Nutrition during pregnancy plays a fundamental role in maternal health, fetal development and the prevention of complications (
Dumas, 2011
). The energy and nutritional needs of pregnant women increase, requiring a sufficient, varied and balanced diet (
Badiou, 2012
). Adequate nutrition is essential to promote fetal growth and maintain maternal well-being (
Alamu et al., 2019
). Nutrient deficiency can lead to adverse effects for both the mother and the unborn child (
Black et al., 2013
).
African women are exposed to nutritional deficiency problems (Russel, 2023). Limited access to sufficient and nutritious food is a reality in developing countries (
FAO, 2021
). This shows that maternal and child health is a major issue. During pregnancy, a diet lacking sufficient key nutrients such as iodine, iron, folate, calcium and zinc can cause anemia, preeclampsia or hemorrhage and lead to the death of the mother (
OMS, 2019
). In addition, these deficiencies sometimes cause stillbirths, low birth weight, wasting and developmental delays in children (
Dangura & Gebremedhin, 2017
).
Iodine deficiency with hypothyroidism during the first half of pregnancy is thought to be responsible for a decrease in intellectual abilities in children aged 4 to 7 linked to neurocognitive alterations (
David, 2012
). Marked iodine deficiency is associated with an increase in spontaneous abortions, perinatal mortality, hypotrophy at birth and neonatal hypothyroidism (
Larry, 2023
). Globally, malnutrition in all its forms affects millions of women: Approximately 800 million women of childbearing age suffer from iron deficiency, and nearly 10% of pregnant women suffer from anemia (
OMS, 2023
). Malnutrition affects more than 30% of pregnant women worldwide, of which approximately 20% are in sub-Saharan Africa (
OMS, 2023
).
Far too many adolescent girls and women are not receiving the nutrition services they need to stay healthy and give their infants the best chance of survival, growth and development (
Chiabi et al., 2019
). In many African countries, women’s diets are poor in terms of fruits, vegetables, dairy products, fish, and meat (
Christian, 2004
). UNICEF estimates that each year, more than 20 million newborns are underweight due to maternal nutritional status (
UNICEF, 2020
).
Various factors influence women’s diets, including access to food products and their costs (which limit women’s ability to ensure optimal nutrition) as well as access to health care (
FAO & PAM, 2021
).
In Africa, a study conducted in Benin reported that 40% of pregnant women experienced the consequences of malnutrition on maternal health (
Sossa et al., 2023b
). Similarly, a study conducted in Tanzania in 2021, found a prevalence of anemia of 80.8% among pregnant women (
Gibore et al., 2021
).
In Burundi, according to the report on the state of food security, low incomes and increasing market prices do not allow many households to obtain a diversified diet on a regular basis (
FAO & PAM, 2021
). Integrated food security Phase Classification (IPC) analyses show that in 2021, 26,142 pregnant or breastfeeding women were acutely malnourished (
PAM & MINEAGRI, 2023
). For the period from June to September 2024, the province of Bujumbura Mairie and 26 health districts are in the Alert phase (IPC Phase 2). A 2019 study revealed that nearly 30% of pregnant women in Burundi were chronically malnourished (
Niyonzima, 2021
). Data from the 2016 Demographic and Health Survey show that approximately 46% of Burundian women of reproductive age suffer from anemia, a particularly acute problem among pregnant women (
ISTEEBU & MSPLS, 2017
).
The Musaga neighborhood is one of the deprived areas of the capital, with a population that has limited means of living. Many families live in precarious conditions, with limited access to drinking water (
Manirakiza et al., 2024
). This suggests that the nutritional health of women of reproductive age is not optimal. Few studies have focused on the nutritional and dietary status of pregnant women. The available data are results presented in reports of national surveys giving overall figures from the Bujumbura city hall, which motivated this study.
A study of the determinants linked to nutritional and dietary status would be relevant in order to discover the state of the nutritional and dietary status of pregnant women and to develop strategies aimed at positively impacting the nutritional status of women as well as preventing the occurrence of consequences on pregnant women as well as on children.
2. Materials and Methods2.1. Description of the Study Location
Musaga Health Center is located in the Musaga area, in the Muha urban commune. Its area of responsibility covers 7 hills and 28 sub-hills that make up the Musaga area. To the southeast is the Kanyosha area, to the west is the Kinindo area, and to the north is the Rohero area of the Mukaza commune.
Source: Musaga Health Center Annual Action Plan 2023-2024
2.2. Period and Type of Study
This is a cross-sectional analytical study conducted among 417 pregnant women who consulted for pregnancy monitoring at the Musaga Health Center, during the three-month period from August 20 to November 20, 2024.
2.3. Inclusion and Exclusion Criteria
Pregnant women who attended prenatal care and agreed to participate in the survey were included in our study. However, pregnant women who attended prenatal care but did not want to participate were excluded.
2.4. Study Sample
We used an empirical method with a purposive sampling technique, surveying all pregnant women who came for a prenatal consultation and met the inclusion criteria and provided consent. A total of 417 pregnant women were registered during the study period.
2.5. Data Collection Tools and Techniques
A letter requesting authorization to conduct the study at the health center was sent to the chief district medical officer of the Bujumbura South District. The study was conducted after obtaining authorization from him and the agreement of the Musaga Health Center Manager.
Data collection was conducted by previously trained interviewers from August 20 to November 20, 2024. We used a questionnaire coded in the Kobocollect data collection tool. Information related to sociodemographic determinants, diet-related determinants, and socio-health determinants was obtained by completing the questionnaire face-to-face based on the responses provided by the respondents. Dietary diversification and food consumption were assessed based on the food groups consumed by the individuals. Nutritional status was determined by measuring mid-upper arm circumference using a MUAC tape.
A database was created by exporting the data collected from Kobotoolbox to Microsoft Excel 2016 for grooming, then exported to SPSS 25.0 for statistical analysis. A sample description was performed by calculating means, standard deviations, and frequencies. A bivariate descriptive analysis was then performed using Pearson’s chi-square test. Finally, a multiple logistic regression was performed by calculating the odds ratio and its 95% confidence interval between the response variable and the independent variables whose p-value was <0.20 in bivariate analysis. Only variables with a p-value < 0.05 at this level were considered predictors of malnutrition among pregnant women in the Musaga Health Center. The discriminatory power of the final model was tested to assess its reliability using the ROC curve.
Before data collection, the purpose of the study was presented to participants. Only respondents who gave their consent were included in our study. The survey was conducted with strict respect for confidentiality and respondent anonymity.
2.6. Operational Definition of Variables
Table 1
shows an operational definition of the response variable.
<xref ref-type="bibr" rid="scirp.143733-"></xref>Table 1. Description of the nutritional status of pregnant women.
Dependent variable
Description
Operational definition
Nutritional status
Binary categorical variable expressed in brachial circumference
Normal status (MUAC ≥ 23 cm)
Malnutrition (MUAC < 23 cm)
Tables 2-4
show an operational definition of the explanatory variables.
Figure 1
below provide the conceptual framework of factors assumed to be associated with the malnutrition of pregnant women at the Musaga Health Center.
Figure 1. Conceptual framework of factors associated with malnutrition in pregnant women at the Musaga Health Center.
3. Results3.1. Description of the Sample
The graph and tables below provide a detailed description of the various characteristics of our study sample. The graph shows the nutritional status of pregnant women, while
Tables 5-7
illustrate the sociodemographic, diet-related, and socio-health determinants of pregnant women, respectively. The results show that the majority of pregnant women, 65.7%, are classified as having normal nutritional status. In contrast, 34.3% of pregnant women are malnourished (
Figure 2
).
<xref ref-type="bibr" rid="scirp.143733-"></xref>Table 5. Sociodemographic determinants of pregnant women seen for prenatal consultation at the Musaga Health Center.
Variables
Mean ± Standard deviation
Age of the pregnant woman (in years)
29 ± 12
Effective
Frequency
Marital status
Single
31
7.5%
Married
324
77.7%
Widowed
2
0.5%
Common-law
60
14.4%
Educational level of women
None
21
5%
Primary education
170
40.8%
Technical training
5
1.2%
Secondary education.
140
33.6%
Bachelor’s or equivalent level
81
19.4%
Household head occupation
Farmer
58
13.9%
Trader, Shopkeeper
62
14.9%
Housewife
223
53.5%
Civil servant
28
6.7%
Others
46
11%
Household income
Less than 100,000 BIF (34 USD)
107
25.7%
100,000 to 200,000 BIF (34 to 68 USD)
112
26.9%
200,000 to 300,000 BIF (68 to 100 USD)
74
17.7%
More than 300,000 BIF (100 USD)
124
29.7
Figure 2. Nutritional status of pregnant women.
<xref ref-type="bibr" rid="scirp.143733-"></xref>Table 6. Description of determinants linked to the diet of pregnant women seen in prenatal consultation at the Musaga Health Center.
Variables
Mean ± Standard deviation
Individual dietary diversity score (IDDS)
4 ± 2
Food Consumption Score (FCS)
55.25 ± 26.9
Number of meals
2 ± 1
Effectives
Frequency
Individual dietary diversity score (IDDS)
Low diversity
49
11.8%
Average diversity
163
39.1%
High diversity
205
49.2%
Food Consumption Score (FCS)
Poor
116
27.8%
Limit
44
10.6%
Acceptable
257
61.6%
Number of meals
One meal
84
20.1%
Two meals
122
29.3%
Three meals
211
50.6%
Fruit Consumption
No
275
66.1%
Yes
142
33.9%
Vegetable consumption
No
203
48.6%
Yes
214
51.4%
Snacking
No
299
71.7%
Yes
118
28.3%
<xref ref-type="bibr" rid="scirp.143733-"></xref>Table 7. Description of the socio-health determinants of pregnant women seen for prenatal consultation at the Musaga Health Center.
Effectives
Proportion (%)
Physical activity
No
191
45.8%
Yes
226
54.2%
Consultation with a specialist
No
417
100%
Yes
0
0%
Gestational age
1st trimester
78
18.7%
2nd trimester
173
41.5%
3rd trimester
166
39.8%
The results also show that the mean age of pregnant women is 29 ± 12 standard deviation. The youngest woman is 17 years old while the oldest is 43 years old. Most women, or 77.7%, are married. Those in a common-law relationship represent 14.4% and single women 7.5%. A minority, or 0.5%, are widowed. Regarding the educational level of pregnant women, primary level is the most represented, followed by secondary level with respective frequencies of 40.8% and 33.6%. Among pregnant women, 25.7% have an income of less than 100,000 BIF, while 26.9% earn between 100,000 and 200,000 BIF. In addition, 17.7% have an income between 200,000 and 300,000 BIF, and 29.7% have an income above 300,000 BIF (
Table 5
).
The results also show that women with low dietary diversity are represented at 11.8% while 39.1% have medium diversity and 49.2% high diversity. Regarding the food consumption score, 27.8% of women are in the poor category, 10.6% have a borderline consumption and 61.6% an acceptable food consumption. Regarding meal frequency, the results of our study show that pregnant women consume on average 2 meals per day, with 20.1% of women having only one meal per day. Regarding fruit and vegetable consumption, 66.1% do not consume fruit and 48.6% do not consume vegetables (
Table 6
).
Finally, 54.2% of pregnant women who engage in physical activity are represented, compared to 45.8% who do not. Regarding the age of pregnancy, 18.7% of women are in the first trimester, compared to 41.5% in the second trimester and 39.8% in the third trimester. No pregnant woman consulted a nutrition or dietetics specialist (
Table 7
).
3.2. Bivariate Analysis
Tables 8-10
show the association between the nutritional status of pregnant women and sociodemographic, diet-related, and socio-health determinants using Pearson’s chi-square test.
<xref ref-type="bibr" rid="scirp.143733-"></xref>Table 8. Analysis of the association between nutritional status and sociodemographic determinants of pregnant women in the Musaga Health Center.
Pregnant women with malnutrition
Explanatory variables
Effectives
n (%)
Chi 2
p-value
Age of pregnant woman
0.349
0.840
Less than 18 years
9
5 (55.55)
18 to 35 years old
370
130 (35.13)
Over 35 years old
38
8 (21.05)
Marital status
4.614
0.202
Single
31
6 (19.4)
Married
324
117 (36.1)
Widowed
2
0
Common-law
60
20 (33.3)
Educational level of pregnant women
16.826
0.014*
None
21
3 (14.3)
Primary education
170
62 (36.5)
Technical training
5
1 (20.0)
Secondary education.
140
50 (35.7)
Bachelor’s or equivalent level
81
28 (34.6)
Household head occupation
8.475
0.076
Farmer
58
13 (22.4)
Trader, Shopkeeper
62
29 (46.8)
Housewife
223
76 (34.1)
Civil servant
28
8 (28.6)
Others
46
17 (37.0)
Household income
6.979
0.081
Less than 100.000 BIF
107
44 (41.1)
100.000 to 200.000 BIF
112
30 (26.8)
200.000 to 300.000 BIF
74
30 (40.5)
More than 300.000 BIF
124
39 (31.5)
<xref ref-type="bibr" rid="scirp.143733-"></xref>Table 9. Analysis of the association between nutritional status and determinants related to the diet of pregnant women in the Musaga Health Center.
Pregnant women with malnutrition
Explanatory variables
Effectives
n (%)
Chi 2
p-value
Individual dietary diversity score (IDDS)
63.891
0.001
Low diversity
49
41 (83.67)
Average diversity
163
54 (33.12)
High diversity
205
48 (23.41)
Food Consumption Score (FCS)
48.429
0.002
Poor
116
69 (59.5)
Limit
44
16 (36.4)
Acceptable
257
58 (22.6%)
Fruit Consumption
56.573
0.001
Yes
283
63 (22.3)
No
134
80 (5.7)
Number of meals
39.676
0.0013
One meal
84
24 (28.57)
Two meals
122
37 (30.3)
Three meals
211
53 (25.1)
Vegetable consumption
2,15
0,81
No
203
33 (16.25)
Yes
214
41 (19.16)
<xref ref-type="bibr" rid="scirp.143733-"></xref>Table 10. Analysis of the association between nutritional status and socio-health determinants of pregnant women in the Musaga Health Center.
Pregnant women with malnutrition
Explanatory variables
Effectives
n (%)
Chi 2
p-value
Physical activity
4.818
0.090
Yes
209
82 (39.23)
No
208
61 (29.32)
Gestational age
33.412
0.001
1st trimester
78
5 (6.4)
2nd trimester
173
68 (39.3)
3rd trimester
166
70 (42.2)
Thus, the variables significantly associated with the nutritional status of pregnant women are: educational level (p = 0.014) (
Table 9
); IDDS (p = 0.001), FCS (p = 0.002), fruit consumption (p = 0.001), and number of meals per day (p = 0.0013) (
Table 10
); and gestational age (p = 0.001) (
Table 11
).
3.3. Multivariate Analysis
Table 11
shows the multiple logistic regression analysis after introducing variables with a p < 0.20 in the bivariate analysis and after adjusting for other variables. Indeed, the variables significantly associated with malnutrition in pregnant women seen for prenatal care at the Musaga Health Center (p-value < 0.05) are: Dietary Diversity Score, Food Consumption Score, Fruit Consumption, Level of Education, and Age at Pregnancy (
Table 11
).
<xref ref-type="bibr" rid="scirp.143733-"></xref>Table 11. Predictors of malnutrition in pregnant women.
Variables
Coefficients
OR
95% confidence interval for OR
p-Value
Inferior
Upper
Individual dietary diversity score (IDDS)
0.003**
High Diversity
1
Average Diversity
0.483
1.620
1.024
2.565
0.039**
Low Diversity
0.000
16.763
7.356
38.199
0.001***
Food consumption score (FCS)
0.014**
Acceptable
Limit
0.673
2.023
1.961
0.993
0.052*
Poor
1.617
5.037
3.141
8.077
0.003***
Fruit Consumption
0.002***
Yes
1
No
1.130
3.095
1.710
5.601
0.001***
Educational level of pregnant women
0.003***
None
1
Primary education
−2.524
0.080
0.014
0.458
0.004**
Technical training
−0.492
0.611
0.304
1.231
0.168
Secondary education
−20.248
0.000
0.000
0.999
Bachelor’s or equivalent level
−0.099
0.906
0.440
1.863
0.788
Gestational age
0.000
1st Trimester
1
2nd Trimester
−2.814
0.060
0.019
0.187
0.000***
3rd Trimester
−0.002
0.998
0.591
1.685
0.993
Number of meals
0.048**
Three meals
1
Two meals
0.798
2.222
1.102
4.479
0.026**
One meal
0.187
1.205
0.655
2.219
0.549
Constant
−1.078
0.340
0.002
Indeed, women with average dietary diversity are 1.620 times more likely to be malnourished, and those with low diversity are 16.763 times more likely to be malnourished than women with high diversity. Women with a limit dietary intake score are 2.023 times more likely to be malnourished, and those with a poor dietary intake score are 5.037 times more likely to be malnourished than women with an acceptable dietary intake score. Women who do not consume fruit are 3.095 times more likely to be malnourished than women who do. Women with only a primary education are 2.524 times less likely to be malnourished than women who have not studied at all. Women in the second trimester of pregnancy are 2.814 times less likely to be malnourished than those in the first trimester.
Discriminatory power of the model
Figure 3
shows the ROC curve obtained from the results of the final estimated model. The area under the ROC curve is 0.80, which means that in 80% of cases, the model will correctly classify the observations. It can be concluded that this model has predictive power, with excellent discrimination, and is effective in predicting the nutritional status of pregnant women.
Figure 3. ROC curve, AUC = 0.80.
4. Discussion
This study aimed to assess nutritional status and identify factors associated with malnutrition among pregnant women seen for antenatal care at the Musaga Health Center. This study found that 34.3% of pregnant women were malnourished. This demonstrates that malnutrition is a public health problem in the locality and that pregnant women are vulnerable to it. This prevalence is lower but comparable to that found in the study conducted in Ethiopia in 2021 by Zewdie et al., which found that 41.2% of pregnant women were malnourished (
Zewdie et al., 2021
) However, these results are significantly lower than those found in the study conducted in Congo by Gloire Kasongo et al. in 2022, where they reported a frequency of 80.27% of pregnant women with an MUAC less than 23 cm (
Gloire Kasongo et al., 2022
).
This study revealed a significant relationship between the individual dietary diversity score (IDDS) and the risk of malnutrition in pregnant women, according to logistic regression analysis (p-value of 0.003; CI = 95%). This confirms the conclusion of the study conducted by Savy and colleagues in 2005, which demonstrated that increased dietary diversity is correlated with better nutritional status in women (p-value = 0.004) (
Savy et al., 2005
).
The results of this study also revealed a significant association between poor food consumption score and malnutrition in pregnant women (p-value = 0.003; OR = 5.037, 95% CI: (95% CI: 3.141 - 8.077), these results suggest that a decrease in food consumption score is associated with an increased risk of malnutrition. These results correspond to those of the study carried out in Burkina Faso by Ousmane Ouédraogo in 2020, which demonstrated that the food consumption score was correlated with underweight women (p-value = 0.01; CI = 95%) (
Ouédraogo, 2020
).
The results of this study show that 66.1% of pregnant women do not consume fruit. In multivariate analysis, there was a significant association between fruit consumption and malnutrition in pregnant women, with a P-value of 0.000. The results of the study carried out in Kenya by Shrestha in 2019, show that pregnant women who regularly consume fruit have higher levels of vitamin C and folic acid, which is associated with better nutritional status (p-value = 0.027) (
Shrestha, 2019
). The results of this study finally show that the number of meals is associated with malnutrition in pregnant women (p-value of 0.048; CI = 95%). These results confirm those found in the study carried out in Benin by Sossa et al., in 2023 where the number of daily meals was significantly associated with the risk of malnutrition (p = 0.01) (
Sossa et al., 2023a
).
5. Limits of the Study6. Conclusion
This study on the nutritional status of pregnant women was conducted at the Musaga Health Center with the aim of assessing nutritional and dietary status and identifying factors associated with malnutrition among pregnant women seen for prenatal care. This study revealed a prevalence of malnutrition of 34.3% among pregnant women, representing a maternal health concern. The results of this study showed that unacceptable dietary diversity, poor food consumption scores, insufficient fruit consumption, and fewer than three meals a day are predictors of malnutrition among pregnant women at Musaga Health Center. Targeted interventions are needed to improve the nutritional health of pregnant women. These interventions should aim to correct malnutrition and diversify food consumption.
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