Nutritional Status of Pregnant Women Attending the Prenatal Consultation Service at Musaga Health Center ()
1. Introduction
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 Methods
2.1. 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
2.5.1. Data Collection Procedures
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.
2.5.2. Data Analysis
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.
2.5.3. Ethical Considerations
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.
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.
Table 2. Sociodemographic determinants.
Independent variable |
Description |
Operational definition |
1. Age of woman |
Discrete quantitative variable
in completed years categorized into three modalities |
Under 18 18 to 35 Over 35 |
2. Marital status |
Categorical variable |
Single Married Widowed Common-law |
3. Household
manager
occupation |
Categorical variable |
Farmer Shopkeeper Civil Servant Housewife Other |
4. Income |
Quantitative variable |
Less than 100,000 BIF (34 USD) 100,000 to 200,000 BIF
(34 to 68 USD) 200,001 to 300,000 BIF
(34.01 to 100 USD More than 300,000 BIF (100 USD) |
5. Level of study |
Categorical variable |
Without any level Primary Technical training Secondary University |
Table 3. Diet-related determinants.
Independent variable |
Description |
Operational definition |
1. Individual dietary diversity score (IDDS) |
Categorical variable |
Low Diversity ≤ 3 groups Average diversity > 3 groups
and ≤5 groups High Diversity > 5 groups |
2. Food Consumption Score (FCS) |
Categorical variable |
Poor ≤ 28 Borderline > 28 and ≤42 Acceptable > 42 |
3. Number of meals |
Categorical variable |
One meal Two meal Three meals |
4. Fruit Consumption |
Binary categorical variable |
Yes No |
5. Vegetable consumption |
Binary categorical variable |
Yes No |
6. Snacking |
Binary categorical variable |
Yes No |
Table 4. Socio-health determinants.
Independent variable |
Description |
Operational definition |
1. Physical activity |
Categorical variable |
Yes No |
2. Consultation with
a specialist |
Categorical variable |
Yes No |
3. Gestational age |
Categorical variable |
1st trimester < 12 weeks Second trimester ≥ 12 weeks to 24 weeks Third trimester > 24 weeks |
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. Results
3.1. 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).
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.
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% |
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.
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) |
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) |
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).
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 Study
Our study focused on a single health center, which limits the extrapolation of results to the whole of the Bujumbura Sud health district, of which the Musaga health centre is part of.
Information on dietary habits was collected by means of a face-to-face interview based on respondents’ statements. This may introduce a memory bias or respond in a socially expected manner.
6. 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.