To compare the prevalence of protein energy malnutrition between urban and rural pregnant women and its association with some traditional risk factors such as maternal age, gestational age parity and indices of socioeconomic status.
Methods: This is a community based, cross-sectional study. Two hospitals (one each from urban and rural settlements) were randomly selected for the study. Three hundred and five pregnant women (200 and 105 from urban and rural setting respectively) were selected for the study. Obstetric and sociodemographic data were obtained through semi-structured questionnaire, while anthropometric method was employed for the assessment of the energy status. Statistical analysis was done with computer software "Statistical Program for Social Sciences" (SPSS® for Windows® version 16.0).
Results: The result showed that 5.9 % of the women were malnourished, which was significantly (p < 0.05) more prevalent in the rural area (11.1%) than in the urban (2%). No significant relationship was identified between the prevalence of PEM and the predisposing factors considered. We conclude that the high prevalence of PEM in economically disadvantaged population may not be attributed to the traditional predisposing factors of malnutrition alone. While further research is desired to unmask the additional factor(s) responsible for this important public health problem, efforts at mitigating malnutrition in such setting should go beyond improving on these traditional risk factors.
Key words: Nutrient requirement; Food accessibility; Energy; Pregnant women; Reproduction; Abakaliki.
Introduction
Nutrition is all about the processes of ingestion, digestion, absorption and utilization of nutrient contents of foods by the living organisms, for growth, maintenance, work and reproduction. Besides good quality water, a well-balanced diet of essential macronutrients and micronutrients is required to achieve optimal health condition [1]. For obvious reasons, pregnancy is the most nutritionally demanding period of a woman's life [2]. When the nutrients intake is in disproportion with the basic body's needs, the pathophysiological consequences known as malnutrition sets in [1, 3]. Malnutrition has been the major public health issue attracting the highest of world's attention [4]; with its burden more on poorer communities [5] and developing countries [6, 7] with infants, young children, adolescents, pregnant and lactating women being the most vulnerable [6, 8].
Protein energy malnutrition (PEM) during pregnancy has been noted to be one of the common causes of intrauterine growth retardation (IUGR) [9, 10] and the growing rate of infants and childhood morbidity and mortality [11], observed in sub-Saharan region of Africa. Apart from the genetic influence, the problems of malnutrition in Africa are consequent upon the demographic influences on the dietary patterns and habits [12, 13]. The prevalence of PEM in a given population depends on several predisposing factors [6], some of which include poverty [8, 14], cultural and religious customs [8, 15], socio-political and economic instability among others that characterize the people of most African countries including Nigeria. Previous study [8] showed that 100% of a Nigerian population does not meet the daily energy requirement.
With increased demands for nutrients, including energy during pregnancy, we hypothesise that the nutrition of pregnant women may be negatively affected, especially pregnant women from rural population, where factors predisposing to PEM, such as poverty, infections, and illiteracy are prevalent. Hence, the present study targets pregnant women as a special population, to determine the prevalence of PEM as well as the relationship of some risk factors with the incidence of PEM.
Methods
Study setting and sample
The study was conducted at the antenatal clinics of Federal Medical Centre (FMC), Abakaliki (representing urban population) and St. Vincent Hospital, Ndubia (rural population), both of which, are referral hospitals in Ebonyi State. Ebonyi State, created out of old Enugu and Abia States in 1996 is located on longitude 8° E and latitude 6° N with moderate relief of between 125 and 245 m above sea level. The vegetation characteristic is that of the tropical rain forest with an average annual rainfall of about 1600 mm and average atmospheric temperature of about 30 °C. Two main seasons dominate the climate of the state. These are the rainy season, which usually begins in late April, and ends in early October; and the dry season, which lasts from late November to early April. It has a population of approximately 2.2 million [16] primarily populated by the Igbos on a land mass of 5935 km2, giving a population density of 371 persons/km2. Abakaliki and the environs are inhabited mainly by subsistence-level population. Their main occupation is subsistence-level farming-mainly rice, yam and cassava-with some animal husbandry. Other professions including civil service, trading, and artisanry are also practiced. The transmission of malaria is intense and perennial.
A total of three hundred and eight (308) pregnant women attending antenatal clinic at the two hospitals, who gave their consent, were recruited for the study using simple random method. Two hundred (200) of them were from Abakaliki (urban area), while hundred and eight (108) were from Ndubia (rural area). At recruitment, the obstetric and sociodemographic data of the participants were obtained through semi-structured questionnaires. Their weights (kg) and heights (m) were measured in light clothing without shoes and standing erect against a pre-marked scale attached to the weighing balance, and body mass index (BMI) (kg/m2) was calculated. Two of the pregnant women later tested HIV seropositive and were excluded, while one opted out for her reserved reason(s). Pregnant women with complicated singleton or multiple gravidas were also excluded from the study. In all, data for 305 subjects were finally analysed. PEM classification was in accordance with the United Nations report of 1992 on the World Nutrition Situation Classification [17].
Analysis of data
Basic statistical analyses were done by student's t-test and one-way analysis of variance (ANOVA). Statistical program for Social Sciences (SPSS® ver. 15.0 for Windows®) was employed for all statistical analysis. Data were expressed either as mean and standard deviation or proportion/percentage. The statistical significance was set at the p-value of < 0.05.
Ethical approval
Ethics and Research Committees of the Federal Medical Centre, Abakaliki and St. Vincent Hospital Ndubia, Ebonyi State, Nigeria respectively approved the protocol for this study.
Results
The age of the pregnant women ranged from 15-40 years, with the mean age of 27.3 ± 5.2 years (Table 1). Comparison of the general characteristics of the pregnant women from urban and rural area showed that women from urban setting had significantly (p < 0.05) higher body mass index (BMI) in comparison to women from rural area (27.3 ± 3.9 vs. 24.5 ± 3.2 kg/m2). However, urban pregnant women had lower (p < 0.05) gestational age than do rural pregnant women (21.9 ± 3.1 vs. 24.9 ± 5.8 wks).
Table 1: General Characteristics of pregnant women
Parameters
Range
Mean
Standard deviation
Urban
Age (years)
15-40
27.3
4.6
Gestational Age (weeks)Ç‚
12-29
21.9
3.1
Weight (kg) Ç‚
49-109
69.9
11.9
Height (m) Ç‚
1.5-1.8
1.6
0.1
BMI (kg/m2) Ç‚
17.8-42.6
27.3
3.9
Rural
Age (years)
18-40
27.3
6.2
Gestational Age (weeks)â€
11-38
24.9
5.8
Weight (kg) â€
40-76
55.0
7.1
Height (m) â€
1.3-1.7
1.5
0.1
BMI (kg/m2) â€
17.3-38.8
24.5
3.2
Overall
Age (years)
15-40
27.3
5.2
Gestational Age (weeks)
11-38
23.0
4.5
Weight (kg)
40-109
64.8
12.7
Height (m)
1.3-1.8
1.6
0.1
BMI (kg/m2)
17.3-42.6
26.3
3.9
Variables with different superscripts are statistically different (p<0.05).
Out of the 305 pregnant women recruited into the study, 18 (5.9%) were underweight (malnourished) of which 0.7 % were of severe (Table 2). The prevalence of PEM was significantly (p < 0.05) higher in the rural women (13.4%) than in the urban women (2%).
Table 2: Comparison of body mass index (BMI) between urban and rural pregnant women
BMI classification¶
Urban
Rural
Overall
n
%
n
%
n
%
Severe Underweight
1
0.5â€
1
1‡
2
0.7
Moderate Underweight‡
3
1.5â€
13
12.4‡
16
5.2
Normal
57
28.5â€
45
42.9‡
102
33.4
Obese
41
20.5â€
30
28.6‡
71
23.3
Morbidly obese
98
49â€
16
15.2‡
114
37.4
Total
200
100
105
100
305
100
Values with different superscript are statistically different (p < 0.05). ¶BMI classification (kg/m2), < 18 = severely underweight, 18-20 = moderately underweight, 21-24 = normal, 25-27 = overweight, >27 = obese (UN ACC/SCN, 1992).
From table 3, the pregnant women in their early adulthood (<25 years) and those >39 years generally have significantly lower mean BMI (p < 0.05) than the rest. The pregnant women in the age group 25-29 years showed the highest prevalence of PEM (3.5%), followed by those in the 20-24 years and 30-34 years age groups with equal proportion of PEM prevalence (1.4%), but there was no significant difference. Among the urban women, while the prevalence of PEM were 2.2 % and 1.8 % for age groups 20-24 and 30-34 yrs respectively against none in the same age groups in the rural area, rural women in the age group 25-29 yrs had PEM prevalence of 11.4 % while no incidence of PEM was recorded in the same age group in the urban area. However, in general, significantly (p < 0.05) higher prevalence of PEM was observed in rural women in comparison to urban women (1.0 % vs. 3.8 %).
Again, significantly higher prevalence of PEM was recorded in all the trimesters of pregnancy among rural women in comparison to urban women. Nevertheless, no case of PEM was recorded at gestational age of < 12 wks, 30-35 wks and > 35 wks in both urban and rural women.
Table 3: Effects of maternal age, gestational age and parity on the incidence of protein energy malnutrition (PEM) among pregnant women
Parameters
Urban
Rural
Overall
N
BMI (Kg/m2)
% PEM
n
BMI (Kg/m2)
% PEM
n
BMI (Kg/m2)
% PEM
Age-groups
<20
8
24.1 ± 2.5
0.0
8
25.1 ± 2.1
0.0
16
24.6 ± 2.3
0.0
20-24
46
26.1 ± 3.5â€
2.2¶
25
24.5 ± 2.9‡
0.0Ç‚
71
25.6 ± 3.4
1.4
25-29
78
27.3 ± 3.8â€
0.0¶
35
23.9 ± 3.9‡
11.4Ç‚
113
26.2 ± 4.1
3.5
30-34
55
28.5 ± 4.5â€
1.8¶
19
25.7 ± 2.5‡
0.0Ç‚
74
27.8 ± 4.2
1.4
35-39
12
28.3 ± 2.2â€
0.0
12
23.9 ± 2.7‡
0.0
24
26.1 ± 3.3
0.0
>39
1
28.5â€
0.0
5
23.7 ± 3.5‡
0.0
6
24.5 ± 3.7
0.0
Total
200
27.3 ± 4.0â€
1.0¶
104
24.5 ± 3.2‡
3.8Ç‚
304
26.3 ± 3.9
2.0
Gestational age (wks)
<12
-
-
-
2
25.4 ± 0.4
0.0
2
25.4 ± 0.4
0.0
12-17
19
26.9 ± 3.9â€
0.1¶
10
21.9 ± 2.4‡
20.0Ç‚
29
25.2 ± 4.2
6.9
18-23
103
27.5 ± 3.9â€
1.0¶
25
24.2 ± 3.4‡
4.0Ç‚
128
26.8 ± 4.0
1.6
24-29
78
27.1 ± 4.0â€
1.0¶
48
24.6 ± 2.6‡
2.1Ç‚
126
26.2 ± 3.7
1.6
30-35
-
-
-
14
25.0 ± 2.8
0.0
14
25.0 ± 2.8
0.0
>35
-
-
-
6
27.7 ± 5.5
0.0
6
27.7 ± 5.5
0.0
Total
200
27.3 ± 3.9â€
1.0¶
105
24.5 ± 3.2‡
3.8Ç‚
305
26.3 ± 3.9
2.0
Parity (n)
Nullipara
72
26.7 ± 3.6â€
0.0¶
28
24.4 ± 3.0‡
3.6Ç‚
100
26.0 ± 3.6
1.0
Primipara
45
26.8 ± 4.2â€
4.4¶
16
24.5 ± 2.7‡
0.0Ç‚
61
26.2 ± 3.9
3.3
2
36
27.3 ± 4.0â€
0.0
7
24.6 ± 2.6‡
0.0
43
26.9 ± 3.9
0.0
3
18
29.9 ± 4.2â€
0.0¶
12
23.2 ± 2.4‡
16.7Ç‚
30
27.2 ± 4.9
6.7
>3
29
27.9 ± 3.7â€
0.0¶
42
24.9 ± 3.6‡
2.4Ç‚
71
26.1 ± 3.9
1.4
Total
200
27.3 ± 3.9â€
1.0¶
105
24.5 ± 3.2‡
3.8Ç‚
305
26.3 ± 3.9
2.0
Values are expressed as mean ± S. D. Values with different superscript are significantly different (p < 0.05)
The parity of the women did not show any significant pattern of relationship with the mean BMI and incidence of PEM (Table 3). However, the parity of 3 showed the highest mean BMI (27.2±4.9 kg/m2) and prevalence of PEM (6.7%) than the rest, with the primipara ranking next in prevalence (3.3%), while the nullipara showed the least mean BMI (26.0±3.6 kg/m2) and prevalence of PEM (1%). Comparison of urban and rural women showed that, while mean BMI was significantly (p < 0.05) higher in all the parity groups in the urban women than in rural women and PEM observed only in urban primiparous women (4.4 %), PEM was recorded in nulliparous (3.6 %), parity 3 (16.7 %) and > 3 (2.4 %) in rural women.
Table 4 shows the effect of maternal occupation, level of education and living accommodation on the incidence of PEM. Overall, the pregnant women who were engaged in one kind of occupation or the other (including students) were at the risk of PEM. The prevalence was higher in farmers (3.8 %) and students (3.0 %) than the rest, though this did not show any statistical difference. The house wives (H/W) and the civil servants (C/S) showed significantly (p < 0.05) higher mean BMI than the more actively engaged (Table 4). While in the urban women PEM was observed only in students and C/S with prevalence of 3.2% and 1.3% respectively, in the rural women, PEM was rather identified in farmers and artisans with prevalence of 4.1% and 4.7% respectively. In terms of education, women who have acquired primary or no formal education showed significantly (p < 0.05) lower mean BMI than the others. The highest prevalence of PEM was identified among the subjects with only primary education (4.1%), followed by those with tertiary education, but there was no significant difference. Comparison between rural and urban women showed that rural women with least formal education (primary) and women without formal education had significantly (p < 0.05) lower mean BMI than their urban counterparts. The same class of rural women were also found to have PEM (6.0 % & 2.2 % for primary and no formal education respectively) against none in their urban counterparts. Table 4 also shows that overall, women whose living accommodation was a single room and bungalow showed significantly (p < 0.05) lower mean BMI than the rest.
Table 4: Effects of maternal occupation, educational level and living accommodation on the incidence of protein energy malnutrition (PEM) among pregnant women
Parameters
Urban
Rural
Overall
N
BMI (Kg/m2)
% PEM
n
BMI (Kg/m2)
% PEM
n
BMI (Kg/m2)
% PEM
Occupation
H/W
40
27.6 ± 3.1
0.0
10
25.0 ± 3.5
0.0
50
27.1 ± 3.4
0.0
C/S
76
27.4 ± 3.8
1.3¶
1
27.57‡
0.0Ç‚
77
27.4 ± 3.8
1.3
Artisans
49
27.5 ± 3.9â€
0.0¶
43
24.0 ± 2.6‡
4.7Ç‚
92
25.9 ± 3.8
2.2
Students
31
26.7 ± 5.1â€
3.2¶
2
30.3 ± 2.6‡
0.0Ç‚
33
26.9 ± 5.0
3.0
Farming
4
24.2 ± 2.9
0.0¶
49
24.5 ± 3.4
4.1Ç‚
53
24.5 ± 3.3
3.8
Total
200
27.3 ± 3.9â€
1.0¶
105
24.5 ± 3.2‡
3.8Ç‚
305
26.3 ± 3.9
2.0
Educational level
None
6
26.0 ± 3.6
0.0¶
46
24.4 ± 2.8
2.2Ç‚
52
24.6 ± 2.9
1.9
Pry.
24
27.2 ± 3.4â€
0.0¶
50
23.8 ± 2.5‡
6.0Ç‚
74
24.9 ± 3.2
4.1
Sec.
99
26.9 ± 3.6
0.0
7
27.3 ± 4.0
0.0
106
26.9 ± 3.6
0.0
Tertiary
68
27.8 ± 4.3â€
2.9¶
2
33.2 ± 8.0‡
0.0Ç‚
70
28.0 ± 4.4
2.9
Total
197
27.2 ± 3.8â€
1.0¶
105
24.5 ± 3.2‡
3.8Ç‚
302
26.3 ± 3.8
2.0
Living accommodation
S/ room
113
26.8 ± 3.2â€
0.0¶
8
23.4 ± 3.1‡
12.5Ç‚
121
26.6 ± 3.4
0.8
Flat
73
27.9 ± 4.4
2.7
-
-
-
73
27.9 ± 4.4
2.7
Bungalow
13
27.0 ± 5.7â€
0.0¶
94
24.5 ± 3.2‡
3.2Ç‚
107
24.8 ± 3.6
2.8
Duplex
-
-
-
1
27.6
0.0
1
27.6
0.0
Total
199
27.2 ± 3.9â€
1.0¶
103
24.5 ± 3.2‡
3.9Ç‚
302
26.3 ± 3.9
2.0
Values are expressed as mean ± S. D. Values with different superscript are significantly different (p < 0.05)
Those living in bungalow ranked highest in the prevalence of PEM (2.8%), followed by those living in flats (2.7%), but there were no significant difference (p >0.05). Comparison of urban and rural women showed that urban women whose living accommodation was a single room have significantly (p < 0.05) higher mean BMI in comparison to their rural counterparts (26.8 ± 3.2 vs. 23.4 ± 3.1kg/m2). However, while PEM was identified only in urban women whose living accommodation was flat (2.7 %), PEM prevalence of 12.5 % and 3.2 % were observed in rural women whose living accommodation was a single room and bungalow respectively.
DISCUSSION
Earlier studies in different Nigerian population groups [6, 18] reported persistence of protein energy malnutrition in the country. From this study, PEM prevalence of 5.9 % was observed among the surveyed population; thus corroborating those earlier studies. This might be as a result of the food shortage that is hitting hard on the world today in addition to unsatisfactory traditional feeding practice [19]. Ijarotimi [8], in the study among the subjects of south-western Nigeria reported that PEM is prevalently higher in the rural area than in the urban. The result of our own finding is in agreement with that report. Okwu et al [6], in support of their finding, claimed that the prevalence of PEM was 3-4 times higher in the rural area than in urban area. This is in corroboration with the ratio 1: 3 PEM prevalence in urban and rural women observed in the present study. The higher prevalence of PEM in rural area might partly be associated with restricted food accessibility among women in many rural African cultures (a life style still very common within the population of our study) and partly due to food aversion that is common during pregnancy [15]. Poverty, seasonal availability of foods, lack of nutrition education and cultural belief might also play a contributory role to this. It has also been found that the nutrient requirements of the body are dependent on age, gender, size of the body and the activity patterns necessary to adapt in a given environment [17].
Results of the present study showed that the prevalence of PEM does not seem to be affected significantly by any of the predisposing factors tested. This finding is in contrast to the earlier claim of Okwu et al [6] that age, level of education and parity were significant predisposing factors to PEM. Why this relationship was not established in this finding is not quite clear, but we speculate that it might be due to yet to be identified factors; possibly the feeding practice or energy intake of the studied groups. Regrettably, only anthropometric measurements were employed in the present study. Anthropometric measurements rely only on the use of physical characteristics resulting from heredity and environment [20] and may not give real situation of PEM. However, significant relationship existed between these predisposing factors (age, level of education, living accommodation, occupation and parity) and the mean BMI values of our subjects. In this case, the finding agrees with the previous findings [6]. The significantly higher mean BMI in elderly and multiparous women observed in the present study is in corroboration of the findings of Ogbeide et al [21] who reported that weight gain, a major determinant of BMI, was in direct proportion to parity. The present finding is also comparable to the earlier finding of Okwu et al [6] in terms of body weight. The significantly higher BMI in older age groups observed in the present study shows the normal physiological changes during aging. These changes are influenced by both environmental and genetic factors. Also, it has been reported that parous women retain more of their pregnancy weight, a condition that has been linked to long time obesity [22].
Again, the significantly lower BMI in women who were more actively engaged (farmers and artisans) in comparison to those who were rather less actively engaged (civil servants, students and of course, the sedentary house wives) shows the importance of high physical activity practices in burning more of the body's energy which reflected in lower BMI in the physically active pregnant women. Additionally, the higher mean BMI in civil servants may be partly ascribed to higher purchasing power which may lead to excessive calorie intakes in comparison to farmers and artisans. For instances, increased dietary intake (in women from high socioeconomic class) may lead to excessive accumulation of tissues [23] which will ultimately lead to increased BMI as seen in the present study. The impact of higher economic power on maternal BMI is also seen in women whose living accommodation was single room and those without formal education as significantly lower mean BMI was recorded in these women. This association was more pronounced in the rural area than in the urban area. Even though these socioeconomic factors have significant effect on the mean BMI, no such association was found with the prevalence of PEM in the present study and therefore corroborated earlier findings with different populations [6, 24]. This finding is also in tandem with earlier report of Al-Mekhlafi et al. [5] in Malaysia, dissociating socioeconomic factors from prevalence of PEM. We therefore conclude that the high prevalence of PEM in economically disadvantaged population, such as Ebonyi State may not be the consequence of the traditional predisposing factors of malnutrition only. While efforts at mitigating malnutrition in such setting should go beyond improving on these factors, more research is desired to unmask the contributing factors responsible for this public health problem.
Acknowledgment
The authors acknowledge the logistic support of the staff of the Department of Obstetrics and Gynaecology of the Federal Medical Centre, Abakaliki and St. Vincent hospital Ndubia.
Conflict of interest
No conflict of interest associated with this work.
Contribution of authors
IOO was involved in the conception, design, collection of data and drafting of the manuscript. NN was involved in the conception, design and revising of manuscript for important intellectual contents. EIU was involved in the design, analysis and interpretation of data and revising the manuscript for important intellectual contents. OUO participated in the collection, analysis and interpretation of data. All the authors gave final approval of the version to be published.
