# Prevalence of Undernutrition and Associated Factors among Pregnant Women in a Public General Hospital, Tigray, Northern Ethiopia: A Cross-Sectional Study Design

Authors: Ebud Ayele; Guesh Gebreayezgi; Teklewoini Mariye; Degena Bahrey; Gebrekiros Aregawi; Gebregziabher Kidanemariam
Journal: Journal of Nutrition and Metabolism
 (2020)

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## Abstract

Background. Undernutrition is a global health problem, particularly in pregnant women. Despite the limited studies performed in different parts of Ethiopia, the information about the prevalence of undernutrition of pregnant women in the current study area is not documented. Therefore, this study aimed to assess the prevalence of undernutrition and associated factors in pregnant women. Methods. An institution-based cross-sectional study design was conducted in the Tigray region from August 01 to December 30, 2018. Study subjects were selected by systematic sampling technique from the respective hospitals. An interviewer-administered questionnaire was used to collect the data. Data were cleaned and entered using Epi-Data version 3.1 and then exported to statistical package for social science (SPSS) version 23.0 for analysis. Multivariate analyses were carried out, and adjusted odds ratios (AORs) with 95% CI and significance level (p value) <0.05 were considered. Results. Out of the total 844 selected pregnant women, 840 participated in the study, yielding a response rate of 99.5%; of this, respondent’s undernutrition prevalence was found to be 40.6% with 95% confidence interval (38.93% and 42.27%). Agriculture as occupation (AOR = 2.6, 95% CI: 1.5, 4.5), women who wanted the pregnancy (AOR = 0.25, 95% CI: 0.14, 0.448), no history malaria during pregnancy (AOR = 0.291, 95%: (0.152, 0.555)), coffee intake during pregnancy (AOR = 1.6, 95% CI: 1.04, 2.69), and hemoglobin < 11 g/dl (AOR = 4.9, 95% CI: 3.09, 7.8) were the factors that were significantly associated with undernutrition, p value (<0.05). Conclusion. In this study, occupation, history of having malaria during pregnancy, wanted type pregnancy, coffee intake during pregnancy, and hemoglobin < 11 g/dl were factors significantly associated with undernutrition in pregnant mothers. So, healthcare providers, policymakers, and other stakeholders should give special focus on these factors.


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## Body

## 1. Introduction

Pregnancy strongly depends on the health and nutritional status of women, and a high proportion of pregnant women are affected by poor nutrition which leads them to unhealthy and distress conditions [1]. Optimal nutrition in mothers is not only crucial for their health but also for the health of future generations [2]. Undernutrition is among the most common causes of maternal mortality [3, 4].Nutrition-related problems form the core of many current issues in women's health, and poor nutrition can have profound effects on reproductive outcomes [1]. Nutrition plays a vital role in reducing some of the health risks associated with pregnancy such as the risk of fetal and infant mortality, intrauterine growth retardation, low birthweight, premature births, decreased birth defects, cretinism, poor brain development, and risk of infection [5].The global prevalence of undernutrition in mothers is about 462 million [6, 7]. And, this becomes the most important risk factor for morbidity and mortality, and hundreds of millions of pregnant women and young children are affected by undernutrition [8]. Undernutrition is a significant global health concern, particularly in pregnant women [9]. Adequate nutrition is essential for a woman throughout her life cycle, and pregnant women require varied diets and increased nutrient intake to cope with the extra needs during pregnancy. The use of dietary supplements and fortified foods should be encouraged for pregnant women to ensure an adequate supply of nutrients for both mother and fetus [5].If adolescents or women are undernourished during pregnancy, the cycle of maternal malnutrition, fetal growth restriction, child stunting, subsequent lifetime of impaired productivity, and increased maternal and fetal morbidity and mortality are perpetuated [10]. Pregnancy is an anabolic process, and a woman's normal nutritional requirement increases during pregnancy to meet the needs of the growing fetus and the maternal tissues associated with pregnancy. Thus, the nutritional status of the expectant mother is one of the most important determinants affecting pregnancy outcomes [11].Therefore, to address maternal undernutrition through intervention, a scientific study was of paramount importance to assess the present image of undernutrition and to identify factors that are affecting for undernutrition, and the available pieces of literature in Ethiopian were limited in addressing factories that influence undernutrition among pregnancy mothers. Therefore, this study aims to assess the prevalence and factors associated with undernutrition among pregnant women in a public general hospital of Tigray, Northern Ethiopia.


## 2. Methods and Materials

### 2.1. Study Design, Area, and Period

An institution-based cross-sectional study design was carried out. This study was conducted in public general hospitals of the Tigray region; in the region, there were 14 total public general hospitals from those five hospitals: Mekelle public general Hospital found in the regional administration, St. Marry public general hospital found in the central zone of the region, Lemlem Karl public general Hospital found in the southern zone of the region, Kahsay Abera public general Hospital west zone of the region, and Adigrat public general hospital found in the southeast were the selected study area. Data collection for this study was undertaken from August 01 to December 30, 2018.


### 2.2. Source and Study Population

The source populations were all third-trimester pregnant women who were coming for delivery and antenatal care visits in the selected public general hospitals of the Tigray region. Third-trimester pregnancy women who were coming for delivery and antenatal care visits in general public hospitals of the Tigray region were selected as the study population.


### 2.3. Inclusion and Exclusion Criteria

All selected third-trimester pregnant women who were coming for delivery and ANC in public general hospitals during the study period were included, whereas pregnancy women with bilateral edema were excluded.


### 2.4. Sample Size and Sampling Techniques

Sample size was calculated using single population proportion formula by assuming precision (d) = 5%, confidence level = 95% (Ζα/2 = 1.96), and proportion of undernutrition (P) = 50%. By considering a 10% nonresponse rate, it becomes 422. Finally, 844 pregnant women were taken as a final sample size after using the design effect two. Two-stage sampling was employed to select the study participants. In Tigray, there were 14 public general hospitals; from those, five hospitals were selected randomly and the sample size was proportionally allocated to each hospital. A systematic random sampling technique was used to select every (determined interval K = 2) study subjects from all the five hospitals.


### 2.5. Data Collection Procedures

A semistructured questionnaire was initially prepared in English and then translated into the local language; Tigrigna was used. Tigrigna version was again translated back to English to check for any inconsistencies or distortion in the meaning of words. Data were collected using an interviewer-administered, and MUAC measurement questionnaire was adapted from the literature. Data collection was performed by five B.Sc. nurses. To assure the quality of the data properly designed data collection instrument and training of data collectors and supervisors was done, the enumerators and the supervisor were given training for three days on procedures, techniques, ways of collecting the data, and monitoring the procedure. Ten percent pretest was done at the Shul public general hospital to check the consistency of the questioner. The collected data were reviewed and checked for completeness by supervisors and principal investigators each week. MUAC was measured by considering the mothers in Frankfurt plane and sideways to measure the left side, arms hanging loosely at the side with the palm facing inward, taken at marked midpoint of upper left arm, a flexible nonstretchable tape should be used, and difference between trainee and trainer should be 0–5 mm.


### 2.6. Independent and Dependent Variables

Nutritional status of pregnant mothers is the outcome variable, and the independent variables were all the sociodemographic characteristics and maternal obstetrical and gynecology history. A brief description of how some of these variables were measured is as follows.


### 2.7. Dependent Variable

The mid-upper arm circumstance values below a cutoff point <23 cm were considered as undernutrition in this study, whereas for the individual in the third-trimester (23 cm and above), it was considered normal [12].


### 2.8. Potential Confounding Factors

Potential confounding variables measured in the study were sociodemographic characteristics and obstetrics and gynecology including the age of mother, marital status, religion, educational background of mothers and household, income, occupation, ethnicity, number of antenatal care visits, type of pregnancy, maternal previous surgery, malaria, parity, iron and folic acid supplementation, marriage at age, hemoglobin level, coffee intake, husband’s support, depression, difficulty to access food during the last three months, and history of low birthweight.


### 2.9. Anthropometric Measurement

The anthropometric measurement midupper arm circumstance was taken from individual third-trimester pregnant women.


### 2.10. Data Management and Analyses

After data were entered into Epi-Data 3.1, they were exported to (SPSS) Version 23 for analysis. Binary logistic regression analysis was executed to see the association between independent and outcome variables. All explanatory variables associated with the outcome variable withp<0.25 were entered into multivariable logistic regression analysis, and a significant association was identified based on p<0.05 and AOR with 95% CI.


## 2.1. Study Design, Area, and Period

An institution-based cross-sectional study design was carried out. This study was conducted in public general hospitals of the Tigray region; in the region, there were 14 total public general hospitals from those five hospitals: Mekelle public general Hospital found in the regional administration, St. Marry public general hospital found in the central zone of the region, Lemlem Karl public general Hospital found in the southern zone of the region, Kahsay Abera public general Hospital west zone of the region, and Adigrat public general hospital found in the southeast were the selected study area. Data collection for this study was undertaken from August 01 to December 30, 2018.


## 2.2. Source and Study Population

The source populations were all third-trimester pregnant women who were coming for delivery and antenatal care visits in the selected public general hospitals of the Tigray region. Third-trimester pregnancy women who were coming for delivery and antenatal care visits in general public hospitals of the Tigray region were selected as the study population.


## 2.3. Inclusion and Exclusion Criteria

All selected third-trimester pregnant women who were coming for delivery and ANC in public general hospitals during the study period were included, whereas pregnancy women with bilateral edema were excluded.


## 2.4. Sample Size and Sampling Techniques

Sample size was calculated using single population proportion formula by assuming precision (d) = 5%, confidence level = 95% (Ζα/2 = 1.96), and proportion of undernutrition (P) = 50%. By considering a 10% nonresponse rate, it becomes 422. Finally, 844 pregnant women were taken as a final sample size after using the design effect two. Two-stage sampling was employed to select the study participants. In Tigray, there were 14 public general hospitals; from those, five hospitals were selected randomly and the sample size was proportionally allocated to each hospital. A systematic random sampling technique was used to select every (determined interval K = 2) study subjects from all the five hospitals.


## 2.5. Data Collection Procedures

A semistructured questionnaire was initially prepared in English and then translated into the local language; Tigrigna was used. Tigrigna version was again translated back to English to check for any inconsistencies or distortion in the meaning of words. Data were collected using an interviewer-administered, and MUAC measurement questionnaire was adapted from the literature. Data collection was performed by five B.Sc. nurses. To assure the quality of the data properly designed data collection instrument and training of data collectors and supervisors was done, the enumerators and the supervisor were given training for three days on procedures, techniques, ways of collecting the data, and monitoring the procedure. Ten percent pretest was done at the Shul public general hospital to check the consistency of the questioner. The collected data were reviewed and checked for completeness by supervisors and principal investigators each week. MUAC was measured by considering the mothers in Frankfurt plane and sideways to measure the left side, arms hanging loosely at the side with the palm facing inward, taken at marked midpoint of upper left arm, a flexible nonstretchable tape should be used, and difference between trainee and trainer should be 0–5 mm.


## 2.6. Independent and Dependent Variables

Nutritional status of pregnant mothers is the outcome variable, and the independent variables were all the sociodemographic characteristics and maternal obstetrical and gynecology history. A brief description of how some of these variables were measured is as follows.


## 2.7. Dependent Variable

The mid-upper arm circumstance values below a cutoff point <23 cm were considered as undernutrition in this study, whereas for the individual in the third-trimester (23 cm and above), it was considered normal [12].


## 2.8. Potential Confounding Factors

Potential confounding variables measured in the study were sociodemographic characteristics and obstetrics and gynecology including the age of mother, marital status, religion, educational background of mothers and household, income, occupation, ethnicity, number of antenatal care visits, type of pregnancy, maternal previous surgery, malaria, parity, iron and folic acid supplementation, marriage at age, hemoglobin level, coffee intake, husband’s support, depression, difficulty to access food during the last three months, and history of low birthweight.


## 2.9. Anthropometric Measurement

The anthropometric measurement midupper arm circumstance was taken from individual third-trimester pregnant women.


## 2.10. Data Management and Analyses

After data were entered into Epi-Data 3.1, they were exported to (SPSS) Version 23 for analysis. Binary logistic regression analysis was executed to see the association between independent and outcome variables. All explanatory variables associated with the outcome variable withp<0.25 were entered into multivariable logistic regression analysis, and a significant association was identified based on p<0.05 and AOR with 95% CI.


## 3. Results

### 3.1. Socioeconomic and Demographic Characteristics of Pregnant Women

A total of 840 pregnant women were included in the study with a response rate of 99.5%. The mean age of the pregnant women was 36 (SD + 1.3) years. About 747 (88.9%) pregnant women were married, 322 (38%) of them were pregnant doing agriculture, 280 (33.3%) of mothers were aged from 30 to 34 years, about 772 (91.9%) of the pregnant women were of Tigray ethnicity, and 794 (71.2%) were orthodox in religion. Out of 747 married mothers, 268 (31%) of their husbands had a high school degree, 322 (38.3%) pregnant women were of elementary school, and 280 (33.3%) pregnancy women had a monthly income less than 1000 Ethiopian Birr (Table1).Table 1
Socioeconomic and demographic characteristics of pregnant women in public general hospitals of Tigray Ethiopia 2018 (n = 840).
VariableCategoryFrequencyPercentMarital statusDivorced9311.07Married74788.9OccupationHousewife14216.9Agriculture32238.3Government employee26831.9Nongovernment employee11013.09Housewife14216.9ReligionOrthodox79494.5Muslim465.47Mother’s age15–19485.720–24364.2825–2942530–3428033.335–3922326.540–4415418.345–49576.78Husband’s educationNo education16719.88Elementary23127.5High school26831.9Preparatory school586.9College and above11613.8EthnicityTigray77291.9Others688Mother’s educationNo education14216.9Elementary32238.3High school26831.9College and above10812.8Income (Ethiopian Birr)Less then 100028033.31001–200022526.782001–300024629.2Above 30018910.59


### 3.2. Health and Health Care Service Characteristics of Pregnant Women

Out of 840 participating in the study yielding to a response rate of 99.5%, undernutrition was found in 40.6%. About 606 (78.8%) had two-time antenatal care visit, 435 (51.7%) out of pregnant women had wanted type pregnancy, 732 (5178%) had no history of previous surgery, 771 (91.78%) had no history malaria during pregnancy, 596 (70.95%7) had no history of low birthweight, 435 (51.78%) pregnant women had multiple ≥ 5 children, \about 710 (84.5%) pregnant women were in iron and folic acid supplementation during the current pregnancy, 652 (77.6%) pregnant women were married at the age >18 year, 590 (70.2%) pregnant women had a hemoglobin level >11 g/dl, 560 (66.6%) women were daily coffee drinkers, 450 (53.5%) women’s husbands were supported them during the current pregnancy, about 650 (77.38%) women had no history of depression, and 598 (71.19%) had difficulty to access food in the last three months (Table2).Table 2
Frequency distribution of health and health-related variables among pregnant women in public general hospitals of Tigray, Ethiopia 2018 (n = 840).
VariableCategoryFrequencyPercentType of pregnancyUnwanted15918.9Unplanned14617.38Wanted43551.78Maternal previous surgeryYes374.8No73295.2MalariaYes698.2No77191.78How many ANCs?One253.3Two60678.8Three and above13817.9Coffee intakeYes67680.4No16419.5History of low birthweightYes24529.16No59670.95ParityPrimipara15918.92–4 children24629.28Multiple ≥ 543551.78Iron and folic acid supplementationYes71084.5No13015.47Age at marriage<18 year18822.38>18 year65277.6Hemoglobin < 11 g/dlYes25029.76No59070.2MUAC < 23 cm>23 cm49959.4<23 cm34140.59Husband’s supportAlways45053.57Sometimes15218.09Never23828.3DepressionYes9010.7No65077.38Difficulty to access food in the last three monthsYes14216.9No59871.19


### 3.3. Results of Logistic Regression Analysis

The bivariate analysis showed that malaria, maternal educational status, type of pregnancy, coffee intake, history of low birthweight, hemoglobin, iron and folic acid supplementation, occupation, age of mothers, income, age during marriage, type of pregnancy, and difficulty in food supplementation were crudely associated with 25% level of significance.Variables associated with adjusted analysis: pregnant women whose occupation is agriculture were more likely [AOR = 2.6, 95% CI: 1.5, 4.5] to have undernutrition than their counterpart, women who had wanted type pregnancy were less likely [AOR = 0.25, 95% CI: 0.14, 0.448] to be undernutritioned than that of planned-pregnancy women; mothers with no malaria infection during pregnancy were less likely [AOR = 0.291, 95%: (0.152, 0.555)] to become undernutritioned than their counterpart; mothers who were coffee drinkers were more likely [AOR = 1.6, 95% CI: 1.04, 2.69] to be undernutritioned than their counterpart; mothers with hemoglobin < 11 g/dl were more likely [AOR = 4.9, 95% CI: 3.09, 7.8] to be undernutritioned than their counterpart (Table3).Table 3
Factors associated with undernutrition in pregnant women in public general hospitals of northern Ethiopia 2018 (n = 840).
VariablesCategoryInstrumentCOR 95% CIAOR 95% CIYesNoDifficulty in food supplementation during the last 3 monthsNo4693030.510 (0.309, 0.841)0.716 (0.374, 1.369)Yes303811Age of the mother15–1928201120–2424120.700 (0.285, 1.721)0.774 (0.239, 2.504)25–2926160.862 (0.369, 2.009)1.798 (0.596, 5.426)30–341781020.802 (0.430, 1.496)1.219 (0.514, 2.891)35–391111121.413 (0.752, 2.655)2.093 (0.857, 5.109)40–44102520.714 (0.367, 1.386)1.260 (0.510, 3.114)45–4930271.260 (0.581, 2.733)1.497 (0.516, 4.34)OccupationHousewife895311Agriculture1661561.578 (1.053, 2.364)2.643 (1.548, 4.513)∗∗Government employee180880.821 (0.537, 1.256)1.576 (0.848, 2.927)Nongovernment employee64441.154 (0.691, 1.928)2.295 (0.971, 5.42)Type of pregnancyUnwanted3612311Planned1151310.333 (0.213, 0.522)0.777 (0.447, 1.350)Wanted348870.073 (0.047, 0.114)0.253 (0.143, 0.448)∗∗Mother’s educational statusNo education140270.396 (0.225, 0.697)0.617 (0.319, 1.193)Elementary153781.046 (0.651, 1.681)0.988 (0.558, 1.74)High school1031653.288 (2.077, 5.205)1.649 (0.941, 2.89)Preparatory school25332.709 (1.417, 5.182)1.048 (0.452, 2.427)College and above783811Malaria statusNo4792920.249 (0.145, 0.427)0.291 (0.152, 0.555)∗∗Yes204911Coffee intakeYes3982781.120 (0.789, 1.589)1.678 (1.046, 2.690)∗No1016311History of low birthweight and pretermYes148970.943 (0.696, 1.277)1.146 (0.643, 2.043)No35124411Age at marriage<18 years144440.365 (0.252, 0.529)0.685 (0.383, 1.226)>18 years35529711Hemoglobin < 11 g/dlYes591919.496 (6.720, 13.4)4.943 (3.097, 7.888)∗∗No44015011Iron and folic acid supplementationYes3983122.730 (1.761, 4.2)1.202 (0.613, 2.36)No1012911IncomeLess than 100019090111001–2000129961.571 (1.091, 2.261)1.344 (0.842, 2.146)2001–30001371091.680 (1.177, 2.396)1.288 (0.821, 2.022)Above 300143462.258 (1.39, 3.6)1.126 (0.582, 2.18)∗∗p≤0.001; ∗p<0.05.


## 3.1. Socioeconomic and Demographic Characteristics of Pregnant Women

A total of 840 pregnant women were included in the study with a response rate of 99.5%. The mean age of the pregnant women was 36 (SD + 1.3) years. About 747 (88.9%) pregnant women were married, 322 (38%) of them were pregnant doing agriculture, 280 (33.3%) of mothers were aged from 30 to 34 years, about 772 (91.9%) of the pregnant women were of Tigray ethnicity, and 794 (71.2%) were orthodox in religion. Out of 747 married mothers, 268 (31%) of their husbands had a high school degree, 322 (38.3%) pregnant women were of elementary school, and 280 (33.3%) pregnancy women had a monthly income less than 1000 Ethiopian Birr (Table1).Table 1
Socioeconomic and demographic characteristics of pregnant women in public general hospitals of Tigray Ethiopia 2018 (n = 840).
VariableCategoryFrequencyPercentMarital statusDivorced9311.07Married74788.9OccupationHousewife14216.9Agriculture32238.3Government employee26831.9Nongovernment employee11013.09Housewife14216.9ReligionOrthodox79494.5Muslim465.47Mother’s age15–19485.720–24364.2825–2942530–3428033.335–3922326.540–4415418.345–49576.78Husband’s educationNo education16719.88Elementary23127.5High school26831.9Preparatory school586.9College and above11613.8EthnicityTigray77291.9Others688Mother’s educationNo education14216.9Elementary32238.3High school26831.9College and above10812.8Income (Ethiopian Birr)Less then 100028033.31001–200022526.782001–300024629.2Above 30018910.59


## 3.2. Health and Health Care Service Characteristics of Pregnant Women

Out of 840 participating in the study yielding to a response rate of 99.5%, undernutrition was found in 40.6%. About 606 (78.8%) had two-time antenatal care visit, 435 (51.7%) out of pregnant women had wanted type pregnancy, 732 (5178%) had no history of previous surgery, 771 (91.78%) had no history malaria during pregnancy, 596 (70.95%7) had no history of low birthweight, 435 (51.78%) pregnant women had multiple ≥ 5 children, \about 710 (84.5%) pregnant women were in iron and folic acid supplementation during the current pregnancy, 652 (77.6%) pregnant women were married at the age >18 year, 590 (70.2%) pregnant women had a hemoglobin level >11 g/dl, 560 (66.6%) women were daily coffee drinkers, 450 (53.5%) women’s husbands were supported them during the current pregnancy, about 650 (77.38%) women had no history of depression, and 598 (71.19%) had difficulty to access food in the last three months (Table2).Table 2
Frequency distribution of health and health-related variables among pregnant women in public general hospitals of Tigray, Ethiopia 2018 (n = 840).
VariableCategoryFrequencyPercentType of pregnancyUnwanted15918.9Unplanned14617.38Wanted43551.78Maternal previous surgeryYes374.8No73295.2MalariaYes698.2No77191.78How many ANCs?One253.3Two60678.8Three and above13817.9Coffee intakeYes67680.4No16419.5History of low birthweightYes24529.16No59670.95ParityPrimipara15918.92–4 children24629.28Multiple ≥ 543551.78Iron and folic acid supplementationYes71084.5No13015.47Age at marriage<18 year18822.38>18 year65277.6Hemoglobin < 11 g/dlYes25029.76No59070.2MUAC < 23 cm>23 cm49959.4<23 cm34140.59Husband’s supportAlways45053.57Sometimes15218.09Never23828.3DepressionYes9010.7No65077.38Difficulty to access food in the last three monthsYes14216.9No59871.19


## 3.3. Results of Logistic Regression Analysis

The bivariate analysis showed that malaria, maternal educational status, type of pregnancy, coffee intake, history of low birthweight, hemoglobin, iron and folic acid supplementation, occupation, age of mothers, income, age during marriage, type of pregnancy, and difficulty in food supplementation were crudely associated with 25% level of significance.Variables associated with adjusted analysis: pregnant women whose occupation is agriculture were more likely [AOR = 2.6, 95% CI: 1.5, 4.5] to have undernutrition than their counterpart, women who had wanted type pregnancy were less likely [AOR = 0.25, 95% CI: 0.14, 0.448] to be undernutritioned than that of planned-pregnancy women; mothers with no malaria infection during pregnancy were less likely [AOR = 0.291, 95%: (0.152, 0.555)] to become undernutritioned than their counterpart; mothers who were coffee drinkers were more likely [AOR = 1.6, 95% CI: 1.04, 2.69] to be undernutritioned than their counterpart; mothers with hemoglobin < 11 g/dl were more likely [AOR = 4.9, 95% CI: 3.09, 7.8] to be undernutritioned than their counterpart (Table3).Table 3
Factors associated with undernutrition in pregnant women in public general hospitals of northern Ethiopia 2018 (n = 840).
VariablesCategoryInstrumentCOR 95% CIAOR 95% CIYesNoDifficulty in food supplementation during the last 3 monthsNo4693030.510 (0.309, 0.841)0.716 (0.374, 1.369)Yes303811Age of the mother15–1928201120–2424120.700 (0.285, 1.721)0.774 (0.239, 2.504)25–2926160.862 (0.369, 2.009)1.798 (0.596, 5.426)30–341781020.802 (0.430, 1.496)1.219 (0.514, 2.891)35–391111121.413 (0.752, 2.655)2.093 (0.857, 5.109)40–44102520.714 (0.367, 1.386)1.260 (0.510, 3.114)45–4930271.260 (0.581, 2.733)1.497 (0.516, 4.34)OccupationHousewife895311Agriculture1661561.578 (1.053, 2.364)2.643 (1.548, 4.513)∗∗Government employee180880.821 (0.537, 1.256)1.576 (0.848, 2.927)Nongovernment employee64441.154 (0.691, 1.928)2.295 (0.971, 5.42)Type of pregnancyUnwanted3612311Planned1151310.333 (0.213, 0.522)0.777 (0.447, 1.350)Wanted348870.073 (0.047, 0.114)0.253 (0.143, 0.448)∗∗Mother’s educational statusNo education140270.396 (0.225, 0.697)0.617 (0.319, 1.193)Elementary153781.046 (0.651, 1.681)0.988 (0.558, 1.74)High school1031653.288 (2.077, 5.205)1.649 (0.941, 2.89)Preparatory school25332.709 (1.417, 5.182)1.048 (0.452, 2.427)College and above783811Malaria statusNo4792920.249 (0.145, 0.427)0.291 (0.152, 0.555)∗∗Yes204911Coffee intakeYes3982781.120 (0.789, 1.589)1.678 (1.046, 2.690)∗No1016311History of low birthweight and pretermYes148970.943 (0.696, 1.277)1.146 (0.643, 2.043)No35124411Age at marriage<18 years144440.365 (0.252, 0.529)0.685 (0.383, 1.226)>18 years35529711Hemoglobin < 11 g/dlYes591919.496 (6.720, 13.4)4.943 (3.097, 7.888)∗∗No44015011Iron and folic acid supplementationYes3983122.730 (1.761, 4.2)1.202 (0.613, 2.36)No1012911IncomeLess than 100019090111001–2000129961.571 (1.091, 2.261)1.344 (0.842, 2.146)2001–30001371091.680 (1.177, 2.396)1.288 (0.821, 2.022)Above 300143462.258 (1.39, 3.6)1.126 (0.582, 2.18)∗∗p≤0.001; ∗p<0.05.


## 4. Discussion

In this study finding, the magnitude of undernutrition in pregnant women was 40.6% with 95% CI (38.93% and 42.27%), which is higher than that in the study conducted in Alamata (22.3%), Bangladesh (23.5%) [13], Amhara Gonder (14.4%) [14], eastern Ethiopia (23.3%) [15], southern Ethiopia (20.9%) [16], Sri Lanka [17], and southern Ethiopia (31.4%) with MUAC < 22 [18] of the participated pregnant women who were undernutritioned. This discrepancy may be because of the study setting and the cutoff point that was used between the studies and the term of the study participants. However, this study was almost similar to the study performed in Oromia Region (38.3%) [19]. Whereas compared with our finding, a lower prevalence of undernutrition was reported in Alta Choko, southern Ethiopia (71.1%) [20] and Ardal country (51.3%) [21]. This may be due to the MUAC cutoff point <19–22 cm that they used, sample size difference, and sociodemographic difference.Mothers whose occupation is agriculture were 2.6 times more likely [AOR = 2.6, 95% CI: 1.5, 4.5] to have undernutrition than those who are housewives. This study is similar to the study performed in pregnant women living in Bangladesh doing agriculture who were more undernutritioned and to the other studies which were conducted in Ethiopia [13, 16, 22]. This is because mothers in low-income countries are spending their time to do agriculture than caring for themselves, and the working intensity might also be hard for pregnant women.In this study, mothers with wanted type pregnancy were 75% less likely [AOR = 0.25, 95% CI: 0.14, 0.448] to became undernutritioned than those with unwanted pregnancy. This finding is similar to the evidence from low- and middle-income countries which indicated that unwanted pregnancy can affect maternal nutrition, health service, and child nutrition [23, 24]. This is because mothers with wanted pregnancy type have planned programs on how to feed themselves and prepare to have diversified food types in their homes.Pregnancy mothers with no malaria during pregnancy were 70.09% less likely [AOR = 0.291, 95%: (0.152, 0.555)] to become undernutritioned than those having malaria during pregnancy. This finding was similar to that of the study performed in Nigeria, in which it was found that malaria can cause anemia, another study performed in Alamata Tigray, a study performed in Amazonian Region [14, 16, 25–27], and others. This is because malaria is one of the proximal factors that cause undernutrition in pregnancy.Mothers consuming coffee was 1.6 times more likely [AOR = 1.6, 95% CI: 1.04, 2.69] to be undernutritioned than those in the counterpart. This finding was similar to that of the study performed in Costa Rica, Ethiopia, and also that of other studies [28–30]. This because coffee intake affects iron bioavailability, and due to its potency as an inhibitor of absorption, it is likely to aggravate anemia at times of increased physiological need or when dietary iron intake is precarious and can compete with other nutrients [31]. Drinking coffee can cause nutrient depletion of important nutrients, such as vitamin B6, and interfere with nutrient absorption of essential minerals, including calcium, iron, magnesium, and vitamin B.Mothers with hemoglobin < 11 g/dl were 4.9 times more likely [AOR = 4.9, 95% CI: 3.09, 7.8] to have undernutrition than their counterparts. This finding was similar to that of the study performed in Wonde Genetin, southern Ethiopia, Nigeria and study done in Reft valley [4, 25, 26, 32, 33]. Women who were anemic were also more likely to have a low MUAC. A study demonstrated that an MUAC measurement above 23 cm reduced odds of anemia by 0.41.


## 5. Conclusion and Recommendation

Above one-third of pregnant mothers were undernutritioned in this study, it was found to be higher than that in other similar studies; it should be considered as a major public health problem in this study area. Our study also identified malaria during pregnancy, occupation, coffee intake, hemoglobin level, and type of pregnancy were factors associated with pregnant mothers’ undernutrition, so healthcare providers and health organizations should give special focuses on these factors. Multisectoral collaboration and coordination between national and international organizations will also be needed for prevention. Counseling services for pregnant mothers might also help to improve their nutritional status. The mothers shall also be counseled about the effect of frequent coffee consumption to their fetus and their health.


### 5.1. Limitation of This Study

The cross-sectional nature of the study might affect the establishment of a causal relationship between identified risk factors and undernutrition; this study is an institution-based study, and other health services were not included. This study failed to incorporate the dietary diversity score as a risk factor of undernutrition which might have also introduced a residual confounding problem.


## 5.1. Limitation of This Study

The cross-sectional nature of the study might affect the establishment of a causal relationship between identified risk factors and undernutrition; this study is an institution-based study, and other health services were not included. This study failed to incorporate the dietary diversity score as a risk factor of undernutrition which might have also introduced a residual confounding problem.



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*Source: 2736536-2020-10-07.xml*