Prevalence and predictors of iron deficiency anemia among pregnant women in Bolosso Bomibe district, Wolaita Zone, Southern Ethiopia Community-based cross-sectional study

Abstract

: Iron-deficiency anemia is defined as anemia, a condition in which there less than the normal hemoglobin (Hb) accompanied by an indication of iron deficiency. Nearly half of all pregnant women worldwide suffer from anemia, and iron deficiency accounts for about half the world’s anemia burden. Most of the studies conducted on iron-deficiency anemia during pregnancy were conducted at the institution level and associated factors are not well studied and documented. Hence, the aim of this study is to determine the prevalence of iron-deficiency anemia and associated factors among pregnant women in Bolosso Bomibe District, Southern Ethiopia, 2019. Community-based cross-sectional quantitative study was conducted from April to June 2019. Multistage sampling was used to include 270 study participants in 6 kebeles. The structured and pretested questionnaire, middle upper arm circumference (MUAC), Hb, serum ferritin, and C-reactive protein were used as a tool to collect data. The data was compiled and entered to EpiData version 3.1 and exported to Statistical Package for Social Sciences (SPSS) version 23.00 packages for analysis. Univariate, bivariate, and multivariable logistic regression with odds ratio along with the 95% confidence intervals (CI) was computed and interpreted. p value < 0.05 was declared as statistically significant. The prevalence of iron-deficiency anemia was 11.3% (95% CI: 8.9–15.7). Factors associated with iron-deficiency anemia were ANC follow-up for the recent pregnancy (adjusted odds ratio [AOR] = 3, 95% CI: 1.15−8.9, and years b/n successive pregnancy (AOR = 4,95% CI: 1.2–12.4). Prevalence of iron-deficiency anemia was low, which is a mild public health concern among pregnant women in Bolosso Bombe District. The factors associated with iron-deficiency anemia in this study population were MUAC, ANC follow-up, birth spacing among successive pregnancy, and duration of menstrual bleeding. Thus, there should be the integration of concerned stakeholders for implementation of different interventions to solve these problems.

Keywords:

• iron deficiency
• anemia
• pregnant mothers
• hemoglobin
• serum ferritin

1. Background

Anemia in pregnancy is defined as a decrease in the concentration of circulating red blood cells or in the hemoglobin (Hb) concentration (hemoglobin levels of below 11 g/dl) and a concomitant impaired capacity to transport oxygen (World Health Organization, 2011). When anemia is accompanied by an indication of iron deficiency (e.g., low ferritin levels), it is referred to as iron-deficiency anemia (IDA) (WHO, 2012). Iron deficiency results from inadequate iron intake and absorption, increased iron requirements during growth, and large amount of iron losses. Pregnancy results in a net iron loss of 580–680 mg because of fetal and placental requirements and bleeding during delivery (Pasricha et al., 2013). IDA consists of decreased concentration of circulating RBCs, which results in decreased concentration of Hb within blood cells. This results in compromised transport of oxygen to tissues, iron stores are further depleted, and the concentrations of iron-dependent oxidative enzymes are reduced (Mawani et al., 2016). IDA during pregnancy affects growth and development both in uterus and in the long term may also be associated with negative pregnancy outcomes such as premature delivery and low birth weight (LBW), preterm birth, maternal and per natal mortality, and poor appearance, pulse, grimace, activity and respiration (APGAR) score of new born (Ezzati et al., 2021; WHO, 2012). Serum ferritin (SF) level, which is the cells’ storage form of iron, has the highest sensitivity and specificity for diagnosing iron deficiency in anemic patients. The generally accepted cutoff level for SF below which iron stores is considered to be depleted is <15 μg/L. Ferritin is a sensitive indicator of IDA in pregnant women, but it is an acute-phase reactant, and levels can be elevated in inflammatory states. C-reactive protein (CRP) is used to rule out infection (Kriplani, ; SA Maternal & Neonatal Community of Practice, 2016). During pregnancy, there is an increase in both red cell mass and plasma volume to accommodate the needs of the growing uterus and fetus (Mawani et al., 2016). Physiological iron requirements are three times higher in pregnancy than they are in the menstruating women with increasing demand as pregnancy advances (Gupta & Gadipudi, 2018). Other causes of anaemia include megaloblastic anemia due to vitamin B12 and folic acid deficiency, thalassaemias, blood loss, hemolytic states (sickle cell disease, malaria, and pre-eclampsia), helminthes infection, and underlying malignancy and chronic disease (SA Maternal & Neonatal Community of Practice, 2016; Pasricha et al., 2013).

Anemia is a global public health problem affecting both developing and developed countries with major consequences for human health as well as social and economic development. It affects more than 2 billion people worldwide (Clara Camaschella, 2015). The World Health Organization (WHO) estimates that nearly half of all pregnant women worldwide suffer from anemia, and iron deficiency accounts for about half the world’s anemia burden (Aikawa et al., 2006; Pasricha et al., 2013). The highest prevalence was found in Africa (47.5%) and South East Asia (35.7%). It is 17.8% in Americans (Taha et al., 2014). In Ethiopia, the prevalence rate of anemia among pregnant women was 29% and IDA accounts about half of anemia burden (Central Statistical Agency (CSA) [Ethiopia] and ICF, 2016]. In spite of the fact that most ministries of health in developing countries have policies to provide pregnant women with iron in a supplement form, maternal anemia prevalence has not declined significantly. IDA during pregnancy has been associated with multiple adverse outcomes for both mother and infant, including an increased risk of hemorrhage, sepsis, maternal mortality, perinatal mortality, and low birth weight (Duffy et al., 2010). Therefore, this study aimed to assess prevalence and associated factors of IDA among pregnant women in Bolosso Bombe, Wolaita Zone, Southern Ethiopia.

2. Methods and materials

2.1. Study setting

This study was conducted in Bolosso Bomibe District, Wolaita zone, South Nations, Nationalities and Peoples (SNNP) Region. As indicated in the figure 1, it is located in the Southwest of the Wolaita Zone, 300 km from Addis Ababa, 219 km from Hawassa, and 56 km from Wolaita Sodo town (Zonal town). The district has a total population of an estimated 111,079 with 55,319 males and 55,760 females with households of 1199, and females in the reproductive age group (15–49 years) is 25,881, and the number of pregnant mothers is 3621. The district has o1 primary hospital, 3 health centers, 21 health post, 2 medium clinics, and 7 primary clinics.

Figure 1. Map of study area. Source: Bolosso Bombe Woreda Health Office.

The districts’ altitude varies from 1250 to 2371 M, and the mean annual temperature over most part of Woreda is 23°C. The Woreda annual rainfall varies between 1500 mm and 1800 mm. The Woreda was mainly divided into three climatic zones, which account for Dega 5%, Weinadega 80%, and Kola 15%. It has 23 kebeles (21 rural and 2 urban). Common staple foods in the area are cereals, roots, and tuber crops.

2.2. Study design

Community based cross-sectional study design was used.

2.3. Study period

April–June 2019.

2.4. Source population

All pregnant mothers in Bolosso Bomibe District.

2.5. Study population

Randomly selected pregnant mothers with known pregnancies who were in second and third trimester in selected kebeles.

2.6. Sampling unit

Randomly selected pregnant women who were in second and third trimester from randomly selected kebeles during data collection period and that fulfill the inclusion criteria.

2.7. Inclusion and exclusion criteria

2.7.1. Inclusion criteria

Pregnant women who were second and third trimester and permanent residents of the community.

2.7.2. Exclusion criteria

Pregnant women who were severely ill and cannot give socio-demographic and anthropometric data and who have no willingness to participate in the study.

2.8. Sample size determination

● The sample size of the study was calculated by Open-Epi version 303 for single

$\mathrm{P}\mathrm{o}\mathrm{p}\mathrm{u}\mathrm{l}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}\mathrm{p}\mathrm{r}\mathrm{o}\mathrm{p}\mathrm{o}\mathrm{r}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}\mathrm{s}=\frac{{\left(\mathrm{Z}\alpha /2\right)}^2\mathrm{p}\left(1-\mathrm{p}\right)\mathrm{D}\mathrm{E}\mathrm{n}}{{\mathrm{d}}^2}=\frac{{\left(1.96\right)}^{2}0.087\left(0.923\right)2}{{\left(0.05\right)}^2}$

P = established prevalence from previous studies = 8.7 % (25)

d = margin of error of 0.05

DE = design effect = 2, sample size = 245,

10% non-responsive rate = 25; the total sample size will be 270.

As indicated in the Table 1, to determine the required sample size for the second objective of the study, various factors significantly associated with the outcome variables were considered with a confidence level of 95%, the margin of error of 5%, and power of 80%. After calculating the required sample size for those selected variables, the maximum sample size was taken. The possible calculated sample size adds an additional 10% contingency for non-response.

Table 1. Sample size determination for second specific objectives, associated factors with IDA among pregnant women in Boloso Bomibe District, Southern Ethiopia, 2019

S. no.	Variables/ factors	Confidence level (%)	Power (%)	Ratio unexpexposed	Outcome in unexposed (%)	Outcome in exposed (%)	None response	Sample size	Reference
1	History of ANC	95	80	1	94.4	48.2	10%	36 + 4	(Brhanie & Sisay, 2016)
2	Eating three main meals	95	80	1	75.8	24.2	10%	36 + 4	(Rehab et al., 2014)
3	Age 25–34 years	95	80	1	68.4	31.6	10%	66 + 7	(Rehab et al., 2014)

Sample sizes for secondary objectives were 40, and finally, the required sample size for this study was decided by taking the maximum from the objectives, which is 270.

2.9. Sampling procedure and technique

As indicated on the Figure 2, multi-stage sampling technique was employed to select study participants. Bolosso Bombe woreda was selected from Wolaita zone because there was recurrent malnutrition epidemics in this woreda. There are 21 rural kebeles and 2 urban kebeles in the district. Out of these, by cluster sampling six (30%) kebeles, one urban kebele, and five rural kebeles were selected by simple random sampling method based on cluster sampling principle. The sample size was distributed to each of the selected kebele proportional to their population size. The sampling frame was prepared for each kebele after identifying pregnant women through reviewing pregnant registration in health post. Pregnant women were then selected using simple random sampling technique from the sampling frame prepared.

Figure 2. Schematic presentation of sampling procedure.

2.10. Study variables

The dependent variable for this study is IDA.

2.10.1. Independent variables

• Socio-demographic and economic factors: age, religion, marital status, family size, maternal education and paternal education, wealth, and occupation of both.

• Gynecological and obstetric factors: Number of gestational age gravidae, parity, birth spacing, duration of period and its regularity, abortion and ANC follow-up, iron pills during pregnancy.

• Nutritional factors: malnutrition, type of diet.

• Health service-related factors: a shortage supplements within the facility, health education during ANC visits.

2.10.2. Data collection instrument

Data was collected using structured and pretested questionnaire with a face-to-face interview. The part of the questionnaire on dietary diversity (DD) was adopted from a standard tool (The Food and Agriculture Organization of the United Nations And USAID’s Food and Nutrition Technical Assistance III Project (FANTA), 2016). The DD was assessed using 24 h recall method. Respondents were asked whether they had taken any food from predefined 10 minimum dietary diversity for women (MDD-W) food groups. Accordingly, the level of dietary diversity score (DDS) was computed out of 10 consistent with the recommendation of Food and Agriculture Organization (FAO) of the United Nations (Food and Agriculture Organization of the United Nations, 2011). Other sections were developed by the principal investigators (PIs) by using different peer-reviewed published literatures. Principal component analysis (PCA) was applied in the computation of the wealth index.

The questionnaire addresses the sections women’s socio-demographic and economic factors; gynecological and obstetric-related factors, dietary-related health service-related factors; and others. The questionnaire was prepared in English and translated into Wolaitato (local language) then back to English. The two versions were examined to identify any inconsistency in the wording. Middle upper arm circumference (MUAC) of left arm measured on half way between the olecranon and acromion process using MUAC measurement to the nearest 1 mm was taken to assess nutritional status of pregnant mothers.

2.11. Blood sample collection and laboratory analysis

Three milliliters of venous blood was collected by disinfecting the puncture site by alcohol using plain Monovette system and stainless steel needles by laboratory technician. Hb label was adjusted for altitude and determined using HemoCue Hb 301 instantly after sample collection. The whole blood was clotted for 20 min, centrifuged at 3000 × g for 10 min, and serum extracted. The samples was transported in icebox and stored frozen at −70°C until analysis. Serum ferritin concentrations and CRP to identify inflammation or infection were determined at Hawassa referral and teaching hospital.

2.12. Data collectors and collecting procedures

Data collectors and supervisors were trained prior to the actual data collection. After pretest, some correction was made to the questioner. Three diploma nurses were recruited as data collectors, three laboratory technicians to collect sample, and two BSc nurses were assigned as supervisor, and they had checked the data every day after data collection for the completeness of questionnaire. Training was given to data collectors and supervisors for 2 days on purpose of the study, details of the questionnaire, data collection procedure, and filling the questioner.

2.13. Data quality assurance and management

Data collectors and supervisors were recruited and trained for 2 days prior to conducting the data collection on purpose of the study, details of the questionnaire, standardization in MUAC measurement, data collection procedure, and filling the questioner and about confidentiality. Three diploma nurses will be recruited as a data collectors, three laboratory technicians for blood sample collection, and two BSC nurses were assigned for supervisions. They had checked the data every day after data collection for the completeness of questionnaire. Before the actual data collection, the questionnaire was pretested on 5% outside the study area on the same category of population to check accuracy of data. After pretest adding some variables, language consistency corrections were made to the questioner. For blood sample, the whole blood was clotted for 20 min, centrifuged at 3000 × g for 10 min, and serum was extracted. The samples were transported in icebox and stored frozen at −70°C until analysis. One blind quality control specimen was randomly selected every 20 specimen for analysis. Informed consent was obtained. Double entry was made to cross-check the data for completeness before analysis.

2.14. Data processing and analysis

The data was entered into EpiData version 3.1, checked for completeness, and exported to Statistical Package for Social Sciences (SPSS) version 23.00 packages for analysis. The wealth index was constructed using 19 variables related to the ownership of household assets using a PCA. The wealth index values were calculated by summing up the scores of four components using items having eigen values greater than one. Finally, the three socioeconomic categories were generated by splitting the wealth index values into three equal classes.

A univariate, bivariate, and multivariable analysis was done using frequencies, binary and multiple logistic regressions to show the relationship between IDA and its associated factor. A variable whose bivariate test has a p-value <0.25 was a candidate for multivariable analysis to control for confounding. Once the variables were identified, multivariable analysis was begun with all of the selected variables The Hosmer and Lemeshow’s goodness-of-fit test model coefficient was considered to assess whether the necessary assumptions for the application of multiple logistic regressions were fulfilled.

Finally, multivariable logistic regression model was done to determine independent predictors of IDA. Adjusted odds ratios (AOR), together with their corresponding 95%

confidence interval, was computed to see the strength of association. All tests were two-sided, and P < 0.05 was considered statistically significant.

2.15. Ethical considerations

Officially written approval letter from the Wolaita Sodo University Institutional Health Research Ethics Review Committee (IHRERC) was obtained prior to the data collection. Then, the supportive letter was obtained from the Wolaita Zone Department of Health. Written consent from each respondent was taken. The benefits of data that was collected and documented are possibly to influence policy creation, planning, and decision-making approaches in the future. The collected data was kept confidential.

3. Results

3.1. Socio-demographic and economic characteristics

Of 270 study participants, 256 pregnant women had participated in the study, with a response rate of (95%). Sixteen samples, which were CRP-positive, were excluded from analysis and 240 (88.9%) were analyzed. As indicated in the Table 2 & 3, the mean age ± (standard deviation) of the participants was 24.47 ± (4.7) years. The mean family size was 4.44 ± (1.7). Most of the participants 190 (79%) were in the age range of 20–35 years. All of the participants were married. Most of the participants 85 (35.4%) had nonformal education followed by 69 (28.8%) illiterate and least 5 (2.1%) had above secondary. Among the study participants, 103 (42.9%) were in medium class, 85 (35.4%) in low, and 52 (21.7%) were in high class of wealth index, and the majority of respondents were housewives.

Table 2. Socio-demographic and economic characteristics of study participants in Boloso Bombe District, Southern Ethiopia, 2019

Variables (n = 240)	Category	Number and percentage
Age group (years)	<20	42 (17.5)
	20–35	190 (79.2)
	>35	8 (3.3)
Residence	Rural	216 (90)
	Urban	24 (10)
Educational level of respondent	Illiterate	69 (28.8)
	Nonformal education	85 (35.4)
	Primary (grades 1–8)	53 (21.1)
	Secondary (9–12)	28 (11.7)
	Above secondary	5 (2.1)
Educational level of husband	Illiterate	39 (16.3)
	Nonformal education	51 (21.3)
	Primary (grades 1−8)	92 (38.3)
	Secondary (9–12)	44 (18.3)
	Above secondary	14 (5.8)

Table 3. Obstetrics factors and gynecological factors

Variables (n = 240)	Category	Number and percentage
Gravidity	Primigravida	51 (21.3)
	Multigravida	189 (78.7)
Parity	<5	136 (71.9)
	≥5	53 (22.1)
Gestational age	Second trimester	100 (41.7)
	Third trimester	140 (58.3)
Level of birth spacing	<2 years	49 (30)
	>2 years	140 (70)
ANC follow-up for the recent pregnancy	Yes No	179 (74.6) 61 (25.4)
Health education given during ANC visit	Yes No	140 (80) 39 (20)
Collect iron tablets during ANC visit	Yes No	149 (82.8 30 (19.2)
Duration of menstrual bleeding	<5 days ≥5 days	136 (56.7) 102 (42.5)

As indicated in the Table 4, from a total of 240 pregnant women, 200 (83.3%) ate meals three and below three times in the last 24 h. About half 124 (51.7%) of the pregnant women consumed cereals followed by dark green leafy vegetables and vitamin A-rich fruits and vegetables 123 (51.3%) in the previous 24 hprior to the survey. More than half of the pregnant women, 180 (75%) have consumed inadequately diverse food, which implies that they consume less than five food groups and only 25% have consumed adequately diverse food. The majority 168 (72.7%) of pregnant had no history of eating meat, poultry, and fish, 136 (58.9%) of pregnant mothers had no history of eating eggs, and (92.1)% of pregnant mothers had no history of eating dairy.

Table 4. Nutrition-related characteristics and feeding habit

Variables (n = 240)	Category		Number and percentage
Tea/coffee with every meal	Yes		148 (61.7)
	No		92 (38.3)
Eat per day	< 3 times 3 times 4–5 times		48 (19.9) 152 (63.3) 40 (16.6)
Pica	Yes		39 (16.3)
	No		201 (83.7)
History of getting the following food groups in the last 24 h
Grain, roots, and tubers	No	124 (51.7)
	Yes	116 (48.3)
Vitamin A-rich vegetables and tubers	No	170 (70)
	Yes	70 (30)
Dark leafy greens and vegetables	No	117 (48.8)
	Yes	123 (51.3)
Other vegetables	No	150 (63)
	Yes	90 (37)
Other vitamin A-rich fruits and vegetables	No	163 (68)
	Yes	77 (32)
Eggs	No	213 (88.8)
	Yes	27 (11.3)
Meat poultry and fish	No	229 (95)
	Yes	11 (5)
Nuts and seeds	No	222 (92.5)
	Yes	18 (7.5)
Dairy	No	221 (92.1)
	Yes	19 (7.9)
DDS	Adequate	60 (25)
	Inadequate	180 (75)

DDS: dietary diversity score.

3.2. The prevalence of iron deficiency anemia

From 240 pregnant mothers included in this study, the overall prevalence of IDA 11.3% was based on laboratory investigation of serum ferritin and hemoglobin. Thirty-eight (15.8%) pregnant mothers had serum ferritin level <15 µg/l and 56 (23.3%) had a hemoglobin level <11 g/dl. The mean (±SD) of serum ferritin was 29 (±18.7) µg/l, and hemoglobin was 11.8 (±1.35) g/dl, respectively.

3.3. Factors associated with iron deficiency anemia

As indicated in the Table 5, both bivariate and multivariable logistic regression analyses were done to identify the independent predictors of IDA among pregnant women. All the variables were analyzed in bivariate logistic regression analysis, and those variables with p-value < 0.25 were considered in multivariable logistic regression analysis. In multivariable logistic regression analysis, p-value < 0.05 at 95% CI was considered statistically significant. By doing this ANC follow-up, years between successive pregnancy, duration of menstrual bleeding, and MUAC were statistically associated variables with IDA in multivariable logistic regression analysis at a p-value < 0.05 and at 95% CI. Those who had no ANC follow-up were three times more likely to develop IDA compared to those who had ANC follow-up for the recent pregnancy (AOR = 3, 95% CI: 1–8.9). Nutritional status and MUAC were significantly associated with IDA of pregnant mothers (AOR = 4, 95%: 1.5–10.8). Years between successive pregnancy <2 years increase odds of IDA by 4 (AOR = 4, 95% CI: 1.2–12.4).

Table 5. Multivariable analysis of variables associated with IDA among pregnant mothers of Bolosso Bombe District, Southern Ethiopia, 2019

Variables (n = 240)		Iron deficiency anemia		OR (95% CI)
		Yes	No	Crude		Adjusted
		Number (%)	Number (%)
ANC follow-up for current pregnancy	Yes	14 (7.8)	165 (92.2)	1		1
	No	13 (21.3)	48 (78.7	3.2 (1.4–7.25)		3 (1–8.9)**
Parity	<5	16 (8.6)	120 (91.4)	1		1
	≥5	42 (79)	11 (20.8)	2.8 (1.210−6.47)		1.9 (0.65–5.5)
Years b/n successive pregnancy	< 2 years	10 (20.4%)	39 (79.6%)	2.9 (1.2–7.25)		4 (1.2–12.4)
	>2 years	13 (8%)	150 (92%)	1		1
Tea or coffee with meal	Yes	22 (14.9)	126 (85.1)	3 (1.1–8.3)		2.6 (0.8–8.2)**
	No	5 (5.4)	87 (88.8)	1		1
Dietary diversity for women	Inadequate	25 (14.2)	151 (85.80)	5 (1.18–22.3)		5.2 (0.9–28.5)*
	Adequate	2 (3.1)	62 (96.9)	1		1
MUAC	<23	15 (20.5)	58 (79.5)	3.3 ((1.5–7.6)		4 (1.5–10.8)**
	≥23	12 (7.2)	155 (92.8)		1	1

**p < 0.001 and *p < 0.05 and 95% CI = confidence interval. 1 = reference groups.

4. Discussion

The prevalence ofIDA among pregnant women in this study (11.3%) was similar with the study conducted in Sidama (Gebreegziabher & Stoecker, 2017) but lower than the average prevalence of IDA in pregnant women in Ethiopia (Haidar, 2010). This might be due to smaller sample size.

WHO report indicates that from all anemic pregnant about half is IDA, the finding of this study is consistent with this report. The prevalence of IDA in Mardan, Pakistan, pregnancy was 76.7% (Shams et al., 2017). This difference might be due to the geographical location, and the difference of the feeding habits and related factors.

Birth interval <2 years has significantly associated with IDA, which is fourfold higher than birth interval <2 years; this is because depletion of the iron storage and losses of blood during delivery by close birth spacing. This is comparable to the findings from the studies conducted in Yemen and Bahrain (Ahmed et al., 2018; Rehab et al., 2014). There is also association with antenatal care follow-up: the pregnant women who had no history of ANC follow-up for the recent pregnancy had three times more likely to develop IDA compared to those who had no ANC follow-up for the recent pregnancy. This is might be due to ANC follow-up benefits: like iron supplementations health education on appropriate feeding including dietary diversity during pregnancy. This result is in line with the findings of studies in Indonesia and Ethiopia (Brhanie & Sisay, 2016; Suega et al., 2014). The association between MUAC IDA is pregnant women who had MUAC <23 cm were four times more likely to develop IDA compared to those had MUAC >23 cm. This finding is consistent with the finding from the study conducted in Bahrain (Rehab et al., 2014). This is might be due to under-nutrition result in malabsorption of iron and also the under nutrition was due to low intake of iron-rich diets.

This study showed a higher prevalence of IDA among participants with age group<20 years and >35 years. These age groups are classified as high-risk age groups. IDA in these age groups is associated with a greater number of complications (Ahmed et al., 2018). The prevalence of IDA was lowest in the second trimester of pregnancy owing to lower iron demand compared with that of the third trimesters. This is consistent with the findings from the previously conducted studies in Ethiopia (Brhanie & Sisay, 2016; Gebremedhin et al., 2014). In this study, IDA was lower in primigravidae than in multigravidae. Pregnancy has a negative effect on iron stores, especially when iron intake is compromised. The prevalence of iron anemia increased with increased gravidity, but the increase was not statistical.

4.1. Study strength and limitations

Strength: Being community based, using simple random sampling, which is the golden one and biochemical nutritional assessment method.

Limitation: Smaller sample size and not including early pregnancy.

5. Conclusion

In conclusion, prevalence of IDA is relatively low, which is a mild public health concern among pregnant women in Bolosso Bombe District, Wolaita Zone, and Southern Ethiopia. The factors associated with IDA in this study population were MUAC, antenatal care follow-up for the recent pregnancy, birth spacing among successive pregnancies, and duration of menstrual bleeding.

5.1. Recommendations

Promotion of family planning and collaborates with agricultural office and other sectors and NGOs regarding to dietary diversity. Maternal child health teams do strongly on pregnant mothers on importance of family planning, Antenatal care follow-up and also maternal nutrition including dietary diversity. Health education on ANC follow-up and diversified feeding practice should be given. Further community-based studies are needed to see the effect of IDA among pregnant mothers nutritional status with a larger sample size.

Acknowledgements

We would like to give my heartily thank to the Wolaita Sodo University College of Health Science and Medicine, School of Public Health, for facilitation of the thesis. We would also like to extend my grateful thank to all pregnant women who participated in this study, data collectors, and supervisors. Finally, I have a great respect for Bolosso Bomibe District Health office staffs and health extension workers for providing important information for this research work.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.