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Nutrition Survey - Nyaminyami District (Kariba Rural)

 
4. RESULTS

The table below shows the demographic distribution of the sample. A total sample size of 918 children was achieved, with both sexes and all age groups being adequately represented. The fact that the observed is similar to the expected profile shows that the sampling procedure did not introduce any bias and thus the sample can be considered to be representative of the under five population in Nyaminyami.

Table 1: Distribution by age and gender, Nyaminyami, July 2002
Age group Female Male Both sex Sex ratio
In months
n %
n %
n %
(M/F)
06-17.99
140 29.9
113 25.2
253 27.6
0.81
18-29.99
106 22.6
94 21.0
200 21.8
0.89
30-41.99
95 20.3
102 22.8
197 21.5
1.07
42-53.99
88 18.8
95 21.2
183 20.0
1.08
54-59.99
39 8.3
44 9.8
83 9.1
1.12
Total
468 51.1
448 48.9
918 100
0.96


7.7% of the children in the sample had lost one or both parents; specifically, 3.7% had lost a mother, 3.3% a father and 0.7% had lost both parents. This compares with a national average of approximately 14.3%.11 12.2% (112) of the children included in the sample were from female-headed households. These have increased from 10.7% since February 2002.There were no child headed households in the sample.

1. Prevalence of acute malnutrition expressed in Z-scores

Using Weight for Height Z-scores 5.1% of the measured children were found to be acutely malnourished, that is, with a W/H Z-score<-2 or oedema. The rate has decreased slightly from 5.8% in February 2002. Severe acute malnutrition (W/H Z<-3 or oedema) was found in 2.1% of the children, marking an increase from 1.9% from February 2002.

Table 2: Acute malnutrition rates in Z-scores, Nyaminyami, July 2002

  Percentage 95% Confidence Interval

Global acute malnutrition 5.1% from 3.1% to 7.1%
Severe acute malnutrition 2.1% from 0.8% to 3.4%



The W/H distribution curve (Graph 1) exhibited a slight shift to the left compared to that of the reference population. The mean Z - score was -0.2 with a standard deviation of 1.0, which in comparison with a mean Z - score of 0 and a SD of 1 in the reference indicates that the study population exhibited a poorer nutritional status with normal variance within the sample. Comparison with the February 2002 distribution, which had a mean Z - score of -0.49 and standard deviation of 1.0, indicates an overall decrease in wasting. This is evident in the thinness and height of the curve, which indicates low variance in Z - scores. It is apparent that the differences in nutritional status between the two sexes was reduced as illustrated by the disappearing "two - peaked" distribution.



Table 3 below gives a breakdown of the sample by wasting (W/H < -2) and oedema status. It is apparent that marasmus was the major form of acute malnutrition. This was in comparison with kwashiorkor, which accounted for 1.5% of the sample. This difference is indicative of dietary energy deficit. Severe protein deficiency is also becoming increasingly significant as exhibited by increased prevalence of oedematous malnutrition.

Table 3: Distribution according to W/H in Z-scores and presence of oedema, Nyaminyami, July 2002
  W/H < -2 Z-Scores W/H >= - 2 Z-Scores
Oedema
+
Kwash/marasmus
2 0.2%
Kwashiorkor
14 1.5%
Oedema
-
Marasmus
31 3.4%
Normal
871 94.9%


Table 4 below compares the global acute malnutrition rates for both sexes of the 6 - 29 month and 30 - 59 month age groups.

Table 4: Acute malnutrition rates in Z-scores by age group and gender, Nyaminyami, July 2002
  Male Female Both Sexes 95% Confidence Interval
Children 6-29 months 9.2% 7.3% 8.1% 4.6% to 11.6%
Children 30-59 months 1.2% 3.2% 2.2% 0.3% to 4.1%
All Children (6-59 months) 4.9% 5.3% 5.1% 3.1% to 7.1%


There seems to be a marked age and gender difference in acute malnutrition. Whilst more females are acutely malnourished than boys at the age of 6 to 29 months, the opposite is true as they grow older (30-59 months). Acute malnutrition was found to be more prevalent in children 6 -29 months, unlike in February 2002 when it was higher in the 30 --59 month age group.

Table 5 compares the global malnutrition rates in each of the three Food Economy Zones. Whilst disaggregation of nutritional status by zone and especially cluster is not statistically reliable in studies of this design, it gives an indication of trend or distribution and can provide an indication of areas of concern.

Contrary to the expected profile, all indicators exhibit higher prevalence of Malnutrition in Kanyati than the other two FEZs. This could be due to the loss of livestock over the last 18 months, which are normally an important source of income. The other two FEZs are chronically food insecure and thus have more diverse coping strategies, hence the lack of alternative mechanisms could be responsible for the decline in nutrition status. This, however, needs further investigation.

Table 5: Global Malnutrition according to Food Economy Zone, Nyaminyami, July 2002
 
Acute Malnutrition
(W/H Z-Scores)
Chronic Malnutrition
(H/A Z-Scores)
Underweight
(W/A Z-Scores)
Mola, Negande FEZ
23 5.2%
159 36.1%
105 23.9%
Nebiri, Msamba, Kasvisva FEZ
13 4.2%
104 33.9%
91 17.6%
Kanyati FEZ
11 6.4%
93 54.4%
31 15.2%
Nyaminyami
47 5.1%
356 38.8%
185 20.2%


2. Prevalence of acute malnutrition expressed as a percentage of the median

Tables 6 to 8 represent some of the results outlined in the previous section expressed as a percentage of the median.

The Z-score index is a more sensitive index and thus recommended for reporting prevalence of acute malnutrition. The Z score recognizes the variation in the spread of weights, which is different from one height to the other, and it classifies children of different heights/ages equally, based on a more correct statistical analysis. The difference in the results obtained from z-scores and percentage of the median become larger among taller children. The W/H% is thus presented here to allow comparison with other surveys, as W/H% is sometimes used/understood by some institutions or may have been (or may be) utilised in another survey.

The rate of global malnutrition is the percentage of children with a W/H index < 80% of the median and/or oedema. Severe acute malnutrition defines the percentage of children with a W/H index < 70% of the median and/or oedema.

Table 6: Acute malnutrition rates in % of the median,Nyaminyami , July 2002

  Percentage 95% Confidence Interval

Global acute malnutrition 3.6% from 1.9% to 5.3%
Severe acute malnutrition 1.7% from 0.5% to 2.9%



Table 7: Distribution according to W/H in % of the median and presence of oedema, Nyaminyami, July 2002
  W/H < 80% W/H >= 80%
Oedema
+
Kwash/marasmus
2 0.2%
Kwashiorkor
14 1.5%
Oedema
-
Marasmus
17 1,9%
Normal
885 96.4%


Table 8: Acute malnutrition rates in % of the median by age group, Nyaminyami, July 2002

  Percentage 95% Confidence Interval

Children 30-59 months 1.7% from 0% to 3.4%
Children 6-29 months 5.5% from 2.5% to 8.5%



3. Underlying Causes of Malnutrition

  • Socio-Economic Status


  • This survey made an attempt to disaggregate the sample population by socio-economic status or by "wealth groups", as is done in Household Economy Assessments, to see whether there were significant differences between malnutrition rates among the poor and better off. The study defined the poor wealth group as those who do not have draught power (have one or no head of cattle) and may offer agricultural labour (i.e. go for work on others' fields). The better off category was defined as those who farm cotton, have two or more head of cattle and sometimes hire agricultural labour. The poor group was observed to have a GAM rate of 5.7%, more than twice that of the better off, 2.4%. The poor group comprised 64.9% of the sample, whilst the better off accounted for 16.9%. The remaining 16.9% were difficult to classify using the specified criteria.

  • Water and Sanitation


  • The majority of the children in the sample (82.1%) are from households that do not have access to a toilet. 1.5% of the households have pit latrines and 16.3% have Blair toilets. Some of these blair toilets are owned by institutions eg. pre-schools. As such, some of these toilets may run the risk of filling up and may be poorly maintained.

    The table below shows the various water sources available in the district and the populace's access thereto.

    Table 9: Sources of Drinking Water, Nyaminyami, July 2002

    Water source n Percentage

    Dam/river 91 9.9%
    Unprotected well 253 27.6%
    Protected well 33 3.6%
    Borehole 423 46.1%
    Spring 118 12.9%

    Total 918 100%



    The majority of households (62.6%) obtain water from relatively safe sources, i.e. protected wells, boreholes and springs. This does not necessarily mean that they drink safe water though since this would require analysis of the path from the source to the actual consumption. It is alarming that 27.6% of the sample access drinking water from unprotected wells. In addition, a number of boreholes in Mola and some in Negande have/produce saline and/or coloured water.

    Water sources had no influence on wasting in this context as evidenced by a relative risk12 (RR) of 1.02 (the exposure being unsafe water source). This means that a child from a household with an unsafe water source was 1.02 times more likely to be acutely malnourished than those with access to a safe water source.

    The low sanitation coverage may however be a contributing factor to the high prevalence of diarrhoea, which affected 10.5% of children between 6 and 59 months of age in the month prior to the survey (Table 10).

  • Morbidity


  • Almost half of the children in the sample, 416 (45.3%), complained of illness during the survey or in the 2 weeks prior to the survey. Wasting was significantly associated with recent morbidity, with 6.4% of those who had been ill in the last 2 weeks being wasted compared to 3.8% of those who had not been ill. A statistical test suggested that ill children were 1.6 times more likely to be acutely malnourished than well children were (RR= 1.6, 95% CI 0.9 to 2.8).

    Table 10 below, which outlines morbidity by age group and shows prevalence in the sample, illustrates that illness was age related, with younger children more likely to have been ill recently.

    Table 10: Morbidity in the Two Weeks Prior to the Survey, Nyaminyami, July 2002
    Age group Malaria Fever Measles Diarrhoea Cough/ ARI Other Total
    6-17 18 17 0 45 28 42
    150 16.3%
    18-29 10 16 0 24 18 15
    83 9.0%
    30-41 13 20 0 17 28 13
    91 9.9%
    42-53 9 15 0 7 21 11
    63 6.9%
    54-59 7 3 0 3 9 7
    29 3.2%
    Total 57 71 0 96 104 88
    416  
      6.2% 7.7% 0 10.5% 11.3% 9.6%
      45.3%


    Coughs and Acute Respiratory Infections accounted for the highest morbidity followed by diarrhoea. This indicates that personal health and hygiene is an issue of public health concern in the district. Increased prevalence of ARI is probably due to the winter season, whilst diarrhoea is high because of poor water and sanitation in the district. Despite the fact that the malaria season has passed, the disease continues to account for 14% of total morbidity.
4. Measles vaccination coverage

Children in the sample were assessed for measles vaccination since it is a highly infectious disease, particularly when nutrition status is low. Only children from 9 to 59 months were included in the analysis, since the immunisation schedule for measles stipulates that the vaccine should be administered at 9 months of age. When the vaccine is given before that age, the child should receive a second booster at 12 months.

Children are considered to be vaccinated when the vaccination date is specified on their health card. . The portion of the population who both have an immunization card and have confirmation based only on the word of the mother is 79.9 %. The word of the mother("history" in the table below), however, can not be regarded as reliable information when considering immunization coverage rates. Therefore, among the children 9 to 59 months in the sample population, 41.4% had verification of receiving the measles immunization.

Table 11: Measles vaccination coverage, Nyaminyami, July 2002

  N Percentage

Card 356 41.4%
History 331 38.5%
Not vaccinated 173 20.1%

Total 860 100%




Footnote:
  1. Figure calculated from data in UNAIDS Zimbabwe Epidemiological Fact Sheets on HIV/AIDS and Sexually Transmitted Infections, 2002 Update.
  2. Relative Risk (or Risk Ratio) estimates the likelihood of exposure to a certain factor (e.g. poor water and sanitation) resulting in a disease outcome (e.g. acute malnutrition).
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