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Abstract

Background:

Maternal undernutrition remains a life-threatening public health problem. Women of childbearing age’s diets are monotonous in several resource-limited countries. The current study investigated the minimum dietary diversity (MDD) of childbearing-age women and its determinants. The findings of this study will assist in the execution of a maternal nutrition program to improve their intake of diversified diets.

Materials and Methods:

Data from the most recent demographic and health surveys of three countries were used. A total weighted sample of 62,015 women was included in the study. Multilevel mixed-effects logistic regression was used to determine the factors associated with the outcome variable. Variables with a p value <0.05 and an adjusted odds ratio (AOR) with a 95% confidence interval (CI) were declared statistically significant.

Results:

The prevalence of achieving MDD among women of childbearing age was 35.44% (95% CI: [35.06%, 35.82%]). Factors such as women’s age (AOR = 0.90; 95% CI: [0.84, 0.97]), educational status (AOR = 2.14; 95% CI: [1.90, 2.40]), current marital status (AOR = 1.15; 95% CI: [1.07, 1.23]), occupation (AOR = 1.39; 95% CI: [1.30, 1.47]), media exposure (AOR = 1.64; 95% CI: [1.51, 1.78]), wealth index (AOR = 2.07; 95% CI: [1.90, 2.24]), distance to health facility (AOR = 1.20; 95% CI: [1.13, 1.28]), nutritional status (AOR = 0.84; 95% CI: [0.77, 0.92]), sex of the household head (AOR = 0.83; [0.78, 0.88]), community poverty level (AOR = 1.24; 95% CI: [1.12, 1.37]), and community literacy level (AOR = 0.76; 95% CI: [0.68, 0.85]) were significantly associated with MDD.

Conclusion:

The prevalence of achieving MDD among women of childbearing age was relatively low. Therefore, promoting women’s education status, improving household economic status, prioritizing women’s decisions, disseminating nutrition information through the media, and giving prior attention to younger and nonworking women who reside far from health facilities are strongly recommended.

Background

The physiological burdens of pregnancy and lactation make women of childbearing age nutritionally vulnerable.^1,2 Pregnant and lactating women had higher requirements for most nutrients than adult men.³ Inadequate micronutrient intakes before, during, and after pregnancy can have an impact on developing babies as well as their mothers, particularly in the crucial first thousand days of life.⁴ The diets of women of childbearing age are monotonous in several resource-limited environments, with higher consumption of starchy staple foods, and do not deliver adequate micronutrients.^5,6

Women are generally responsible for food choice, preparation, and child-feeding practices, and they are vital to household food security and nutrition.^7–10 It is habitual for women and men to eat alone in some regions of Africa and Asia.¹¹ Most of the time, women eat last, after they have served food to their children and husbands.¹² Hence, other family members consume qualified and nutrient-rich foods (e.g., animal-source foods), to the disadvantage of women.¹¹ The adequacy of household dietary diversity could not guarantee that women are consuming diets that meet their daily nutritional requirements, and when women’s diets are diverse, it is likely that the other household members are also consuming diverse diets.¹³

Maternal undernutrition remains a life-threatening public health problem, with large regional and within-country disparities in the burden of underweight, anemia, and micronutrient deficiencies across the globe.¹⁴ Women of childbearing age (15–49 years) necessitate appropriate nutrition and health status both for themselves and their children through harmless pregnancy, normal delivery, and healthier pregnancy outcomes.¹⁵ During conception and lactation, women need extra nutrition to support additional physiological desires.¹⁶ The nutritional status and well-being of childbearing-age women depend on their dietary practices as a result of genetic and environmental factors.³ The supreme vehicle to maintain adequate intake of micronutrients is high-quality and ideal diversified diets.^17,18

Minimum dietary diversity for women (MDD-W) is a food group diversity indicator that reflects one crucial dimension of diet quality: micronutrient adequacy, summarized across 11 micronutrients: vitamin A, thiamine, riboflavin, niacin, vitamin B-6, folate, vitamin B-12, vitamin C, calcium, iron, and zinc.¹⁹ A higher prevalence of MDD-W among women who are childbearing is a proxy for better micronutrient adequacy in a given population.¹⁷ Studies conducted elsewhere showed that the proportion of women attaining minimum dietary diversity (MDD) was 40.3% at St. Martin’s Island, Bangladesh,²⁰ 56.5% in Cumilla district, Bangladesh,²¹ 46.2% in India,²² 47% in Burla,²³ 57.7% in Latin America,²⁴ and 28% in Tanzania.²⁵ Literature also revealed that factors such as distance to a health facility, women’s age, nutritional status of women, family size, socioeconomic status, educational level, and occupation were determinants of MDD.^20,22,23,25

Even though there are studies conducted on the magnitude and determinants of MDD in different countries, there is no evidence of them conducted using the most recent demographic and health survey (DHS) dataset. Therefore, the current study investigated the MDD of childbearing-age women and its determinants in three sub-Saharan African (SSA) and South Asian (SA) countries using the most recent DHS datasets (2022). The findings of this study will fill this gap by providing information on MDD and its determinants among women of childbearing age residing in SSA and SA countries. It will also assist in the execution of a maternal nutrition and reproductive health program to improve their health and nutritional status, as well as their intake of diversified diets.

Methods and Materials

Sampling, data sources, and study populations

This is a secondary analysis of the most recent DHS data from the three SSA and SA countries of Ghana (2022), Kenya (2022), and Nepal (2022). These three countries are selected depending on the availability of the most recent DHS dataset and outcome variable. The DHS is a community-based cross-sectional study that is conducted every 5 years to produce updated population and health-related indicators. All childbearing-age women in the three SSA and SA countries were the source population of the study. Childbearing-age women living in the randomly selected enumeration areas (EAs) of each country during the survey year were the study population of this study. The data from the three countries were appended to one file to figure out the minimum dietary diversity (MDD) and associated factors among women of childbearing age in three SSA and SA countries. The survey for each country contains different datasets, including those for children, males, women, births, and households. The current study used the individual record file. The data used for this analysis are accessible online at https://dhsprogram.com/data/available-datasets.cfm. The DHS is a nationwide survey mostly collected every 5 years across low- and middle-income countries. It uses standard procedures for sampling, questionnaires, data collection, cleaning, coding, and analysis, which allows for cross-country comparison.²⁶ A total weighted sample of 62,015 women in the reproductive age group who answered all variables of interest completely was included in the study (Table 1). The DHS employs a stratified two-stage sampling technique.²⁷ The first stage involves the development of a sampling frame, consisting of a list of primary sampling units or EAs, which covers the entire country and is usually developed from the latest available national census. The second stage is the systematic sampling of households listed in each cluster or EA. Further information on the survey sampling strategies is available in the DHS guideline.²⁸

Table 1.

Sample Size for Minimum Dietary Diversity and Associated Factors among Women of Childbearing Age in Three Sub-Saharan African and South Asian Countries

Country	Year of survey	Weighted sample (n)	Weighted sample (%)
Ghana	2022	15,014	24.21
Kenya	2022	32,156	51.85
Nepal	2022	14,845	23.94
Total sample size		62,015	100

Study variables and measurements

Dependent variable

The outcome variable of this study was MDD, which is a dichotomous indicator of whether or not women aged 15–49 years have consumed at least 5 out of 10 defined food groups the previous day or night.¹⁹ The 10 food groups are as follows: (1) grains, white roots and tubers, and plantains; (2) pulses (beans, peas, and lentils); (3) nuts and seeds; (4) milk and milk products; (5) meat, poultry, and fish; (6) eggs; (7) dark green leafy vegetables; (8) other vitamin A-rich fruits and vegetables; (9) other vegetables; and (10) other fruits. Women who consumed at least five different food groups during the previous day or night are considered to have achieved MDD (yes) and no otherwise.

Independent variables

As DHS data are hierarchical, variables at the individual and community levels were considered for this analysis. Individual-level variables are as follows: respondents’ age (15–24, 25–34, 35–49 years), educational status (no education, primary, secondary, higher), household wealth status (poor, middle, rich), current marital status (unmarried, married), distance to health facility (big problem, not a big problem), working status (not working, working), media exposure (no, yes), household size (≤5, >5), sex of household head (male, female), and nutritional status (normal, underweight, overweight). Community-level variables: place of residence (rural or urban), community-level media exposure (low or high), community literacy level (low or high), and community poverty level (low or high) were considered community-level factors.

Description of independent variables

Nutritional status

Classified as normal, underweight, or overweight based on a woman’s body mass index: normal: 18.5–24.9 kg/m²; underweight: <18.5 kg/m²; and overweight: ≥25.0 kg/m².²⁸

Media exposure

It is generated by combining whether women read newspapers or magazines, listen to the radio, or watch television, and is coded as “yes” if women were exposed to at least one of these media and “no” otherwise.

Community literacy level

The proportion of women with a minimum primary level of education derived from data on respondents’ level of education. Then, it was categorized using the national median value into two categories: low (communities with ≤50% of women having at least primary education) and high (communities with >50% of women having at least primary education).

Community-level media exposure

The proportion of women who had been exposed to at least one media (television, radio, or newspaper) and categorized based on the national median value as low (communities with ≤50% of women exposed) and high (communities with >50% of women exposed) community-level media exposure.

Community poverty level

An aggregated variable from household wealth status (proportion of women from poor and rich wealth status), and it was recorded as low and high community poverty level, as above.

Data management and analysis

Data extracted from the most recent DHS datasets were cleaned, recoded, and analyzed using STATA/SE version 14.0 statistical software. To manage sampling errors and nonresponses, a sample weight was applied. Descriptive statistics were employed to present both the individual- and community-level variables. The variables in the DHS dataset were organized in clusters; women are nested within households, and households are nested within clusters. The assumptions of independent observations and equal variance across clusters were broken to employ the traditional logistic regression model. This is an indication that using a sophisticated model to take into account between-cluster factors is necessary. As a result, multilevel mixed-effects logistic regression was used to determine the factors associated with the outcome variable. Multilevel mixed-effects logistic regression follows four models: the null model (outcome variable only), Model I (only individual-level variables), Model II (only community-level variables), and Model III (both individual- and community-level variables). The model without independent variables (the null model) was used to check the variability of MDD across the cluster. The association of individual-level variables with the outcome variable (Model I) and the association of community-level variables with the outcome variable (Model II) were assessed. In the final model (Model III), the association of both individual- and community-level variables was fitted simultaneously with the outcome variable (MDD). Through the use of the intraclass correlation coefficient (ICC) and proportional change in variance (PCV), the magnitude of the clustering effect and the extent to which community-level factors explain the unexplained variance of the null model were assessed. A model with the lowest deviance was selected as the best-fitted model. Finally, variables with a p value <0.05 and an adjusted odds ratio (AOR) with a 95% confidence interval (CI) were reported as statistically significant variables associated with MDD. A variance inflation factor (VIF) falling within acceptable bounds of 1–10 was used to test for multicollinearity between covariates; a mean VIF of 1.63 indicated the lack of significant collinearity among independent variables.

Random effects

To estimate random effects or measures of variation of the outcome variable, the median odds ratio (MOR), ICC, and PCV were used. The ICC and PCV were used to measure the variation between clusters. Taking clusters as a random variable, the ICC reveals that the variation of MDD between clusters is computed as follows: ICC = VC/(VC + 3.29) ×100%, where VC is the cluster-level variance. The MOR is the median value of the odds ratio between the area of the highest risk and the area of the lowest risk for MDD when two clusters are randomly selected, using clusters as a random variable: MOR = e^0.95√VC. In addition, the PCV demonstrates the variation in MDD explained by factors and is computed as: PCV = (Vnull-VC)/Vnull × 100%, where Vnull is the variance of the null model and VC is the cluster-level variance.²⁹ The fixed effects calculated the relationship between the likelihood of minimum dietary variety and independent variables at the individual and community levels.

Results

Individual- and community-level characteristics of women of childbearing age

A total of 62,015 women were included in the current study. The mean age of study subjects was 29.36 ± 0.04 years, and 37.20% of them fall in the age range of 15–24 years. Only 10.75% of women had completed higher education, and 61.89% of them were married. More than three-fourths (81.57%) of women had media exposure, and 64.32% of them had jobs. More than two-thirds (68.34%) of women perceived that distance to health facilities was not a big problem, and 43.33% of them had poor socioeconomic status. More than half (55.50%) of women had normal nutritional status, and 59.89% of them had five or fewer household members. More than half (55.23%) of women were from rural areas, and 62.74% of them were from male-headed households. More than half (59.91%), 52.72%, and 50.71% of women were from communities with high media exposure level, high literacy level, and low poverty level, respectively (Table 2).

Table 2.

Individual- and Community-Level Characteristics of Women of Childbearing Age, Pooled Data from Three Sub-Saharan African and South Asian Countries: DHS 2022

Variables	Category	Frequency (n)	Percentage (%)
Respondent’s age	15–24 years	23,070	37.20
	25–34 years	19,044	30.71
	35–49 years	19,901	32.09
Educational status	No formal education	11,198	18.06
	Primary	18,803	30.32
	Secondary	25,348	40.87
	Higher	6,666	10.75
Current marital status	Unmarried	23,634	38.11
Current marital status	Married	38,381	61.89
Working status	Not working	22,128	35.68
Working status	Working	39,887	64.32
Media exposure	No	11,427	18.43
Media exposure	Yes	50,588	81.57
Household wealth index	Poor	26,873	43.33
	Middle	12,318	19.86
	Rich	22,824	36.80
Distance to health facility	Big problem	14,803	31.66
Distance to health facility	Not a big problem	31,957	68.34
Nutritional status	Normal	17,564	55.50
	Underweight	3,626	11.46
	Overweight	10,457	33.04
Household size	≤5	37,142	59.89
Household size	>5	24,873	40.11
Sex of the household head	Male	38,911	62.74
Sex of the household head	Female	23,104	37.26
Residence	Urban	27,767	44.77
Residence	Rural	34,248	55.23
Community-level media exposure	Low	24,864	40.09
Community-level media exposure	High	37,151	59.91
Community poverty level	Low	31,445	50.71
Community poverty level	High	30,570	49.29
Community literacy level	Low	29,318	47.28
Community literacy level	High	32,697	52.72

DHS, demographic and health survey.

MDD among women of childbearing age

The proportion of women of childbearing age who met the MDD in three SSA and SA countries was 35.44% (95% CI: [35.06%, 35.82%]). MDD among women of childbearing age varied by country, ranging from 29.80% in Kenya to 44.33% in Nepal (Fig. 1). Out of the total women of childbearing age included in the study, only 13.16%, 14.21%, and 14.98% of them consumed other vitamin A-rich fruits and vegetables, nuts and seeds, and eggs, respectively (Fig. 2).

FIG. 1.

Variation of minimum dietary diversity by country among women of childbearing age in three sub-Saharan African and South Asian countries: DHS 2022 (n = 62,015). DHS, demographic and health survey.

FIG. 2.

Proportion of women of childbearing age who consumed the 10 defined food groups in three sub-Saharan African and South Asian countries: DHS 2022 (n = 62,015). DHS, demographic and health survey.

Measures of variation and model fitness

To determine whether the data supported the decision to assess randomness at the community level, a null model was used. According to findings from the null model, there were significant differences in MDD between communities, with a variance of 0.705 and a p value of <0.001. The variance within clusters contributed 82.34% of the variation in MDD, whereas the variance across clusters was responsible for 17.66% of the variation. In the null model, the odds of MDD differed between higher- and lower-risk clusters by a factor of 2.23 times. The ICC value for Model I indicated that 11.17% of the variation in MDD accounts for the disparities between communities. Then, with the null model, community-level variables were used to generate Model II. According to the ICC value from Model II, cluster variations were the basis for 14.22% of the differences in MDD. In the final model (Model III), the likelihood of MDD varied by 1.82 times across low and high MDD (Table 3).

Table 3.

Model Comparison and Random Effect Analysis for Minimum Dietary Diversity Among Women of Childbearing Age in Three Sub-Saharan African and South Asian Countries: DHS 2022

Parameter	Null model	Model I	Model II	Model III
Variance	0.7054067	0.4138377	0.5455975	0.3953581
ICC	17.66%	11.17%	14.22%	10.73%
MOR	2.23	1.84	2.01	1.82
PCV	Reference	41.33%	22.65%	43.95%
Model fitness
LLR	−38669.097	−18704.489	−38336.452	−18680.018
Deviance	77,338.194	37,408.978	76,672.904	37,360.036

DHS, demographic and health survey; ICC, intracluster correlation coefficient; LLR, log-likelihood ratio; MOR, median odds ratio; PCV, proportional change in variance.

Individual- and community-level factors associated with MDD

In the final fitted model (Model III) of multivariable multilevel logistic regression, women’s age, educational status, marital status, working status, media exposure, wealth index, distance to a health facility, nutritional status, sex of the household head, community poverty level, and community literacy level were factors significantly associated with MDD among women of childbearing age. Accordingly, women aged 15–24 years were 10% less likely to achieve MDD than those aged 25–34 years (AOR = 0.90; 95% CI: [0.84, 0.97]). Women who completed primary, secondary, and higher education were 1.12, 1.70, and 2.14 times more likely to achieve MDD compared with women who had no formal education, respectively (AOR = 1.12; 95% CI: [1.02, 1.22]), (AOR = 1.70; 95% CI: [1.55, 1.86]), and (AOR = 2.14; 95% CI: [1.90, 2.40]). The odds of achieving MDD were 1.15 times higher among married women compared with their counterparts (AOR = 1.15; 95% CI: [1.07, 1.23]). Working women were 1.39 times more likely to achieve MDD compared with nonworking women (AOR = 1.39; 95% CI: [1.30, 1.47]).

Likewise, the odds of achieving MDD were 1.64 times higher among women who had media exposure compared with those who were not exposed to media (AOR = 1.64; 95% CI: [1.51, 1.78]). Women with middle and rich household wealth status were 1.44 and 2.07 times more likely to achieve MDD than women with a poor wealth index, respectively (AOR = 1.44; 95% CI: [1.33, 1.55]) and (AOR = 2.07; 95% CI: [1.90, 2.24]). Women who perceived that distance to health facilities was not a big problem were 1.20 times more likely to achieve MDD compared with their counterparts (AOR = 1.20; 95% CI: [1.13, 1.28]). Women with normal nutritional status were 16% less likely to achieve MDD than those who are underweight (AOR = 0.84; 95% CI: [0.77, 0.92]). Women from male-headed households were 17% less likely to achieve MDD compared with their counterparts (AOR = 0.83; 95% CI: [0.78, 0.88]). The odds of achieving MDD were 1.24 times higher among women from communities with low poverty levels compared with those from communities with high poverty levels (AOR = 1.24; 95% CI: [1.12, 1.37]). Finally, women from communities with low literacy levels were 24% less likely to achieve MDD than those from communities with high literacy levels (AOR = 0.76; 95% CI: [0.68, 0.85]) (Table 4).

Table 4.

Multivariable Multilevel Logistic Regression Analysis of Individual- and Community-Level Factors Associated with Minimum Dietary Diversity Among Women in Three Sub-Saharan African and South Asian Countries: DHS 2022 (n = 62,015)

Variables	Category	Model IAOR (95% CI)	Model IIAOR (95% CI)	Model IIIAOR (95% CI)
Respondent’s age	15–24 years	1		1
	25–34 years	0.90 (0.84, 0.97)^*		0.90 (0.84, 0.97)^*
	35–49 years	0.95 (0.88, 1.02)		0.96 (0.89, 1.04)
Educational status	No education	1		1
	Primary	1.07 (0.98, 1.17)		1.12 (1.02, 1.22)^*
	Secondary	1.63 (1.49, 1.79)^*		1.70 (1.55, 1.86)^*
	Higher	2.03 (1.81, 2.28)^*		2.14 (1.90, 2.40)^*
Marital status	Unmarried	1		1
Marital status	Married	1.15 (1.08, 1.23)^*		1.15 (1.07, 1.23)^*
Working status	Not working	1		1
Working status	Working	1.40 (1.31, 1.48)^*		1.39 (1.30, 1.47)^*
Media exposure	No	1		1
Media exposure	Yes	1.60 (1.48, 1.74)^*		1.64 (1.51, 1.78)^*
Wealth index	Poor	1		1
	Middle	1.49 (1.38, 1.60)^*		1.44 (1.33, 1.55)^*
	Rich	2.21 (2.06, 2.38)^*		2.07 (1.90, 2.24)^*
Distance to health facility	Big problem	1		1
Distance to health facility	Not a big problem	1.20 (1.13, 1.28)^*		1.20 (1.13, 1.28)^*
Nutritional status	Normal	1		1
	Underweight	0.85 (0.78, 0.93)^*		0.84 (0.77, 0.92)^*
	Overweight	1.04 (0.98, 1.10)		1.04 (0.98, 1.10)
Household size	≤5	1.01 (0.96, 1.07)		1.01 (0.95, 1.07)
Household size	>5	1		1
Sex of the household head	Male	1		1
Sex of the household head	Female	0.83 (0.78, 0.88)^*		0.83 (0.78, 0.88)^*
Residence	Urban		1.59 (1.52, 1.67)^*	1.07 (0.99, 1.14)
Residence	Rural		1	1
Community media exposure	Low		1	1
Community media exposure	High		1.20 (1.07, 1.35)^*	0.98 (0.87, 1.09)
Community poverty level	Low		1.66 (1.52, 1.83)^*	1.24 (1.12, 1.37)^*
Community poverty level	High		1	1
Community literacy level	Low		1	1
Community literacy level	High		0.93 (0.83, 1.04)	0.76 (0.68, 0.85)^*

Statistically significant at p value <0.05.

AOR, adjusted odds ratio; CI, confidence interval; DHS, demographic and health survey.

Discussion

The quality of diet and lifestyle choices among mothers significantly influences their nutritional status and other health consequences for themselves and their children. The present study was conducted to determine the prevalence of MDD and identify its associated factors among women of childbearing age in three SSA and SA countries using the most recent DHS datasets. Accordingly, the prevalence of MDD among women of childbearing age was 35.44% (95% CI: [35.06%, 35.82%]). This finding was lower than studies conducted in Cumilla district, Bangladesh (56.5%),²¹ India (46.2%),²² Burla (47%),²³ Latin America (57.7%),²⁴ and at St. Martin’s Island, Bangladesh (40.3%).²⁰ In contrast, this finding was higher than a study conducted in Tanzania (28%).²⁵ The possible reason for this discrepancy might be due to sociocultural, topographical, and agroeconomical differences between countries. The difference might also be due to differences in sample size, study settings, and study subjects. The present study uses nationally representative data from three SSA and SA countries with a large sample size, whereas some of the previous studies were conducted in a single area of countries with a small sample size. Some of the previous studies were also conducted among pregnant women only, whereas the current study incorporated women of reproductive age.

This study also identified factors significantly associated with MDD. Hence, women aged 15–24 years were less likely to achieve MDD than those aged 25–34 years. This finding was consistent with a study conducted in Burla.²³ This might be due to the fact that older women are more conscious of food choices accessible locally than younger ones. When women get older, they often disregard the advice of elderly people or their neighbors when they are pregnant or nursing. Furthermore, as women age, they gain greater autonomy in making decisions about their homes, including what they eat.³⁰ The odds of MDD increased with an increasing level of education. Similarly, women from communities with low literacy levels were less likely to achieve MDD. This finding was in agreement with studies conducted in Bangladesh.^20,31 This might be due to the fact that highly educated women are more likely to become financially independent by earning their own income. Financial independence does, in fact, have a proven beneficial effect on women’s nutrition.³² This is because women have more negotiating power when it comes to buying food because they have greater financial autonomy.³³ The odds of MDD were higher among married women compared with their counterparts. This finding was in line with studies conducted in Ghana.^34,35 The assistance they received from their husbands may help to explain this. Individual stress and anxiety would be reduced for women who received help from their husbands, allowing them to share the burden of food and household assets, income shortages, and other health care-related events.³⁶ This would then lead to the intake of an adequate variety of foods.

Likewise, working women were more likely to achieve MDD compared with nonworking women. This finding was consistent with studies conducted in Kenya³⁷ and India.²² This would make sense given that those with jobs have steady incomes, which raises the likelihood that they will have access to food. The odds of achieving MDD were higher among women who had media exposure. One possible explanation for this could be the ongoing media campaign, which uses television and radio to highlight and publicize dietary practices. This could serve as a general reflection of the ability of the media to enhance appropriate dietary practices among women. The odds of MDD increased with an increasing level of wealth status. Similarly, women from communities with low poverty levels had higher odds of achieving MDD compared with their counterparts. This finding was in line with studies conducted in Kenya,³⁷ India,²² and Bangladesh.²¹ The most likely explanation is that women in the richest quintile are probably in possession of other resources as well as disposable income. This could improve the women’s capacity to make household purchases in these homes. It has been established that the availability of nutritional diversity in homes depends significantly on one’s capacity to purchase food.³⁸ Wealthy households could afford a wider range of meals for their families, which would positively contribute to a diversified food intake.

In addition, women who perceived that distance to health facilities was not a big problem were more likely to achieve MDD. This finding was in agreement with a study conducted in Tanzania,²⁵ in which, compared with women who lived far away, those who lived close to their health facility had a higher chance of meeting enough dietary diversity. This might be due to the fact that women who live nearby frequently arrive at the clinic early and without much trouble, which might cause fatigue and poor focus during the education session, especially for those who travel a long distance. Women with normal nutritional status were less likely to achieve MDD than those who were underweight. This finding was consistent with studies conducted in India³⁹ and Burla.²³ This might be due to the fact that women who are underweight might consume a variety of foods to improve their nutritional status. Underweight women might also be obliged to take diversified diets to achieve MDD compared with women with normal nutritional status. Finally, women from male-headed households were less likely to achieve MDD compared with their counterparts. This finding was in contrast with a study conducted in Tanzania,⁴⁰ in which women in female-headed households have low dietary diversity compared with those in male-headed households. The higher odds of MDD among women in female-headed households in the present study could be due to the autonomy of women’s decision-making in altering their own and their family members’ nutritional status.⁴¹ It is documented that, compared with women who do not have a say in household purchases, those who do have a say are more likely to attain higher dietary diversity.⁴² This implies that women have the autonomy to purchase nutrient-dense foods, indicating that enhancing women’s decision-making autonomy may benefit women’s dietary intake.

Strengths and limitations of the study

The main strength of this study is using a nationally representative, large sample size across three countries in SSA and SA countries to determine the prevalence of MDD and identify its determinant factors among women of childbearing age. The use of advanced statistical models that consider individual- and community-level factors is another strength of the current study. The study also uses the most recent DHS data from the three SSA and SA countries. This study also has some limitations. First, the findings may not be generalizable to all SSA and SA countries, as only three countries are included for the sake of using the most recent data. Second, the causal relationship between the outcome variable and independent variables could not be established due to the cross-sectional nature of the study design. Finally, there might be an introduction of recall and social desirability biases, as the DHS survey depends on study subjects’ self-reporting.

Conclusion

The prevalence of MDD among women of childbearing age in three SSA and SA countries was relatively low. Factors such as women’s age, educational status, current marital status, occupation, media exposure, wealth index, distance to a health facility, nutritional status, sex of the household head, community poverty level, and community literacy level were significantly associated with MDD. Therefore, promoting women’s education status, improving household economic status, prioritizing women’s decisions, disseminating nutrition information through the media, and giving prior attention to younger and nonworking women who reside far from health facilities are strongly recommended for the attainment of an adequate MDD.

Footnotes

Acknowledgments

The author is grateful to the DHS program for letting me use the relevant DHS data in this study.

Authors’ Contributions

E.G.M: conceptualization, data curation, formal analysis, investigation, methodology and software, supervision, validation, visualization, writing the original draft, reviewing, and editing.

Ethical Approval and Consent to Participate

Permission was granted to download and use the data from before conducting the study. Ethical clearance was obtained from the Institution Review Board of the DHS Program, ICF International. The Institution Review Board approved procedures for DHS public-use datasets. Identifiers for respondents, households, or sample communities were not allowed in any way, and the names of individuals or household addresses were not included in the data files. The number for each EA in the data file does not have labels to show their names or locations. There were no patients or members of the public involved since this study used a publicly available dataset.

Availability of Data and Materials

The data from the three SSA and SA countries are publicly available online at .

Author Disclosure Statement

The author declared that there was no competing interest.

Funding Information

No funding was received for this article.

Abbreviations Used

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Minimum Dietary Diversity and Its Determinants Among Women of Childbearing Age in Three Sub-Saharan African and South Asian Countries: Evidence from the Most Recent Nationally Representative Surveys (2022)