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close this book Measuring drought and drought impacts in Red Sea Province
close this folder 5. Nutritional status of children in Red Sea Province, November 1985 to November 1987. Mary Cole and Roy Cole
View the document Summary
View the document Introduction
View the document Methods
View the document Results
View the document Conclusions
View the document Discussion
View the document Future directions.
View the document Appendix 5.1. Data collection form, nutritional surveillance teams, Oxfam
View the document Appendix 5.2. Claasifications of coded variables.
View the document Appendix 5.3. Ecozones in Red Sea Province (from Watson, 1976).
View the document Appendix 5.4. Seasons by month and ecozone, Red Sea Province.
View the document Appendix 5.5. Classification of fled Sea Province into food security zones, 1987.
View the document Appendix 5.6. Locations of sampled sites, nutritional surveillance cycle 1.
View the document Appendix 5.7. Names of sampled sites, nutritional surveillance cycle 1.
View the document Appendix 5.8. Locations of sampled sites, nutritional surveillance cycle 2.
View the document Appendix 5.9. Names of sampled sites, nutritional surveillance cycle 2.
View the document Appendix 5.10. Locations of sampled sites. nutritional surveillance cycle 3.
View the document Appendix 5.11. Names of sampled sites, nutritional surveillance cycle 3.
View the document Appendix 5.12 Locations of sampled sites, nutritional surveillance cycle 4.
View the document Appendix 5.13. Names of sampled sites, nutritional surveillance cycle 4.
View the document Appendix 5.14 Locations of sampled sites, nutritional surveillance cycle 5.
View the document Appendix 5.15. Names of sampled sites, nutritional surveillance cycle 5.
View the document Appendix 5.16. Locations of sampled sites, nutritional surveillance cycle 6.
View the document Appendix 5.17. Names of sampled sites, nutritional surveillance cycle 6.

5. Nutritional status of children in Red Sea Province, November 1985 to November 1987. Mary Cole and Roy Cole

 

Summary

The nutritional surveillance teams at Oxfam Port Sudan undertook six province wide tours of Red Sea Province between November 1985 and November 1987. Weight for height measurements were taken on approximately 2000 children less than or equal to 115 cm in height during each tour (cycle). The present study is a reanalysis and a synthesis of the data collected during this time.

In Red Sea Province as a whole, the nutritional status of children <=115 cm in height improved significantly between cycles 1-3 inclusive and 4-6 inclusive (approximating 1986 and 1987). Female children had a marginally better mean percent weight for height than male children in 1987, but equal proportions of males and females were malnourished (less than 80% reference median weight for height) in both cycles 1-3 and 4-6. Significantly more females than males were severely malnourished in cycles 13 (less than 70% reference median weight for height). There was a significantly higher proportion of males than females in the sample in cycles 13 and 4-6. Unfortunately we lack mortality data and details of refusal rates by sex which would be required to interpret these results. The percentage of the sample less than 75 cm in height increased significantly between cycles 1-3 and 4-6.

At the district level, Rural Port Sudan and North Tokar Districts had a percentage of malnourished children which was lower than the province wide average in 1986. Halaib District had a percentage of malnourished children that was equal to the province wide average in 1986, and Derudeb, Haya, Sinkat and South Tokar Districts had a percentage of malnourished children that was above the province wide average in 1986. Between cycles 1-3 and 4-6, the nutritional status of children in Rural Port Sudan and North Tokar did not change. The nutritional status of children in the remaining districts improved significantly between cycles 1-3 and 4-6, with the important exception of Haya District. The poor nutritional status of children in Haya District remained unchanged between cycles 1-3 and 4-6. This was particularly alarming because it has been estimated that up to 28% of the total population of the province live in Haya District for all or part of the year.

The highest rates of malnutrition were found in children less than 75 cm in height. This approximates to children less than one year of age. These results should be interpreted with caution, however, because the majority of these children were measured upright rather than supine, a technique which has not been validated. In children over 75 em in height, highest rates of malnutrition were found in the height category 75.1-85 cm. This corresponds to weaning age. The poor nutritional status of breast fed babies raises concerns about maternal health and nutrition during pregnancy and lactation. Children of weaning age are at high risk of malnutrition also. More information is needed about infant feeding practices and weaning in Red Sea Province.

Seasonal changes in nutritional status were apparent in both cycles 1-3 and 4-6. Children had the worst nutritional status in August and September, at the end of the dry summer season with the associated lack of cereals, grazing and milk. Children had the best nutritional status in January, after the harvest and when grazing and milk production had improved. By district, Halaib and Rural Port Sudan Districts did not show such seasonal trends in nutritional status. These districts are in the north of the province and have low rainfall with little seasonal variation. Seasonal variations in nutritional status should be considered when comparing the results of surveys undertaken at different times of the year.

Children who were living in settlements described as camps had a significantly worse nutritional status than children living in railway towns, towns or rural areas in 1986. By 1987, however, there was no significant difference in the nutritional status of children in any of the settlement types.

There was no correlation between the mean relief grain ration per family per day by settlement and the mean percent weight for height of children by settlement. This may be a reflection of the limitations of the analysis, which were imposed by the methods of data collection by both the Relief Section and the nutritional surveillance teams. Improved coordination between the Relief section and nutritional surveillance teams must be a priority in the future if nutritional surveillance data are to contribute to relief allocations in a meaningful way.

The smallest unit of analysis (the settlement) explained 12.9% of the variation in percentage weight for height found in cycles 1-6. It is likely that of those variables measured the individual settlement is the single most important factor in determining the nutritional status of children. The practical implications of this finding are that as many settlements as is practical should be sampled during nutritional surveillance in order to obtain an accurate picture of the nutritional status of children, and research into the causes of malnutrition in Red Sea Province should focus on the settlement.

The Ministry of Health of the Sudan government, together with USAID, undertook a survey of the nutritional status of children under five years of age in Northern Sudan during 1986 and 1987. These surveys found that children in Red Sea Province had a consistently higher nutritional status than had been found in the Oxfam surveys. This was probably because the government surveys included urban areas, which were excluded from Oxfam surveys. The results of the government surveys confirmed Oxfam findings with respect to the high risk of malnutrition in weaning age children, and the importance of the settlement in explaining variation in nutritional status.

After an initial phase of famine relief, the emphasis of relief efforts in Red Sea Province has been on a general concept of recovery rather than on nutritional interventions per se. Because of the alarming rates of malnutrition in babies and children of weaning age, pregnant and lactating women and children of weaning age should be targeted with specific interventions aimed at improving their health and welfare. This will involve examining the nutritional value of the relief ration, and developing new methods of targeting and monitoring. This should become primarily a gender issue. Oxfam should encourage innovative approaches to nutritional surveillance in the field in order to develop methods of assessment which are acceptable to the Beja, and locally appropriate standards for nutritional status which could be used in conjunction with internationally accepted standards. Methodological issues which have been raised as a result of practical experience should be addressed promptly and incorporated into guidelines which could be used in the event of future relief efforts in Red Sea Province.

 

Introduction

The present study is a reanalysis and a synthesis of the nutritional surveillance data collected by Oxfam Port Sudan from November 1985 to November 1987. The purpose of the study was to bring together data from six province-wide cycles of nutritional surveillance to provide an overview of the nutritional status of children in Red Sea Province, particularly with reference to significant changes over time and between different areas of the province. At the same time, a more detailed analysis of the data than had previously been undertaken was required to more accurately characterize malnutrition for future targeting and programme planning. This study also forms the basis for comparison with the nutritional status of children in Red Sea Province collected in 1989. These results are presented as a supplement to the present report. The following work was completed as part of a 90 day consultancy between June and November 1989, and in collaboration with the research section, Oxfam Port Sudan.

 

Methods

1. Methods used in the original nutritional surveillance.

This is not intended as an exhaustive review of the methods used in cycles 1-6, nor as a critique. Rather, it is intended to provide a framework within which to assess the results. A description of methods used and an assessment of the practical experiences of the nutritional surveillance teams in the field is planned for early 1990 (Fatima Gebreil).

a. Sampling.

The population targeted for surveillance was children in Red Sea Province less than or equal to 115 cm in height who were receiving food aid. The unit of comparison was the administrative district. The sampling frame was drawn from World Food Programrne/Oxfam lists of settlements receiving food aid, and included the number of families estimated to be associated with that settlement for the purpose of receiving food aid. Each of the seven administrative districts in Red Sea Province was surveyed every four months. Each four month province-wide survey was called a cycle. Six cycles were completed between November 1985 and November 1987.

The method of selecting the sample size varied for cycles 1 and 2. An attempt to weight the sample by district population from the 1983 census was abandoned. From cycle 3 onwards the sample size (n) was derived from a 95% confidence interval for an estimated percentage of malnourished children (p) of 22%, and with a precision of +/- 5% for the estimate of change.

C.I. = p ± 2 {p(100-p)/n}

This resulted in a sample size of 275 for each unit to be compared (the district). Because the proportion of the population to be sampled was small, it was assumed that the sample was drawn from an infinite population, and no correction factor was applied for actual population. The same number of children were therefore surveyed in each district, except in South Tokar, where the sample size was increased to 370 because it was considered to be densely populated.

The sample was divided into clusters of 30 children, a number selected as being both the number of children who could be measured in one day, and the minimum number required to be representative of the total settlement. The required number of children would therefore be selected from approximately nine settlements (12 in South Tokar). These were randomly selected from the lists for each district, after first being weighted for the number of families in each settlement. Settlements in the South Tokar mountains were excluded from the list because of inaccessibility. The sampling procedure was altered in cycle 6 because of complaints of excessive numbers of repeat visits. The majority of those settlements sampled in cycle 5 were excluded from the lists from which the random selection of the settlements to be visited in cycle 6 were made. The selection for cycle 6 was not therefore strictly random.

b. Sampling in the field.

After arriving at each selected site, the team leader met with the local community leader (shaykh) and mapped the approximate locations of the distinct clusters which made up the total settlement. These were then numbered, and another team member not involved in the mapping asked to choose a number. This was the cluster visited. In the majority of cases there would not be 30 children in the cluster, and the selection procedure would be repeated until 30 children had been measured. In larger clusters, individual houses were selected and all the children in them measured until the required number had been reached. Another technique involved collecting all the children in a central location before selecting and measuring them. It is possible that the technique used depended on the receptiveness and cooperation of the shaykh, and that house to house visits may not have been acceptable in some areas. This will be discussed in more detail in the pending discussion of field experiences.

c. Measurement techniques.

Children were weighed to the nearest 0.1 kg using Salter hanging scales. The scales were calibrated using 1, 5 and 10 kg weights at the start of each weighing session. Stales were replaced after a maximum of two cycles, or whenever required. The scales were held by one female team member while the child was weighed by another. Very small children were suspended in a nylon sling, while older children held onto the balance. Children were not required to remove light clothing.

Height or length was measured to the nearest 0.1 cm using a locally made board with a metal tape attached, and with a sliding head board. Children who were too young to stand upright, or who were too weak, were measured lying down. There were no quarditative cut off points for a length versus height measurement; In many cases mothers objected to young or weak children being laid horizontally, a position associated with being laid out after death. These children were supported in a vertical position against the height board.

Sex, height and weight, together with comments on the child's condition (fever, marasmus, other illness, etc.) were recorded on prepared forms (Appendix 5.1). Children with oedema were marked as having kwashiorkor, and their weight for height set at less than 7096 irrespective of the actual measurements.

d. Analysis.

Raw data were brought back to the office for analysis. Percent of median weight for height or length was calculated for each child, using the NCHS/CDC/WHO normalized reference of February 1982, taken from "Refugee Community Health Care" (Ed S.Simmonds, P.Vaughan and S.W.Gunn; Oxford University Press, Oxford 1983). Mean percent weight for height, percent less than 80% weight for height, and percent less than 70% weight for height were calculated for each settlement and for each district. Hand calculators were used throughout.

e. Reporting.

Cycles one to five were written up as individual reports and circulated to the Relief Section of Oxfam Port Sudan, Oxfam Khartoum, the Sudan Desk and Health Unit Oxfam House. Raw data and district summaries were made available on request to the Relief Section in Port Sudan.

2. Methods used In the present study.

The present study is a reanalysis and a synthesis of the nutritional surveillance data collected from cycles 1 to 6. The original raw data were entered into a spreadsheet. For each case the following variables were recorded:

1. Case number.

2. Cycle number.

3. District.

4. Health status of child (good/bad).

5. Height of child.

6. If height or length of child were measured.

7. If child's age was less than one year (mothers' report).

8. Month of survey.

9. Settlement name.

10. Sex of child.

11. Weight of child.

12. Year of survey.

These variables were collected in the original surveys. The variables "less than one year by mothers' report" and "health status" were subsequently dropped because of poor quality and inconsistent reporting, particularly in the later cycles. In addition to the above variables, new variables were added:

1. Average daily ration of relief grain per family.

2. Ecozone.

3. Food security zone.

4. Season.

5. Settlement type.

The classifications used for each variable are given in Appendix 5.2. The first three additional variables were based on a mapping of all the places surveyed in cycles 1-6. Based on latitude, longitude and the major khor system with which it was associated, each settlement was placed within an ecozone. These were taken from Watson's 1976 aerial survey of livestock and human population in Red Sea Province (Appendix 5.3). A season (major wet, minor wet, dry and harvest) was assigned to each ecozone by month (Appendix 5.4). A season was then assigned to each case according to ecozone in which the settlement was located and the month of the survey. "Food security zone" refers to a classification of Red Sea Province carried out by the Research Section, Oxfam Port Sudan, in which zones of high, medium and low drought impacts and food insecurity were identified based on a composite of variables that included rainfall, floods, agriculture, livestock, grazing and economic opportunities (Appendix 5.5). Settlement type was either railway, town, rural or camp. The average ration of grain delivered per family to each settlement was calculated from Oxfam and W.F.P. distribution schedules. Actual quantities and days between deliveries were used rather than allocated quantities and proposed delivery dates. The figure calculated represented the average amount of grain per day delivered to each family for the distribution cycle during which the nutritional surveillance took place. The limitations of this calculation are discussed below.

After completing the data set as outlined above, sex specific percent weight for height/length and z-scores were calculated for each case using WHO/NCHS/CDC references taken from "Measuring change in nutritional status" (WHO 1983). These tables were entered on the spreadsheet and the calculations made using the "Lookup" function. Percent weight for height and z-scores were rounded to two and three decimal places respectively. Each case was coded as being in one of seven height categories and eight percent weight for height categories.

Height categories:

Approximate age equivalent:

<= 55.0 cm

< 3 months

55.1 - 65.0 cm

up to 6 months

65.1 - 75.0 cm

6-12 months

75.1 - 85.0 cm

1-2 years

85.1 - 95.0 cm

2-3 years

95.1 - 105.0 cm

3-4 years 3 months

105.1 - 115 cm

>4 years 3 months

Percent weight for height categories:

<

 

60

60

-

69.9

70

-

79.9

80

-

89.9

90

-

99.9

100

-

109.9

1 10

-

1 19.9

>=

 

120

The data set was then edited by sorting on z-scores and rechecking extreme values with the raw data. Spot checks were also made through the rest of the data. Because of the size of the data set (over 14000 cases and 250000 individual pieces of data), all character based variables such as district and settlement names were converted to numbers, and a code book developed. The data set was then converted to an American Standard Code for Information Interchange (ASCII) file for reading into a statistics package. Copies of the raw data in ASCII format and the code book are available on request.

Although z-scores are a more accurate measure of nutritional status than percent weight for height, percent weight for height is the measure presented in this report. Percent weight for height was chosen because of a general unfamiliarity with the concept of z-scores, unfamiliarity with the relation of z-score cut off points to degrees of malnutrition, and because percent weight for height has historically been the measurement of choice in reporting nutrition surveillance data in Red Sea Province.

The data set was divided into two groups; children 75 cm and under, and children 75.1115 cm. The 75 cm cut off point was selected as approximating the height of a one year old. A height cut off was selected because mothers' reporting of children as being under one year old was inconsistent, particularly in the later cycles. After examining the raw data, and the heights/lengths of children who were recorded as being under one by mothers' report in the early cycles, the WHO recommended cut off of 80 cm for one year olds seemed inappropriately high for this sample. The division of the sample into those less than and equal to or more than 75 cm was carried out for three reasons:

1. Children under one year old tend to be breast fed. Their nutritional status is therefore not as reflective of the food stresses faced by a community as the nutritional status of weaned children, and should be reported separately.

2. There are some technical problems with the data collected on children less than one year old:

a. Sampling of under one year olds may not have been consistent. The policy as to whether children under one should be included in the sample changed between cycles 1 and 2. After cycle 1, any child selected who was less than one year old by mothers' report was measured, but another child more than one year old was added to the sample to make up a quota of 30 children over one year old. The intention was to report the nutritional status of less than one year olds separately, but reports from cycles 1-5 appear to include all children under 115 cm. In the later cycles the sample was restricted to 30 children irrespective of whether they were less than or more than one.

b. A large number of under one year olds were measured in a vertical position, being supported by a team member. It has not been possible to validate this procedure.

The sampling method used in cycles 1-6 had the district as the unit of analysis. The same number of children were sampled in each district. In order to obtain representative provincewide statistics, it was necessary to weight each district statistic by the proportion of the total population of Red Sea Province in that district. These population proportions were obtained from the Oxfam/ERGO 1989 low level aerial survey of Red Sea Province. These were considered the most accurate population data avail-able at the time of the analysis. Figures for population density and inhabited area of each district were taken from the March 1989 survey. This was considered to be when the population was at its' annual peak. Population per district was obtained by multiplying population density per square kilometre by habitable area in square kilometres. Uninhabited areas of Halaib and Haya Districts were excluded from the total area of those districts. In addition, 25,000 square kilometres were excluded from South Tokar District. This was the estimated area of the mountain zone, which was not included in the sampling frame for cycles 1-6. The percentage of population per district was then used as a weighting factor for district statistics when being summed to arrive at province wide figures (Table 5.1). In applying these weighting factors to data from earlier years, the assumption was made that despite population growth, the proportions of the population in each district have not changed significantly.

Table 5.1. Population densities, inhabited area and proportion of population per district, Red Sea Province.

DISTRICT

POP/KM

AREA (KM )

POP

%POP

DERUDEB

2.47

12100

29887

10.1

HALAIB

0.59

29100

17169

5.8

HAYA

3.33

25200

83916

28.4

N.TOKAR

2.18

19300

42074

14.2

R.P.S.

4.93

15800

77894

26.3

SINKAT

3.39

7900

26781

9.0

S.TOKAR*

5.07

3600

18252

6.2

* Coastal strip only (excludes mountains).

Source: ERGO, Preliminary report of phase one aerial and ground survey, February April 1989.

In addition to cycle by cycle results, annual averages were calculated in order to make comparisons of change over time without the influence of intervening variables such as season. Cycles 1-3 and cycles 4-6 were averaged to correspond approximately to 1986 and 1987 respectively.

Statistical analysis was carried out in two statistical packages; Systat and SPSS-PC+. Significance testing was limited to simple l-tests for comparing two means, ANOVA for comparing groups of means, and confidence intervals to compare proportions. The relationship between the relief ration and percent weight for height was examined using Spearman's rank correlation. A 95% confidence level was used throughout; results were considered significant only if there was less than a 5% probability they could have occurred solely by chance.

 

Results

1. Timing and location of Oxfam Port Sudan Nutritional Surveillance.

Table 5.2. Timing of Oxfam Nutritional Surveillance Cycles 1-6.

         

CYCLE BY MONTH

YEAR

J

F

M

A

M

J

J

A

S

O

N

D

1985

                   

1

1

1986

1

1&2

2

2

2

2

3

3

3

3

3

4

1987

4

4

4&5

5

5

5

5

6

6

6

6

 

The average time taken to survey the whole province was four months. Three surveys were completed in 12 months. Cycles 1 and 4, 2 and 5, and 4 and 6 were carried out at comparable times of year (Table 5.2). Within each cycle, the order in which the districts were visited varied (Table 5.3). The locations of the sites sampled in cycles 1-6 are shown in Appendix 5.6.

Table 5.3. Districts visited by monfh, NST cycles 1-6.

     

MONTH BY CYCLE

DISTRICT

1

2

3

4

5

6

DERUDEB

NOV

MAR

SEP

JAN

MAY

SEP

HALAIB

JAN

JUN

OCT

JAN

JUN

OCT

HAYA

DEC

APR

AUG

DEC

MAR

SEP

N.TOKAR

JAN

MAY

OCT

DEC

MAY

OCT

R.P.S.

JAN

JUN

NOV

MAR

JUL

DEC

SINKAT

DEC

APR

SEP

DEC

APR

OCT

S.TOKAR

JAN

MAR

SEP

JAN

MAR

OCT

Table 5.4. Total number of visits and number of places visited by district, cycles 1-6.

DISTRICT

NUMBER OF

NUMBER OF

% VISITED

% VISITED

 

VISITS

PLACES

> ONCE

3 TIMES

DERUDEB

57

37

35

13.5

HALAIB

75

47

45

13

HAYA

77

53

38

7.5

N.TOKAR

72

37

62

19

R.P.S.

67

49

29

8

SINKAT

79

55

35

9

S.TOKAR

77

47

43

17

TOTAL

504

325

40

12

Oxfam received many complaints from community leaders of repeat sampling and excessive visits by nutritional surveillance teams. Table 5.4 shows the total number of visits made to each district over cycles 1-6, together with the total number of different places visited. Over all cycles (two years), 40% of places visited were visited more than once, and 12% of places visited were visited three or more times. The maximum number of times any place was visited was four times.

2. Description of the sample.

a. Sample size

Sample sizes by district and cycle for children 75.1-115 cm and <75 cm are shown in Tables 5.5 and 5.6 respectively.

Table 5.5. Sample sizes (children >75 cm) by cycle and district.

 

CYCLE

DISTRICT

1

2

3

9

5

6

1-3

4-6

DERUDEB

169

322

258

211

195

189

749

595

HALAIB

264

324

231

203

190

190

819

583

HAYA

150

337

252

277

258

248

739

783

N.TOKAR

330

324

257

255

224

232

911

711

R.P.S.

214

327

266

252

264

254

807

770

SINKAT

227

338

257

293

267

262

822

822

S.TOKAR

301

323

241

291

292

279

865

862

TOTAL

1665

2295

1762

1782

1690

1654

5712

5124

Table 5.6. Sample sizes (children <75 cm) by cycle and district.

 

CYCLE

DISTRICT

1

2

3

4

5

6

1-3

4-6

DERUDEB

22

34

42

35

46

51

98

132

HALAIB

66

44

56

41

53

57

166

151

HAYA

30

33

49

53

75

86

112

2;4

N.TOKAR

89

41

44

53

77

70

174

200

R.P.S.

26

41

35

52

96

76

102

224

SINKAT

40

40

44

65

66

68

124

199

S.TOKAR

88

46

59

70

70

81

193

221

TOTAL

361

279

328

369

481

489

968

1339

The percent of the total sample which comprised children less than or equal to 75 cm by district and cycle is shown in Table 5.7. For Red Sea Province as a whole there was a significantly higher percentage of children less than 75 cm tall in cycles 46 than in cycles 1-3. At the district level there was a significantly higher percentage of children less than 75 cm tall in cycles 4-6 than in cycles 1-3 for all districts except Halaib and South Tokar.

Table 5.7. Percent of sample <75 cm by cycle and district.

 

CYCLE

DISTRICT

1

2

3

4

5

6

1-3

4-6

DERUDEB

11.5

9.6

14.0

14.2

19.1

21.3

11.6

18.2**

HALAIB

16.7

8.7

15.6

15.3

23.5

23.5

16.9

20.6

HAYA

16.7

8.9

11.2

16.1

22.5

25.7

13.2

21.2**

N.TOKAR

21.2

11.2

14.6

17.2

25.6

23.2

16.0

22.0**

R.P.S.

10.8

11.1

11.6

17.1

26.7

23.0

11.2

22.5**

SINKAT

15.0

10.6

14.6

18.2

19.8

20.6

13.1

19.5**

S.TOKAR

22.6

12.5

19.7

19.4

19.3

22.5

18.2

19.5

TOTAL

17.9

10.8

15.7

17.2

22.2

22.8

14.5

20.7**

** Significant difference between cycles 1-3 and 4-6 (p<.05)

b. Proportions of males and females in the sample.

The ratios of males to females by district and cycle for children greater than 75 cm and less than or equal to 75 cm are shown in Tables 5.8 and 5.9 respectively.

Table 5.8. Ratios of males:females (children >75 cm) by cycle and district.

 

CYCLE

DISTRICT

1

2

3

4

5

6

1-3

4-6

DERUDEB

1.06

1.37*

1.10

1.22

1.44*

1.33

1.20*

1.32*

HALAIB

0.89

0.98

0.94

0.88

1.18

0.96

0.94

1.00

HAYA

1.14

1.13

0.97

1.16

0.97

1.25

1.08

1.12

N.TOKAR

1.17*

1.13

1.14

0.92

1.20

1.17

1.15*

1.08

R.P.S.

1.55*

1.14

1.53*

1.17

1.28*

1.17

1.36*

1.21*

SINKAT

1.39*

1.28*

0.85*

1.02

1.09

1.02

1.15*

1.04

S.TOKAR

1.01

1.07

0.94

1.12

1.20

1.05

1.01

1.12

TOTAL

1.14*

1.15*

1.05

1.07

1.18*

1.13*

1.12*

1.12*

* Significantly different from 1.00 at p=.05

In the sample 75.1-115 cm, there was a significantly higher proportion of males than females for the whole province in both cycles 1-3 and 4-6. By district, Derudeb and Rural Port Sudan had significantly higher proportions of males than females in both cycles 1-3 and 4-6, while North Tokar and Sinkat districts had significantly higher proportions of males than females in cycles 1-3 only.

Table 5.9. Ratios of males:females (children <75 cm) by cycle and district

 

CYCLE

DISTRICT

1

2

3

4

5

6

1-3

4-6

DERUDEB

0.83

0.62

0.68

0.75

0.92

1.09

0.69

0.91

HALAIB

0.89

0.63

0.60

1.73

1.04

0.68

1.71

1.01

HAYA

1.50

1.06

0.88

1.04

1.08

0.95

1.07

1.02

N.TOKAR

0.98

1.28

1.00

1.41

0.97

1.19

1.04

1.15

R.P.S.

1.36

0.78

1.19

1.36

1.13

1.24

1.04

1.22

SINKAT

1.00

1.35

1.39

0.81

0.83

0.74

1.24

0.79

S.TOKAR

1.26

0.92

0.97

0.89

1.50

1.08

1.08

1.13

TOTAL

1.07

0.91

0.91

1.07

1.06

0.98

0.97

1.03

Despite some apparently large differences between the proportions of males and females less than 75 cm, none were significantly different from a ratio of 1.00 (50% males, 50% females). This is because statistical significance depends not only on the proportions found, but on the size of the sample. The sample sizes for children less than 75 cm were very small. c. Distribution of heights in the sample.

Height distributions for the total sample for cycles 1-3 and 4-6 are shown in Figure 5.1. There was a significantly higher percentage of children in the height category 55.1-65.0 cm in cycles 4-6 than in cycles 1-3. There was a significantly lower percentage of children in the height category 95.1-105.0 cm in cycles 4-6 than in cycles 1-3. No significant differences were found between the percentage of males and females in each height category in either cycles 1-3 or 4-6.


Figure 5.1. Height distributions m the total sample, cycles 1-3 and cycles 4

3. The nutritional status of children, total province.

Unless otherwise specified, results refer to children 75.1-115 cm in height. a. Means and distribution of percent weight for height, total province.

The percentage of children in each percent weight for height class for cycles 1-3 and 4-6 are shown in Figure 5.2. For both cycles 1-3 and 4-6 the largest percent of the sample fell between 80 and 89.9% weight for height. Males and females are aggregated because no significant differences were found between the percentage of males and females in each percent weight for height class in either cycles 1-3 or 4-6. In cycles 46, however, females had a higher mean percent weight for height than males (see Table 5.10).

Table 5.10 Mean percent weight for height and standard deviation by sex, Red Sea Province, cycles 1-3 and 46.

CYCLE

 

MALE

FEMALE

TOTAL

1-3

MEAN

86.68

87.04

87.26

 

SD

7.99

8.12

8.05

4-6

MEAN

88.08*

88.58*

88.34**

 

SD

7.76

8.05

7.90

* Significant difference between males and females (p<0.05).

** Significant difference between cycles 1-3 and 4-6 (p<.051.

For both cycles 1-3 and 4-6 the mean percent weight for height for the total sample was above 85% weight for height. The centre of the distribution therefore fell towards the upper limits of the 80-89.9% class.


Figure 5.2 Distribution of percent weight for height children 75.1-115 cm, Red Sea Province.

There was an upward shift in the distribution of percent weight for height between cycles 1-3 and 4-6. This is reflected in the mean percent weight for height which was significantly higher in cycles 4-6 than 1-3 (p<.05). There was a significant decrease in the percentage of the sample in the 80-89.9% weight for height class between cycles 1-3 and 4-6, and a corresponding increase in the 90-99.9% weight for height class over the same periods. Although there was a decrease in the percentage of the sample in the 70-79.9% weight for height class between cycles 1-3 and 4-6, this was not statistically significant. When all weight for height classes less than 80% weight for height were aggregated, however, the changes between cycles 1-3 and 4-6 became significant (see the Results section).

b. Rates of malnutrition, total province.

Children between 70% and 79.9% of their reference median weight for height are classified as "moderately malnourished". Children less than 70% of their reference median weight for height are classified as "severely malnourished". For the purpose of this report the following definitions have been used:

Malnourished - those children less than 80% of their reference median weight for height. This includes both moderately and severely malnourished children.

Severely malnourished - those children less than 70% of their reference median weight for height.

i. Percentage of malnourished children, total province.

The percentage of the total sample who were malnourished together with the 95% confidence interval for the population estimate, is shown in Table 5.11. Province-wide, there was no significant difference between the percentage of males and females who were malnourished. A significantly lower percentage of children were malnourished in cycles 4-6 than in cycles 1-3.

Table 5.11. Percent less than 80% reference median weight for height (malaourished) by sex, with 95% confidence intervals, Red Sea Province.

CYCLE

MALES

 

FEMALES

 

TOTAL

 
 

%

95% CI

%

95% CI

%

95% CI

1-3

16.68

1.36

16.65

1.43

16.63

0.49*

4-6

13.72

1.32*

12.64

1.35*

13.19

0.47*

* Significant difference in cycles 1-3 and 4-6.

ii. Percentage of severely malnourished children, total province.

The percentage of the total sample who were severely malnourished, together with the 95% confidence interval for the population estimate, is shown in Table 5.12.

Table 5.12 Percent less than 70% reference median weight for height (severely malnourished) by sex, with 95% confidence Intervals, Red Sea Province.

   

SEX

       

CYCLE

MALES

 

FEMALES

 

TOTAL

 
 

%

95% CI

%

95% CI

%

95% CI

1-3

1.89

0.43*

2.74

0.63*

2.27

0.43

4-6

1.36

0.44

1.34

0.47

1.35

0.32**

* Significant difference between males and females (p<.05).

** Significant difference between cycles 1-3 and 4-6 (p<.05).

In cycles 1-3 there was a significantly higher percentage of severely malnourished females than males. There was no significant difference in the percentage of severely malnourished males and females in cycles 4-6. The percentage of severely malnourished females decreased significantly between cycles 1-3 and 4-6, whereas the percentage of severely malnourished males stayed the same.

c. Malnutrition rates by district.

i. Percentage of malnourished children by district.

The percentage of the sample who were malnourished by district and sex, together with the 95% confidence interval for the population estimate, is shown in Table 5.13. There was a significant difference between the percentage of malnourished males and females in South Tokar District in cycles 1-3. A significantly higher percentage of males than females were malnourished. There were no other significant differences between the percentages of malnourished males and females by district, either in cycles 1-3 or 4-6.

Table 5.13. Percent less than 80% reference median weight for height (malnourished) by district and sex, with 95% confidence Intervals for the estimate.

   

CYCLES 1-3

CYCLES 4-6

DISTRICT

 

MALES

FEMALES

TOTAL

MALES

FEMALES

TOTAL

DERUDEB

MEAN

21.57

21.11

21.36

15.04

18.75

16.64**

 

CI

4.07

4.42

3.00

3.88

4.89

3.05

HALAIB

MEAN

14.90

15.13

15.02

8.25

6.51

7.38**

 

CI

3.58

3.49

2.50

3.23

2.89

2.19

HAYA

MEAN

20.30

19.10

19.76

18.12

15.99

17.11

 

CI

4.12

4.17

2.93

3.79

3.79

2.69

N.TOKAR

MEAN

11.50

14.15

12.73

11.65

11.40

11.53

 

CI

2.89

3.39

2.21

3.33

3.44

2.40

R.P.S

MEAN

10.75

11.70

11.15

10.45

9.46

10.00

 

CI

2.87

3.48

2.22

2.98

3.13

2.16

SINKAT

MEAN

18.18

20.94

19.46

14.35

10.45

12.41**

 

CI

3.86

4.16

2.76

3.43

3.05

2.30

S.TOKAR

MEAN

28.28*

20.00*

24.16

14.25

12.56

13.46**

 

CI

4.32

3.86

2.91

3.27

3.29

2.33

PROVINCE MEAN

16.68

16.65

16.63

13.72

12.64

13.19**

 
 

CI

1.36

1.43

0.49

1.32

1.35

0.47

* Significant difference between males and females.

** Significant difference between cycles 1-3 and 4-6.

From Table 5.13 the districts in Red Sea Province can be divided into two groups: those that showed a significant decrease in the percentage of malnourished children between cycles 1-3 and cycles 4-6, and those that showed no significant difference in percentage of malnourished children between cycles 1-3 and 4-6. Derudeb, Halaib, Sinkat and South Tokar Districts all had a significant decrease in the percentage of malnourished children between cycles 1-3 and 4-6. Haya, North Tokar and Rural Port Sudan Districts showed no change in the percentage of malnourished children between cycles 1-3 and 4-6. This is summarized in Map 5.1.

Map 5.2 shows a comparison of the percentage of the sample who were malnourished in each district for cycles 1-3 in relation to the province-wide mean percentage of malnourished children for cycles 1-3. Districts are ranked as above the province-wide mean, equal to the province wide mean, or below the province-wide mean with respect to the percentage of malnourished children. Map 5.3 shows the same information for cycles 4-6.


Map 5.1. Changes in malnutrition rates by district. cycles 1-3 to 46.


Map 5.2. Percentage of malnourished children, district means compared to province-wide mean. cycles 1-3.


Map 5.3. Percentage of malnourished children, district means compared to provincewide mean. cycles 46.

It should be remembered that the province-wide percentage of malnourished children was lower in cycles 4-6 than in cycles 1-3, and that the relation of each district to the province-wide mean should be interpreted in conjunction with the absolute district figures shown in Table 5.13. A district by district summary combining these data is given below.

DERUDEB: The percentage of malnourished children was higher than the provincewide average in cycles 1-3, and although it decreased significantly, still remained worse than average in cycles 4-6.

HALAIB: The percentage of malnourished children was equal to the province-wide average in cycles 1-3 and decreased significantly, resulting in a lower than average percentage of malnourished children in cycles 4-6. Halaib District was the only district with average or higher than average percentage of malnourished children to improve significantly between cycles 1-3 and 4-6.

MAYA: The percentage of malnourished children was higher than the province-wide average in cycles 1-3 and did not change significantly in cycles 4-6. Because the province-wide average percentage of malnourished children decreased between cycles 1-3 and 4-6 while the district average for Haya remained the same, the percentage of malnourished children in Haya was worse in relation to the rest of Red Sea Province in cycles 4-6 than in cycles 1-3. Haya district was the only district with a higher than average percentage of malnourished children that failed to improve between cycles 1-3 and 4-6.

NORTH TOKAR: The percentage of malnourished children was better than average in cycles 1-3 and did not change significantly in cycles 4-6. However because the provincewide average percentage of malnourished children decreased in cycles 4-6, North Tokar moved from being better than the province-wide mean in cycles 1-3 to being equal to the province-wide mean in cycles 4-6.

RURAL PORT SUDAN: The percentage of malnourished children was lower than average in cycles 1-3. The percentage of malnourished children did not change significantly between cycles 1-3 and 4-6, but despite the fact the province-wide mean improved, Rural Port Sudan still had a lower than average percentage of malnourished children in cycles 4-6.

SINKAT: The percentage of malnourished children was higher than average in cycles 1-3 but decreased significantly and were equal to the average in cycles 4-6.

SOUTH TOKAR: The percentage of malnourished children was higher than average in cycles 1-3 but decreased significantly and were equal to the average in cycles 46. The percentage of malnourished males decreased more than the percentage of malnourished females, so the significantly higher percentage of malnourished males than malnourished females found in cycles 1-3 was not found in cycles 4-6.

ii. Percentage of severely malnourished children by district.

The percentage of the sample who were severely malnourished broken down by district and sex, together with the 95% confidence interval for the estimate, is shown in Table 5.14.

Table 5.14. Percent less than 70% weight for height by district and sex, with 95% confidence Intervals for the estimate.

CYCLES 1-3 CYCLES 4-6

DISTRICT

 

MALES

FEMALES

TOTAL

MALES

FEMALES

TOTALS

DERUDEB

MEAN

2.21

4.40

3.20

0.88

2.34

1.51**

 

CI

1.46

2.20

1.29

1.01

1.89

1.00

HALAIB

MEAN

1.77

2.13

1.95

1.03

0.34

0.69**

 

CI

1.36

1.40

0.97

1.18

0.67

0.69

HAYA

MEAN

2.87

3.09

2.98

1.93

1.63

1.79

 

CI

1.71

1.83

1.25

1.35

1.32

0.95

N.TOKAR

MEAN

1.44

1.65

1.54

0.54

0.88

0.70

 

CI

1.08

1.24

0.82

0.76

1.01

0.63

R.P.S

MEAN

0.65

1.75

1.12

1.43

1.43

1.43

 

CI

0.75

1.42

0.74

1.16

1.27

0.86

SINKAT

MEAN

1.18*

3.93*

2.43

1.44*

0.50*

0.97**

 

CI

1.03

1.99

1.07

1.17

0.70

0.68

S.TOKAR

MEAN

4.37

3.95

4.16

1.32

1.23

1.28**

 

CI

1.96

1.88

1.36

1.07

1.09

0.77

PROVINCE MEAN

1.89*

2.74*

2.27

1.36

1.34

1.35**

 
 

CI

0.43

0.63

0.43

0.44

0.47

0.32

* Significant difference between males and females.

** Significant difference between cycles 1-3 and 4-6.

As noted previously, there was a significantly higher percentage of severely malnourished females than severely malnourished males in cycles 1-3. At the district level the significantly higher percentage of severely malnourished females than severely malnourished males was found only in Sinkat district, although many other districts showed a similar pattern but did not reach statistical significance, possibly because of small sample size.

No district had a percentage of severely malnourished children which was significantly different from the province-wide mean. Small sample size may have influenced the lack of statistical significance. The tendency was, however, for those districts with the highest rates of moderate malnutrition to have the highest rates of severe malnutrition.

4. Further characterisation of malnutrition in Red Sea Province.

a. Percentage of malnourished children by height category.

The total sample of children of all heights was broken down into height categories. The percent of children in each height category who were malnourished is shown in Figure 5.3. Children less than 55 cm were excluded because of extremely small numbers. No significant differences were found between the percentage of malnourished males or the percentage of malnourished females in any height category in either cycles 1-3 or 4-6.


Figure 5.3. Percent less than 80% reference median weight for height by height category, cycles 1-3 and 46.

In both cycles 1-3 and 4-6 the highest rates of malnutrition were seen in the height category 65.1-75 cm. High rates of malnutrition were also seen in children 55.165 cm. These results must be interpreted with extreme caution, however, because of the measurement technique used for the majority of children in these height categories. In children aver 75 cm tall highest rates of malnutrition were seen in the category 75.185 cm. Malnutrition rates then declined with height, and the lowest rates were seen in the tallest height category. There was a significant in the percentage of malnourished children in all height categories between cycles 1-3 and 4-6. The largest decreases were seen in the height categories 55.1 to 65 cm and 65.1 to 75 cm.

b. Percentage of malnourished children by season.

The results presented above are annual averages. It is important to understand the seasonal variations in malnutrition rates, for example to identify times of high risk, and to construct a framework with which to interpret the results of future surveys carried out at specific times of the year.

As described in the methods section, a season (major wet, minor wet, dry and harvest) was assigned to each case based on ecozone and time of year. Results from this analysis failed to detect seasonal differences; there were no significant differences between the mean percent reference median weight for height or the percentage of malnourished children between any of the seasons. This did not reflect the experience in the field, where May and June were described as the worst months for children's health, and January the best month, irrespective of ecozone. A further analysis was carried out by cycle, which corresponded to months of the year (see Table 5.3).


Figure 5.4. Percent less than 80% reference median weight for height by cycle. children 75.1-115 cm. Red Sea Province.

For the total province, the percentage of malnourished children peaked in September, then decreased in January and remained at this level until peaking again in September (Figure 6.4). Malnutrition rates were therefore worst in September, but improved by January to a level which is sustained for the rest of the year.

When broken down by district and cycle, two patterns emerge: Figure 5.5 shows the percentage of malnourished children by cycle for Halaib and Rural Port Sudan districts, and Figure 5.6 shows the percentage of malnourished children by cycle for the remaining districts of Derudeb, Haya, North Tokar, Sinkat and South Tokar. The latter group of districts followed the pattern described for the province as a whole. In Halaib district there was a steady decrease in the percentage of malnourished children with no seasonal variation apparent. In Rural Port Sudan there was some variation in the percentage of malnourished children, but it was small and not consistent with seasonality.


Figure 5.5. Percent less than 80% reference median weight for height (malnourished) by cycle, Halaib and Rural Port Sudan districts.


Figure 5.6. Percent less than 80% reference median weight for height (malnourished) by cycle, Derudeb, Haya, North Tokar, Sinkat and South Tokar Districts.

c. Percentage of malnourished children by settlement type.

The settlements visited by the Oxfam Port Sudan nutrition surveillance teams in cycles 16 were classified as being railway towns, towns, rural settlements or camps (settlements of displaced people that grew up during the drought). Table 5.15 gives the percentage of malnourished children by settlement type for cycles 1-3 and 4-6.

Table 5.15. Percentage of children less than 80% reference median weight for height (malnourished) by settlement type, cycles 1-3 and 4-6, with 95% confidence Intervals for the population estimate.

CYCLE

SETTLEMENT

CYCLES 1-3

CYCLES 4-6

 

MEAN

95% CI

MEAN

95% CI

RAILWAY

17.1

4.24

14.0

3.32

TOWN

19.5

2.66

10.6

2.17

RURAL

16.2

1.16

13.0

1.13

CAMP

24.5

4.14*

13.8

3.86

* Significantly different from other settlement types (p<.05).

There was a significantly higher percentage of malnourished children in the camps in cycles 1-3 than in all other types of settlements in cycles 1-3. There was no significant difference in the percentage of malnourished children in railway towns, towns and rural areas in cycles 1-3. There was no difference between the percentage of malnourished children in any of the settlement types in cycles 4-6.

d. Percentage of malnourished children by food security zone.

Red Sea Province was classified into three zones of drought impacts and food security for the years 1987 and 1988. Nutritional surveillance data for 1987 (cycles 4-6) were analyzed by "food security zone" for the same year. The percentage of malnourished children by food insecurity zone in cycles 4-6 are shown in Table 5.16.

Table 5.16. Percentage of children less then 80% reference rnedian weight for height by food security zone, cycles 4-6, with 95% confidence intervals for the population estimate.

FOOD SECURITY ZONE

N

MEAN

95% CI

HIGH SECURITY

1891

11.37

1.46

MEDIUM SECURITY

1535

12.77

1.70

LOW SECURITY

1029

15.74

2.27*

* Significantly different from other food security zones (p<.05).

There was no significant difference between the percentage of malnourished children in the high and medium food security zones. The percentage of malnourished children in the low food security zone was significantly higher than the percentage of malnourished children in the high and medium food security zones.

e. Percentage of malnourished children and the World Food Programme relief grain ration.

Mean percent reference median weight for height of children 75.1-115 cm for each settlement was correlated with the settlement average World Food Programme grain ration per family per day at the time of the nutritional surveillance. Spearman's rank correlation of mean percent weight for height with mean grain relief grain ration gave a correlation coefficient of -0.157 for cycles 1-3 and 0.008 for cycles 4-6. These correlation coefficients were not significantly different from zero. There was no correlation between mean percent weight for height and relief grain ration; the ration neither increased or decreased in a consistent pattern as malnutrition rates increased.

f. Variation In percentage of malnourished children within and between settlements.

One-way analysis of variance (ANOVA) of percent weight for height and settlement for cycles 1-6 showed that 12.9% of the total variation in percent weight for height could be explained by the relationship of percent weight for height to settlement. Unfortunately, Oxfam Port Sudan did not have the facilities to be able to carry out multiple regression on the nutritional surveillance data, so it was not possible to determine the amount of variance in percent weight for height explained by all the variables examined or their inter-relationships.

g. Comparison of Oxfam nutritional surveillance results with Sudan Emergency and Recovery Surveillance System (SERISS) results.

The Republic of Sudan Ministry of Health and USAID conducted four rounds of nutritional surveillance of children under five years old in the six northern regions of Sudan between May 1986 and July 1987. Red Sea Province was included in the surveys. Figure 5.7 shows the comparison of weight for height z-scores for children under five years old collected by SERISS, and weight for height z-scores for children less than or equal to 115 cm in height collected by Oxfam Port Sudan.

SERISS found consistently higher weight for height z-scores than Oxfam Port Sudan; children surveyed by SERISS had a better nutritional status than children surveyed by Oxfam Port Sudan. In the SERISS surveys, however, standard deviations of weight for height z-scores were as high as 2.73, whereas standard deviations for weight for height z-scores in the Oxfam surveys were close to 1.0. A similar seasonal pattern of changes in weight for height z-scores was seen in both surveys. Other SERISS findings which were similar to those of Oxfam Port Sudan included:

1. Nutritional status improved from 1986 to 1987.

2. High malnutrition rates were seen in the 12-21 month age group.

3. The smallest unit of analysis (the village council) was the single most important variable determining nutritional status.


Figure 5.7. Mean weight for height z-scores from SERISS and Oxfam nutritional surveillance, Red Sea Province

 

Conclusions

Unless otherwise specified, conclusions refer to children 75.1-115 cm in height.

1. Gender

a. There was a significantly higher percentage of males than females 75.1115 cm tall in Red Sea Province for both cycles 1-3 and 4-6. There was no significant difference in the proportions of males and females 75 cm or less for cycles 1-3 or 4-6.

b. There was no significant difference between the percentage of males and the percentage of females in each height category over 75 cm either in cycles 1-3 or 4-6.

c. There was no significant difference between the percentage of males and the percentage of females in each percent weight for height class in either cycles 1-3 or 4-6.

d. The mean percent reference median weight for height of females was significantly higher than the mean percent reference median weight for height of males in cycles 4-6, but the difference was so small as to be of little practical significance.

e. There was no significant difference between the percentage of malnourished males and the percentage of malnourished females in either cycles 1-3 or 4-6.

f. There was a significantly higher percentage of severely malnourished females than severely malnourished males in cycles 1-3. In cycle 46 there was no significant difference between the percentage of severely malnourished males and the percentage of severely malnourished females. The percentage of severely malnourished females decreased significantly between cycles 1-3 and 4-6, whereas there was no significant change in the percentage of severely malnourished males between cycles 1-3 and 4-6.

2. Province-wide results.

a. The height distribution of children in Red Sea Province changed between cycles 1-3 and 4-6. There was a significantly higher percentage of children under 75 cm in cycles 4-6 than in cycles 1-3, and a significantly lower percentage of children 95.1-105 cm in cycles 4-6 than in cycles 1-3.

b. For both cycles 1-3 and 4-6, the largest percentage of the sample fell into the 80-89.9% weight for height category, with the centre of the distribution towards the upper end of the class.

c. Mean percent reference median weight for height increased significantly between cycles 1-3 and 4-6, while both the percentage of malnourished children and the percentage of severely malnourished children decreased significantly between cycles 1-3 and 4-6.

3. District level results.

a. In cycles 1-3 Derudeb, Haya, Sinkat and South Tokar districts had a percentage of malnourished children above the mean for the province. Halaib district had a percentage of malnourished children equal to the mean for the province, while North Tokar and Rural Port Sudan districts had a percentage of malnourished children below the province mean.

b. Derudeb, Halaib, Sinkat and South Tokar districts all had a significant decrease in the percentage of malnourished children between cycles 1-3 and 4-6. In Haya, North Tokar and Rural Port Sudan districts there was no significant difference between the percentage of malnourished children in cycles 1-3 and cycles 4-6.

c. Of those districts with a higher than average percentage of malnourished children in cycles 1-3, only Haya district failed to improve in cycle 4-6. Of those districts with a percentage of malnourished children equal or lower than the average, only Halaib district improved in cycle 4-6.

d. In cycles 4-6 Derudeb and Haya districts had a percentage of malnourished children above the mean for the province. North Tokar, Sinkat and South Tokar districts had a percentage of malnourished children equal to the mean for the province, while Halaib and Rural Port Sudan had a percentage of malnourished children below the province mean.

4. Percentage of malnourished children by height category.

a. Highest rates of malnutrition were seen in the 65.1-75 cm height category in both cycles 1-3 and 4-6.

b. High malnutrition rates were found in children 55.1-65 cm.

c. For children over 75 cm tall, highest rates of malnutrition were seen in the 75.1-85 cm height category.

5. Percentage of malnourished chlidren and seasonality.

a. For the whole of Red Sea Province, the lowest percentage of malnourished children were found in January - April. Highest percentages of malnourished children were found in August and September.

b. When broken down by district, Halaib and Rural Port Sudan districts did not show seasonal fluctuations in the percentage of malnourished children.

6. Percentage of malnourished children by settlement types.

a. There was no significant difference in the percentage of malnourished children in railway towns, towns and rural areas.

b. In cycles 1-3 there was a significantly higher percentage of malnourished children in settlements classed as camps than in all other settlement types. In cycles 4-6 there was no significant difference between the percentage of malnourished children in railway towns, towns, rural areas or camps.

7. Nutritional status and the relief grain ration.

a. There was no correlation between settlement means for percent reference median weight for height and average World Food Programme relief grain ration per family per day.

8. Variation in nutritional status by settlement.

a. The smallest unit of analysis (the settlement) explained 12.9% of the total variance in percent weight for height for cycles 1-6.

9. Comparison of Oxfam nutritional surveillance results with SERISS results.

a. SERISS found consistently better nutritional status but greater variability in nutritional status in Red Sea Province than Oxfam nutritional surveillance teams.

b. SERISS and Oxfam nutritional surveillance had similar findings with respect to the improvement in nutritional status between 1986 and 1987: high risk of malnutrition in weaning age children, and the importance of the village in explaining variation in nutritional status.

 

Discussion

1. Gender differences in nutritional status.

From the figures for mean percentage reference median weight for height it would appear that females had a better nutritional status than males in Red Sea Province, although the difference was so small as to be of little practical significance. Equal proportions of males and females were malnourished, but in 1986 significantly more females than males were severely malnourished. In large samples relatively small but significant differences in mean percent reference median weight for height may be a reflection of differences in distributions above 80% weight for height. The mean percentage weight for height in two samples can be significantly different while the percentage of classified as malnourished is not, because of varying proportions in the different categories of well nourished children. Changes in mean percent weight for height can be used as an indicator of a worsening nutritional status in the community before increased malnutrition is seen. Mean percent weight for height also helps to describe the total distribution of percent weight for height in the sample. It is not safe to assume, however, that because one group has a higher mean percent weight for height than another (in this case females versus males), that the higher group has lower rates of malnutrition. From the results of this study, it would appear that females are at higher risk of malnutrition, particularly severe malnutrition, than males.

Significantly higher proportions of males than females were found in the samples from both cycles 1-3 and 4-6. We lack enough information to say whether these significant differences in proportions of males and females are a true reflection of a difference in the population, or are a result of a bias in the sample. If these figures are representative of the population, and assuming that the proportions of males and females are equal at birth, higher proportions of males than females in children 75.1-115 cm suggests a higher mortality rate in females than in males. This would be consistent with higher rates of severe malnutrition in females than in males, but there are no mortality data available to support or refute this hypothesis. Mortality data are important in interpreting change in nutritional status. An apparent improvement in nutritional status may be due to mortality in severely malnourished children. In Red Sea Province it is difficult to collect reliable mortality data as death, particularly in children, is a taboo subject. The health care infrastructure in the province is weak and the health information system extremely incomplete.

An alternative explanation of the unequal proportions of males and females is that the sample was not representative; that it was biased towards sampling males. There are no data on refusal rates by sex to arrive at this conclusion, but there remains the possibility that cultural factors may prevent women from bringing forward female children for measurement. One observation from the raw data: in some settlements the raw data is grouped into batches of males and females. This suggests that there may have been some division by gender during the process of assembling the children to be weighed. If this was the case, it would have added a complicating factor to the process of obtaining a truly random sample. If future surveys are undertaken, accurate records should be kept of refusals.

There is little information on infant and child feeding practices in Red Sea Province, and how they vary with the sex of the child. Colin Alfred undertook 10 weeks of field work in April - June 1986 in which he used open-ended questionnaires and interviews to collect information designed to increase understanding of the effect of food aid on communities. In discussion groups with women, a consensus emerged that "...boys ate more than girls, but not when they were young". The question of whether women felt that males needed more food than females was not addressed. Also, expressed opinions may not reflect practice, especially in times of stress.

2. Co-operation and "survey fatigue".

Although it is not within the scope of this report to comment extensively on survey design and practical difficulties in the field, there are some points that should be highlighted here. The percentage of places receiving repeat visits does reflect the possibility of different areas of a settlement being surveyed at each visit. In the case of larger settlements these areas could have been some distance from each other. Conversely, it is possible that in some areas one community leader would have to be consulted before a number of settlements could be visited. The number of repeat visits to individual community leaders could therefore be considerably higher than the number of repeat visits to individual settlements. The total number of visits by all Oxfam and non-Oxfam teams should be considered in assessing survey fatigue. Oxfam Food Monitors, World Food Programme Monitors, SERISS and Ministry of Health teams all toured Red Sea Province with varying frequency. The British Red Cross, Norwegian Red Cross, Euro-Action Acord, International League of the Red Cross and the Sudanese Red Crescent work in rural areas of Red Sea Province also.

The difficulties associated with survey co-operation increased not only with increasing numbers of visits, but with decreasing relief rations and decreased drought related stress. The unpopular and invasive nutritional surveillance was seen as a necessary evil associated with relief distribution. The nutritional surveillance teams themselves often emphasized the links between nutritional surveillance and the relief ration in an attempt to ensure co-operation. As discussed previously, refusal rates must be accurately recorded in the future. Refusal rates for an invasive and unpopular procedure are in themselves an indicator of stress in the community.

3. Changes in height distributions between cycles 1-3 and 4-6.

The increase in proportions of children less than 75 cm (approximately one year old) between cycles 1-3 and 4-6 was highly significant. Possible explanations for this change include:

a. Sampling bias.

b. An increased birth rate in cycles 4-6.

c. A decreased infant mortality rate in cycles 4-6.

Better maternal nutritional status would contribute to both an increased birth rate and decreased infant mortality. Again, the lack of vital statistics for the province and a lack of detailed data on refusal rates make it impossible to evaluate the change in the height distributions of the sample.

4. Nutritional status of children, total province.

Province-wide figures for mean percentage reference median weight for height and percentage of malnourished children provide a good starting point for describing nutritional status. The advantages of province-wide figures include a large sample size, which increases the probability of finding statistically significant results, and results that are not influenced by population shifts within the province (although Red Sea Province is far from a closed system, and population movement does occur across provincial boundaries). The disadvantage of province-wide results is that they mask variation within the province.

In Red Sea province as a whole mean percentage reference median weight for height increased, while the percentage of malnourished children decreased between cycles 1-3 and 46. The increase in mean percentage reference median weight for height was very small, and despite being statistically significant, is of little practical significance. The decrease in the percentage of malnourished children is encouraging, but we lack the historical data necessary to evaluate the degree of malnutrition in cycles 1-3 and 4-6 or the change between them in the context of what is usual variation in Red Sea Province.

There were no formal relief programme objectives in terms of nutritional status set at the beginning of the programme against which to assess the decrease in malnutrition.

Percentage reference median weight for height is a proxy indicator of nutritional status. The cut off of 80% weight for height for classifying malnutrition is largely arbitrary. We have no means of assessing what shifts in a proxy indicator around this arbitrary cut off really mean in terms of the health and welfare of children in Red Sea Province. More information is needed on the Beja perception of child health, acceptable and unacceptable levels of thinness and shortness in children of varying ages, and the expected variation in these both seasonally and in times of stress. By developing a locally appropriate cut off for malnutrition which could be used alongside international standards, future programme objectives could be both more realistic and aimed at a degree of change which has practical significance and visible impacts for the beneficiaries. Unless we have more detailed local information we are in danger of either devoting time and money to programmes which do not address a real need, or, more alarmingly, withdrawing from programmes when objectives which do not reflect needs have been met "successfully".

5. Variation in nutritional status by district.

With the exception of North Tokar, the nutritional status of children in the southern half of the province was worse than that of children in the northern half of the province. Although there is low rainfall in the north of the province, the absolute variability in rainfall is not great (see the accompanying paper by Roy Cole "Drought, food stress and the flood and rainfall record for Red Sea Province"). The north is therefore adapted to conditions of permanent drought, and did not experience a dramatic worsening of conditions in 1984/85. The worst nutritional status was seen in the south of the province, which has the highest variability in rainfall and flooding and experienced the biggest drought impacts in 1983/84. Nutritional vulnerability is not linked solely to absolute conditions but to variability in conditions and the degree and frequency with which exceptional conditions occur (see the accompanying paper by Roy Cole "Measuring drought and food insecurity in Red Sea Province in 1987 and 1988: a technique for the rapid assessment of large areas.").

The higher nutritional status of children in North Tokar district than the rest of southern Red Sea Province could be a reflection of the relatively rich resources of that district. These include agriculture in the Tokar delta and major khors, the economic opportunities and facilities associated with the town, and rich natural resources for local rural industry. South Tokar, which had the worst nutritional status in Red Sea Province in 1986, is exceptional also in that it has a high population of Beni 'Amer "refugees" who have become destitute through the combined effects of drought and restricted access to traditional grazing and agricultural areas in Eritrea caused by war. In addition, there is a high population of Eritrean refugees both in camps and absorbed into the areas around larger towns such as Garora. Traditional land rights restrict the movement of the Beni 'Amer into the North, the war in Eritrea has cut them off from their own traditional areas in the South, and the situation has been exacerbated by the pressure of incoming refugees. South Tokar district can be cut off from the rest of Red Sea Province for several months during late summer because of flooding in Khor Baraka.

Those districts with above average nutritional status in 1986 (North Tokar and Rural Port Sudan) did not improve in terms of percentage of malnourished children in 1987, despite a general improvement in nutritional status province-wide, regular relief food deliveries and good rainfall. Possible reasons for the lack of improvement include:

a. The level of malnutrition seen in these districts in 1986 represents the best possible level achievable in the prevailing conditions. That is, malnutrition is primarily a result of variables such as poor water supply, infectious disease, poverty and a lack of entitlement rather than a result of inadequate food supplies. This being the case, relief rations would not have a significant impact on malnutrition rates, and an alternative strategy focusing on primary health and a general strengthening of the ability to cope with environmental and economic variability would be more appropriate.

b. The level of malnutrition seen in these districts in 1986 is a level that is accepted either by individual families or the community. That is, the child's health and development are considered to be normal and there is no obvious functional disability, despite being below internationally recognised cut offs for malnutrition. This will be referred to as an "acceptable" level of malnutrition. Once this level is reached, resources may be thought to be more appropriately channelled into strategies to increase future security rather than into immediate household consumption. Given the extremely marginal and variable nature of the environment it is likely that long term strategies for increasing security would have a high priority once children were perceived as being out of danger. The comments made previously about the need to investigate local perceptions of malnutrition and their relation to child health and welfare apply here also. If a threshold of acceptable malnutrition does exist, and it is at a level which compromises child health and development, vulnerable children should be targeted with commodities specifically for their consumption rather than assuming that the benefits of a more general ration will trickle down. Examples such as corn soy mix (CSM) or a more varied basket of local commodities suitable for weaning foods are discussed above. Children are unlikely to benefit from supplementary foods when the food supply for the whole family is inadequate. A twopronged approach that ensures sufficient food for the family together with targeted foods for weaning age children stands the best chance of success.

Halaib district moved from having average rates of malnutrition in cycles 1-3 to having the lowest rates of malnutrition in Red Sea Province in cycles 4-6. Although the nutritional status of children in Halaib was better during cycles 1-3 than in several other districts, the changes seen in cycles 4-6 shows that there was still potential for improvement. Actual changes in nutritional status must be interpreted in terms of potential for change, which includes resource availability, prevailing conditions such as rainfall, and external inputs such as relief rations. The recent ERGO/Oxfam low level aerial survey of Red Sea Province has shown Halaib to be the least densely populated district, with an estimated population of approximately 17,000. This figure is substantially lower than the population estimate used for relief distribution. It is probable that the actual amount of relief grain distributed per family was considerably higher than that allocated. This, coupled with the fact that no severe drought effects were felt in Halaib district, may have contributed to the improvement in nutritional status.

Haya district was the only district with worse than average malnutrition rates that failed to improve between cycles 1-3 and 4-6. In cycles 4-6 Haya district had the worst malnutrition rate of any of the districts in Red Sea Province. The lack of improvement is particularly disturbing in view of the recent aerial survey findings that 28% of the total population of Red Sea Province lives in Haya district for all or part of the year. It was the district which suffered the worst drought impacts in 1983/84, and together with Derudeb the district in which camps for drought displaced people sprang up. There was no improvement in nutritional status between cycles 1-3 and 4-6 despite good rains and floods in Khor Arab, and regular relief distributions. Possible explanations for the lack of improvement must include those hypothesized for the lack of improvement in better than average areas; that the rate of malnutrition represents the best possible rate for the district, and/or the level was considered acceptable. The first explanation seems unlikely in view of the potential grazing, agricultural possibilities of Khor Arab and related areas, and the links and economic opportunities afforded by the railway and tarmac road.

Were relief rations allocated to Haya district inadequate? From the aerial survey figures it appears that the population of Haya district may have been underestimated, and the amount of relief actually available to each family less than that allocated Allocations and monitoring of relief in Haya district (with the exception of Tahamyam) was the responsibility of the Sudanese Red Crescent rather than Oxfam. The SRC has been and still is autonomous, and makes independent judgements on allocation levels. SRC seems to have been in favour of lower rations and more rapid ration reductions than was Oxfam policy for the rest of the province.

Another explanation for the continued poor nutritional status in Haya district could be that as families recovered from the effects of the drought they returned to their home areas, leaving behind a residual population of poor and destitute families. Alternatively, parts of families may have moved away leaving women, the elderly and sick children at the relief dumping points and camps. The populations being measured in cycles 1-3 and 4-6 would therefore not be comparable. With seasonal migration a strong feature of Beja life (and indeed an essential survival strategy) the problem of defining the population being surveyed at various times of the year applies to all districts and adds yet another complicating factor to survey design and the interpretation of results.

There is not enough information available to assess retrospectively the plausibility of any of the proposed explanations for the lack of improvement in nutritional status in Haya district. It is a pity that the emphasis of the nutritional surveillance was on a quantitative statement of the degree of malnutrition to the exclusion of a more holistic investigation of the causes of malnutrition. The quantitative approach was rooted in the original function of the nutritional surveillance teams for rapid assessment in an emergency situation, and failed to evolve with the changing emphasis of the relief programme and the changing conditions in the province. The opportunities to investigate and learn from situations such as the lack of change in Haya district were lost.

6. Malnutrition rates by height of the child.

The highest rates of malnutrition in Red Sea Province were found in children 65.1-75 cm in height. This corresponds to approximately ages 6-12 months. Disturbingly high rates of malnutrition were found in children 55.1-65 cm tall also. It would be expected that breastfeeding would play a major role in meeting nutritional requirements of children in these height categories, and therefore that rates of malnutrition would be relatively low. Aside from the possibility of a sampling bias towards babies who were sick, and the error introduced by the non-standard measurement technique used for children in these height categories, the high rate of malnutrition in under one year olds in Red Sea Province could be a reflection of inadequate breastfeeding and/or low birth weight babies who fail to catch up with expected growth patterns.

Reports from the field suggested that a large percentage of lactating mothers were unwell. Diseases such as scurvy and severe anaemia have been noted on field trips in 1988. Women do not have access to such a variety of foods as men because they are restricted to the tent. Men eat outside the tent, sharing food with neighbours. Men are served first, before women and children eat the remaining food. Men travel away from the community have the opportunity to eat in restaurants and coffee shops.

Maternal malnutrition affects both the quantity and quality of breast milk, particularly when the mothers' own body stores have been depleted by persistently poor nutritional intake and the stress of disease and pregnancy. Poor health could also affect infant feeding practices. Personal observations and anecdotal evidence from midwives suggest that some women who are in poor health will either stop breastfeeding or not establish breastfeeding even if their breast milk is adequate. Breast milk production is stimulated by feeding. If feeding stops or is infrequent a vicious cycle develops where breast milk production decreases and the mothers' perception of her inadequacy for breastfeeding is reinforced. More information is needed on women's perceptions of suitable and unsuitable health and environmental conditions for successful breastfeeding.

Maternal pre-pregnancy nutritional status and weight gain during pregnancy affect the birth weight of the infant. Poor nutritional status and low weight gain in pregnancy can result in low birth weight babies, who are effectively malnourished from birth and subsequently at high risk for diseases which would worsen their nutritional status further. A prerequisite for improving the nutritional status of children is to improve the nutritional status of women. All women of child bearing age should be considered for targeting as a vulnerable group. Particularly at risk are very young women who face the nutritional stresses of pregnancy and lactation before they have completed their own growth. In Red Sea Province it is common for women to be married and bearing children before they are out of their 'teens.

Targeting pregnant and lactating women for what the World Food Programme describes as "vulnerable group feeding" poses many problems besides the most obvious of identifying and reaching such women. Traditional practice is to restrict food intake in the third trimester of pregnancy to avoid large babies. Large babies are thought to cause difficult births, and to have been afflicted with the evil eye (belief in the evil eye is powerful and influences all aspects of rural Beja life). The period of food restriction coincides with the time that it is customary for a woman to leave her husband and return to her family for the birth; an extra mouth to feed in a household that may already be strained. In addition to a general restriction of food intake there are specific food taboos that apply in pregnancy. These seem to vary but examples of forbidden foods include eggs, dates and camel's milk. Any supplementary feeding programme must take such practices into account.

Policing household level food distribution is obviously impossible and undesirable. It is unlikely that commodities distributed for pregnant and lactating women will be consumed by them unless there is sufficient available for the whole family. One approach which has been used successfully in the Gambia is to provide pregnant and lactating women with high protein high energy biscuits which are less likely to be absorbed into the family food supply. This approach is impractical for Red Sea Province as there is no health care infrastructure through which to distribute the biscuits, large external inputs would be required, and the system would not be locally sustainable. Given adequate environmental conditions there is a sufficient quantity and variety of local foods to satisfy the nutritional requirements of pregnant and lactating women. What is needed is a greater understanding of women's support structures, their priorities for and control of their own income, their influence on decision making at various levels and how these change with factors such as wealth and drought related stress.

Of children over 75 cm tall, highest rates of malnutrition were found in the 75.185 cm height category. This corresponds approximately to children 12-24 months of age. In Red Sea Province weaning begins with the gradual introduction of supplementary foods between six and nine months of age, and is completed between 1824 months of age. Breast milk alone becomes inadequate to meet the nutritional needs of a child at around six months of age. Results from this study indicate that children of weaning age were at high risk of malnutrition. Children of weaning age have high energy and nutrient requirements for growth, but cannot take large quantities of food. Weaning foods must therefore have a high energy and nutrient density and be fed in small frequent amounts. In Red Sea Province weaning foods include ground sorghum porridge (asayda), goat milk, goat ghee, rice, and water in which dried dates have been soaked Sour milk and camel milk are not considered suitable for children. Breast feeding continues for up to two years, or until the woman becomes pregnant.

Successful weaning in rural areas is largely dependent on the availability of animal products such as milk, ghee and animal fat. The availability of these products varies both seasonally and from year to year. Replacements would be expensive and available only in towns. The vegetable oil and sugar provided in the relief ration are energy dense, but do not contain the wide range of nutrients found in milk and milk products. The vegetable oil (rape seed oil) is unpopular with some people because it has a fishy smell. It has been difficult to ensure equitable distributions of oil and sugar; they are valuable products which makes them particularly prone to "leakage". Oil and sugar may be sold by the recipients to provide cash. Sugar is highly prized and would be given preferentially to men and guests.

The most satisfactory way of permanently improving the health and welfare of weaning age children would be through long-term strategies to increase the resource base for pastoralism, investment in agricultural infrastructure and increased economic opportunities for the community as a whole. In the meantime, if a relief approach is adopted vulnerable weaning age children need to be targeted with more appropriate foods. Dried full fat vitamin A fortified milk is one, albeit controversial, possibility for the summer months when milk is in short supply. Corn soy mix was briefly distributed in Red Sea Province, with mixed reactions. Oxfam teams reported that the CSM was unpopular, and that women winnowed the mixture to extract the sugar. The Sudanese Red Crescent, however, reported enthusiastically that CSM had "the magical power to remove marasmus from our children" and asked for distribution to be extended. An alternative would be a relief ration that consisted of a more varied basket of nutritionally dense foods which would be suitable as weaning foods. Oxfam has tried this approach in some areas of Darfur, where families with malnourished children were targeted with a ration that included lentils and oil. An essential part of any such targeted programme would be an effective distribution and monitoring system. This should be done by women and through women, despite the difficulties that this poses in Red Sea Province.

Ideally, nutritional support to weaning age children would be part of a wider health care strategy that addresses issues such as immunisation and treatment of diarrhoea which directly affect nutritional status. Some common traditional medical treatments administered to children of weaning age result in either increased nutritional requirements because of physiological stress or decreased intake of nutrients. Examples include burning the head or other areas of the body to treat oedema (a common treatment for many other diseases also), and incising the gums and removing four teeth to treat tasniin. a disease associated with teething. Health and nutrition education for rural women should be increased. What primary health care facilities that are available concentrate on therapeutic rather than preventive and promotive health care. Community health workers are exclusively male.

7. Seasonallty In malnutrition rates.

Classification of seasons by month and ecozone failed to detect changes in malnutrition rate by season. The method used for classification was too rigid and insensitive to the great inter and intra-annual variation in rainfall and flooding seen in Red Sea Province. Seasonal changes in malnutrition rates are lagged behind the actual seasons. Changes are therefore masked, particularly as the rainy season is very short and the dry season long.

When re-examined by cycle, monthly changes in malnutrition rate became apparent. The worst rates of malnutrition were seen at the end of the dry season; the cumulative effect of the associated scarcity in cereals and grazing, reduced milk production and poor water supplies. Red Sea Province in the dry season is extremely harsh, with high temperatures and frequent dust storms. The incidence of respiratory and eye infections increases dramatically in the summer months, part of a vicious cycle of worsening nutritional status and increasing disease susceptibility. Lowest rates of malnutrition were seen in January and February, after the rainy season and harvest, and when improved grazing has resulted in increased milk production and livestock numbers.

When broken down by district the pattern of seasonal changes in malnutrition rates was not seen in the north of the province (Halaib and Rural Port Sudan districts). As discussed previously, the north of the province receives very little rainfall, and the distinction between the seasons is blurred. Seasonal changes in malnutrition rates in Rural Port Sudan district may have been modified by access to the supplies and facilities available in Port Sudan. Greatest seasonal variation in malnutrition rates occurred in the south of the province, which has the highest variability in rainfall and flooding.

It is essential to take seasonality into account when comparing results of nutritional surveys conducted at different times of the year. Otherwise, an improvement in nutritional status could be unjustifiably attributed to an intervention such as provision of relief radons when it is a reflection of normal intra-annual variation.

The provision of a stable relief ration throughout the year did not mask seasonality in malnutrition rates. This could be for three reasons:

a. The ration did not provide the total requirements for energy and nutrients.

b. In terms of nutritional value, sorghum, oil and sugar are not a substitute for milk and milk products which are essential for children and are always in seasonal short supply unless the family has a large herd and is highly mobile.

c. Non-food factors which influence malnutrition rates and vary seasonally, such as disease incidence, are playing an important role in the aetiology of malnutrition in Red Sea Province.

8. Malnutrition rates by settlement type.

Camps for drought displaced families sprang up in early 1985, largely in Haya and Derudeb districts. They were usually associated with an existing town. The nutritional status of children living in settlements classified as camps was significantly worse than the nutritional status of children living in other settlement types in 1986, but not in 1987. The improvement in the nutritional status of children in camps in 1987 could have been due to either or both of the following:

a. Camps being disbanded and families returning to their home areas, leaving a residual urbanised population and blurring the distinction between camps and towns.

b. The nutritional status of the camp inhabitants improved.

There was no difference between the nutritional status of children living in railway settlements, towns and rural areas. It was expected that the nutritional status of children in railway towns would be better than the nutritional status of children in towns or rural areas. Railway workers were relatively well paid in 1986 and 1987 (LS 350 per month), they have access to food supplies delivered by train, and water supplies are delivered by tanker from Atbara or Port Sudan. This was not the case. Possibilities for the lack of difference between railway towns and other settlements include:

a. Within railway towns the children of railway workers were not differentiated from the children of non-railway workers.

b. The majority of people living close to the station are not employed by the railway.

c. Railway towns have large satellite communities who have settled to take advantage of the facilities, often because of stress in their home areas. Railway towns could therefore be made up of two populations; relatively advantaged railway workers, and disadvantaged rural people on the periphery. This would not have been picked up from the previous survey design.

d. There could have been no difference in the nutritional status of wealthy and poor families, either because malnutrition was caused by non-food factors or because the level of malnutrition was one which was acceptable to wealthier families. Oxfam nutritional surveillance teams in Darfur found no difference in rates of malnutrition in the children of wealthy or poor families.

The possibilities outlined above apply to the lack of difference between the nutritional status of children in towns and rural areas also. Towns have more facilities and economic opportunities than rural areas, but also have a peripheral population of very poor families. It is possible that similar populations are being measured in towns and rural areas as families move between them at different times of the year.

9. Rates of malnutrition by food security zone.

As might be expected, areas with low food security and high drought impacts had higher rates of malnutrition than areas with medium and high food security and low drought impacts. There was no difference, however, in malnutrition rates between areas with medium and high food security and low drought impacts. Possible explanations for this include:

a. An acceptable rate of malnutrition is reached in the medium food security zone, and represents a threshold below which malnutrition rates do not improve despite increased food security.

b. Malnutrition in areas with medium or high food security is caused largely by factors not related to food availability.

c. The food security zones are too large and mask variability within them. The zones were based on ecological rather than human criteria. There was a general east-west bias in the zoning, whereas differences in nutritional status seem to vary more in a north-south direction. A more detailed analysis of food security in Red Sea Province has since been carried out, but because of time pressures could not be included in this analysis.

d. Migration between the food security zones could have resulted in the same populations being measured in different food zones at different tames of the year.

If points a and b are valid, the usefulness of nutritional status as an indicator of food security is reduced. If point d is valid, it needs to be a major consideration in planning future sampling strategies.

10. Malnutrition rates and the relief ration.

The calculation of the average relief grain ration per family for the cycle during which the nutritional surveillance took place was not ideal. Nutritional status is a lagged variable; it does not change immediately but reflects conditions at some time in the past. To fully understand the relationship between the relief ration and malnutrition rates a historical perspective is necessary. Changes in malnutrition rate should be examined in relation to relief input over time rather than looking for relationships between absolute figures at one point in time. Unfortunately neither the Relief Section records nor the nutritional surveillance data were in a form which could be used in a longitudinal analysis without considerable work, well outside the time frame for this study. Coordination between the Relief Sections and nutritional surveillance teams to collect data in a form that would be amenable to continuous longitudinal analysis should be a priority in planning future relief efforts. Examples include better co-ordination between relief allocation zones and sampling units for nutritional surveillance, consideration of the timing of relief distributions in planning the frequency and timing of nutritional surveillance, more serious efforts to time relief deliveries on a regular basis, and making available cumulative records of relief inputs and running averages for allocations.

The calculation of the average relief grain ration per family per day at the time of the nutritional surveillance was considered worthwhile despite the limitations outlined above. This was for three reasons:

a. It was the best measure of average relief rations available.

b. As a rule, relief allocations did not vary greatly within one dumping point in 1986 and 1987.

c. The beneficiaries are thought to manage deliveries in such a way as to smooth fluctuations in the availability of grain through the cycle. Examples include borrowing grain against the next relief delivery if that delivery is delayed.

The lack of correlation between malnutrition and the relief ration is probably best explained by the limitation of the analysis outlined above. Other factors which complicate a direct relationship between the relief ration and malnutrition rates include:

a. Food availability may not be reflected directly in malnutrition rates because of the influence of non-food factors in the aetiology of malnutrition, and because of the possibility of an acceptable level of malnutrition (as discussed previously).

b. The nutritional status of children may not necessarily reflect the total needs of an area for relief input. Improving the nutritional status of children was not the only goal of this relief operation, and the need for relief could therefore remain after malnutrition rates have reached their lowest possible rates.

c. Nutritional surveillance data were not, or possibly never could be, appropriate in terms of time and space to make a meaningful contribution to the cycle by cycle management of relief allocations. Could such data be used more appropriately for long term planning and evaluation (for instance setting annual targets for total inputs), and/or more detailed case study type information to address the causes of malnutrition, which in turn would guide policy?

11. Variation in malnutrition rates by settlement.

In the 1986/87 Sudan Emergency and Recovery Surveillance System (SERISS) study of nutritional status in Northern Sudan (Ministry of Health/USAID), 28% of the total variation in weight for height z-scores was explained by the 22 variables examined in the survey. The remaining 72% of variation remained unexplained The Oxfam nutritional surveillance teams collected information on similar but fewer variables than the SERISS survey, and it is unlikely that regression of the Oxfam data set would result in a higher percentage of explained variance in nutritional status than that found in the SERISS study. If the SERISS figure of 28% explained variance is taken as a guide, the effect of settlement in explaining variation in percent weight for height in the Oxfam survey was considerable. It is probable that settlement was the single most important variable in explaining variation in nutritional status in the Oxfam data set, as it was in the SERISS data set. The practical implications of this finding are:

a. Sampling should include as many settlements as is practical in order to ensure an accurate assessment of the district and province In Red Sea Province family size is small and the number of children present in each settlement typically small. Sampling therefore inevitably involves visiting a large number of individual settlements.

b. Investigations of the causes of malnutrition should concentrate on looking at individual settlements and the differences between individual settlements which are influencing nutritional status. This was the approach taken by the nutritional surveillance teams in 1988 and 1989 (see the paper by Fatma Gebreil "An analysis of areas in Red Sea Province with persistently poor nutritional status", Oxfam Port Sudan 1989.).

c. General relief rations and/or supplements for vulnerable groups need to be targeted by settlement. Within one allocation zone there will be a wide range of need. This implies close monitoring by people with detailed local knowledge.

The importance of the settlement in explaining variation in percentage weight for height is probably the result of it being the smallest unit of analysis in the study. Factors affecting even smaller units such as the individual tent or family group may prove even more important in explaining variation in percent weight for height than those which affect settlements, but in rural Red Sea Province at the present time research at the household level is difficult to the point of practical impossibility.

12. Comparison of Oxfam nutritional surveillance results with SERISS results.

The persistently higher nutritional status found in the SERISS surveys than in the Oxfam surveys is most likely to be a result of differences in the sampling frames, the sampling intensity and the distribution of the sampled areas in the two studies. It is not a case of one study being "right" and another "wrong", but rather of them providing different information for different uses. The Oxfam nutritional surveillance had Red Sea Province as the area of study, with the district as the unit of analysis. The SERISS study had Northern Sudan as the area of study, with the province as the unit of analysis. Oxfam sampled exclusively from rural areas, whereas SERISS stratified their sample by urban and rural population.

The urban component of the SERISS study was probably responsible for the higher nutritional status than was found by Oxfam. In addition, SERISS sampled in fewer districts and settlements than Oxfam. As has been discussed previously, this would have important implications for capturing all the variation in nutritional status found in Red Sea Province. It would be valuable for Oxfam to collect or collate more information on the nutritional status of children in Port Sudan. Seasonal migration of families to Port Sudan means that the urban populations in some strongly Beja areas of the city such as Daym al-'arab could be the same as the population being measured by Oxfam nutritional surveillance teams in rural areas. Periods spent living in Port Sudan could be an important factor influencing the nutritional status of such populations.

 

Future directions.

The tendency has been for Oxfam to use the nutritional status of children as an indicator of the wellbeing of the community and to provide general assistance with the hope that benefits will reach all sections within the community. Improving the nutritional status and hence the health and welfare of children in Red Sea Province should become 1) a practical programme goal with both long-term development initiatives and immediate targeted interventions, and 2) focused on gender issues. If Oxfam is not to do this directly, it should act as an advocate with government and other agencies to promote the interests of women and children.

Oxfam should take the initiative in developing innovative ways of assessing nutritional status in the field. The Oxfam philosophy of the bottom-up approach should not be restricted-to development. What is needed in Red Sea Province is a greater understanding of the Beja perceptions of normal and abnormal child health and development. Appropriate local cut offs could then be used alongside the international standards for measuring nutritional status which are currently accepted by donors. This would greatly enhance our ability to both plan programmes and evaluate changes in the field in a context which is appropriate for the local people. Methodological issues which have arisen as a result of Oxfam's experiences in Red Sea Province should be addressed promptly and incorporated into guidelines that could be used in the event of future relief efforts in Red Sea Province.

 

Appendix 5.1. Data collection form, nutritional surveillance teams, Oxfam


Data collection form, nutritional surveillance teams, Oxfam

 

Appendix 5.2. Claasifications of coded variables.

COLUMN

TITLE DESCRIPTION

   

A

NUM

Case number

 

B

DIST

District

1=Derudeb

     

2=Halaib

     

3=Haya

     

4=North Tokar

     

5=Rural Port Sudan

     

6=Sinkat

     

7=South Tokar

C

VILL

Village

 

D

C

Cycle

1-6

E

M

Month

1-12

F

Y

Year

Last two numbers

G

FZ

Food Security Zone

1=High security

     

2=Medium security

     

3=Low security

     

4=No data

H

S

Season

W=Wet

     

H=Harvest

     

D=Dry

I

EZ

Ecozone

1-25

J

T

Settlement type

1=Station

     

2=Town

     

3=Rural

     

4=Camp

K

HL

Measurement type

H=Height

     

L-Length

L

A

Age by report

0=Less than 1 year

     

1=1 year or over

M

H

Health

G=no illness

     

S=Illness recorded

     

K=Oedematous

N

S

Sex

M=Male

     

F=Female

O

HT

Height/Length (cm)

 

P

WT

Weight (kg)

 

Q

%PRW % weight for height, non sex-specific

   

R

%SEXRW % weight for height, sex specific ref.

   

S

Z

Z-score

 

 

Appendix 5.3. Ecozones in Red Sea Province (from Watson, 1976).


Ecozones in Red Sea Province (from Watson, 1976).

 

Appendix 5.4. Seasons by month and ecozone, Red Sea Province.

ECOZONE

   

SEASON

   
 

Major

Minor

Harvest

Best

Worst

 

Rain

Rain

Season

Health

Health

1

Oct-Jan

Jun-Sep

Jan-Mar

Jan

May-Jun

2

Oct-Dec

Jul-Sep

Dec-Jan

Jan

May-Jun

3

Oct-Dec

   

Nov-Jan

Jun-Sept

4

Jul-Dec

Jul-Sep

Jan-Feb

Oct-Dec

May-Jun

5

Oct-Dec

Jul-Sep

Dec-Jan

Jan

May-Jun

6

Jul-Aug

Oct-Dec

Sep-Oct

May-Jun

 

7

Jul-Sep

Nov-Dec

Sep-Oct

Oct

May-Jun

8

Jul-Sep

Oct-Dec

Sep-Nov

Oct-Jan

May-Jun

9

Jul-Sep

 

Sep-Nov

Oct

May-Jun

10

Jul-Sep

   

Dec-Jan

Sep-Oct

12

Jul-Sep

Oct-Dec

Sep-Nov

Oct

May-Jun

14

Jul-Sep

Oct-Dec

 

Sep-Oct

May-Jun

15

Jul-Sep

Oct-Dec

Oct-Jan

Oct-Jan

May-Jun

16

Jul-Sep

Oct-Dec

 

Sep-Oct

May-Jun

17

Oct-Dec

Jul-Aug

Dec

Jan

May-Jun

18

Jul-Sep

Oct-Dec

 

Sep-Oct

May-Jun

20

Oct-Dec

Jul-Sep

 

Jan

May-Jun

23

Jul-Sep

Oct-Dec

 

Sep-Oct

May-Jun

24

Jul-Sep

Oct-Dec

Sep-Nov

Oct

May-Jun

25

Jul-Sep

Oct-Dec

Sep-Nov

Oct-Jan

May-Jun

 

Appendix 5.5. Classification of fled Sea Province into food security zones, 1987.


Classification of fled Sea Province into food security zones, 1987.

 

Appendix 5.6. Locations of sampled sites, nutritional surveillance cycle 1.


Locations of sampled sites, Nutritional Surveillance Cycle 1.

 

Appendix 5.7. Names of sampled sites, nutritional surveillance cycle 1.

DERUDEB

Al-konet, Amberkiateb, Gasangayef, Larlik, Ormis, Shoshoiyeb, Tehilla.

HALAIB

Adaldayb, Al-jarif, Eit, Al-kuk, Fodikwan, Harawary, Kajakabab, Kamoreb, Sharem, Shashoisharab, Shashol, Todor, Tomandra, Tumeray.

HAYA

Alatit, Hamasit, Hormatal, Ibli, Nafti, Okliss, Togna.

NORTH TOKAR

Agwatiro, Aswit, Basugayt, Dolobiay, Enkiateb, Fada, Gabayab, Galalayma, Hamdayab, Keray Salambot, Lui, Mant, Mays, Sitereb, Toraft.

RUKAL PORT SUDAN

Adabay, Agariry, Baldangel, Hamli, Handuub Beja, Handuub Rashayda, Tugalhuush, Yudib.

SINKAT

Abent, Amayt, Enderkwan, Gebeit Ashraf, Hansut, Ikamab, Labba, Nasit, Olhud, Yawana.

SOUTH TOKAR

Adart, Adobana, Agatay, Agig, Aiterba Rural, Aiterba Town, Al-adliba, Andel, Garora Rashayda, Garora Rural, Garora Souk, Halfa, Ma'arafiit.

 

Appendix 5.8. Locations of sampled sites, nutritional surveillance cycle 2.


Locations of sampled sites, nutritional surveillance cycle 2.

 

Appendix 5.9. Names of sampled sites, nutritional surveillance cycle 2.

DERUDEB

Adarot, Ambirkiateb, Arift, Balak, Dirbab, Ganyota, Gasangayef, Hamboli, Matayt, Sugudeb, Tadamay, Tisibrihimit, Tolik.

HALAIB

Aydeb, Deruba, Efangwab, Fodikwan, Gebeit Alma'adiin, Hamiyamayb, Handukteb, Harankuk, Ogra, Sararashambi, Shalatayn, Sofaya, Soodi, Todor.

HAYA

Al-ganay, Amrin, Aryab, Awayt, Eikyab, Halag, Kokryeb Abadab, Kokryeb Magwalla, Oger, Ofin, Sanganeb, Siyeteb, Tiksasit, Timiki, Tolugreb.

NORTH TOKAR

Amayb, Ashat Rural, Dolobiay, Erim, Gabayab, Gabol, Hamdayb, Lui, Mays, Odwaan, Sitareb, Triktiay.

RURAL PORT SUDAN

Adabay, Adryab, Daruur, Handuub, Kalkowi, Li'it, Obo, Sallum, Salwadeb, Tobayn, Tomaala Almasjid, Tomaala Hayli.

SINKAT

Abent, Agwamt Hamlayt, Amigwolad, Biranfi, Dimyat, Erert, Gebeit Ashraf, Hansuut, Haritri, Hilayat, Nasit, Nobahaweb, Saguteb, Tabakly.

SOUTH TOKAR

Adobana, Agatay B. Aiterba Town, Al-adliba, Andel, Aydeb, Derhayb, Agig, Garora Town, Halfa, Ma'arafiit Rural, Ma'arafiit Town, Rikaab, Shabri.

 

Appendix 5.10. Locations of sampled sites. nutritional surveillance cycle 3.


Locations of sampled sites. nutritional surveillance cycle 3.

 

Appendix 5.11. Names of sampled sites, nutritional surveillance cycle 3.

DERUDEB

Adarot, Andrayef, Arift, Awagayt, Delay, Dirbab, Ganyota, Hamboli, Hamteb, Tadamay, Tamlayt.

HALAIB

Al-kuk, Al-shalal, Aydeb, Dait, Dungunab, Gabatit, Hadi, Handukteb, Lolignet, Sofaya, Sukanholt, Todor, Yumunt.

HAYA

Alanabib, Alatit, Alganay, Amrin, Asot, Gadayt Tangaday, Hormatal, Kamoyab, Kilanyeb, Ogrin, Siyeteb, Tiksasit, Tolugreb, Yahamyab.

NORTH TOKAR

Amayb, Ashat, Dolobiay, Fatit, Gabol, Islabib, Nowlabab, Odwaan, Sitareb, Triktiay, Warram.

RURAL PORT SUDAN

Adalgurbab, Al-baab, Darabshashowi, Daruur, Kalkowi, Kisibiyai, Obo, Sallum, Taiglin, Tobayn, Towai.

SINKAT

Adalaweb, Amigwolad, Dabarayt, Erkowit, Hagrab, Hansut, Khor Bolib, Nafilyament, Satim, Tabakly.

SOUTH TOKAR

Adart, Adobana, Agig, Aiterba, Al-adliba, Garora, Halfa, Ma'arafiit, Ma'arafiit Rural, Shabri.

 

Appendix 5.12 Locations of sampled sites, nutritional surveillance cycle 4.


Locations of sampled sites, nutritional surveillance cycle 4.

 

Appendix 5.13. Names of sampled sites, nutritional surveillance cycle 4.

DERUDEB

Abusalarna, Agwatay, Ambirkiateb, Balak, Gawtit, Kinabanayeb, Kodit, Sararatiyef, Sugudeb, Tamlayt, Tindera.

HALAIB

Aljarif, Arakiay, Delaw, 'Is, Eit, Fodikwan, Hadaya, Kelayet, Shashoi Gumadryab, Shenab, Soodi.

HAYA

Adalhuf, Al-rogel, Asot, Enha, Hamadayt, Hankalaweb, Kalhashtribab, Labba, Myas, Ofin, Sanganeb, Shortayn, Taharis, Togna.

NORTH TOKAR

Agwatay, Ashat Bilasiit, Aswit, Dahant, Enkiateb, Gabayab, Hamdayb, Mays, Odwaan South, Sitareb Bushbelt, Sitareb Rashayda.

RURAL PORT SUDAN

'Amuur Maluuf, Batra, Garamayt, Hadayt Guba, Kajaret, Komosana, Oshiri, Serkoyt, Shandoday, Tugalhuush Coast, Tugalhuush Mountain.

SINKAT

Airaweb, Amayt, Ashuf, Asot, Bramyo, Dabitaweb, Geneteb, Haritri, Labba, Nasit, Olhud, Onil, Shakan, Tomasay.

SOUTH TOKAR

Adart, Agatay A, Aiterba Clinic, Aydeb, Ayet, Derhayb, Garora, Gowi, Hambukeb, Mahmuud Salih Bush, Ma'arafiit Hasra, Sebat, Warhat.

 

Appendix 5.14 Locations of sampled sites, nutritional surveillance cycle 5.


Locations of sampled sites, nutritional surveillance cycle 5.

 

Appendix 5.15. Names of sampled sites, nutritional surveillance cycle 5.

DERUDEB

Adarot, Delay, Gadamay, Ganyota, Garada, Gasangayef, Sogodeb, Tolik.

HALAIB

Adaraweb, Aykwan, Aytery, Balwayitsha, Elkuk, Fodikwan, Gebeit Alma'adin, Kuratry, Lolignet, Sararashambi, Shashoisharab, Sofaya.

HAYA

Aldawir, Al-kilo, Amrin, Aryab, Hadaweb, Kasalwadi, Musmar Town, Shora, Siyeteb, Taham, Tiksasit, Timiki, Yahamyab.

NORTH TOKAR

Ashat, Ashat Souk, Dolobiay, Erim, Erwit, Gabayab, Gabol, Galalayma, Nowlabab, Sitareb, Tantiu.

RURAL PORT SUDAN

Abunaggara, Adafab, Adalaweb, Adryab, Daytry, Handuub, Ibrahimayt, Kadaweb, Kisibiyay, Odi, Salwadeb, Shalhat, Toksar.

SINKAT

Adalhuf, Amigwolad, Andraydyab, Arab, Biranfi, Erert, Hamashyameb, Harasab, Harmanab, Hashatribab, Makida, Manaweb, Nobahaweb, Oshger, Summut.

SOUTH TOKAR

Adobana, Agatay B. Agig, Aiterba, Aiterba Ablay, Aiterba Rashayda, Aladliba, Al-adliba Rashayda, Derhayb, Gelhent, Ma'arafiit, Shoshiay, Shoshiay Rural, Warhat.

 

Appendix 5.16. Locations of sampled sites, nutritional surveillance cycle 6.


Locations of sampled sites, nutritional surveillance cycle 6.

 

Appendix 5.17. Names of sampled sites, nutritional surveillance cycle 6.

DERUDEB

Ayob Khalwa, Gadamay Albar, Gahwat Algabab, Makekulay A, Manar Masjid, Matayt, Sugudeb.

HALAIB

Arakiay, Delaw, Dungunab, Eit, Gabatit, Hadaya, Khasham Gabatit, Shenab, Tomandra, Yumunt.

HAYA

Adalhuf, Balatribab, Ebarashit, Eikyab, Eraway, Hamadayt, Kass, Kokreyb Abadab, Myas, Shediyab, Tolugreb, Yomalal.

NORTH TOKAR

Agwatiru, Ashat Bilasiit, Dahant, Enkiateb, Fatit, Komideb, Mays, Odwaan, Odwaan South, Sitareb Bushbelt, Sitareb Rashayda.

RURAL PORT SUDAN

Agwamt Tosaara, Baldangel, Daruur, Gashiyt, Kalkowi, Lakateche, Sallum, Tugalhuush, Tomaala Almasjid, Tomli.

SINKAT

Alla Akbar, Amayt, Baharoty, Bilasiit, Bramyu, Dabarayt, Dabitaweb, Deokwan, Gebeit Ashraf, Halgit, Harmanab, Hilayat, Labba, Tehib, Telya.

SOUTH TOKAR

Adart A, Adart B. Aiterba Clinic, Aiterba Souk, Garora, Hasra, Hilayat Hasra, Mahmuud Salih Bush, Rikaab, Shabri, Traf Alhaysham, Warhat.