Studies on the Predisposing Factors of Protein Energy Malnutrition Among Pregnant Women in a Nigerian Community

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ISSN 0972-5997
Volume 6, Issue 3; Jul-Sep 2007
Original Article
Studies on the Predisposing Factors of Protein Energy Malnutrition Among Pregnant
Women in a Nigerian Community
Authors
Okwu GN,
Ukoha AI,
Nwachukwu N
Agha NC
Department of Biochemistry , Federal University of Technology, Owerri
Address For Correspondence
Gloria N. Okwu
Department of Biochemistry,
Federal University of Technology,
P.O. Box 2572, Owerri, Imo State, Nigeria.
E-mail: gnokwu@yahoo.com
Citation
Okwu GN, Ukoha AI, Nwachukwu N, Agha NC. Studies on the Predisposing Factors of Protein Energy
Malnutrition Among Pregnant Women in a Nigerian Community Online J Health Allied Scs. 2007;3:1
URL
http://www.ojhas.org/issue23/2007-3-1.htm
Open Access Archives
http://cogprints.org/view/subjects/OJHAS.html
http://openmed.nic.in
Submitted Aug 18, 2007; Accepted Dec 17, 2007; Published: Jan 24, 2008
OJHAS Vol 6 Issue 3(1) - Okwu GN, Ukoha AI, Nwachukwu N, Agha NC. Studies on the Predisposing Factors of Protein Energy Malnutrition Among
PregnantWomen in a Nigerian Community
http://ojhas.org
1

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Abstract:
grammes and the availability and quality of health
Protein Energy Malnutrition (PEM) continues to be
services. (2,4)
a major public health problem in developing
countries and affects mostly infants, young
Malnutrition continues to be a major health
children, pregnant and lactating mothers. This
burden in developing countries. It is global y the
study was carried on some of the factors that
most important risk factor for il ness and death
predispose pregnant women to PEM and hence
with hundreds of mil ions of pregnant women and
identify groups at greater risk. A total of 1387
young children particularly affected.(5) Poor
pregnant women (910 in the urban area and 477
nutrition in pregnancy in combination with
in the rural areas) were recruited for the study.
infections is a common cause of maternal and
Anthropometric indices of weight, height and
infant mortality and morbidity, low birth weight
Body Mass Index (BMI) of the pregnant women
and intrauterine Growth Retardation (IUGR).(6) In
were measured and semi structured questionnaires
Nigeria, maternal death per 100,000 births is put
were used to elicit information on possible
at 800 while percentage low birth weight stands at
predisposing factors such as age, level of
twenty.(7)
education, parity, child spacing etc. Results
obtained showed that the mean weight and height
Low birth weight babies have increased risk of
of the rural pregnant women, were significantly
mortality, morbidity and development of
(p<0.0001) lower than those of the urban
malnutrition. Children who suffer from
pregnant women. The mean BMI of the rural
malnutrition are more likely to have slowed
subjects, was also significantly (p< 0.0027) lower
growth, delayed development, difficulty in school
than that of the urban subjects. Analysis of the
and high rates of il ness and they may remain
effect of age showed that the younger age
malnourished to adulthood.(8,9) IUGR is
category (24 years and below) had significantly
associated with poor cognitive and neurological
(p<0.0001) lower mean BMI and higher prevalence
development for the infant and in adulthood,
of PEM while the effect of level of education
susceptibility to cardiovascular disease, diabetes
showed significantly (p<0006) lower mean BMI
and renal disease.(10)
and higher PEM prevalence among the less
Malnutrition remains one of the world’s highest
educated (no formal and primary education).
priority health issues not only because its effects
Those with parity of two, one and primipara
are so widespread and long lasting but also
showed significantly (p<0.0175) lower mean BMI
because it can be eradicated. Eradication is best
while child spacing did not have any significant
carried out at the preventive stage. Hence the
effect on both mean BMI and prevalence of PEM.
need to identify groups of pregnant women at
The implications of these findings are discussed
greater risk of developing PEM. Such high-risk
and recommendations made on how to tackle the
groups can be targeted in any planned
problem.
intervention programme.
Key Words: Protein Energy Malnutrition, Pregnant
Women, Predisposing Factors, Owerri, Nigeria
Materials and Methods:
Subjects
Introduction:
Worldwide, an estimated 852 mil ion people are
A total of 1,387 pregnant women took part in the
undernourished with most (815 mil ion), living in
study, 910 in Owerri urban area and 477 in the
developing countries.(1,2) Poverty is the main un-
rural area surrounding Owerri. The study was
derlying cause of malnutrition and its determi-
carried out at the antenatal clinics of government
nants.(3) The degree and distribution of Protein
hospitals and private clinics in Owerri urban area
Energy Malnutrition (PEM) in a given population
and antenatal clinics of health centres in rural
depends on many factors – the political and eco-
areas surrounding Owerri and covered a period of
nomic situation, level of education and sanitation,
11 months.
the season and climate conditions, food produc-
tion, cultural and religious food customs, breast-
Approval to carry out the study was obtained from
feeding habits, prevalence of infectious diseases,
the appropriate health authorities and informed
the existence and effectiveness of nutrition pro-
consent obtained from the subjects before the
OJHAS Vol 6 Issue 3(1) - Okwu GN, Ukoha AI, Nwachukwu N, Agha NC. Studies on the Predisposing Factors of Protein Energy Malnutrition Among
PregnantWomen in a Nigerian Community
http://ojhas.org
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commencement of the study. Pregnant women
on a weight scale. Standing height was measured
who had complications such as pregnancy induced
without headgear using a stadiometer to the
hypertension, infections, malaria, metabolic
nearest 0.1cm. Body mass index (BMI) was
disorders etc (as indicated in their medical
calculated as weight (kg) divided by height (m)
records) were excluded from the study. All the
2
pregnant women in the study received routine
squared (kg/m ). According to UN classification,
prescriptions of iron, multivitamins, folic acid and
BMI < 18 is considered severely malnourished,
daraprim (as antimalaria prophylaxis). Data on age,
18-20 is moderately malnourished, 21-24 is
educational level, parity, child spacing, etc were
normal, 25-27 is overweight and > 27 is obese.(13)
obtained from the pregnant women through a
Statistical Analysis
semi-structured questionnaire.
Data was analysed using the software package SAS
Sampling Technique And Sample Size
version 8. (SAS Institute Inc, Cary, North Carolina).
For the Owerri urban area, proportionate cluster
Pearson chi Square, Anova and post Hoc Duncan’s
sampling method was used. Five clusters were
multiple range test were used to identify
identified and one was randomly selected. All the
statistical y significant differences. Data was
hospitals and clinics in the selected cluster were
considered significant for p <0.05 at 95%
included in the study. For the rural areas
confidence limit.
surrounding Owerri a total of 12 health centres
were randomly selected from the 55 health
Results:
centres belonging to 55 autonomous communities.
A total of 1,387 pregnant women were included in
Sample size n, for random sampling was calculated
the study (910 in the urban area and 477 in the ru-
using the relationship
ral areas). The mean weight and height of the
pregnant women in the rural areas, 63.65 ±
2
11
14.80kg and 1.58 ± 0.07m respectively were signifi-
n = (Z1-?/?) p(1-p)
cantly lower than those of the urban subjects,
68.92 ± 10.23kg and 1.67 ± 0.08m respectively,
Prevalence, P was taken to be 50, which gives the
p<0.0001 in each case. The mean BMI of the rural
largest sample size.
2
Sampling error, was 5%
subjects, 25.28 ± 4.60kg/m was also significantly
lower than that of the urban subjects, 26.41 ±
Confidence coefficient 1- ? = 95% (Z1- ? = 1.96)
2
Accordingly a minimum sample size of 384 was
3.36kg/m , p<0.0027. In the urban area, 35% of
calculated for the rural areas. To take into account
the pregnant women were public servants, 43%
the cluster design effect, the calculated random
were involved in some business activity and 22%
sampling size, n is multiplied by two.(12) Hence a
were housewives/students not holding any jobs. In
minimum sample size of 768 was obtained for the
the rural sub sample, 8% were public servants, 22%
Owerri urban area.
were involved in some business activity and 70%
were engaged in subsistence farming as a means
Anthropometric Indices
of livelihood.
Anthropometric measurements of the pregnant
women were performed with the help of trained
assistants. Body weights were measured without
shoes and with light clothing to the nearest 0.1kg
OJHAS Vol 6 Issue 3(1) - Okwu GN, Ukoha AI, Nwachukwu N, Agha NC. Studies on the Predisposing Factors of Protein Energy Malnutrition Among
PregnantWomen in a Nigerian Community
http://ojhas.org
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Table 1: Mean BMI And Prevalence Of PEM According To Age Of The Pregnant Women.
2
Age (yrs)
Frequency
BMI (kg/m )
%* PEM
Range
Mean
s.d
Overall
< 20
68
16.94-30.30
c
25.07
2.58
25.00
20-24
443
18.82-42.36
c
25.16
3.21
11.74
25-29
454
17.80-41.80
b
26.50
3.86
6.17
30-34
261
18.65-38.08
b
26.35
3.77
5.36
35-39
130
18.37-38.08
b
26.92
4.86
4.62
> 40
31
22.48-36.57
a
29.04
3.84
0.00
Total
1387
Urban
< 20
35
22.22-30.30
b
27.25
2.63
0.00
20-24
245
18.82-33.15
b
25.72
3.000
4.89
25-29
328
17.80-36.79
b
26.54
3.42
4.57
30-34
186
20.00-35.50
b
26.49
3.32
4.20
35-39
88
18.37-35.63
b
26.58
3.59
3.41
>40
28
25.00-36.57
a
29.54
4.32
0.00
Total
910




Rural
< 20
33
16.94-25.10
b
22.29
2.52
51.52
20-24
198
18.99-42.36
b
24.33
3.72
20.20
25-29
126
19.37-41.80
a
26.36
4.19
10.31
30-34
75
18.65-38.09
a
25.94
4.77
8.00
35-39
42
19.04-38.08
a
27.75
6.53
7.14
> 40
3
22.48-26.49
b
23.51
2.83
0.00
Total
477




Values with different superscripts per column are statistically significant (p<0.05)
2
* % PEM: Overall – p<0.0136, Urban – p<0.4194, Rural – p<0.0001 (Pearson X used)
Table 1 shows mean BMI and prevalence of PEM amongst the pregnant women according to age. Overall the pregnant
women below 20yrs and 20-24yrs age groups showed significantly (p<0.0010) lower mean BMI and significantly (p<0.0136)
higher percentage of PEM than the older age categories. In the urban sub-sample, both mean BMI of the 24 years and
below age category was significantly (p<0.0421) lower than that of above the 40 years age group while prevalence of PEM
OJHAS Vol 6 Issue 3(1) - Okwu GN, Ukoha AI, Nwachukwu N, Agha NC. Studies on the Predisposing Factors of Protein Energy Malnutrition Among
PregnantWomen in a Nigerian Community
http://ojhas.org
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did not show statistical difference (p<0.4194) among the various age groups. In the rural sub-sample, mean BMI of the 24
years and below age group was significantly (p<0.0111) lower than the older age groups and their proportion of PEM was
significantly (p<0.0001) higher.
Table 2: Mean BMI and Prevalence of PEM of the Pregnant Women According to Educational Level
2
Level of education
Frequency
BMI (kg/m )
%* PEM
Range
Mean
s.d
Overall
No Formal Education
104
16.94-36-85
c
2.57
12.50
24.80
Primary Education
362
17.80-40.03
c
3.43
12.71
24.76
Secondary Education
621
20.48-42.36
b
3.98
7.25
25.86
Post Secondary Education
300
18.73-41.80
a
4.10
4.00
27.03
Total
1387




Urban
No Formal Education
62
22.86-34.18
a
3.00
4.84
25.63
Primary Education
188
17.80-35.86
a
2.94
4.79
25.80
Secondary Education
420
18.73-35.56
a
3.08
4.05
26.19
Post Secondary Education
240
18.36-36.79
a
3.70
3.75
27.45
Total
910




Rural
No Formal Education
42
16.94-36.85
b
2.33
23.81
24.72
Primary Education
174
19.04-40.03
b
3.93
21.26
24.35
Secondary Education
201
20.48-42.36
a
4.58
13.93
26.64
Post Secondary Education
60
19.73-41.80
a
4.48
5.00
28.47
Total
477




Values with different superscripts per column are statistical y significant (p<0.05)
2
* % PEM: Overal – p<0.0104, Urban – p<0.0351, Rural – p<0.0476 (Pearson X used)
Table 2 shows mean BMI and prevalence of PEM amongst the pregnant women according to level of education. Overall, the
pregnant women with primary education and no formal education had significantly (p<0.0006) lower mean BMI and
significantly (p<0.0104) higher prevalence of PEM. In the urban area, although there was no statistical difference (p<0.6287)
in mean BMI, there was significant difference (p<0.0351) in prevalence of PEM amongst the pregnant women according to
level of education. In the rural sub-sample the primary and no formal education groups had significantly (p<0.0012) lower
mean BMI and significantly (p<0.0476) higher prevalence of PEM.
OJHAS Vol 6 Issue 3(1) - Okwu GN, Ukoha AI, Nwachukwu N, Agha NC. Studies on the Predisposing Factors of Protein Energy Malnutrition Among
PregnantWomen in a Nigerian Community
http://ojhas.org
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Table 3: Mean BMI And Prevalence Of PEM According To Parity Of The Pregnant Women
BMI (kg/m2)
Parity
Frequency
% PEM*
Range
Mean
s.d
Overall Primipara
106
19.26-33.12
b
25.68
3.24
5.66
1
251
17.44-36.79
25.31b
2.95
12.35
2
304
16.94-42.36
b
25.45
3.85
7.89

3
354
18.64-41.80
26.45 a
4.32
6.50

4
205
19.53-35.56
a
27.13
4.08
7.32

>4
167
17.79-40.03
26.63 a
4.15
9.58
Total
1387




Urban
Primipara
100
19.26-33.12
c
25.82
3.43
6.00
1
185
18.82-36.79
25.12c
3.06
5.41
2
220
18.36-33.96
c
25.66
3.35
5.45

3
240
19.84-35.62
26.79 b
3.02
2.08

4
112
20.89-35.49
a
27.58
3.58
2.68

> 4
53
17.80-36.57
27.84 a
4.45
3.77
Total
910




Rural
Primipara
6
22.66-23.31
a
22.99
0.45
0.00

1
66
17.44-27.88
23.59 a
2.89
31.82

2
84
16.94-42.36
a
24.80
4.68
14.29

3
114
18.64-41.80
25.61 a
5.63
15.79

4
93
19.53-35.56
a
26.49
4.13
13.68

>4
114
19.04-40.03
25.97 a
4.85
12.28
Total
477




Values with different superscripts per column are statistically significant (p<0.05)
* % PEM: Overall – p<0.0136, Urban – p<0.0166, Rural – p<0.1942 (Pearson X2 used)
Table 3 shows mean BMI and prevalence of PEM according to parity. Overal the lower mean BMI of parity of
one, parity of two and primipara showed significant (p<0.0175) differences from the other groups although
their prevalence of PEM was not significantly (p<0.0638) different. In the urban sub sample, the lower mean
BMI of parity of one, two and primipara showed significant (p<0.0244) difference from those of the other
groups. Their prevalence of PEM was also significantly (p<0.0166) different. In the rural sub-sample, although
the mean BMI and prevalence of PEM did not show statistical differences, the pregnant women with parity of
one presented the highest prevalence of PEM of 31.82%.
OJHAS Vol 6 Issue 3(1) - Okwu GN, Ukoha AI, Nwachukwu N, Agha NC. Studies on the Predisposing Factors of Protein Energy Malnutrition Among
PregnantWomen in a Nigerian Community
http://ojhas.org
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Table 4: Mean BMI And Prevalence Of PEM According To Child Spacing Of The Pregnant Women
2
BMI (kg/m )
Child spacing
Frequency
% PEM*
Range
Mean
s.d
Overall
Primipara
106
16.94-32.36
a
25.53
3.34
5.60
<1yr
80
19.98-29.90
25.21 a
3.56
10.00

1-1.5yrs
354
17.79-35.56
a
26.38
3.65
7.34

1.5-2yrs
415
18.73-40.03
26.59 a
4.26
6.70

2-2.5yrs
197
20.00-32.29
a
25.74
3.34
9.64

Above 2.5yrs
235
18.36-42.36
26.10 a
4.62
11.91
Total
1387




Urban

Primipara
100
18.82-32.29
a
26.17
3.24
6.00

< 1yr
50
21.83-28.26
26.01 a
2.09
6.00

1-1.5yrs
250
17.80-34.89
a
26.40
3.13
3.20

1.5-2yrs
295
18.73-34.89
26.70 a
3.48
3.05

2-2.5 yrs
102
20.00-32.29
a
25.87
2.86
5.88

Above 2.5yrs
113
18.36-36.79
26.58 a
4.33
5.31
Total
910




Rural
Primipara
6
16.94-32.36
a
23.15
4.00
0.00
< 1yr
30
19.98-29.90
a
23.88
4.14
16.67
1-1.5yrs
104
19.15-35.56
a
26.30
3.87
17.31
1.5-2yrs
120
20.00-40.03
a
26.36
4.60
16.67
2-2.5yrs
95
20.96-32.03
a
24.69
5.01
18.68
Above 2.5yrs
122
18.65-42.36
a
25.51
5.31
16.39
Total
477




Values with different superscripts per column are statistically significant (p<0.05)
2
* % PEM: Overall – p<0.2192, Urban – p<0.1991, Rural – p<0.1081 (Pearson X used)
Table 4 shows mean BMI and prevalence of PEM according to child spacing. Overal no statistical difference in
mean BMI and prevalence of PEM was found among the pregnant women according to child spacing. The same
was the case in both the urban and rural sub-samples.
OJHAS Vol 6 Issue 3(1) - Okwu GN, Ukoha AI, Nwachukwu N, Agha NC. Studies on the Predisposing Factors of Protein Energy Malnutrition Among
PregnantWomen in a Nigerian Community
http://ojhas.org
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Discussion
as has been pointed out the rural women live phys-
Majority of the subjects in the rural sub sample
ical y arduous lives17 and so the usual weight gain
were subsistence farmers and as is the case in most
with increase in parity may not be observed.
sub-Saharan African countries although they spend
long hours farming they stil have limited access to
Although the nutritional demands of frequent cy-
food since the men control the family resources.14
cles of pregnancy and lactation (child spacing) have
The rural women therefore consumes
always been known to impact negatively on the nu-
systematical y below their minimum daily calorie
tritional status of women17, results from the present
requirement.15 This would explain the lower mean
study showed that child spacing did not have any
weight, height and BMI of the rural subjects
significant effect on both the mean BMIs and the
compared to the urban subjects. A previous study
prevalence of PEM amongst pregnant women both
by the authors showed prevalence of PEM to be 3-4
in the urban and rural areas. The reason for this is
times higher in the rural area compared with the
not immediately obvious but it might be that the
urban area (unpublished finding).
education intervention programmes (usual y a com-
mon feature of antenatal clinics) on birth control
The effect of age on the prevalence of PEM showed
measures and child spacing may be yielding divi-
that the age groups, below 20years and 20-24
dends.
years, presented the higher prevalence of PEM of
25% and 11.74% respectively. Their mean BMIs were
In conclusion, Protein Energy Malnutrition among
significantly lower than those of the other age
pregnant women remains a major public health
group. The 24 years and below age group is appar-
problem in Nigeria especial y in the rural areas.
ently the group at greater risk for PEM especial y in
Those who are at greater risk are the teenage and
the rural areas. The age effect although not seen in
young mothers, the less educated, the primigravi-
the urban area was quite prominent in the rural ar-
dae and those with parity of one or two especial y
eas.
in the rural areas. In view of the adverse effects of
PEM on both mother and child it is recommended
The effect of level of education on the prevalence
that appropriate intervention programmes be insti-
of PEM showed that those with no formal educa-
tuted to tackle the problem and the fol owing rec-
tion and primary education had significantly lower
ommendations are hereby made:
BMI and higher percentages of PEM than those of
other groups. Hence it can be concluded that the
1. Introduction of feeding programmes in
less educated are at greater risk of developing PEM.
antenatal clinics and health centers or in
Level of education did not show any effect in the
the alternative, provision of food subsidies
urban area but was a significant factor in the rural
to targeted groups,
areas. The more educated pregnant women in the
2. Counseling on dietary intake and reduced
rural areas are the ones that are likely to be en-
energy expenditure before and during
gaged in occupations other than farming which wil
pregnancy.
fetch them more income and hence greater food
3. Nutrition education and efficient nutrition
purchasing power. In the urban area on the other
monitoring systems at al levels of care.
hand, even the less educated pregnant woman is
4. Subsidized agricultural inputs and labour
likely to be engaged in some economic activity
saving devices for women.
which wil earn her some income and thus guaran-
5. Hygiene education, improved access to
tee her reasonable food purchasing power.
potable water and adequate sanitation
and health care services
Parity of two, one and primipara recorded mean
6. Providing opportunities for women’s in-
BMIs that were significantly lower than those of the
volvement in development through access
other groups. This effect was more pronounced in
to education, paid employment, assets
the urban area than the rural areas. This can be ex-
such as land and credit facilities.
plained by the fact that weight gain increases with
increase in parity.16 Hence those with lower parity
are likely to have lower BMIs. However, in the rural
areas, this might not necessarily be the case since
OJHAS Vol 6 Issue 3(1) - Okwu GN, Ukoha AI, Nwachukwu N, Agha NC. Studies on the Predisposing Factors of Protein Energy Malnutrition Among
PregnantWomen in a Nigerian Community
http://ojhas.org
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Acknowledgements
12. Lwanga SK, Lemeshow S. Sample Size De-
The authors thank Miss Dorothy Nwaneri, Miss
termination in Health Studies: A Practical
Maryjoe Keke and Miss Chinemerem Anyanwu for
Manual, Geneva, WHO, 1991.
their assistance in data col ection. We also thank
13. United Nations Administrative Committee
the numerous proprietors of the private hospitals
on Coordination/Subcommittee on Nutri-
and clinics in Owerri and the nurses at the Govern-
tion. Second Report on the World Nutri-
ment hospitals in Owerri and health centers in the
tion Situation, vol. 1, 1992. Global and Re-
rural areas for their co-operation
gional Results. Geneva; United Nations Ad-
ministrative Committee on Co-ordination/
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OJHAS Vol 6 Issue 3(1) - Okwu GN, Ukoha AI, Nwachukwu N, Agha NC. Studies on the Predisposing Factors of Protein Energy Malnutrition Among
PregnantWomen in a Nigerian Community
http://ojhas.org
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