Abstract
Background:
Poor dietary diversity and undernutrition is a major public health concern for pregnant mothers. Optimal dietary diversity is essential during pregnancy since nutritional deficiencies may have a significant impact on both the mother’s and the fetus’s health. Pregnant women in Ambo district had inadequate dietary diversity and were undernourished, but their status during the first trimester of pregnancy was not known. As a result, the objective of this research was to assess the dietary diversity, nutritional status, and associated factors among pregnant women in the Ambo district.
Methods:
A community-based cross-sectional study design and a multi-stage sampling technique were used among 750 pregnant women. Data was gathered using a semi-structured questionnaire. The Food and Nutrition Technical Assistance questionnaire was modified to collect data on dietary diversity. The nutritional status of pregnant women was assessed by measuring the mid-upper arm circumference. First, descriptive statistics like mean, Standard deviation, frequency and percentage were used, then bivariable and, finally, multivariable logistic regression analysis was used to assess the association of the predictors with the outcome variable.
Result:
The study revealed that 73.6% and 23.9% of pregnant women had low dietary diversity and were undernourished, respectively. Being in food secured household (AOR = 4.44, 95% CI: 2.14-9.15), having good knowledge (AOR = 3.32, 95% CI: 2.10-5.23) and favorable attitude toward nutrition and health (AOR = 1.71, 95% CI: 1.10-2.66) were significantly associated with dietary diversity, whereas household size (having 1-3 household members AOR = 6.59, 95% CI: 2.53-17.21, having 4-5 household members AOR = 5.62, 95% CI: 3.15-9.99), being in food secured household (AOR = 5.64, 95% CI: 2.79-11.38), having high dietary diversity (AOR = 8.49, 95% CI: 2.47-29.23), and having optimal practice on nutrition and health (AOR = 6.85, 95% CI: 3.23-14.55) were significantly associated with undernutrition (P < .05).
Conclusions:
The current study revealed that pregnant women in the study area had inadequate dietary diversity practices and a high prevalence of undernutrition. Knowledge and attitude, and households’ food security status were the predictors of dietary diversity, while household size, household food security status, dietary diversity and nutrition and health practice were predictors of undernutrition. Hence, behavior change communication needs to be designed to improve the dietary diversity and nutritional status of pregnant women.
Keywords: Dietary diversity, nutritional status, pregnant women, Ambo, Ethiopia
Introduction
Dietary diversity is defined as the ingestion of a wide variety of foods or food groups over a set period of time. It is regarded as a critical component in defining a person’s or a family’s diet access, utilization, and quality. 1 In pregnant women, dietary diversity can be used as a proxy indicator for nutritional adequacy. 2 Dietary diversification is strongly recommended for pregnant women due to their higher nutrient requirements. Pregnant women, as a result, require a varied diet to suit their nutritional needs and thereby improve their nutritional status and which will benefit both the mother and child outcome. 3
The diets of pregnant low and middle-income countries are predominantly cereal-based, with minimal animal products, vegetables, or fruits consumed. 4 According to a nationwide nutritional end-line survey conducted in Ethiopia in 2015, only 20.3% of adult women consumed 5 or more food groups out of 10, (which is extremely low). 5 Similarly, a study conducted in Ethiopia’s Amhara region indicated that a substantial majority of women (98.3%) relied on monotonous food groups. 6
Undernutrition is a major public health concern for women of reproductive age, particularly pregnant women, because it has a negative impact on pregnancy outcomes. 7 Despite this, maternal malnutrition is common worldwide, especially in Asia and Sub-Saharan Africa. 8 High rates of undernutrition among pregnant women can be found in China (21%), Sri Lanka (15%), and Nigeria (10%-40%).9-11
Ethiopia is one of Sub-Saharan Africa’s countries with the highest rates of maternal undernutrition. 12 Undernutrition among pregnant women in Ethiopia ranges from 19.5% to 41.2%. Dessie, Northern Ethiopia (19.5%), Silte Zone, Southern Ethiopia (21.8%), Ethiopia’s Central Rift Valley (31.7%), and Shashemene, Southern Ethiopia (41.2%) have all recorded it.13-16
A pregnant woman with poor dietary diversity who becomes malnourished has a variety of fetal problems, like increased risk of intrauterine growth restriction, low birth weight, preterm birth, congenital impairment, prenatal mortality, and newborn mortality and morbidity, and maternal problems like maternal anemia, increased infection, and pre-eclampsia. 17 Undernutrition is a major public health concern for women of reproductive age, particularly pregnant women, because it has a negative impact on pregnancy outcomes. 7
Household food security, maternal care practices, a healthy environment, and access to health care facilities are underlying factors of mother nutritional status, according to a United Nation Children Fund (UNICEF) report from 2016. 18 Furthermore, socio-demographic factors such as age, wealth index, residence, occupation, farmland size, and illiteracy or low education, as well as maternal and health-care-related factors such age at marriage, ANC visits, counseling during antenatal care, level of knowledge, awareness, attitude, and practices regarding nutritious food intake, food taboos, women’s empowerment, and craving, all have an impact on the dietary diversity and nutritional status of pregnant women.15,19-22
The dietary practices of Ethiopian pregnant women were similar to their practices before pregnancy, and unfortunately, they may not realize that they are pregnant during the first trimester of pregnancy.23,24 Therefore, this study differs from the other studies in that it was done in the first trimester of pregnancy, which most studies have not done at this gestational age.
Socio-demographic and economic factors and other maternal-related factors were assessed by previous researchers, whereas maternal factors, specifically level of knowledge, attitude, and practices regarding optimal nutrition and health, and women’s decision-making power and support from the family and community affecting the dietary diversity and nutritional status of pregnant women, were not assessed in the Ethiopian context.
By assessing the predisposing factors like level of knowledge, attitude, and practices regarding optimal nutrition and health as well as the reinforcing factors (women’s decision-making power and support from family and community since giving birth), this study contributes to the body of literature on predictors of dietary diversity and nutritional status of pregnant women in the first trimester of pregnancy. In addition, risk factors for dietary diversity and undernutrition might not be the same across different regions due to differences in socioeconomic characteristics, culture, ethnicity, and geographical location.
To design appropriate and effective interventions for the next stage of pregnancy, it is timely to identify the factors that influence the dietary diversity and nutritional status of pregnant women during the early pregnancy period. In a similar vein, health service providers might use the study’s findings to concentrate on important modifiable risk factors.
The dietary practices of Ethiopian pregnant women were similar to their practices before pregnancy, and unfortunately, they may not realize that they are pregnant during the first trimester of pregnancy.23,24 Therefore, this study differs from the other studies in that it was done in the first trimester of pregnancy, which most studies have not done at this gestational age. As a result, the aim of this research was to determine the dietary diversity, nutritional status, and associated factors among pregnant women in the first trimester of pregnancy in the Ambo district, Western Ethiopia.
Methods and Materials
Study period and setting
The study was conducted in the Ambo district from April 1 to June 1, 2018. Ambo district is located in West Shoa Zone, Oromia Regional State, and west-central Ethiopia. Ambo is one of 22 districts in the west Shoa Zone, located at 8°59′N and 37°51′E and divided into 39 kebeles (Ethiopia’s smallest administrative units), 6 of which are urban and 33 of which are rural (Figure 1). Ambo Town is the capital town of both the district and the zone, which is located 114 km from Addis Ababa, the capital city of Ethiopia. According to the Central Statistical Agency’s (CSA) 2007 national census report, the district has a total population of 152 143. Of this number, 5668 are men and the remaining 76 475 are women. 25 Based on 2017 district health office data, it has 37 454 and 6976 reproductive age groups and pregnant women, respectively. 26 In the district, there are 2 public hospitals, 8 health centers, and 6 health posts. Livestock (like cattle, sheep, goats, and poultry) and cereals and pulses (like maize, wheat, teff, barley, and sorghum, beans, lentils, and peas) and vegetables and fruits (like cabbage, collard greens, tomatoes, potatoes, mangoes, and avocados) are the dominant agricultural livelihoods in the districts. 27
Figure 1.
Map of study area, Ambo district, with respective kebeles (Source: West Shoa Zone, Health Office, 2022).
Study design
A community based cross-sectional study design was used.
Source population and study population
The source population consisted of all pregnant women in the district, while the study population consisted of randomly selected pregnant women from the selected kebeles.
Inclusion criteria
This study included pregnant women between the ages of 18 and 49 who had resided in the study kebeles for at least 6 months, and the women must be in their first trimester of pregnancy, as determined by a laboratory test.
Exclusion criteria
Severely sick pregnant women and/or those unable to respond to the questions were not included.
Because the nutritional needs and dietary practices of pregnant women with chronic diseases like hypertension, diabetes mellitus, tuberculosis, and Human Immune Virus/Acquired Immune Disease and Syndrome (HIV/AIDS) were different from those of their counterparts; these pregnant women were excluded from the study.
Sample size determination and sampling technique
Using a 5% margin of error, a 95% confidence level, a design effect of 2, and a 10% non-response rate, the sample size was estimated using a single population proportion under the premise that 36.1% 28 of pregnant mothers had poor diet diversity during pregnancy and this gave us a sample size of 770 pregnant women.
The participants in the study were chosen using a multi-stage sampling technique. The Ambo district’s whole Kebele was divided into rural and urban areas. Twelve Kebeles (2 urban and 10 rural) were chosen via simple random sampling (SRS) of the lottery technique out of the 39 already existing Kebeles (ie, 6 urban and 33 rural). Eligible households were chosen via simple random sampling from among selected kebeles using a computer-generated random number that was proportional to the size allocation of each kebele. Each kebele’s identified eligible pregnant women were registered to create a sample frame, and then the necessary numbers of pregnant women were chosen using a computer-generated simple random sampling procedure. An inquiry regarding the woman’s most recent period and a pregnancy test confirmation were used to determine whether she was pregnant.
Data collection tools and procedures
Data were gathered using a semi-structured English questionnaire. Language experts translated the questionnaire into Afan Oromo and Amharic before returning it to English in order to maintain consistency. In order to identify any ambiguity, completeness, consistency, and acceptability of the questionnaire, it was pretested on 5% of the total sample size.
To gather data, 8 diploma nurses were recruited. Pregnant women who were eligible were found by asking about the first day of their last menstrual cycle and utilizing a pregnancy test to confirm their pregnancy (a lab test). Three female laboratory technicians were also recruited to do pregnancy tests at each pregnant woman’s home. The data collectors received instruction on the purpose and relevance of the study, information confidentiality, respondent rights, informed consent, and interviewing techniques. At the pregnant women’s residences, data collectors conducted face-to-face interviews with them to deliver the questionnaire. Other family members were not allowed free access to the location of the interviews in order to protect the women’s privacy. Four supervisors who all had BSc degrees in nursing and the investigator checked the completed questionnaires every day for consistency and completeness.
Household wealth index and women decision-making power adapted from Ethiopian Demographic and Health Survey (EDHS). 29 The latent factors describing the wealth data were generated using principal components analysis (PCA) and then grouped into wealth tertiles (ie, 3 categories: first, second, and third). Eight questions were used to assess the decision-making power of women. Code one was given to each question when a decision was taken by the woman independently or jointly with her husband; otherwise, code zero was given to each question. The power of a woman to make decisions was categorized using the mean. 30
Knowledge, attitude, and practice on nutrition and health and the dietary diversity score questionnaire were adapted from the Essential Nutrition Action (ENA) message’s recommendation, the formative research conducted by the Manoff group to promote maternal nutrition in developing countries, and the Food and Nutrition Technical Assistance III Project (FANTA), respectively.31-33
Principal component analysis (PCA) was used to measure the knowledge, attitude, and practice of pregnant women about nutrition and health using 26, 11, and 20 questions, respectively. Based on how many of the knowledge assessment questions were successfully answered, each participant was given a knowledge score. Each response that was right was given a 1, and each that was wrong was given a 0. The scores were added together and divided into 3 categories called tertiles. The highest tertile, or third tertile, was then classified as having good knowledge. 34 A similar procedure was conducted for attitude. The highest tertile (or third tertile) was classified as having a favorable, if not unfavorable, attitude. 35 In the same way, the respondents were asked to choose between Yes or No for each of the nutrition and health practices, indicating whether or not they engaged in that particular practice. The factor scores were added up and divided into 3 groups called tertiles. Then the highest tertile (ie, the third tertile) was labeled as optimal nutrition and health practice, if not suboptimal nutrition and health practice. 34
To measure household food insecurity, the household food insecurity access scale (HHFIAS), which was validated in developing countries, 36 is utilized. Nine previously validated questions, which summed together to give a maximum score of 27, were used to assess the state of food security. A household was classified as being food secure, mildly, moderately, or severely food insecure if it falls 0 to 1, 2 to 10, 11 to 17, and >17 food insecurity indicators.
Dietary diversity score questioner has 10 different food groups based on their nutrients: (1) grains, white root, tubers, and plantains, (2) pulses (beans, peas, and lentils), (3) nuts and seeds, (4) dairy, (5) meat and fish (poultry and fish), (6) eggs, (7) dark green leafy vegetables, (8) vitamin A-rich fruits and vegetables, (9) others vegetables, and (10) other fruits. Each food group that was consumed over the course of the previous 24 hours received one point in the evaluation, which used 24-hour open dietary recall procedures. The interviewer questioned the participants about all the food and drinks they had consumed the previous day, night, and any snacks, as well as any food groups they could have forgotten. On a predetermined list, each meal or drink that the respondent described was circled or underlined. The investigator either categorized the items that weren’t on the predetermined list into an already-existing predefined food category or noted them separately on the questionnaire and afterward coded and arranged them into one of the predefined food groups. 1 A non-consecutive longitudinal dietary assessment was conducted for 4 days 37 and a maximum score of 40 was available for each respondent. To determine the women’s dietary diversity score, the number of food groups they consumed over the course of 4 separate days was tallied (dietary diversity score (DDS)). The dietary diversity score was changed into a tertile, with the highest tertile used to define a “high” dietary diversity score and the 2 lower tertiles taken together being referred to as a “low” dietary diversity score. 38
The nutritional status of the pregnant women was assessed by measuring the mid-upper arm circumference (MUAC). The mid-upper arm circumference of a pregnant woman was measured using a non-stretchable MUAC tape. The upper left arm’s MUAC was taken for the right handed pregnant women and the upper right arms for those left handed with no clothing on it. The left arm was used as it shows malnutrition while the right arm, which is frequently used, will show lean muscle mass as a result of work. 39 During the procedure, the midpoint of the upper arm was located by flexing the women’s elbows to 90° with the palm facing upwards. Then the distance from the acromion to the olecranon processes was measured and the midpoint was marked with ink. Finally, the MUAC measuring tape was placed snugly around the arm at the midpoint mark while the arm was hanging freely, palm facing toward the thigh. Two measurements were taken and read to the nearest 0.1 cm on the same day for each study subject. Women with MUACs ⩾ 23 cm were considered normally nourished, whereas those with MUACs of less than 23 cm were considered undernourished.40,41
Data quality control
The questionnaire was pretested and modified using standard data collection tools. The instrument was found to have a Cronbach’s alpha value of knowledge and practice > .7 for the entire scale, making it appropriate for use in the research area. The lab personnel, supervisors, and data collectors were trained for 3 days. To make the questionnaire easier for the respondents to understand, it was also translated into 2 languages that are more widely spoken in the study area: Afan Oromo and Amharic. MUAC was measured following a standard procedure. While assessing DDS, multiple-pass 24 hours probing method was used to minimize the recall bias. The investigator and supervisors closely monitored the data collection procedure. Daily checks were made to ensure that all required information was included, and any missing or incorrect data was corrected. The standard approaches were used to verify key assumptions.
Data processing and analysis
Prior to data entry, the data were carefully reviewed for accuracy and consistency during data gathering. Once it had been put into the Epi Info software, version 7.2.2, it was exported to statistical package for social science (SPSS) for Windows, version 21, where it was cleaned up and analyzed.
For continuous variables, descriptive statistics like mean and standard deviation were first calculated; for categorical data, frequency and percentage.
To assess the association between the predictors and the outcome variables, bivariate and multivariable logistic regression analyses were performed. Model goodness of fit is assessed using the Hosmer-Lemeshow goodness-of-fit statistic. The Pearson Correlation Coefficient was used to determine the correlation between independent variables. The use of variance inflation factors (VIF) to test for multicollinearity was used and the result revealed that there was no correlation between the independent variables. The association between dietary variety, nutritional status during pregnancy, and each of the related variables was analyzed using bivariable binary logistic regression model. Their odds ratios (OR) with 95% confidence intervals (CI) and P-values were obtained. Factors that were significantly associated with dietary diversity and nutritional status at P-value < .25 in bivariate logistic regression analysis were entered to multivariable binary logistic regression. P-values < .05 were used to declare statistical significance.
Results
Socio demographic and economic characteristics
Seven hundred seventy eligible pregnant women were invited to participate in the study. A total of 750 pregnant women were interviewed, and 97.4% of them responded. Twenty pregnant women, for the reasons listed below: 4 were unable to respond to the question because of illness; 8, 5, and 3 had confirmed hypertension; diabetic mellitus; and tuberculosis, respectively, were excluded from the study. Six hundred eight (81.1%) pregnant women were from rural kebeles. The ages of the pregnant women ranged from 18 to 38, and the mean (±SD) age of the respondents was 27 (±4.4). Two hundred eighty six (38.1%) of pregnant mothers and 219 (29.5%) of their husbands had no formal education, according to data on educational attainment. The majority of pregnant women 607(80.9%) were housewives, while their husbands 457 (60.9%) were farmers. One hundred eighty (24%) pregnant women had a family size of more than 5 members, with a mean (±SD) family size of 4.5 (±1.6) individuals. Approximately one-third of respondents 219 (29.2%) and 202 (26.9%) were rich and poor, respectively. Two hundred seventy (36.0%) of pregnant women were from food-insecure households (Table 1).
Table 1.
Socio-demographic and economic characteristics of the pregnant women in Ambo district, Ethiopia, 2018.
| Variables | Category | Frequency | Percent |
|---|---|---|---|
| Religion of the respondents | Orthodox | 346 | 46.1 |
| Protestant | 319 | 42.5 | |
| Wakefeta | 40 | 5.3 | |
| Others* | 45 | 6.0 | |
| Ethnicity | Oromo | 672 | 89.6 |
| Amhara | 58 | 7.7 | |
| Others** | 20 | 2.7 | |
| Occupation of respondents | Employed | 40 | 5.3 |
| Housewife/daily laborers | 618 | 82.4 | |
| Merchant | 42 | 5.6 | |
| Farmers | 50 | 6.7 | |
| Husband occupation | Employed | 82 | 10.9 |
| Merchant | 82 | 10.9 | |
| farmer | 457 | 60.9 | |
| Daily laborer | 71 | 9.5 | |
| Private workers | 58 | 7.7 | |
| Respondent educational status | No formal education | 286 | 38.1 |
| 1-4 Grade | 179 | 23.9 | |
| 5-8 Grade | 184 | 24.5 | |
| 9-12 Grade | 74 | 9.9 | |
| Diploma and Higher | 27 | 3.6 | |
| Husband educational status | No education | 219 | 29.2 |
| 1-4 Grade | 140 | 18.7 | |
| 5-8 Grade | 189 | 25.2 | |
| 9-12 Grade | 146 | 19.5 | |
| Diploma and higher | 56 | 7.5 | |
| Head of your household | Husband | 703 | 93.7 |
| Myself | 41 | 5.5 | |
| Other*** | 6 | 0.8 | |
| Household wealth index | Poor | 219 | 29.2 |
| Medium | 329 | 43.9 | |
| Rich | 202 | 26.9 |
Catholic, **Tigre, Gurage, and Silte ***Grandmother, Grandfather.
Maternal related characteristics
One hundred thirty seven (18.3%) marry before they turn 18. The majority of pregnant women, 454 (60.5%) and 464 (61.9%), had 2 to 4 live births and pregnancies, respectively. During this pregnancy, the majority of pregnant women, 522 (69.6%) and 529 (70.5%), had self-decision making power and received health and nutrition information, respectively (Table 2).
Table 2.
Maternal related characteristics of the pregnant women in Ambo district, Ethiopia, 2018.
| Variables | Category | Frequency | Percent |
|---|---|---|---|
| Age at marriage | <18 y | 137 | 18.3 |
| 18-24 y | 578 | 77.1 | |
| 25-34 y | 35 | 4.7 | |
| Parity | <1 | 229 | 30.5 |
| 2-4 | 454 | 60.5 | |
| >5 | 67 | 8.9 | |
| Gravidity | 1 | 119 | 15.9 |
| 2-4 | 464 | 61.9 | |
| >5 | 167 | 22.3 | |
| Gap duration between pregnancy | <3 y | 625 | 83.3 |
| ⩾3 y | 125 | 16.7 | |
| Women decision making power | Yes | 522 | 69.6 |
| No | 228 | 30.4 | |
| Had health and nutrition information | Yes | 529 | 70.5 |
| No | 221 | 29.5 |
Knowledge, attitude and practices of pregnant women on nutrition and health
Two hundred fifty (33.3%) of the respondents were knowledgeable on nutrition and health. The mean (±SD) nutrition and health knowledge score was 4.96 ± 1.43. Regarding attitude toward nutrition and health, 235 (31.3%) of respondents expressed a favorable attitude toward nutrition and health. The mean (±SD) attitude toward nutrition and health was 3.79 ± 2.2. About three-fourths, or 545 (72.7%) of the study participants exhibited suboptimal nutrition and health practices. The mean (±SD) nutrition and health practice score was 9.84 ± 3.10 (Table 3).
Table 3.
Knowledge, attitude, and practices on nutrition and health of the pregnant women in Ambo district, Ethiopia, 2018.
| Variables | Category | Frequency | Percent |
|---|---|---|---|
| Overall nutrition and health knowledge | Poor | 500 | 66.7 |
| Good | 250 | 33.3 | |
| Overall attitude toward nutrition and health | Unfavorable | 515 | 68.7 |
| Favorable | 235 | 31.3 | |
| Overall practice on nutrition and health | Suboptimal | 545 | 72.7 |
| Optimal | 205 | 27.3 |
Dietary diversity of the women
Around one-fourths (26.4%) (95% CI: (23.5, 29.6)) of pregnant women had high dietary diversity. The mean (±SD) dietary diversity score of pregnant women was 4.57 ± 1.73. Nearly all of the pregnant women, 741 (98.8%) said that they had consumed cereal-based foods in the last 24 hours before the survey and pulses were the second most consumed food group, 609 (81.2%). Notably, eggs, meat, and fish were consumed at a low rate of 200 (26.7%) and 198 (26.4%), respectively (Table 4).
Table 4.
Food groups consumed by the pregnant women, in Ambo district, Ethiopia, 2018.
| Food groups | Frequency | Percent |
|---|---|---|
| Starchy foods | 741 | 98.8 |
| Pulses | 609 | 81.2 |
| Nuts and seeds | 218 | 29.1 |
| Dairy products | 424 | 56.5 |
| Meat and fish | 198 | 26.4 |
| Eggs | 200 | 26.7 |
| Dark green leafy vegetables | 327 | 43.6 |
| Vitamin A-rich fruits and vegetables | 306 | 40.8 |
| Other vegetables | 210 | 28.0 |
| Other fruits | 205 | 27.3 |
| Overall dietary diversity | ||
| High | 198 | 26.4 |
| Low | 552 | 73.6 |
Factors associated with dietary diversity
Bivariable logistic regression analysis showed that there was an association between dietary diversity of pregnant mothers and age, husband’s occupation, respondent educational status, husband’s educational status, household size, food security status, wealth status, parity, gravidity, having health and nutrition information, knowledge on nutrition and health, and attitude toward nutrition and health. Whereas, residence, marital status, religion, ethnicity, respondent occupation, gap duration between pregnancy, estimated time to reach health institution and estimated distance to reach health institution had no association with dietary diversity of mothers. However, household food security status, knowledge on nutrition and health and attitude toward nutrition and health were found significantly associated with dietary diversity of pregnant mothers in multivariable logistic regression analysis. Accordingly, pregnant mothers from food-secure households were 4.44 times more likely to consume a diversified diet compared to severely insecure households [(AOR = 4.44, 95% (2.15, 9.16)]. Another factor significantly associated with the dietary diversity of the respondents was their knowledge of nutrition and health. Knowledge on nutrition and health was another factor significantly associated with the dietary diversity of the respondent. Pregnant mothers who had good knowledge on nutrition and health were 3.32 times more likely to consume a diversified diet than their counter parts [(AOR = 3.32, 95% CI (2.10-5.23)]. Similarly attitude toward nutrition and health also significantly associated with the dietary diversity of the pregnant mothers. Pregnant mothers who had favorable attitude toward nutrition and health were 1.7 times more likely to consume a diversified diet compared to their counterparts [(AOR = 1.71, 95% (1.09, 2.66)] (Table 5).
Table 5.
Multivariable logistic regression analysis of factors associated with dietary diversity among pregnant women in Ambo district, Ethiopia, 2018 (n = 750).
| Variables | Category | WDDS | COR (95% CI) | AOR (95% CI) | |
|---|---|---|---|---|---|
| High N (%) | Low N (%) | ||||
| Food security status | Food secured | 163 (36.2) | 287 (63.8) | 4.62 (2.61-8.17) | 4.44 (2.15-9.16) * |
| Mild insecure | 11 (15.1) | 62 (84.9) | 1.44 (0.63-3.32) | 2.13 (0.81-5.61) | |
| Moderately insecure | 9 (10.0) | 81 (90.0) | 0.90 (0.38-2.16) | 1.29 (0.49-3.35) | |
| severely insecure | 15 (10.9) | 122 (89.1 | 1 | 1 | |
| Knowledge on nutrition and health | Poor | 77 (15.4) | 423 (84.6) | 1 | 1 |
| Good | 121 (48.4) | 129 (51.6) | 5.15 (3.64-7.29) | 3.32 (2.10-5.23) * | |
| Attitude toward nutrition and health | Unfavorable | 101 (19.6) | 414 (80.4) | 1 | 1 |
| Favorable | 97 (41.3) | 138 (58.7) | 2.88 (2.05-4.04) | 1.71 (1.09-2.66) * | |
Abbreviations: AOR, adjusted odds ratio; COR, crude odds ratio; WDDS, women’s dietary diversity score.
Significant at P-value < .05. Parameter estimates were adjusted for the tabulated variables.
Nutritional status of the respondents
Based on the mid upper arm circumference, 23.9% of the pregnant women were found to be undernourished (MUAC < 23.0 cm).
Factors associated with nutritional status of the pregnant women
Age, respondent educational status, husband’s educational status, household size, food security status, wealth index, parity, gravidity, having health and nutrition information, dietary diversity practice, knowledge about nutrition and health, attitude toward nutrition and health, and practice on nutrition and health were all found to be significantly associated with nutritional status in bivariable logistic regression analysis. However, in a multivariable logistic regression analysis, household size, household food security status, dietary diversity practice, and practice on nutrition and health were found to be significantly associated with nutritional status (P < .05). Accordingly, pregnant women having 1 to 3 household size had 6.6 times higher odds of having good nutritional status compared to pregnant women who had >5 household size [(AOR = 6.59, 95% CI: (2.53, 17.21)]. Similarly, those pregnant women having 4 to 5 household size had 5.6 times higher odds of having good nutritional status compared to pregnant women who had >5 household size [(AOR = 5.62, 95% CI: (3.15, 9.99)]. Household food security status is significantly associated with nutritional status. Pregnant mothers from food-secure households had 5.6 times higher odds of having good nutritional status than severely insecure households [(AOR = 5.64, 95% (2.79, 11.38)]. Those pregnant women who consumed diversified diet had 8.5 times more likely to have good nutritional status compared with those with their counter parts [(AOR = 8.49, 95% (2.47, 29.23)]. The study revealed that those pregnant women who had optimal nutrition and health practices had 6.9 times higher odds of having good nutritional status than their counterparts (AOR = 6.85, 95% CI (3.23, 14.55)] (Table 6).
Table 6.
Multivariable logistic regression analysis of factors associated with the nutritional status among pregnant women in Ambo district, Ethiopia, 2018 (n = 750).
| Variables | Category | Nutritional status | COR (95% CI) | AOR (95% CI) | |
|---|---|---|---|---|---|
| Normal N (%) | Undernourished N (%) | ||||
| Household size | 1-3 | 193 (90.6) | 20 (9.4) | 11.28 (6.53-19.47) | 6.59 (2.53-17.21) * |
| 4-5 | 295 (82.6) | 62 (17.4) | 5.56 (3.72-8.30) | 5.62 (3.15-9.99) * | |
| >5 | 83 (46.1) | 97 (53.9) | 1 | 1 | |
| Food security status | Food secure | 405 (90.0) | 45 (10.0) | 8.13 (5.15-12.81) | 5.64 (2.79-11.38) * |
| Mild insecure | 42 (57.5) | 31 (42.5) | 1.22 (0.69-2.17) | 1.67 (0.72-3.86) | |
| Moderately insecure | 52 (57.8) | 38 (42.2) | 1.24 (0.72-2.11) | 2.08 (1.01-4.27) | |
| severely insecure | 72 (52.6) | 65 (47.4) | 1 | 1 | |
| Dietary Diversity score | Low | 376 (68.1) | 176 (31.9) | 1 | 1 |
| High | 195 (98.5) | 3 (1.5) | 30.43 (9.59-96.49) | 8.49 (2.47-29.23)* | |
| Practice on nutrition and health | Suboptimal | 378 (69.4) | 167 (30.6) | 1 | 1 |
| Optimal | 193 (94.1) | 12 (5.9) | 7.11 (3.86-13.09) | 6.85 (3.23-14.55) * | |
Abbreviations: AOR, adjusted odds ratio; COR, crude odds ratio.
Significant at P-value < .05. Parameter estimates were adjusted for the tabulated variables.
Discussion
This study aimed to assess the magnitude of dietary diversity, nutritional status, and associated factors among pregnant women in the first trimester of pregnancy.
In our findings, the prevalence of high dietary diversity among pregnant women in the first trimester of pregnancy was 26.4% (95% CI: (23.5, 29.6). Our finding was comparable with the study done in Shashemene town (25.4%) 42 and the Southern Nations, Nationalities, and Peoples’ Region government (SNNPRG) region of Ethiopia (28%). 43 However, it is superior to those of a comparable study conducted in Kenya, 2 Ethiopia (20.3%) and (10.2%), respectively,5,6 and while it is lower than research in the United States (54%), 44 Ghana (40.3%), 45 Kenya (60.6%), 46 and Bahir Dar, North West Ethiopia (39.3%). 47 The possible disparity could be attributed to their study technique, gestational age, geographical location, socio-demographic, and seasonal differences. Differences in the measurement of dietary diversity also result in differences in the prevalence of dietary diversity; some studies used 10 food groups, while other studies used 14 food groups, and others differ in the cutoff points for categorization of dietary diversity as high or low.
The study indicated that household food security status was significantly associated with the dietary diversity of pregnant women. In comparison to severely food insecure households, mothers from food secure households were more likely to consume a diverse diet. This finding is consistent with findings from a study conducted in the llu Aba Bor Zone and Tigray, Ethiopia, which found that those who were food secure were more likely to have good dietary diversity than those who were food insecure.48,49 Similarly, this study was in agreement with the study in Nepal. 50 The possible explanation for this could be due to poor respondents’ economic level that likely resulted in their consuming fewer diversified diets.35,41
Another factor that was found to be associated with the dietary diversity of pregnant women was knowledge of nutrition and health. Pregnant women who had good nutrition and health knowledge were more likely to consume a diversified diet compared to their counterparts. This finding is in line with the finding from Malawi. 51 Similarly, the finding was also in line with the Ethiopian studies.47,48 The explanation for this could be, knowing the benefits of proper nutrition might inspire pregnant women to set goals to enhance their dietary diversity. 52
The study further showed that attitudes toward nutrition and health were significantly associated with dietary diversity practice. Pregnant women who had a favorable attitude toward nutrition and health were more likely to consume a diversified diet compared to their counterparts. This is because food choices and nutritional habits are influenced by attitudes. 19 This finding is supported by an Indian study that revealed 63.7% of mothers believed that certain foods and fruits should be avoided during pregnancy. Similarly, a study conducted in Addis Ababa, Ethiopia, showed that 31.3% of pregnant women exclude at least one food group from their meals due to traditional food taboos. 53
The current study revealed that the overall prevalence of undernutrition among the study participants was 23.9% (95% CI = 22.3, 25.5). According to World Health Organization (WHO) Prevalence cut-off values for public health significance, if the prevalence of undernutrition among adults, including pregnant women, is between 20% and 30%, then it is considered a high prevalence (serious situation), so based on the cut-off value, the study participants in our study area are in a serious situation. 54 This finding is comparable to that of research conducted in southern Ethiopia (21.8%) 14 and Sri Lanka. 9 However, this study finding is higher than the finding of the study conducted in southwest and north west Ethiopia, 17.4% and 14.4% respectively,48,55 and Kenya (19.3%). 46 The finding of this research is lower than those of studies conducted in Ethiopia’s Central Rift Valley (31.7%), 13 Shashemene, Southern Ethiopia (41.2%), 15 the Gumay district of Jimma Zone, South West Ethiopia (44.9%), 56 and India (61.5%). 57 The difference could be due to the fact that some studies included participants from lower social classes, such as India, study design, such as Silte zone, Southern Ethiopia uses facility based study design and stage of pregnancy that our study population includes first trimester of pregnancy. Furthermore, the inconsistencies could be explained by seasonal variations in consumption of food, and the use of different cutoff points for MUAC (mid-upper arm circumference) measurement in different studies.
The study indicated that household size was significantly associated with the nutritional status (undernutrition) of pregnant women. The odds of having good nutritional status were higher among pregnant women with a household size of less than 5. This is consistent with research from the Illu Aba Bor Zone in southwest Ethiopia and Western Regional Hospital, Nepal.48,58 This implies that as the number of household members’ increases, pregnant women will have lower consumption of diversified foods, which can affect their nutritional status.
The study indicated that household food security was significantly associated with the nutritional status (undernutrition) of pregnant women. Pregnant mothers from food-secure households had higher odds of having good nutritional status than severely insecure households. This finding is in line with research from the Illu Aba Bor Zone in south-west Ethiopia, 48 the Gambella region South West Ethiopia, 59 the Gumay district, Jimma Zone, South West Ethiopia, 56 in rural communities of Southern Ethiopia, 60 and in rural Nepal. 61 The reason for this could be that food insecurity has an impact on the diet quality and portion size of the food consumed by pregnant women 62 and this resulted in undernutrition.
Dietary diversity score was another factor shown to have a significant association with the nutritional status of pregnant women. Women with high dietary diversity scores were more likely to be well nourished compared to their counterparts. This finding is in congruent to the study done in central refit valley of Ethiopia 15 and Kenya. 46 The possible reason for this might be that dietary diversity can be utilized as a proxy indicator for nutritional adequacy so that it enhances immunity and thus improves their nutritional status. 63
Nutrition and health practiced was also significantly associated with the nutritional status of pregnant women. Those women having optimal nutrition and health practice were more likely to be well nourished compared to their counterparts. This could be explained by the fact that when women have optimal nutrition and health-related habits, their nutritional status is affected as a result of the dietary practice. This data is replicated by the study in northern 14 and Southwest Ethiopia. 64
Major strengths of this study included assessing the magnitude of dietary diversity, nutritional status, and associated factors in pregnant women in the first trimester of pregnancy, which most studies have not done at this gestational age, which is very important to provide effective intervention for the next stage of pregnancy; predisposing factors like level of knowledge, attitude, and practices regarding optimal nutrition and health; and reinforcing factors (women’s decision-making power and support from family and community) that affect both were also included. The study also tried to use standardized tools and procedures (like WHO ENA guidelines and the formative research conducted by the Manoff group to promote maternal nutrition in developing countries) in measuring Knowledge, attitude and Practice (KAP) of nutrition and health and validated tools in Ethiopia to assess household food insecurity, which could be mentioned as a strength of the study.
As a constraint, there may be recall bias on dietary consumption because respondents may forget what they ate the day before. To reduce recall bias, a multiple pass 24 hours probing method was adopted. Due to the cross-sectional nature of the study, it does not show the temporal or causal effect relationships between the independent and the outcome variables, and seasonal variations were not considered.
Conclusion
According to the current study, there was a high prevalence of a low dietary diversity and undernutrition among pregnant women in the study area who were in their first trimester. Having good knowledge and favorable attitude, and residing in food secured households were significantly associated with the dietary diversity score of the pregnant women while household size, household food security status, dietary diversity score and nutrition and health practices were predictors of nutritional status (undernutrition). As a result, nutrition and health behavior change and communication must be adjusted to suit the needs of pregnant women in terms of dietary diversity and nutritional status.
Research Data
This article is distributed under the terms of the Creative Commons Attribution 4.0 License (http://www.creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).
sj-sav-1-nmi-10.1177_11786388231190515 for Dietary Diversity, Nutritional Status, and Associated Factors Among Pregnant Women in Their First Trimester of Pregnancy in Ambo District, Western Ethiopia by Mitsiwat Abebe Gebremichael and Tefera Belachew Lema in Nutrition and Metabolic Insights
Footnotes
Funding: The author(s) received no financial support for the research, authorship, and/or publication of this article.
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Authors’ Contributions: MA, TB were involved in the design and operation of the study, approved the proposal with some revisions, edited and finalized the manuscript, coordinated the study, analyzed the data, drafted the first manuscript, involve in critical review of the manuscript, the corresponding author submitted the manuscript to the Journal. Both authors read and approved the final manuscript.
Data Availability (Where Applicable): SPSS Data have sent with the manuscript.
Ethical Approval and Consent: All methods in the study were performed in accordance with relevant institutional/ national/international guidelines. Institutional Review Board of Jimma University approved the study (ref no: RPGC/40724/2016). Permission to conduct the study in the respective kebeles was granted by Ambo District health offices (ref no: ADHO/134/2018). Prior to participation in the study, the nature of the study, the importance of their participation, and withdraw at any time was clearly disclosed to the study participants in order to gain their written informed consent, and all information obtained was kept anonymous. Soft copy data is password protected, while hard copy data is secured with a key and lock to guarantee confidentiality. Personally identifiable information will not be used in any form in the presentation of the findings. There are no ethical challenges encountered in conducting this study.
References
- 1. FAO. Minimum Dietary Diversity for Womens: A Guidelines to Measurement. FAO; 2016. [Google Scholar]
- 2. Kiboi W, Kimiywe J. Dietary diversity, nutrient intake and nutritional status among pregnant women in Laikipia County, Kenya. Int J Health Sci Res. 2016;6:378-379. [Google Scholar]
- 3. Pasricha S-R, Drakesmith H, Black J, Hipgrave D, Biggs B-A. Control of iron deficiency anemia in low- and middle-income countries. Blood. 2013;121:2607-2617. [DOI] [PubMed] [Google Scholar]
- 4. Lee SE, Talegawkar SA, Merialdi M, Caulfield LE. Dietary intakes of women during pregnancy in low- and middle-income countries. Public Health Nutr. 2013;16:1340-1353. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Ayana G, Hailu AA, Tessema M, Belay A. Ethiopian national nutrition program end-line survey. Ethiopian Public Health Institute Final Report; 2015: 1-53. [Google Scholar]
- 6. Abel A. Assessment of Dietary diversity among pregnant and lactating women and 6 to 23 months age children, in rural areas of western Gojjam, Amhara Region. The Ethiopian Public Health Institute; 2014. [Google Scholar]
- 7. Dalky HF, Qandil A, Alqawasmi AA. Factors associated with undernutrition among pregnant and lactating Syrian refugee women in Jordan. Global J Health Sci. 2018;10(4):1-58. [Google Scholar]
- 8. Biswas T, Townsend N, Magalhaes RJS, Islam MS, Hasan MM, Mamun A. Current progress and future directions in the double burden of malnutrition among women in South and Southeast Asian countries. Curr Dev Nutr. 2019;3:nzz026. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Adikari AMNT, Sivakanesan R, Wijesinghe DGNG, Liyanage C. Assessment of nutritional status of pregnant women in a rural area in Sri Lanka. Trop Agric Res. 2016;27:203. [Google Scholar]
- 10. Ugwa EA. Nutritional practices and taboos among pregnant women attending antenatal care at General Hospital in Kano, Northwest Nigeria. Ann Med Health Sci Res. 2016;6:109-114. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Liu FL, Zhang YM, Parés GV, et al. Nutrient intakes of pregnant women and their associated factors in eight cities of China: a cross-sectional study. Chin Med J. 2015;128:1778-1786. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Central Statistical Agency (CSA) [Ethiopia] and ICF. Ethiopia Demographic and Health Survey. CSA, ICF; 2016;6:1-7. [Google Scholar]
- 13. Belete YNB, Firehiwot M. Undernutrition and associated factors among adolescent pregnant women in Shashemene, west Arsi zone, Ethiopia: a community-based study. J Nutr Food Sci. 2016;6:1-7. [Google Scholar]
- 14. Diddana TZ. Factors associated with dietary practice and nutritional status of pregnant women in Dessie town, northeastern Ethiopia: a communitybased cross-sectional study. BMC Pregnancy Childbirth. 2019;19:1-10. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Mariyam AF, Dibaba B. Epidemiology of malnutrition among pregnant women and associated factors in central refit valley of Ethiopia, 2016. J Nutr Disord. 2018;08:1-8. [Google Scholar]
- 16. Muze M, Yesse M, Kedir S, Mustefa A. Prevalence and associated factors of undernutrition among pregnant women visiting ANC clinics in Silte zone, southern Ethiopia. BMC Pregnancy Childbirth. 2020;20:707-708. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. Suhag A, Berghella V. Intrauterine growth restriction (IUGR): etiology and diagnosis. Curr Obstet Gynecol Rep. 2013;2:102-111. [Google Scholar]
- 18. UNICEF. UNICEF conceptual framework of undernutrition. 2016. Accessed May 8, 2018. http://www.unicef.org/nutrition/training/2.5/4.html.
- 19. Chakona G, Shackleton C. Food taboos and cultural beliefs influence food choice and dietary preferences among pregnant women in the Eastern Cape. South Africa. Nutrients. 2019;11:2668. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20. FDRE, USAID. Federal Democratic Republic of Ethiopia: A Tool to Support Nutrition Advocacy in Ethiopia: Ethiopia PROFILES Estimates. Final Report, FDRE, USAID; 2012:15-25. [Google Scholar]
- 21. USAID, JSI. Understanding the Essential Nutrition Actions and Essential Hygiene Actions Framework. USAID, JSI; 2015:1-5. [Google Scholar]
- 22. USAID/ENGINE. Save the children. Maternal diet and nutrition practices and their determinants engine: A project supported by the Feed the Future and Global Health Initiatives A report on formative research findings and recommendations for social and behavior change communication programming in the Amhara, Oromia, SNNP and Tigray regions of Ethiopia. USAID/ENGINE; 2014:5-10. [Google Scholar]
- 23. Kedir H, Berhane Y, Worku A. Magnitude and determinants of malnutrition among pregnant women in eastern Ethiopia: evidence from rural, community-based setting. Matern Child Nutr. 2016;12:51-63. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24. Kumera G, Gedle D, Alebel A, Feyera F, Eshetie S. Undernutrition and its association with socio-demographic, anemia and intestinal parasitic infection among pregnant women attending antenatal care at the University of Gondar Hospital, Northwest Ethiopia. Matern Health Neonatol Perinatol. 2018;4:18. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. CSA. Population Size by Sex, Area and Density by Region, Zone and Wereda. CSA; 2022. [Google Scholar]
- 26. West Shoa Zone. West Shoa Zone, Health Office; 2017. [Google Scholar]
- 27. ABAD. Agricultural bureau of Ambo District. Communication Office; 2021. [Google Scholar]
- 28. Hawi G, Melese ST, Kalkidan HA. Maternal dietary and nutritional characteristics as predictor of newborn birth weight in Jimma town, Southwest Ethiopia, 2017. J Public Health Epidemiol. 2018;10:155-164. [Google Scholar]
- 29. EDHS. Central Statistical Agency Addis Ababa, Ethiopia and ORC Macro Calverton, USA. Ethiopia demographic and health survey; 2011; 2011:13. [Google Scholar]
- 30. CSAICF. Central Statistical Agency (CSA) [Ethiopia] and ICF, Ethiopia Demographic and Health Survey. CSA and ICF; 2016. [Google Scholar]
- 31. FANTA III. Food and Nutrition Technical Assistance, Participant-Based Survey Sampling Guide for Feed the Future Annual Monitoring Indicators; 2018. [Google Scholar]
- 32. WHO. Essential Nutrition Actions: Improving maternal, newborn, infant and young child health and nutrition; 2013:3-45. Accessed October 10, 2020. https://apps.who.int/iris/handle/10665/84409. [PubMed] [Google Scholar]
- 33. MOG. Guidance for Formative Research on Maternal Nutrition: Prepared for the Infant and Young Child Nutrition Project; 2011:4-6. [Google Scholar]
- 34. Belachew T, Lindstrom D, Gebremariam A, Hogan D, Lachat CLH. Food insecurity, food based coping strategies and suboptimal dietary practices of adolescents in Jimma zone Southwest Ethiopia. PLoS One. 2013;8(3):e57643. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35. Mulugeta YD, Degu GA, Belachew T. Dietary practices and associated factors among pregnant women in West Gojjam Zone, Northwest Ethiopia. BMC Pregnancy Childbirth. 2020;20:18. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36. Gebreyesus SH, Lunde T, Mariam DH, Woldehanna T, Lindtjørn B. Is the adapted household food insecurity access scale (HFIAS) developed internationally to measure food insecurity valid in urban and rural households of Ethiopia? BMC Nutr. 2015;1:1-10. [Google Scholar]
- 37. Chowdhury M, Raynes-Greenow C, Alam A, Dibley MJ. Making a balanced plate for pregnant women to improve birthweight of infants: a study protocol for a cluster randomised controlled trial in rural Bangladesh. BMJ Open. 2017;7:e015393. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38. Workicho A, Belachew T, Feyissa GT, et al. Household dietary diversity and animal source food consumption in Ethiopia: evidence from the 2011 welfare monitoring survey. BMC Public Health. 2016;16:1192. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39. Fakier A, Petro G, Fawcus S. Mid-upper arm circumference: A surrogate for body mass index in pregnant women. S Afr Med J. 2017;107:606-610. [DOI] [PubMed] [Google Scholar]
- 40. Ghosh S, Spielman K, Kershaw M, et al. Nutritionspecific and nutrition-sensitive factors associated with mid-upper arm circumference as a measure of nutritional status in pregnant Ethiopian women: implications for programming in the first 1000 days. PLoS One. 2019;14:e0214358. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41. Tang AM, Chung M, Dong K, Terrin N, Edmonds A, Assefa N, et al. Determining a Global Mid-upper Arm Circumference Cutoff to Assess Malnutrition in Pregnant Women. FHI 360/Food and Nutrition Technical Assistance III Project (FANTA); 2016. [Google Scholar]
- 42. Desta M, Akibu M, Tadese M, Tesfaye M. Dietary diversity and associated factors among pregnant women attending antenatal clinic in Shashemane, Oromia, Central Ethiopia: a cross-sectional study. J Nutr Metab. 2019;2019:1-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43. IFPRI. Nutrition Interventions in Antenatal Care and Immediate Postnatal Care Findings From A Baseline Survey in SNNPRG, Ethiopia; 2019. [Google Scholar]
- 44. Shehab L. Nutritional awareness of women during pregnancy. J Am Sci. 2012;8:494-502. [Google Scholar]
- 45. Gyimah LA, Annan RA, Apprey C. Dietary diversity and its correlates among pregnant adolescent girls in Ghana. medRxiv. 2020;1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46. Willy K, Judith K, Peter C. Dietary diversity, nutrient intake and nutritional status among pregnant. Int J Health Sci. 2016;6:378-385. [Google Scholar]
- 47. Nana A, Zema T. Dietary practices and associated factors during pregnancy in northwestern Ethiopia. BMC Preg Childbirth. 2018;18:183. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48. Tsegaye D, Tamiru DTB. Factors associated with dietary practice and nutritional status of pregnant women in Rural Ethiopia. Dove Press J. 2020;12:103-112. [Google Scholar]
- 49. Jemal K, Awol M. Minimum dietary diversity score and associated factors among pregnant women at Alamata General Hospital, Raya Azebo Zone, Tigray Region, Ethiopia. Hindawi J Nutr Metab. 2019;2019:1-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 50. Lama N, Lamichhne R, Bhandari R, et al. Factors influencing dietary diversity of pregnant women attending antenatal care in western regional hospital, Nepal: a cross-sectional study. J Karnali Acad Health Sci. 2019;2:189-196. [Google Scholar]
- 51. Katenga-Kaunda LZ, Kamudoni PR, Holmboe-Ottesen G, et al. Enhancing nutrition knowledge and dietary diversity among rural pregnant women in Malawi: a randomized controlled trial. BMC Preg Childbirth. 2021;21:644-711. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 52. Contento IR. Nutrition education: linking research, theory, and practice. Asia Pac J Clin Nutr. 2008;17:176-179. [PubMed] [Google Scholar]
- 53. Walelgn T, Tsegahun WB, Mamo D. Dietary diversity practice and associated factors among pregnant women attending ANC in Kolfe Keranyo sub city health centers, Addis Ababa, Ethiopia. doi: 10.1101/2020.04.27.20081596; 2020. [DOI] [Google Scholar]
- 54. WHO. Nutrition Landscape Information System (NLIS) Country Profile Indicators Interpretation Guide. 2nd ed. World Health Organization; 2019. [Google Scholar]
- 55. Desyibelew HD, Dadi AF, Ciccozzi M. Burden and determinants of malnutrition among pregnant women in Africa: a systematic review and meta-analysis. PLoS One. 2019;14:1-19. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 56. Shiferaw A, Husein G. Acute under nutrition and associated factors among pregnant women in Gumay District, Jimma Zone, South West Ethiopia. J Womens Health Care. 2019;8:459. [Google Scholar]
- 57. Nisal KM. Women belonging to low income group. J Sci. 2015;5:580-582. [Google Scholar]
- 58. Lama N, Lamichhane R, C S K., Bhandari GP, Wagle RR. Determinants of nutritional status of pregnant women attending antenatal care in Western Regional Hospital, Nepal. Int J Community Med Public Health. 2018;5:5045-5051. [Google Scholar]
- 59. Nigatu M, Tsegaye T, Gebrehiwot DHG, Gemeda DH. Household food insecurity, low dietary diversity, and early marriage were predictors for undernutrition among pregnant women residing in Gambella. Hindawi Adv Public Health. 2018:1-10. [Google Scholar]
- 60. Solomon Z, Sagni GF, Abera KT, Fitsum W. Undernutrition among Pregnant Women in Rural Communities in Southern Ethiopia. Int J Women Health. 2021;13:73-79. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 61. Acharya SR, Bhatta J, Timilsina DP. Factors associated with nutritional status of women of reproductive age group in rural, Nepal. Asian Pac J Health Sci. 2017;4:19-24. [Google Scholar]
- 62. Kim HJ, Oh K. Household food insecurity and dietary intake in Korea: results from the 2012 Korea National Health and Nutrition Examination Survey. Public Health Nutr. 2015;18:3317-3325. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 63. Wen L, Flood VM, Simpson JM, Rissel C, Baur LA. Dietary behaviours during pregnancy: findings from firsttime mothers in southwest Sydney, Australia. Int J Behav Nutr Phys Act. 2010;7:13. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 64. Shemsu S, Argaw A, Zinab B. Dietary practice and nutritional status among pregnant women attending antenatal care at Mettu Karl referral hospital, Southwest Ethiopia. Open Pub Health J. 2020;13:3. [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
This article is distributed under the terms of the Creative Commons Attribution 4.0 License (http://www.creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).
sj-sav-1-nmi-10.1177_11786388231190515 for Dietary Diversity, Nutritional Status, and Associated Factors Among Pregnant Women in Their First Trimester of Pregnancy in Ambo District, Western Ethiopia by Mitsiwat Abebe Gebremichael and Tefera Belachew Lema in Nutrition and Metabolic Insights