Prevalence and Determinants of Low Birth Weight in Ethiopia: A Systematic Review and Meta-Analysis

Katiso, Nuradin Abusha; Kassa, Getachew Mullu; Fekadu, Gedefaw Abeje; Kidanemariam Berhe, Abadi; Muche, Achenef Asmamaw

doi:https://doi.org/10.1155/2020/7589483

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Abstract Introduction Methods Results Discussion Conclusions Abbreviations Data Availability Conflicts of Interest Authors’ Contributions Acknowledgments Supplementary Materials References Copyright Related Articles

Review Article | Open Access

Volume 2020 | Article ID 7589483 | https://doi.org/10.1155/2020/7589483

Prevalence and Determinants of Low Birth Weight in Ethiopia: A Systematic Review and Meta-Analysis

Nuradin Abusha Katiso,¹Getachew Mullu Kassa,²Gedefaw Abeje Fekadu,³Abadi Kidanemariam Berhe,⁴and Achenef Asmamaw Muche⁵

Academic Editor: Jagdish Khubchandani

Received27 Nov 2019

Revised29 Apr 2020

Accepted16 May 2020

Published15 Sept 2020

Abstract

Introduction. Low birth weight (LBW) is the most significant risk factor for neonatal and infant mortality. It is one of the major public health problems in developing countries. Although there are various studies on low birth weight, findings were inconsistent and inconclusive. Therefore, this study was conducted to estimate the national-pooled prevalence of low birth weight and its associated factors in Ethiopia. Method. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline was followed. This meta-analysis employed a review of both published and unpublished studies conducted in Ethiopia. The databases used were PubMed, Google Scholar, CINAHL, and African Journals Online. Relevant search terms for prevalence and determinants of LBW were used to retrieve articles. The meta-analysis was conducted using STATA 14 software. Forest plots were used to present the findings. The Cochran Q test and I² test statistics were used to test heterogeneity across studies. Egger’s test was used to assess the publication bias of included studies. The pooled prevalence and the odds ratios (OR) with 95% confidence intervals (CI) were computed and were presented using forest plots. Results. A total of 28 studies, 50,110 newborn babies, were included in this meta-analysis. The pooled prevalence of LBW in Ethiopia was 14.1% (95% CI = 11.2, 17.1). Higher variation in the prevalence of LBW in different regions across the country was observed. Significant association of LBW with sex of the newborn baby, higher odds among female babies (OR = 1.5 (95% CI = 1.2, 1.7)), prematurity (OR = 4.7 (95% CI = 1.5, 14.5)), not attending prenatal care (OR = 1.7 (95% CI = 1.4, 2.2)), pregnancy-induced hypertension (OR = 6.7 (95% CI = 3.5, 12.9)), and newborn babies whose mothers were from rural areas (OR = 1.8 (95% CI = 1.2, 2.6) were the factors associated with low birth weight. Conclusions. The prevalence of LBW in Ethiopia was high. LBW was associated with several maternal and newborn characteristics. The large disparity of LBW among the different regions in the country needs targeted intervention in areas with higher prevalence. Particular emphasis should be given to mothers residing in rural areas. Community-based programs are important to increase the use of prenatal care.

1. Introduction

World Health Organization (WHO) defined low birth weight (LBW) as infants whose weight is less than 2500 gram at birth irrespective of gestational age [1]. It is one of the major public health problems globally, with a higher prevalence in developing countries [1–4]. Reports showed that 16% of all births globally were low birth weight, representing more than 20 million births a year [5, 6]. Furthermore, LBW is a significant predictor of prenatal mortality and morbidity, especially in sub-Saharan African countries. It is also associated with a range of short- and long-term consequences including chronic disease in later life [1–5, 7].

Several studies identified prematurity and intrauterine growth restriction as predominant causes of low birth weight. Prematurity is the leading reason in developed countries, whereas intrauterine growth restriction is in developing ones [1, 6, 8]. Additionally, inadequate weight gain during pregnancy, low prepregnancy weight, short stature, sex of the newborn (being female), hard physical work during pregnancy, illness(especially infections), the lower status of women, and lack of antenatal care (ANC) are also identified as risk factors for low birth weight in developing countries [9–12]. Multiple gestations, young maternal age, primiparity, low education level, poor maternal nutrition before and during pregnancy, and lower maternal anthropometric measurements are also associated with low birth weight [13–20].

Various studies were conducted to estimate the prevalence and determinants of low birth weight in Ethiopia. The prevalence of low birth weight in various studies conducted in Ethiopia ranged from 6% to 29.1% [21–47]. Additionally, sex of the infant, pregnancy-induced hypertension, ANC follow-up, prematurity, parity, and residence were found to be associated with low birth weight [21–50].

However, most of these studies found inconclusive findings, especially on the factors associated with LBW. Moreover, most of the available studies in Ethiopia were conducted using small sample size limited to a certain region or area. Therefore, this study was conducted to pool the available studies in the country and to present the national estimate of LBW and selected factors associated with it. The findings of this study will be important to design better health policies to prevent LBW and to design policies which can target the identified factors. Additionally, it will assist the country to provide information that can suggest areas of intervention and help achieve the national and international maternal and child health-related goals and targets.

2. Methods

2.1. Study Design and Search Strategy/Data Sources

A systemic review and meta-analysis on the prevalence and determinant factors of low birth weight in Ethiopia were performed using the published and unpublished research articles. Major databases such as PubMed, Google Scholar, CINAHL, and African Journals Online (AJOL) were used to review published studies. In addition, we have used Google to retrieve unpublished studies from the official website of Addis Ababa University electronic database [51]. All published articles up to July 30, 2017 were included in this review. The reference list of already identified studies was also searched to retrieve additional articles. The online search was carried out using the following search terms and phrase: “prevalence of low birth weight, low birth weight, abnormal birth weight, newborn, neonate, underweight, infant, factors affecting low birth weight; pregnancy-induced hypertension (PIH) and low birth weight, parity and low birth weight, ANC and LBW, preterm delivery and LBW, and residence and LBW, Ethiopia” separately, and/or in combination using the Boolean operators to fit PubMed database, the following search strategy was applied: birth weight [MeSH Term] OR low birth weight OR abnormal birth weight OR normal birth weight OR underweight OR big baby weight OR macrosomia AND newborn [MeSH Term] OR neonate OR infant AND Ethiopia.

2.2. Study Selection and Eligibility Criteria

The following criteria were used to include and exclude studies from the review.

2.2.1. Inclusion Criteria

(i)Participants: this review included all studies, irrespective of characteristics of participants, that were conducted on prevalence and factors associated with low birth weight in Ethiopia(ii)Setting: all studies conducted in community or institution level(iii)Definition: all research studies that define LBW as birth weight less than 2,500 grams(iv)Publication types: journal articles, master’s thesis, and dissertations(v)Studies published only in the English language were included in the review(vi)Timeframe: all studies irrespective of the time of data collection or publication were included

2.2.2. Exclusion Criteria

(i)Research studies that did not report outcome variable of this review were excluded(ii)Qualitative studies were excluded from the analysis(iii)Studies with methodological problems and review articles were also excluded from the review

2.3. Data Extraction

The data extraction was performed by five researchers using a data extraction tool. The researcher developed the data extraction tool. The tool included information on the name of the author, publication year, study design, data collection period, sample size, study area, the age of study participants, response rate, the definition used for low birth weight, the prevalence of low birth weight, and maternal and newborn-associated factors.

2.4. Quality Assessment and Data Collection

Retrieved articles were assessed for inclusion using their title and abstract, and then, a full review of articles for quality assessment was performed before selecting for final review. Researchers used the Joanna Briggs Institute Meta-Analysis of Statistics Assessment and Review Instrument (JBI-MAStARI) for critical appraisal. Two reviewers independently assessed the articles for inclusion in the review using the instrument. The disagreement (on article selection and data extraction), which arose between the reviewers, was resolved through discussion and by involving a third reviewer. The quality of all studies included in this review after the final full article review was also assessed.

2.5. Publication Bias and Heterogeneity

Publication bias and heterogeneity were also assessed. Egger’s test was used to check the publication bias of studies. Statistically significant publication bias was declared at a value less than 0.05. The heterogeneity of studies was tested using I² test statistics. I² test statistics result of 25%, 50%, and 75% was declared as low, moderate, and high heterogeneity, respectively. The random effect model was used for meta-analysis test results, which indicates statistically significant heterogeneity. Also, trim and fill analysis was conducted for studies with statistically significant publication bias using the random effect methods of analysis.

2.6. Statistical Methods and Analysis

The analysis was conducted using STATA version 14 software. The forest plot was used to present the combined estimate and odds ratio (OR) with the 95% confidence interval. For studies, which did not present standard error (SE), it was calculated using the formula . The calculated standard error and prevalence rate of each study was then entered into STATA software to calculate the overall prevalence and its 95% CI. The pooled OR was used to show the effect of sex of neonates, pregnancy-induced hypertension, antenatal care booking, gestational age, parity, residence, and pregnancy intention on low birth weight.

3. Results

3.1. Study Selection

This systematic review and meta-analysis included all published and unpublished studies on LBW conducted in Ethiopia. Search for articles using databases resulted in a total of 1,004 articles. After screening all articles using their title, abstract, and full paper review, a total of 28 articles were selected for the final meta-analysis (Figure 1).

3.2. Characteristics of Included Studies

The mean birth weight of babies included in the review ranged from 2,077 to 3,147 grams, and the sample size ranges from 253 to 11,872. Overall, this review included a total of 50,110 newborn babies (Table 1). The majority [17] of the included studies were cross-sectional, 8 were cohort, and 3 were case-control studies. Most of the regions in Ethiopia (Oromia, Amhara, Tigray, SNNPR regions, Addis Ababa city, and Dire Dawa city) were represented in this review. Two of the studies were nation-based studies. The publication year of the studies was from 1990 to 2017 (Table 2).

The quality of included studies was assessed using a quality assessment toot based on the JBI critical appraisal tool checklists for cross-sectional (19 studies), case-control (3 studies), and cohort (7 studies). Accordingly, the quality score of studies that used a cross-sectional study design ranged from 75% to 93.75%. For research studies that used cohort study design, the quality score ranged from 64% to 77%, and for case-control studies, the quality score ranged from 72% to 89% (Additional files 1–3).

3.3. Prevalence of Low Birth Weight

The prevalence of LBW ranged from 6% to 29.1%. The highest prevalence of LBW was observed in Tigray region (15.4% (95% CI = 08.6, 22.2)) and the lowest was in Addis Ababa city (8.7% (95% CI = 8.0, 9.3)). The final pooled prevalence of LBW in Ethiopia was 14.1% (95% CI = 11.2, 17.1). The I² test result showed the presence of high heterogeneity (I² = 98.9%, value =< 0.001). To address this heterogeneity, the random effect method of analysis was used for the meta-analysis (Figure 2).

3.4. Sex Difference in LBW

Seven of the studies included in this review showed that the sex of the newborn is associated with low birth weight [24, 25, 30, 39, 45–47]. In four studies, there was no significant difference in birth weight and sex of the newborn [26, 34, 43, 48]. However, the final pooled estimate showed that female newborn babies are at a higher risk of low birth weight than males, OR = 1.46 (95% CI = 1.22, 1.73) (Figure 3).

3.5. Prenatal Care and LBW

A total of 14 studies were included in this category of meta-analysis [22, 24–26, 30, 34, 40, 43, 45–50]. Eight of these studies showed a significant association between the absence of antenatal follow-up with a higher risk of LBW [24, 34, 40, 43, 45, 47–49]. Altogether, this meta-analysis showed that babies born from women who had no prenatal care were 1.7 times more likely to have low birth weight babies, OR = 1.72 (95% CI = 1.35, 2.2) (Figure 4).

3.6. Preterm Delivery and LBW

Six of the studies included in this meta-analysis showed that preterm babies (delivered before 37 weeks of gestation) were more likely to be low birth weight [29, 34, 40, 45, 46, 50]. While one study showed the opposite finding [22], and one study showed no difference in prevalence of LBW between preterm and term babies [26]. This meta-analysis showed a significant association between preterm delivery and low birth weight, OR = 4.7 (95% CI = 1.53, 14.42) (Figure 5).

3.7. Pregnancy-Induced Hypertension and LBW

Five studies were included in this category of meta-analysis [22, 39, 43, 46, 50]. The pooled meta-analysis showed the presence of an association between pregnancy-induced hypertension and low birth weight, OR = 6.7 (95% CI = 3.48, 12.92) (Figure 6).

3.8. Parity and LBW

Ten studies were included in this category of meta-analysis [22, 24–26, 30, 34, 43, 45, 48, 50]. The final pooled meta-analysis showed no statistically significant difference in the rate of LBW among women who were primipara and multipara, OR = 1.2 (95% CI = 0.9, 1.5) (Figure 7).

3.9. Residence and LBW

From 13 studies included in this category of meta-analysis [22,24–26, 29, 34, 40, 43, 45–49], two studies showed higher LBW risk among babies born from urban resident mothers [34, 40]. But, nine studies showed an increased risk of LBW among rural residents [24–26, 29, 43, 46–49]. Two studies showed no statistically significant difference in the rate of LBW among rural and urban resident mothers [22, 45]. The final pooled meta-analysis showed that babies born from rural mothers were at a higher risk of low birth weight compared to those born from an urban area, OR = 1.75 (95% CI = 1.19, 2.56) (Figure 8).

3.10. Heterogeneity and Publication Bias

Egger’s test was conducted to assess the publication bias of studies included in this meta-analysis. Accordingly, nonsignificant publication bias was observed in all categories of meta-analysis, value >0.05. For studies that present with high heterogeneity between studies, the random effect method of analysis was used.

3.11. Meta-Regression

The meta-analysis showed evidence of heterogeneity of included studies, and therefore, meta-regression was conducted to assess the source of heterogeneity. However, none of the covariates tested for source of heterogeneity were statistically significant. Therefore, the heterogeneity could be explained by other variables not included in this meta-analysis (Table 3).

4. Discussion

This systematic review and meta-analysis were carried out to assess the pooled prevalence of low birth weight and its determinant factors in Ethiopia. The weight of the newborn at birth is considered as the most important factor related to perinatal and infant morbidity and mortality [52, 53]. Low birth weight babies are almost 20 times more likely to die compared to normal birth weight babies [1].

This review found a higher prevalence of low birth weight in Ethiopia. The prevalence was 14.1% (95% CI = 11.2, 17.1). This is almost similar to the prevalence of LBW in sub-Saharan Africa, which ranges from 13% to 15% [1]. A study conducted in Cameroon showed a 13.5% prevalence of LBW [54]. A lower prevalence of LBW (8.7%) was also observed in a study undertaken in Colombia [53]. While a higher prevalence (30.3%) of LBW was found in a study conducted in India [55]. The sociodemographic, economic, nutritional, and healthcare seeking behaviors of pregnant mothers and health service differences could be possible reasons for the higher and lower estimates.

A variety of differences in the prevalence of LBW between the different regions in Ethiopia was found. A higher prevalence of LBW (15.4%) was seen in Tigray region, while the lowest (8.66%) was in Addis Ababa, Capital city of Ethiopia. Such disparities could be due to the difference in the sociodemographic, economic, healthcare seeking behavior, and nutritional status of pregnant women in different regions of the country. The subgroup analysis of the prevalence of LBW by publication year showed an increasing trend from 1990 to 2017. The rising prevalence in the LBW could be attributed to the increasing health care seeking behavior and reduced home delivery, which all increased the case detection rate over time in Ethiopia.

The finding of this meta-analysis also showed the presence of a statistically significant association between the sex of the newborn and low birth weight, OR 1.5 (1.2, 1.7). Female newborn babies had a 46% higher odd of being low birth weight than male babies. This finding was congruent with the study conducted in Arunachal Pradesh [56]. The higher LBW risk among females than male newborn babies could be due to the difference in chromosomal factors among male and female babies. Term male infants weigh between 150 and 200 g more than females [16].

Mothers who did not attend ANC for that pregnancy were 1.7 (OR 1.4, 2.2) times more likely to have low birth weight. Studies conducted in other countries also support this idea [16, 57–60]. Mothers who attended ANC are likely to get health education and counseling about pregnancy, which enables them to avoid modifiable risk factors for low birth weight. For example; studies have shown that prenatal interventions such as prevention of smoking during pregnancy, nutritional education, and prevention and early treatment of medical conditions during pregnancy are responsible for the reduction of low birth weight babies among women who follow antenatal care [60]. However, the effectiveness of prenatal care in reducing pregnancy complications depends on the number of visits and the time the women booked for prenatal care. Therefore, studies showing the effect of frequency of antenatal follow-ups and the birth of the baby are needed.

In this meta-analysis, preterm babies were 4.7 (OR 1.5, 14.5) times more likely to be low birth weight compared to term babies. A baby born early has less time in the mother’s uterus to grow and gain weight, and much of a fetus’s weight is increased during the latter part of the mother’s pregnancy [16].

Mothers who had pregnancy-induced hypertension were 6.7 times more likely to have low birth weight babies, OR 6.7 (3.5, 12.9). This finding is in line with the studies conducted in Karnataka, India, and Campinas, Brazil [61, 62]. This might be due to the trophoblast invasion into the spiral arteries that supply the placenta is incomplete due to pregnancy-induced hypertension. The reduced uteroplacental blood perfusion leads to intrauterine growth retardation and low birth weight [63].

Parity is not associated with low birth weight in this meta-analysis. This could be because the studies included in this analysis have only two categories for parity (primipara and multipara). Previous studies showed that first and four and above births are more likely to be low birth weight compared to second and third births. The reason for this is that the uterus, especially the vascular structures, is not well matured for all purposes during the first pregnancy. The uterine vascular conditions start to decline increasing the risk of low birth weight beginning with the fourth pregnancy [16].

Maternal place of residence was associated with low birth weight (OR 1.6 (1.2–2.6)). Mothers living in rural areas had a 75% higher risk of giving low birth weight babies than mothers living in the urban areas. This finding is consistent with the study conducted in Punjab [64]. Rural resident pregnant women are more prone to maternal psychosocial stress, which is implicated in increasing the release of catecholamine and cortisol, and subsequently stimulates the release of corticotrophin-releasing hormone (CRH) through cortisol. The release of CRH has been hypothesized to initiate the onset of labor via a series of biochemical processes, while cortisol has been linked with intrauterine growth restriction and low birth weight [65].

Although this study has assessed the effect of selected variables on the occurrence of low birth weight, the factors are not limited to these. Previous studies have also reported additional factors associated with low birth weight. These include poverty, nonuse of antenatal care, low social support and unwanted pregnancy [43], preterm birth, maternal chronic illness and maternal weight below 50 kg [29], and a history of perinatal death and antepartum hemorrhage [22]. Therefore, future review and meta-analysis studies should consider addressing other factors not included in the current study.

This review used a comprehensive search strategy to retrieve both published and unpublished studies. Several authors were involved in each step of the meta-analysis. The review followed the PRISMA guideline for meta-analysis. On the other hand, because of the nature of the studies included, parity was dichotomized only into two, which may lead to a lack of association with low birth weight. Therefore, further studies are recommended to identify the effect of parity on low birth weight in Ethiopia. This study included the effect of selected factors on low birth weight. The factors are not limited to those included in this review, and further reviews to show the association of LBW with other sociodemographic, maternal, and newborn characteristics are needed.

5. Conclusions

The prevalence of low birth weight was high in Ethiopia, with high variation across the different regions in the country. This requires targeted interventions to regions with a higher prevalence of LBW. This review also showed the association of LBW with several factors. Variables such as sex of the newborn, prenatal care follow-up, pregnancy-induced hypertension, preterm delivery, and residence of the mother were significantly associated with LBW. No statistically significant association was observed in the rate of LBW among primigravida and multigravida mothers. Emphasis should be given to mothers living in rural areas. Community-based health promotion activities on maternal nutrition during pregnancy, antenatal follow-up, prevention, and early treatment of PIH are needed to reduce the high burden of LBW in Ethiopia.

Abbreviations

ANC:	Antenatal care
LBW:	Low birth weight
OR:	Odds ratio
PIH:	Pregnancy-induced hypertension
SNNPR:	Southern Nations, Nationalities and Peoples’ Region of Ethiopia
WHO:	World Health Organization.

Data Availability

The data used to support this study are included within this article.

Conflicts of Interest

The authors declare that they have no conflicts of interest

Authors’ Contributions

NAK was involved in the design, selection of articles, study quality assessment, data extraction, statistical analysis, and writing the first draft of the manuscript. GMK, GAF, AAM, and AKB were also involved in the article selection, statistical analysis, and writing and editing of the manuscript. All authors approved the final version of the manuscript.

Acknowledgments

The authors would like to acknowledge all authors of the studies included in this review.

Supplementary Materials

Additional file 1. Table: quality assessment based on the JBI critical appraisal tool checklists of included case-control study with their authors and scores. Additional file 2. Table: quality assessment based on the JBI critical appraisal tool checklists of included cohort studies with their authors and scores. Additional file 3. Table: quality assessment based on the JBI critical appraisal tool checklists of included analytical cross-sectional studies with their authors and scores. (Supplementary Materials)

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Copyright © 2020 Nuradin Abusha Katiso et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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