Hotspot analysis of anaemia among pregnant women in Ethiopia: hotspot analysis of national demographic and health survey data

Abstract

Background

Anaemia is a significant global health problem, especially, in developing nations like Ethiopia. Despite increasing rates over the past two decades, there is limited research on the specific prevalence of anaemia among pregnant women in the country.

Objective

To identify hotspot areas of anaemia-associated factors among pregnant women in Ethiopia.

Study design

Cross-sectional.

Setting

Ethiopian demographic study from 2005 to 2016.

Participants

This study analysed 3350 pregnant women.

Primary and secondary outcome measures

Hotspot area of anaemia among pregnant women, trend of anaemia and associated factors.

Results

The prevalence of anaemia among pregnant women has shown significant fluctuations over the years. Between 2005 and 2011, there was a notable decrease from 30.9% to 21.5% while the prevalence increased from 21.5% in 2011 to 29.58% in 2016. The identified determinants of anaemia among pregnant women were female-headed household, belonging to the highest wealth quintile, being in the second or third trimester of pregnancy, being a working woman and residing in the Somalia region. Hotspot areas, where the prevalence of anaemia was particularly high, were identified in Somalia, Dire Dawa, Afar and Harari regions.

Conclusion

Anaemia during pregnancy is a major public health concern in Ethiopia, with a concerning increase between 2011 and 2016. Hotspot areas like Somali, Dire Dawa, Afar and Harari are particularly affected. Shockingly, nearly one in three pregnant women in Ethiopia suffer from anaemia. To address this issue effectively, targeted interventions prioritising economically disadvantaged households and pregnant women in their second and third trimesters are crucial. Monitoring spatial patterns and contributing factors is vital to develop tailored interventions and improve maternal health outcomes in these high-risk areas. By strategically targeting hotspot areas nationwide, significant progress can be made in reducing anaemia among pregnant women.

STRENGTH AND LIMITATION OF THE STUDY.

• Using a substantial sample size of individuals from various regions across the country, making it representative of the entire population.

• The study used a combination of statistical methods, including spatial and trend analysis, to identify changes over time.

• The study also took into account vulnerable populations (pregnant women).

• Due to the cross-sectional nature of the data, it was not possible to establish a cause-and-effect relationship or temporal relationship.

• The analysis was unable to incorporate essential factors such as dietary intake, parasite infections, hospitalisation history and the use of dietary supplements containing iron and folic acid.

Introduction

The WHO describes anaemia during pregnancy as having a haemoglobin level below 110 g/l.¹ When a woman is pregnant, her body requires a significant increase in iron to support various processes. Iron is essential for expanding the plasma volume, increasing the red blood cell mass and aiding the growth of the placenta. Without sufficient iron supplementation, the haemoglobin concentration in the blood of pregnant women can decrease from the usual non-pregnant average of approximately 133 g/L to an average of around 110 g/L by 36 weeks of pregnancy.²

Globally, anaemia affects 38% of pregnant women and 29% of non-pregnant women³ contributing to 20% of all maternal deaths.⁴ The prevalence of anaemia among pregnant women is 40% in low and middle-income countries,⁵ Africa 41.7%⁶ and varies between 5.2% and 65.7% worldwide as of 2019.⁵ The highest rates of anaemia are found in low-income and middle-income countries, with Central and West Africa reporting a rate of 56%, South Asia at 52% and East Africa at 36%.^{3 7} Anaemia, primarily caused by iron deficiency, is responsible for approximately 20% of maternal mortality in Sub-Saharan Africa and South Asia. In Sudan, 20.3% of maternal deaths are associated with anaemia.⁷

In Ethiopia, numerous pocket studies have been conducted to assess the prevalence of anaemia among pregnant women. These studies have revealed a wide range of occurrence rates, ranging from 9.7% in the North Shoa Zone to 56.8% in Eastern Ethiopia.^{8 9} According to the 2005 Ethiopia Demographic and Health Survey report, the prevalence of anaemia in pregnant women was found to be 30.6%.¹⁰ It is estimated that approximately 50% of anaemia cases in pregnant women are caused by iron deficiency.¹¹ Anaemia resulting from iron deficiency in pregnant women is a significant factor associated with various negative outcomes. These include an increased risk of maternal, fetal and neonatal mortality, as well as poor pregnancy outcomes such as low birth weight and preterm birth. Furthermore, anaemia contributes to decreased productivity in adults, particularly, in developing countries like Ethiopia.¹²

Several studies conducted in various countries have identified multiple factors that are associated with a woman’s anaemia such as history of past abortion,¹³ knowledge about anaemia,13,18 knowledge about iron and folic acid (IFA),^{14 17 18} received health education,^{13 16} counselled on IFA,¹⁵ have four and more antenatal care (ANC) visit,^{15 17} maternal education,^{18 19} husband education,¹⁸ achieved secondary school,^{20 21} age of mother,^{16 18 22} history of anaemia during previous pregnancy,¹⁶ complication of previous pregnancy,²² distance from health facility,²³ being government employee²³ and early start of antenatal care.^{19 20 23}

Anaemia during pregnancy is a significant health challenge in Ethiopia, leading to complications such as abortion, premature births, low birth weight and infant deaths. Anaemia affects the physical and cognitive development of children and increases the risk of ill-health among older adults.^{3 4 12} Inadequate intake of essential nutrients like vitamins, iron and folic acid contributes to this problem, especially, in resource-limited areas. The WHO has set a global target to reduce anaemia among women of reproductive age by 50% by 2025.³ Despite interventions like iron and folate supplementation, anaemia prevalence remains high. There is no spatial distribution and trend analysis on anaemia among pregnant women in Ethiopia. Therefore, this study aims to assess spatial distribution and factors influencing anaemia among pregnant women in Ethiopia using data from the Ethiopian Demographic and Health Survey.

Methods and materials

Patient or public involvement

Patients or the public were not involved in the design, or conduct, or reporting, or dissemination plans of our research.

Study setting and study design

The study was conducted in Ethiopia (3°−14° N and 33°−48° E), which is located at the horn of Africa. There are nine regional states and two city administrations subdivided into 68 zones, 817 districts and 16 253 Keeble’s in the administrative structure of the country.²⁴ A community-based cross-sectional study design was conducted at the national level as one part of the periodic demographic and health survey. The survey was conducted with nationally in each region and city administrative.

Data source and sampling

The Demographic and Health Survey (DHS) Programme provides publicly free access to survey data for responsible researchers. Therefore, accessed the datasets using the website (https://www.measuredhs.com)(http://www.measuredhs.com). After the reasonable request of the Demographic and Health Survey, the data were freely accessible. Accordingly, the Ethiopian Demographic and Health Survey (EDHS) of 2005 to 2016 was used for the current study. From individual record data, we extracted the pregnant women with important variables which were identified in previous literatures.

Source population

All pregnant women aged 15 to 49 years in the three surveys (2005–16).

Study population

Pregnant women who lived in the selected clusters and presented during data collection period were the study population in this study.

Inclusion criteria

All pregnant women whose anaemia level was recorded in demographic and health data.

Exclusion criteria

Pregnant mothers due to missed haemoglobin data were excluded.

Data extraction methods

We obtained permission from the Measure DHS website to download the data in STATA format. After carefully reviewing the detailed data coding, we conducted further data recoding. The 2005, 2011 and 2016 EDHS datasets had 1133, 1104 and 1113 pregnant mothers, respectively. We extracted variables related to sociodemographic, economic, household and obstetric characteristics, as well as anaemia level and other indicators.

Measurement and operational definition

Anaemia in pregnancy is determined by measuring the haemoglobin level adjusted for altitude at sea level, based on the criteria established by the WHO. Specifically, if a pregnant woman’s haemoglobin level is less than 11 gm/dL, she is considered to have anaemia according to the WHO’s definition. Conversely, if a pregnant woman’s haemoglobin level is 11 g/dL or higher, she is considered to not have anaemia according to the same definition.²⁵

Study variables

Dependent variable

Anaemia in pregnant women.

Independent variable

Sociodemographic characteristics: such as educational status, age, region, residence, Wealth Index, partner educational status, occupation, sex of household head and family size.

Reproductive characteristics: such number of parities, ever terminating pregnancy, lactation, taking iron pills, age at first sex, gestational age and Body Mass Index.

Data quality control and analysis

An initial analysis was performed to explore the data and identify any outliers, missing data or inconsistencies. The results of this study were adjusted for sampling probabilities by applying the weighting factor provided in the demographic and health survey data. The data was then combined into three datasets and analysed using STATA V.16.0.

Spatial analysis

The spatial autocorrelation statistic was used to evaluate whether the anaemia patterns were dispersed, clustered or randomly distributed during the 2016 survey periods in Ethiopia. The decision was made based on the calculated Moran’s I values. When the Moran I value is close to 1, it indicates anaemia is dispersed, whereas, a Moran I value close to +1 indicates anaemia is clustered in the study area. However, Moran’s I value of zero shows a random distribution of anaemia. Local Moran’s identify hotspot clusters and cold spot clusters. It also measures outliers in which high values were surrounded primarily by low values (high-low) and outliers in which low values were surrounded primarily by high values (low-high).²⁶ This spatial analysis technique is employed to detect the local-level risk areas of anaemia and their outliers on a separate map. Hotspot analysis computes the Z-score and p value to determine the statistical significance of the clustering of anaemia over the study area at different significance levels simultaneously.²⁶ In this analysis, the p value associated with a 95%, 90% and 99% confidence level would have been used to determine the existence of significant clustering. Areas at hotspot of pregnant anaemic women and areas at cold spot of anaemia during pregnancy were detected.^{26 27} The spatial interpolation technique was applied to predict the non-sampled areas from sampled measurements.²⁸ The ordinary Kriging spatial interpolation method is used to predict raster surfaces from point data. Therefore, a smooth surface for the risk areas of anaemia among pregnant women was indicated on the anaemia risk map. Identifying the most likely clusters was done using the spatial scan statistical method, a method that is widely recommended as it is very important in detecting local clusters and has higher power than other available spatial statistical methods.²⁸ Women with anaemia were taken as cases, and non-anaemic ones were considered controls to fit the Bernoulli model. The default maximum spatial cluster size of 50% of the population was used as an upper limit, which allowed both small and large clusters to be detected and ignored clusters that contained more than the maximum limit. For each potential cluster, a likelihood ratio test statistic was used to determine if the number of observed anaemia cases within the potential cluster was significantly higher than expected or not. The primary and secondary clusters are identified and ranked based on their likelihood ratio based on 999 Monte Carlo replications. Therefore, the most likely risk areas for anaemia among pregnant women in the 2016 survey were indicated in the spatial map.

Result

Background characteristics and trends of anemia among pregnant women

In three surveys, it was observed that more than one-fourth of the total respondents fell within the age range of 25–29. When considering the educational status of women, the percentage of illiteracy decreased from 78.9% in 2005 to 53% in 2016. Regarding the occupational status of women, approximately 74%, 44% and 55.6% were found to be employed during the three surveys. The average number of household members remained consistent across the three surveys, with an average of 5 members per household. In all three surveys, more than 90% of respondents reported never having had a terminated pregnancy. The average age at first birth for women was 18 during these three surveys (refer to table 1).

Table 1. Background characteristics of study participants.

Variables	Category	EHDS-2005 (weighted n=1133)	EHDS-2011 (weighted n=1104)	EHDS-2016 (weighted n=1113)	% difference in anaemia 2005–11	% difference anaemia 2011–16
Age of women in years	15–19	35.3	17.7	33.35	−17.6	15.65
	20–24	24.5	17.6	30.63	−6.9	13.03
	25–29	33.8	20.7	25.9	−13.1	5.2
	30–34	33.5	24.85	29.2	−8.65	4.35
	35–39	32.3	28.6	41	−3.7	12.4
	40–44	21.1	26.3	16.5	5.2	−9.8
	45–49	21.3	1.45	7.6	−19.85	6.15
Marital status	Single	4.8	44.5	5.3	−39.7	−39.2
	Married	31.6	21.1	30.5	−10.5	9.4
Place of residence	Urban	12.4	21.3	22.3	8.6	1
	Rural	31.5	21.6	30.8	−9.9	9.2
Educational status of	Not educated	33.66	24.36	33.7	−9.36	9.34
women	Primary	23.5	16.9	26.5	−6.6	9.6
	Secondary	6.9	17.3	25.74	10.4	10.44
	Higher	16.2	21.9	4.3	5.7	−17.6
Partner’s educational status	Not educated	34.5	25.9	32.4	−8.6	6.5
	Primary	30	16.74	29	−13.26	12.26
	Secondary	10.57	14.46	29.4	3.89	14.94
	Higher	7.6	28	26.4	20.4	−1.6
Occupation	Working	33.12	19.28	26.2	−13.8	6.92
	Not working	30.25	24.69	33	−5.59	8.31
Region	Tigray	36.77	19.57	21.3	−17.2	1.73
	Afar	40.72	38.9	56.7	−1.82	17.8
	Amhara	40	29.28	15.15	−10.72	−13.78
	Oromia	24	21.15	34.15	−2.81	33.99
	Somali	42.94	50.8	62.1	−7.86	11.3
	Benishangul Gumuz	30.25	25.16	33.77	−5.09	8.61
	SNNP	31.5	11.94	27.8	−19.56	15.86
	Gambela	38	38.2	0	0.2
	Harari	17.94	34.7	0	16.79
	Addis Ababa	19	15.2	0	−3.8
	Dire Dawa		52.6	0
Wealth Index	Poorest	26.9	23.87	40.14	−3.03	16.27
	Poorer	43.1	23.1	30.36	−20	7.26
	Middle	27.96	19	22.55	−8.96	3.55
	Richer	35	23.4	33.54	−11.6	10.14
	Richest	10.24	17	18.9	−6.6	1.9
Sex of household head	Male	31.6	21.3	28.6	−10.3	7.3
	Female	24.6	23.4	36.8	−1.2	13.4
Family size	Small	28.93	13.9	25.66	−15.03	11.76
	Middle	34	24.94	28.8	−9.06	3.86
	Large	27.8	19.5	32.27	−8.3	12.77
Ever had terminated pregnancy	Yes	24.25	28.52	23.86	4.27	4.66
	No	31.6	20.5	30.32	−11.1	9.82
Parity	None	19.4	16.89	22.46	−2.6	5.57
	Prim parous	37.3	19.36	29.5	−17.94	9.64
	Multiparous	34.4	23.43	31.9	−10.97	8.47
Currently breastfeeding	Yes	36.17	20.57	15.4	−15.6	−5.17
	No	30.22	21.7	31.2	−8.52	9.5

Factors associated with anemia among pregnant women

A bivariate and multivariate analysis was conducted to identify significant variables associated with anaemia. In the bivariate analysis, variables such as age, place of residence, marital status, educational status of the respondent and their partner, occupational status, region, Wealth Index, sex of household head, parity, duration of pregnancy and Body Mass Index were considered. Among these variables, those with a p value of 0.2 or less were eligible for the multivariable analysis. In the year 2016, the EDHS found that several factors were significantly associated with a high prevalence of anaemia. These factors included living in the regions of Somalia and Oromia, belonging to a female-headed household, having a large family size and being in the second and third trimesters of pregnancy. On the other hand, belonging to a household with a medium or large Wealth Index was significantly associated with a lower prevalence of anaemia. Specifically, pregnant women living in Somalia had a 6.4 times higher prevalence of anaemia compared with those living in Addis Ababa, with a CI of 2.13 to 13.13. Pregnant women from the Oromia region had a 2.51 times higher risk of anaemia compared with those living in Addis Ababa, with a CI of 1.05 to 5.98. Pregnant women from female-headed households had a 2.77 times higher prevalence of anaemia compared with their counterparts, with a CI of 1.58 to 4.83. From 2005 to 2016, the EDHS found that being a working woman, belonging to the richest household, being the female head of the household, living in Somalia and being in the second and third trimesters of pregnancy were significantly associated with anaemia. Working women had a 24% lower prevalence of anaemia compared with their counterparts, with an adjusted OR (AOR) of 0.76 and a CI of 1.60 to 6.50. Women from the richest households were 50% less likely to develop anaemia compared with women from the poorest households, with an AOR of 0.5 and a CI of 0.29 to 0.86. Female-headed households had a 39% higher prevalence of anaemia compared with male-headed households, with an AOR of 1.39 and a CI of 1.01 to 1.91. Women living in Somalia had a 3.55 times higher prevalence of anaemia compared with women in Addis Ababa, with an AOR of 3.55 and a CI of 1.94 to 6.50. Pregnant women in the second and third trimesters were 2.56 and 2.64 times more likely to develop anaemia, respectively, compared with those in the first trimester, with CIs of 1.87 to 3.53 and 2.07 to 6.41, respectively (online supplemental table 1).

Spatial distribution results and spatial autocorrelation of anemia among pregnant women

The analysis of anaemia among pregnant women during the 2016 EDHS revealed that the distribution of cases was not random, as demonstrated by figures1 2. The global Moran’s I values, ranging from 0.101 to 0.26, indicated a significant clustering of anaemia among pregnant women. In fact, the clustering pattern observed in 2016 was highly significant, exceeding a 90% confidence level. These clustered patterns clearly depicted higher rates of anaemia in the study area.

Figure 1. Spatial distribution of anaemia among pregnant women in Ethiopia (source: shape file from CSA, 2003, URL: https://africaopendata.org/dataset/ethiopia-shapefile). SNNPR: Southern Nations, Nationalities, and People’s Region.

Figure 2. Spatial autocorrelation of anaemia among pregnant women in Ethiopia, 2016 Ethiopia (Source: shape file from CSA, 2003, URL: https://africaopendata.org/dataset/ethiopia-shapefile).

Spatial epidemiology of anemia among pregnant women

According to the findings presented in figure 3, the survey conducted in 2016 depicted the geographical distribution of risk areas for anaemia among pregnant women. Each point on the map represented a specific enumeration area. The survey identified hotspot areas with a high risk of anaemia in Somali, Dire Dawa, Afar and Harari. On the other hand, Addis Ababa, Oromia, Amhara, Tigray, SNNP, Benshangul Gumizi and Gambela were categorised as cold spot regions with a low risk of anaemia. Outliers were found in Addis Ababa, Dire Dawa, Oromia, SNNP and the southern parts of Afar, Southwest Amhara and Benshangul-Gumuz. The survey results from 2016 (figure 3) highlighted that the Somali (west) region, along with certain parts of Oromia, exhibited a higher predicted risk for anaemia during pregnancy compared with other regions. These findings provide valuable insights into the distribution of anaemia risk among pregnant women in different areas, which can help inform targeted interventions and healthcare strategies.

Figure 3. Interpolation of anaemia among pregnant mothers in Ethiopia EDHS 2016 Ethiopia (source: shape file from CSA, 2003, URL: https://africaopendata.org/dataset/ethiopia-shapefile). SNNPR: Southern Nations, Nationalities, and People’s Region.

Spatial scan statistical analysis

In the 2016 survey, a total of 259 primary clusters were identified. These clusters were located in the entire Somalia region and the eastern border areas of Dire Dawa and Oromia regions. The centre of these clusters was at coordinates (6.023458 N, 44.807507 E), with a spatial window radius of 462.80 km. The clusters had a relative risk of 1.80 and a log-likelihood ratio of 29.37 at a p value of 0.001 (online supplemental table 2). It was observed that pregnant women within this spatial window had a 1.80 times higher risk of anaemia compared with women outside the spatial window (online supplemental figure 1).

Discussion

This study aimed to investigate the prevalence of anaemia among pregnant women in Ethiopia and identify potential contributing factors. The analysis of three national demographic and health surveys revealed that the prevalence of anaemia among pregnant women decreased from 30.9% in 2005 to 21.5% in 2011 but then increased to 29.58% in 2016. These findings are consistent with studies conducted in other African countries,²⁹ a systematic review of anaemia in Ethiopia,³⁰ Yemen³¹ and Tanzania.³² However, the prevalence of anaemia among pregnant women in Ethiopia was lower than in India,³³ Egypt,³⁴ Ghana,³⁵ Bangladesh,³⁶ Pakistan,³⁷ Somalia,³⁸ Saudi Arabia,³⁹ Kenya (40.7%),⁴⁰ Sudan(73.2%),⁴¹ Ghana (40.8%),⁴² Bangladesh (58.9%)⁴³ and Sub-Saharan Africa (49.7%)⁴⁴ while higher than in Mekelle (19.7%),⁴⁵ Southern Ethiopia (23.1%)⁴⁶ and Morocco (16.8%).⁴⁷ The variation in anaemia prevalence across different countries may be due to various factors such as geographical, cultural and dietary differences, the distribution and prevalence of communicable diseases, feeding practices among pregnant women and access to healthcare facilities. Early screening for anaemia during antenatal care⁴⁸ and cultural practices related to the nutrition of pregnant women may also play a role in these regional disparities. This implies that considering cultural factors on feeding and nutritional mixture of pregnant women were highly effective the anemia occurence among pregnant women.

According to this study, the regions with the highest prevalence of anaemia in Ethiopia were Somalia, Dire Dawa, Afar and Harari. These regions were found to be less developed compared with other Ethiopian states in terms of economy, gender equality, healthcare facilities and food availability.⁴⁹ The variation in anaemia rates across regional states could be attributed to differences in food consumption preferences, the occurrence of communicable diseases^{50 51} and disparities in healthcare facility availability.⁵² Furthermore, the lack of clean water and inadequate sanitation facilities may contribute to the spread of soil-transmitted infections,⁵³ which in turn, can lead to anaemia.⁵⁴ Additionally, the higher prevalence of malaria in this region could also contribute to anaemia.⁵⁵ Therefore, addressing the increasing prevalence of anaemia among pregnant women in Ethiopia may require comprehensive and context-specific interventions.

This study also identifies the determinants of anaemia among pregnant women in Ethiopia. The determinants of anaemia among pregnant women were being from a female-headed household, being from a household of the highest wealth quintile, being in the second and third trimesters of pregnancy, having a large family size and living in the Somalia region. According to our research findings, pregnant women in the second and third trimesters are more likely to experience anaemia. This aligns with previous studies,56,61 conducted elsewhere, which also indicated a higher prevalence of anaemia among women in these trimesters. The possible explanation for this finding is that many women do not have an adequate amount of iron during the second and third trimesters of pregnancy. This could be attributed to the loss of iron and other nutrients during repeated pregnancies, as well as the sharing of resources with the fetus. These findings emphasise the importance of early intervention and iron supplementation for pregnant women to improve their anaemia levels. It is recommended that pregnant women receive daily oral supplementation of 30 mg to 60 mg of elemental iron and 400 µg (0.4 mg) of folic acid to prevent maternal anaemia. By following this recommendation, the overall health and well-being of pregnant women can be significantly enhanced.⁶² Another possible reason could be due to haematological changes and increased oxygen consumption during pregnancy due to the gradual increase in the body’s demand for iron, which is essential for fetal development. This demand for iron also increases as the pregnancy progresses through each trimester.^{61 63}

The probability of experiencing anaemia is significantly lower among individuals with higher wealth compared with those with lower socioeconomic status. This finding is supported by various studies conducted in Ethiopia,^{12 61} other developing countries,⁶⁴ Benin⁶⁵ and India.⁶⁶ The reason behind this disparity could be attributed to the fact that individuals with lower income have limited resources to purchase nutritious foods or maintain a balanced diet,⁶⁷ resulting in inadequate nutrient intake and compromised nutritional status. It is worth noting that over 38% of the Ethiopian population falls into the poorer and poorest wealth quintile,⁶⁸ indicating a substantial proportion of women at risk of anaemia due to their low socioeconomic position. Lower socioeconomic status is frequently linked to adverse outcomes when it comes to maintaining a healthy diet, an increased risk of infectious diseases and limited access to healthcare services.69,71 On the other hand, individuals from higher socioeconomic groups enjoy a relative advantage that allows them to afford an ample and diverse range of high-quality food, thereby, reducing the likelihood of developing anaemia.⁴

The likelihood of developing anaemia among female-headed households was higher. Being in female-headed households was one of the determinants of anaemia among pregnant women in Ethiopia. Based on our research findings, being pregnant women from female-headed households were 39% more likely to experience anaemia during pregnancy compared with their counterparts. This finding aligns with previous studies^{72 73} that have also highlighted female-headed households were determinants of anaemia during pregnancy. There are several possible justifications for conducting this study. One potential reason could be the issue of food insecurity and limited access to diverse food options that women in female-headed households may face during pregnancy.⁷⁴ In the case of resource control, females have limited control and social resources in Ethiopia. Food and nutrients are allocated inequitably within the households with an obvious male benefit. Moreover, in Africa, women tend to have lower levels of education and fewer opportunities for paid employment, which further increases their vulnerability to malnutrition and inadequate food access. It is important to address these challenges and find effective interventions to support pregnant women in female-headed households, ensuring they have access to nutritious food and adequate healthcare.^{75 76} By doing so, we can work towards reducing the prevalence of anaemia and promoting better maternal and child health outcomes.

According to a recent study, pregnant women from larger families are more likely to develop anaemia compared with those from smaller families. Specifically, the study found that women with larger family sizes had a 34% higher chance of experiencing anaemia during pregnancy. This finding is consistent with previous research^{30 59 72 77 78} that suggests that inadequate food access and food competition within households may be contributing factors. In some communities in Ethiopia, women have fewer resources to feed themselves compared with other family members and often eat their meals after everyone else has finished. Larger family sizes may also result in household food insecurity and a decreased intake of nutrients that could prevent anaemia. Another study has also reported a similar association between larger family size and anaemia during pregnancy.^{30 37}

The odds of developing anaemia among women who have work were less likely as compared with their counterpart. Working women have been found to have lower odds of developing anaemia compared with those who do not work, as supported by previous studies79,83 which indicated that women with women with less experienced occupations are less likely to develop anaemia. These studies suggest that women with occupations are less likely to experience anaemia due to their increased financial resources, which enable them to purchase more nutritious foods, and their lower levels of stress. Additionally, women with their own occupations have greater decision-making autonomy, which can enhance access to food and other essential nutrients.⁸⁴ Moreover, women who work are more likely to attend antenatal care, which reduces the risk of anaemia and increases the intake of iron and folate during the early stages of pregnancy.85,87 These findings highlight the need for comprehensive interventions, ranging from socioeconomic support to healthcare services, to address maternal anaemia during pregnancy.

In this study, it was found that women living in Somalia have a higher prevalence of anaemia compared with women in other regions. This could be attributed to their dietary habits, such as consuming iron-deficient foods like macaroni and spaghetti, as well as drinking goat milk, which is low in folic acid.^{88 89} The Somali region showed the most significant clusters of anaemia during pregnancy, and anaemia risk prediction maps indicated that the eastern part of the country had a higher risk of anaemia during pregnancy. These regional differences in anaemia prevalence could be due to variations in food consumption preferences, income levels or working status.^{89 90} Furthermore, there are significant barriers to accessing healthcare, attending ANC and religious factors affecting female feeding practices.⁹¹ This highlights the importance of strong integration between the community and healthcare facilities to address the issue of anaemia among pregnant women in the region and the country as a whole.

Strengths and limitations

This study used a robust methodology, leveraging large-scale population-based data and a substantial sample size that accurately represented various regions of Ethiopia. The inclusion of three demographic and health surveys further enhanced the study’s reliability. Notably, the researchers employed a combination of sophisticated statistical techniques, including spatial analysis and trend analysis, to gain insights into the contextual and geographical factors influencing the occurrence of anaemia among pregnant women.

As limitations in this study. The data collected for the demographic and health survey relied solely on self-reported information, which may have been susceptible to social desirability and recall bias. Furthermore, due to the study’s cross-sectional design, it is not possible to establish causal relationships between anaemia and the identified predictors. Additionally, the reliance on publicly available secondary data restricted the inclusion of other crucial factors associated with anaemia, such as dietary habits, parasite infections (including hookworm), hospitalisation history and the use of dietary supplements containing iron and folic acid.

Conclusion

In Ethiopia, the prevalence of anaemia during pregnancy varies across different regions. Significant clusters of anaemia were identified in the Somali, Dire Dawa, Afar and Harari regions. According to the 2016 demographic and health survey, these clusters were mainly located in the entire Somali region and the eastern border areas of the Dire Dawa and Oromia regions. The prevalence of anaemia showed that almost one in three pregnant women in Ethiopia had anaemia. Several sociodemographic factors were found to be associated with maternal anaemia. These factors include belonging to a female-headed household, being in the highest wealth quintile, being a working woman and residing in the Somalia region. Additionally, reproductive and obstetric history, such as being in the second or third trimester of pregnancy, also showed a significant association with anaemia during pregnancy.

To tackle this issue, programmatic interventions should prioritise targeting the poorest households, pregnant women in their second and third trimesters, and regions with a high prevalence of anaemia, such as Somalia. It is crucial to reduce the prevalence of anaemia, as it can lead to complications for both the mother and the fetus. Implementing interventions that strengthen the current quality of maternal health services is highly recommended, with a focus on multiple centres and tailored approaches.

For future research, longitudinal data and assessments of cultural factors related to nutrition during pregnancy are needed. Furthermore, conducting further studies using micronutrient assay techniques that can detect latent anaemia before changes in red blood cell morphology and indices occur would be beneficial.

Data availability statement

All data relevant to the study are included in the article or uploaded as supplementary information.