camps in Sudan. The Kosti findings may even be applicable to the general population of Sudan. Linking the Kosti study to the general population may even encourage further studies of IDP camps in Sudan (Hynes, at al, 2002). Even in the absence of a subsequent program, such an extensive survey of IDP still has considerable value in terms of assessing the conditions of the IDP in Kosti, as well as being medium in which to improve similar such surveys in the future.

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References Action Contre la Faim (2003) Food Security Update. Internally Displaced People in Ingushetia. Adventist Development and Relief Agency (ADRA). Food Security Initiative. ANTHRO software. De Onis, M., and Blossner, M. (2003) The World Health Organization Global Database on Child Growth and Malnutrition: methodology and applications. International Journal of Epidemiology. 32: 518-526. De Onis, M., Monteiro, C., Akre, J., Clugston, G. (1993) The worldwide magnitude of protein-energy malnutrition: an overview from the WHO Global Database on Child Growth. Bulletin of the World Health Organization. 71(6) p703. Emergency Nutrition Network (ENN) Online. Shared experiences in infant and young child feeding in emergencies. http://www.ennonline.net/ife/ifecasestudies.pdf Gorstein, J., Sullivan, K., Yip, R., de Onis, M; Trowbridge, F., Fajans P., Clugston, G. (1994) Issues in the assessment of nutritional status using anthropometry. Bulletin of the World Health Organization: 72(2). Haug, R. “Forced Migration, Processes of Return and Livelihoods Construction among Pastoralists in Northern Sudan,” Disasters. 26(1): 70-84, 2002. Hovy, B. (2002) Statistics on Forced Migration, Migration Information Source. Migration Policy Institute (MPI). http://www.migrationinformation.org/Feature/print.cfm?ID=49 Hynes, M., Sheik, M., Wilson, H.G., and Spiegel, P. (2002) Reproductive Health Indicators and Outcomes Among Refugee and Internally Displaced Persons in Postemergency Phase Camps. JAMA. 288(5): 595-603. IFPRI. Adult equivalent consumption units differentiated by age and gender.

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http://www.ifpri.org/pubs/abstract/120/rr120apxb.pdf Karim, R., et al. (2002) Challenges to the Monitoring and Evaluation of Large Nutrition Programs in Developing Countries: Examples form Bangladesh. Tufts University. Friedman School of Nutrition. Food Policy Applied Nutrition Program. Discussion Paper No. 1. Lamstein, Sascha. Conceptual framework. Sautreau, R. (2000) Anthropometric Survey. Luuq Town and IDP Camps, Somalia, 16-20 April 2000. Action Contre la Faim. Shrimpton, Roger and Kachondham, Yongyout. Analyzing the Causes of Child Stunting in DPRK. 2003. UN Office for the Coordination of Humanitarian Affairs. SOMALIA: High child malnutrition and mortality due to food shortages. http://www.irinnews.org/report.asp?ReportID=42262&SelectRegion=Horn_of_Africa&SelectCountry=SOMALIA SPSS 12.0 Taha, Zubair Bashir. The Socio-Economic Impact of the War in Southern Sudan. Speech delpieved at the Conference on ‘The Southern Sudan Probel and the Porspeects of Peace’, London, 1998. www.sufo.demon.co.uk/conf001g.htm UN Office for the Coordination of Humanitarian Affairs. United Nations Consolidated Inter-Agency Appeal for Sudan. January - December 1998. ReliefWeb. http://www.reliefweb.int/w/rwb.nsf/0/ab48a45f5e6211c6c12565ae004af3d6?OpenDocument UN Office for the Coordination of Humanitarian Affairs (OCHA). Sudan: Affected Populations by District. Internally Displaced People (October 2003). http://www.reliefweb.int/w/map.nsf/wByCLatest/C4F3426BFCAA4301C1256E0F00792035?Opendocument

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United Nations Sub Committee on Nutrition. Refugee Nutrition Information System (RNIS), No. 29 - Report on the Nutrition Situation of Refugee and Displaced Populations. http://www.unsystem.org/scn/archives/rnis29/begin.htm#Contents

Unicef Conceptual Framework for Nutrition. Causes of Malnutrition. www.tulane.edu/~panda2/Analysis2/nutrdata/causes_of_malnutrition.htm. UNICEF. Iraq: Unsafe water making children unhealthy http://www.unicef.org/newsline/2003/03nn34iraq.htm

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BCG vaccination

Nutrition Status

Characteristics of household

Total number

of household members Literacy of HH head

Health Environment

& services

Immunization

OPV vaccination

DPT vaccination

Measles vaccination

Sanitation practices

Keeping utensils

Ways of garbage disposal

Drainage of stagnant water

Conceptual Framework:Causality of Malnutrition (predictors)

Diarrhea

Characteristics of child

Water treatment

Keeping water

Food security

Social Economic Status

Food availabilityInadequate dietary intake

Age/W/H Gender

Food consumption