- Open Access
Determinants of fetal macrosomia among live births in southern Ethiopia: a matched case–control study
BMC Pregnancy and Childbirth volume 22, Article number: 465 (2022)
Fetal macrosomia defined as birth weight of 4000 g and above regardless of gestational age and associated with adverse maternal and fetal outcomes, especially among women in developing countries like Ethiopia. Despite the observed burden, there is limited evidence on determinants of fetal macrosomia. This study aimed to identify determinants of fetal macrosomia among live births at Wolaita Sodo town Southern Ethiopia.
A facility-based matched case–control study design involved 360 singletons deliveries attended at hospitals in Wolaita Sodo town, southern Ethiopia, with 120 cases and 240 controls included. Cases and control were matched by maternal age. Cases were neonates with a birth weight of ≥ 4000, while controls were neonates with a birthweight between 2500gm and less than 4000gm. Data were collected by interviews, measuring, and reviewing mothers' medical documents. Conditional logistic regression analysis was carried to identify the independent predictor variables. Statistical significance was set using a p-value < 0.05 and 95% CI for AOR.
Male neonates were four times more likely to be macrosomia than female neonates MAOR = 4.0 [95%CI; 2.25–7.11, p < 0.001]. Neonates born at gestational age ≥ 40 weeks were 4.33 times more likely to be macrosomia with MAOR = 4.33 [95%CI; 2.37–7.91, p < 0.001]. Neonates born from physically inactive mothers were 7.76 times more likely to be macrosomia with MAOR = 7.76 [95CI; 3.33–18.08, p < 0.001]. Neonates born from mothers who consumed fruits and dairy products in their diet frequently were 2 and 4.9 times more likely to be macrosomia MAOR = 2.03 [95%CI; 1.11–3.69, p = 0.021] and AOR = 4.91[95%CI; 2.36–10.23, p < 0.001] respectively.
Mothers' physical exercise and consumption of fruit and dairy products were significant predictor variables for fetal macrosomia. Hence, health care providers may use these factors as a screening tool for the prediction, early diagnosis, and timely intervention of fetal macrosomia and its complications.
Fetal macrosomia is defined as a total birth weight of 4000 g and above regardless of gestational age or greater than 90 percentile for gestational age [1,2,3,4,5]. The most commonly used threshold of fetal macrosomia in developed countries is the weight above 4500 gm . The grading system used for decision-making regarding operative delivery has suggested grade I 4000—4499 g, grade II 4500 to 4999 g, and grade III for over 5000 g for infants [2, 7].
Globally, macrosomia affects 12% of normal pregnancy and 15%-45% of mothers with gestational diabetes . The magnitude of fetal macrosomia varied from region to region, from one community to another, and has shown temporal changes in the same community due to various factors investigated in different studies [9,10,11,12,13,14]. Its prevalence is higher in industrialized nations, in affluent countries where their nutritional levels are among women of high socioeconomic status within a given population .
In developing countries, fetal macrosomia ranges from 0.5 in India to 15% in Algeria, though there has been a rise in prevalence from 15–25% in the last two decades .
Recent studies reveal that the prevalence of fetal macrosomia in Ethiopia is estimated from 6.7% to 19.1% [14,15,16].
Fetal macrosomia is a significant contributor to obstetric morbidity and mortality. Due to the maternal and neonatal morbidities associated with macrosomia fetuses' pregnancies, such pregnancies are often considered high-risk pregnancies. Macrocosmic baby has a higher threat of developing both short and long term health outcomes in later life; Short term health outcomes: including birth asphyxia, stillbirth, shoulder dystocia, hypoglycemia, skeletal injuries, meconium aspiration, fetal death, and low Apgar score [14, 17, 18]. Similarly, evidence shows that being born macrosomic is associated with health risks in later life, including diabetes mellitus, hypertension, and obesity in adulthood and a higher risk of certain cancers in a future life .
Fetal macrosomia is also related to maternal complications like postpartum haemorrhage, prolonged labor, perineal laceration, cesarean delivery, failed instrumental delivery, maternal death, uterine rupture, and wound infection [17,18,19,20]The government of Ethiopia has implemented different strategies to improve maternal and newborn health through increasing demand for maternal health services and more accessibility to basic and essential obstetric services, expansion of health facilities, increasing availability of supplies, and deployment of skilled health professionals .
Despite the above efforts and strategies, fetal macrosomia is still the significant contributor to maternal and neonatal mortality and morbidity in Ethiopia [15, 16, 22].
Macrosomia can be a more significant obstetric hazard for women in Ethiopia, where undernutrition during childhood can inhibit the growth of the pelvis to its full potential. Pregnancy before the pelvis fully develops joint; delivering a giant baby is distressing to the mother, her baby, obstetrician, and neonatologist. It may lead to unfavourable outcomes during the whole process from pregnancy through delivery and finally after giving birth .
Some studies conducted in Ethiopiaidentified gestational age, GDM, sex, weight gain during pregnancy, pre-pregnancy overweight/obesity, maternal age, and parity as predictor variables [12,13,14].
Since all studies mentioned above were cross-sectional, they were focused on prevalence rate rather than its predictors and focused on clinical factors.
Understanding specific modifiable determinants for macrosomia is crucial for health care providers to prevent macrosomia complications and used to design specific cost-effective interventions. Studies on determinants of macrosomia in Ethiopia is insufficient, and most of those studies were cross-sectional or retrospective focused on clinical factors. Therefore this study was aimed to identify determinants of fetal macrosomia in Ethiopia.
Methods and materials
Study area and period
The study was conducted in Wolaita Sodo teaching and referral hospital (WSUTRH) and Sodo Christian general hospital (SCGH), located in Sodo town, Wolaita zone of South Ethiopia. Wolaita Sodo teaching and referral hospital serves about 3 million people. The hospital has one big maternity ward, around 70 beds, about 6000 deliveries per year. Pre-operative and post-operative, inpatient services, abortion care, labour and delivery services, ART services for all pregnant women, and Obstetric/Gynecologic Ultrasound services delivered.
Sodo General Christian hospital is a private hospital in Sodo town, containing four surgical, maternity, medicine/pediatric, and orthopaedic wards. The maternity ward has 25-beds facilities and 750 deliveries per year.
The study was conducted from June to July 2021.
The study was a facility-based, matched case–control study. The age of mothers was used for matching, and age strata were created using five-year intervals and a 1:2 case to control ratio.
The study population
The cases were macrosomic neonates whose birth weight was ≥ 4000 gm regardless of gestational age and controls: controls were average birth weight neonates whose birth weight was between 2500 and less than 4000gm regardless of gestational age at birth.
For cases: Neonates with a birth weight of ≥ 4000gm delivered at hospitals of Sodo town during the study period were included in the study as a case.
For controls: Selected neonates with birth weights between 2500 and 3999gm, delivered at hospitals in Sodo town during the study period, were included as control after matching for maternal age.
Exclusion criteria for cases and controls
Those deliveries faced pregnancy complications like abruptio placenta, placenta praevia, multiple pregnancies, and congenital anomalies for both cases, and controls were excluded from the study.
Sample size determination
The two population proportion formula was used to estimate the sample size required using two different exposure variables, and variables (male sex) with the small odds ratio were selected considering the proportion of exposure among controls 48.8%, and and 8.l% among cases from the study done in Hawassa public health institution, Southern Ethiopia . Based on the following assumption; a ratio of fetal macrosomia cases to controls 1:2, Power 90%, Confidence level 95%, Odds ratio 2.2. total sample size of 366 study participants (122cases and 244 controls) was included in the study adding a 10% non-response rate.
Sampling procedure and technique
The client registration book of two months before the data collection time was reviewed from two hospitals, and then the total numbers of deliveries during data collection time were estimated which is as (1030 deliveries per two months). The sample size was split between these two hospitals based on the proportionality of their delivery service attendants. A convenient sampling method was used to select cases and controls because pregnant women come to health institutions randomly. Two controls from the source population were selected for every case after matching maternal age until the desired sample size was attained.
Data collection tools and techniques
We adapted a structured questionnaire from relevant articles and related literature. Data was collected through direct interviews, measurements and supported by reviewing medical records. Age of mothers were used for matching, and age strata was created using five years intervals. For one case, mothers aged between 21–25 years; two control mothers aged 21–25 years were selected, giving a 1:2 case to control ratio. Others were also selected in this way.
The neonate weight was measured within one hour of delivery using a beam balance accurate to 100gm.
The last normal menstrual period (LNMP) was confirmed from her chart and through the interview. Gestational age was estimated based on LNMP and chart review for ultrasound reports.
The dietary habit was assessed based on the number of days per week, based on the Harvard university food frequency questionnaire.
Physical exercise was measured in walking for at least 30 min per day during pregnancy time as a WHO recommendation for pregnant mothers.
History of stillbirth, abortion, and using contraceptive methods used were assessed in terms of the history just before the current pregnancy.
Cases: Are neonates whose birth weight were ≥ 4000gm regardless of gestational age.
Controls: Are neonates whose birth weight were between 2500 and 3999gm regardless of gestational age.
Frequently consumption of fruits and dairy products: consumption of fruits and dairy products more than five times per week respectively .
Macrosomia is a newborn baby with a birth weight ≥ 4000gm .
Average (normal) birth weight: A newborn weighs between 2500 and 3999gm .
Birthweight: the fetus or newborn's first weight measured within one hour of birth .
Live birth: live birth is the complete expulsion or extraction from its mother of a product of conception, irrespective of the duration of the pregnancy, which, after such separation, breathes or shows any other evidence of life .
Physically active pregnant mothers who walk for more than thirty minutes per day .
Physically inactive: are those pregnant mothers who walk for less than thirty minutes per day .
Birth-interval: is the time interval between live birth and conception of current pregnancy recommended as at least 24 months .
Gestational age: is the period between the first day of the last normal menstrual period and date of delivery weeks of pregnancy measured by completed weeks 
Data quality management
Data quality was assured by pretesting on 5% of sample size in Dubo Hospital located in Areka town Wolaita Zone. The data collection tool was prepared in English, translated into local Amharic, and returned to English for consistency. Three data collectors and two supervisors were trained on the content and administration of the questionnaire. The data collectors were three midwife nurses, and two health officers supervised the data collectors. Supervisors checked the filled questionnaire for completeness at the end of each data collection day.
Data processing and analysis
The collected data were manually checked for completeness and consistency. Then the data was coded and entered into Epidata 22.214.171.124 version and exported to Stata 17 version software for cleaning and further analysis. Data cleaning was performed to check for accuracy, consistency, and mean values. Univariate analysis using frequency technique was performed to describe the data according to the study subjects' essential characteristics. Then the data was expressed in terms of frequency, percentages, and mean. Bivariate conditional logistic regression analysis examined the crude associations between the independent and dependent variables. A variable with a P-value of 0.2 and less was taken to multivariable conditional logistic regression to measure the strength of associations and expressed in terms of adjusted odds ratio with 95% confidence interval by adjusting for confounders. Significance was declared at P-value ≤ 0.05. Multicollinearity was checked using Variance inflation factor/VIF < 10 running the regress and vif syntaxes in the Stata software. Post estimation command (Hosmer and Lemeshow test) in the logistic regression was run using the estat gof to check the model fitness., The normality of continuous variables was checked using a histogram. Thus, the p-value for the Hosmer and Lemeshow chi-square was greater than 0.05, which indicated the model's fitness. The area under the ROC/receiver operating characteristic/ curve was done to classify accuracy.
Socio-demographic characteristics of the study population
In this study, a total of 360 participants was interviewed and measured. Overall, 120 cases matched by maternal age with 220 controls taken part in the study to identify risk factors of neonatal macrosomia, producing 98% of response rates. Six (2%) of the participants' interviews were omitted due to incomplete data. This study indicated that 87(72.5) cases and 170(70.8%) participants reside in urban areas. Concerning neonatal sex, 87 (72.5%) cases and 90(37.5%) of controls were males.
Regarding the occupation, the majority of the mothers were housewives, and comparable proportions were reported among cases and controls (56.7% Vs 55.8%).
Concerning educational status, 74(61.7%) of cases and 158(65.8%) controls belongs to secondary and above (Table 1).
Obstetric history, medical conditions, and health services utilization of participants.
Eighty-three (69.2%) cases and 130(54.2%) controls were multiparas.
When we look for gestational age, among case 1(0.8%) were born preterm, 101 (84.2%) were born term, and 18 (15.0%) were born post-term. Among controls 4(1.7%) were born preterm, 234 (97.5%) were born term and 2 (0.8%) were born post-term.
Among those who visited ANC utilization services, 75(64.7%) cases and 159(69.4%) controls got dietary counselling.
Among the participants who used contraceptive methods before pregnancy, 61(89.7%) cases and 98(86.7%) of controls used hormonal methods.
Nearly two-thirds of mothers, 234 (65%), were not screened. Among screened mothers, 6(4.8%) had diabetes. Among cases, five (6.8%) were diabetic, and from the controls, one(1.9%) was diabetic (Table 2).
Dietary and lifestyle-related factors
Among the total participants, 102(85%) cases and 200(83.3%) of controls involved in this study frequently consumed cereals.
The proportion of participants who consumed dairy products was higher among cases than controls (29.2% Vs 9.6%).
Regarding consumption of eggs among participants, 21(17.5%) of cases and 27(11.3%) of controls were consumed eggs ≥ five times per week. The proportion of participants who consumed fruits was found to be higher among cases as compared to controls (44.2% Vs 22.1%). When we look for physical exercise during pregnancy, 37(30.8%) of cases and 16(6.7%) of controls were physically inactive (Table 3).
Predictors of neonatal macrosomia
Bivariate analysis was run in the conditional logistic regression to check the association between dependent and independent variables.
Sex of newborn, history of abortion, history of stillbirth, contraceptive use, physical exercise, family size, average monthly income, parity, gestational age, frequent use of egg, fruit, and dairy products were candidate variables for multiple logistic regression having p-value < 0.2 in bivariate analysis.
However, multiple conditional logistic regression analysis showed no difference among cases and controls concerning the history of abortion, history of stillbirth, use of family planning methods, average monthly income, and parity.
Only neonatal sex, physical exercise, gestational age, frequent consumption of fruit and dairy products were independent predictors of neonatal macrosomia at p < 0.05.
The sex of neonates has shown a significant association with neonatal birth weight in the study. Male neonates were four times more likely to be macrosomia than female neonates MAOR = 4.0 [95%CI; 2.25–7.11, p < 0.001].
Gestational age has shown a significant association with neonatal birth weight in the study. Neonates born at gestational age ≥ 40 weeks were 4.33 times more likely to be macrosomia than neonates from their control groups with MAOR = 4.33 [95%CI; 2.37–7.91, p < 0.001].
Physical exercise during pregnancy has shown a significant association with neonatal birth weight in the study. Neonates born from mothers physically inactive mothers (< 30 min per day) during pregnancy were 7.76 times more likely to be macrosomia as compared to neonates born from physically active mothers (≥ 30 min per day)with MAOR = 7.76 [95CI; 3.33–18.08, p < 0.001].
Consuming fruits and dairy products have shown a significant association with neonatal birth weight in the study. Neonates born from mothers who consumed fruits and dairy products in their diet frequently (≥ 5 per week) were 2.03 and 4.91 times more likely to be macrosomia as compared to neonates from mothers who consume a fewer amount of fruits and dairy products in their diet with MAOR = 2.03 [95%CI; 1.11–3.69, p = 0.021] and MAOR = 4.91 [95%CI; 2.36–10.23, p < 0.001] respectively.
In contrast to this, average monthly income, family size, parity, history of abortion before current pregnancy, history of stillbirth before current pregnancy, use of family planning methods, and consumption of eggs were not significantly associated with macrosomia in the final model (Table 4).
The model fitness was checked by Hosmer–Lemeshow = 4.58 (p-value = 0.8017), and 80.56% of variables were correctly classified. The area under the ROC curve was under excellent discrimination (82.56%) Fig. 1.
This study was conducted to assess determinants of macrosomia among neonates delivered at hospitals of Sodo town, Southern Ethiopia, 2021. An institutional-based matched case–control study was employed to answer the research question. The finding of this study revealed that neonatal sex, gestational age, physical exercise, consumption of fruit and dairy products were found to be a positive statistically significant association with macrosomia.
Male neonates were 4.1 times more likely to be macrosomia than female neonates. This result is consistent with reports from cross-sectional studies conducted in Gondar Northern Ethiopia, Hawassa Southern Ethiopia, Cameroon, and retrospective cohort studies in Japan [11, 12, 26, 27]. This might be due to male newborns usually around 150–200 g weights greater than female newborns of the same gestational age near term . Boys were heavier, longer, and had greater head circumference than girls at birth [26, 29]. This might be because of genetic factors and different bodyweight patterns between males and females. However, this study was inconsistent with the study done in Saud found that the proportion of female infants was remarkably higher than males . This might be due to methodological difference, which is descriptive.
Neonates born at gestational age ≥ 40 weeks were 3.7 times more likely to be macrosomia. This was in line with a cross-sectional study in Gondar, Northern Ethiopia, and a case–control study in Tanzania [11, 30]. This might be because an advanced gestational age may cause a large birth weight at delivery by letting growth process in the uterus. Moreover, this is expected as newborns gain weight around 150–200 g near term .
Physical exercise was significantly associated with macrosomia. Neonates from physically inactive mothers were 6.8 times more likely to be macrosomia than their control groups. This is consistent with reports from many other studies, case–control studies in Morocco, prospective cohort studies in France and Brazil [23, 31, 32]. This might be due to low-level physical activity during pregnancy may result in gestational weight gain. This, in turn, results in an increased risk of macrosomia.Moreover, it is expected to be a 1 kg increase in the pregnancy weight was associated with a 94 g increase in birth weight . This might be because exercise has been shown to reduce maternal fat storage and fetal adiposity; therefore, it may effectively prevent EGWG and promote healthy birth weight . Physical activity during pregnancy is a modifiable health risk factor and can contribute to the maternal health of women and newborns. In contrast, this study was inconsistent with a study conducted in Canada that indicated that physical exercise during pregnancy was associated with a 2.5 g reduction in an infant's birth weight . This might justify the difference in exercise intensity, which is vigorous exercise.
Neonates born from mothers who consumed fruit frequently were 1.96 times more likely to have been macrosomia than their controls. This aligns with a systematic review conducted in the USA and a prospective study conducted in Japan [35, 36]. This might be because fruits contain many vitamins to promote fetal growth and development .
Neonates born from mothers who consumed milk products frequently were 4.1 times more likely to be macrosomia than their controls. This result is consistent with reports from other studies [38, 39]. This might be because milk can promote anabolism and serve as an endocrine signalling system for postnatal growth by activating the nutrient-sensitive kinasemTORC1, thus increasing gestational age and placental and fetal weight .
There was no significant difference among parity in this study. This is consistent with reports from other studies on macrosomia [14, 16, 40]. In contrast, there is a significant difference among parity in the prevalence of macrosomia [22, 28, 41, 42]. Although the history of stillbirth , miscarriage (45), contraceptive use (45), and hypertension (46) was independently associated with macrosomia in many studies, no such association was found in this study.
Strength and limitations of the study
Strength of the study
The strength of this study was its study design, a matched case–control and used matched analysis,
The sample size in this study was large enough, and the findings can be generalized to similar settings in other parts of the country.
Multiple data collections were used, such as interviews, measurement, and medical record reviews.
Limitation of the study
Any random and systematic measurement error in self-reported data might attenuate the associations observed in this study.
Self-reported pre-pregnancy body weight, dietary habits, and data regard to menstrual dates may lead to recall bias.
There is no data on the wealth index; only average monthly income was assessed.
This study identified multiple predictors of fetal macrosomia. These predictors include; male sex, physical exercise, Gestational age, consumption of fruit and dairy products.
This implies there are modifiable factors such as physical exercise, fruit, and dairy products consumption. Since most of them are modifiable, early recognition and management of these factors at the community level and in ANC providing settings could reduce a significant amount of associated maternal and neonatal complications in this resource limited country.
Health professionals should provide dietary counselling during pregnancy to minimize the consumption of fruit and dairy products, especially after the third trimester, Since the period is vulnerable to birth weight gain.
Health professionals should provide counselling on Physical exercise (e.g., Walking) during the pregnancy period.
Health care providers can use these factors as a screening tool for fetal macrosomia prediction and early diagnosis that allows timely intervention to prevent adverse maternal and neonatal-associated complications.
Gestational diabetic screening and documentation should be taken as standard during ANC visits in these hospitals.
A large-scale facility-based follow-up study recommended exploring further risk factors associated with fetal macrosomia.
Availability of data and materials
All data generated or analyzed during this study are included in its supplementary information files. Supplementary files has been included in supplementary material section. [Legend; Supplementary data of all data generated for neonatal macrosomia among mothers delivered at hospitals in Wolaita Sodo town, 2021].
American College of Obstetricians and Gynecologists
Anti Natal Care
Adjusted Odds Ratio
Body Mass Index
Estimated Gestational Weight Gain
Gestational Diabetes Mellitus
Large for Gestational Age
World Health Organization
ACOG Practice Bulletin. Obstetrics and gynecology. 2020;135(1):e18-e35.
Boulet SL, Alexander GR, Salihu HM, Pass M. Macrosomic births in the united states: determinants, outcomes, and proposed grades of risk. Am J Obstet Gynecol. 2003;188(5):1372–8.
Ng S, Olog A, Spinks AB, et al. Risk factors and obstetric complications of large for gestational age births with adjustments for community effects: results from a new cohort study. BMC Public Health. 2010;10:460. https://doi.org/10.1186/1471-2458-10-460.
Silasi M. Obstetric Imaging: Fetal Diagnosis and Care. Second Edition ed. 2018.
WHO. International Statistical Classification of Diseases and Related Health Problems 10th Revision. 2011 2
Ye J, Torloni MR, Ota E, Jayaratne K, Pileggi-Castro C, Ortiz-Panozo E, et al. Searching for the definition of macrosomia through an outcome-based approach in low- and middle-income countries: a secondary analysis of the WHO Global Survey in Africa, Asia and Latin America. BMC Pregnancy Childbirth. 2015;15(1):324.
Abramowicz J. OB/GYN ultrasound symposium the fore front UChicago medicine. 2018.
Kc K, Shakya S, Zhang H. Gestational diabetes mellitus and macrosomia: a literature review. Ann Nutr Metab. 2015;66(Suppl 2):14–20.
Asplund CA, Seehusen DA, Callahan TL, Olsen C. Percentage change in antenatal body mass index as a predictor of neonatal macrosomia. Ann Fam Med. 2008;6(6):550–4.
Koyanagi A, Zhang J, Dagvadorj A, Hirayama F, Shibuya K, Souza JP, et al. Macrosomia in 23 developing countries: an analysis of a multicountry, facility-based, cross-sectional survey. Lancet. 2013;381(9865):476–83.
Mengesha HG, Wuneh AD, Weldearegawi B, Selvakumar DL. Low birth weight and macrosomia in Tigray, Northern Ethiopia: who are the mothers at risk? BMC Pediatr. 2017;17(1):144.
Adugna DG, Enyew EF, Jemberie MT. Prevalence and associated factors of macrosomia among newborns delivered in University of Gondar Comprehensive Specialized Hospital, Gondar, Ethiopia: an institution-based cross-sectional study. Pediatric Health Med Ther. 2020;11:495–503.
Biratu AK, Wakgari N, Jikamo B. Magnitude of fetal macrosomia and its associated factors at public health institutions of Hawassa city, southern Ethiopia. BMC Res Notes. 2018;11(1):888.
Tela FG, Bezabih AM, Adhanu AK, Tekola KB. Fetal macrosomia and its associated factors among singleton live-births in private clinics in Mekelle city, Tigray, Ethiopia. BMC Pregnancy Childbirth. 2019;19(1):219.
Wondie T, Jara D, Ayana M. Factors Associated with Macrosomia among Neonates Delivered at Debre Markos Referral Hospital, Northwest Ethiopia, 2014: A Case Control Study. J Diabetes Metab. 2014;5:468. https://doi.org/10.4172/2155-6156.1000468
Jasim SK, Al-Momen H, Majeed BA, Hussein MJ. Rate of fetal macrosomia with maternal and early neonatal complications in internally moved people affected by violence. Med Res Health Sci. 2018;7(7):141–6.
Usta A, usta CS, Yildiz A, Ozcaglayan R, Dalkiran ES, Savkli A, et al. Frequency of fetal macrosomia and the associated risk factors in pregnancies without gestational diabetes mellitus. Pan Afr Med J. 2017;26.
Amini P, Moghimbeigi A, Zayeri F, Mahjub H, Maroufizadeh S, Omani-Samani R. Evaluating the impact of risk factors on birth weight and gestational age: a multilevel joint modeling approach. Int J Fertil Steril. 2018;12(2):106-13. https://doi.org/10.22074/ijfs.2018.5330.
Nkwabong E. Maternal and neonatal complications of macrosomia. Trop Doct. 2014;44(4):201-4. https://doi.org/10.1177/0049475514539479. Epub 2014 Jun 23.
The Federal Democratic Republic of Ethiopia Ministry of Health:Health Sector Transformation Plan 2016 -2020. Augest 2015.
WHO. WHO recommendations on antenatal care for a positive pregnancy experience. 2016.
Said AS, Manji KP. Risk factors and outcomes of fetal macrosomia in a tertiary centre in Tanzania: a case-control study. BMC Pregnancy Childbirth. 2016;16(1):243.
WHO. Report of a WHO technical consultation on birth spacing Geneva, Switzerland. 2005.
Duryea ELMH, Josiah S, McIntire DD, Casey BM, Leveno KJ. A revised birth weight reference for the United States. Obstet Gynecol. 2014;12(1):16–22.
Galjaard S, Ameye L, Lees CC, Pexsters A, Bourne T, Timmerman D, et al. Sex differences in fetal growth and immediate birth outcomes in a low-risk Caucasian population. Biol Sex Differ. 2019;10(1):48.
Fazzi C, Saunders DH, Linton K, Norman JE, Reynolds RM. Sedentary behaviours during pregnancy: a systematic review. Int J Behav Nutr Phys Act. 2017;14(1):32.
Vargas-Terrones M, Nagpal TS, Barakat R. Impact of exercise during pregnancy on gestational weight gain and birth weight: an overview. Braz J Phys Ther. 2019;23(2):164–9.
Pui-Ying WONGWW. Risk factors and pregnancy outcomes of macrosomia: a retrospective cohort study. J Gynaecol Obstet Midwifery. 2018;18(1):18–23.
Al-Qashar F, Al-Ghamdi M, Agab W, Al-Sayed M, Jabari M, Al-Shehri H, et al. Prevalence and outcomes of macrosomic infants born to non-diabetic mothers: a ten years’ experience at tertiary care center. J Am Sci. 2016;12(12):89.
Pui-Ying WONG, William WKTO. Risk factors and pregnancy outcomes of macrosomia: a retrospective cohort study. Hong Kong J Gynaecol Obstet Midwifery. 2018;18(1):18–23.
Tela FG, Bezabih AM, Adhanu AK. Effect of pregnancy weight gain on infant birth weight among mothers attending antenatal care from private clinics in Mekelle City, Northern Ethiopia: a facility based follow-up study. PLoS ONE. 2019;14(3):e0212424.
Bisson M, Croteau J, Guinhouya BC, Bujold E, Audibert F, Fraser WD, et al. Physical activity during pregnancy and infant’s birth weight: results from the 3D birth cohort. BMJ Open Sport Exerc Med. 2017;3(1):e000242.
Murphy M, Stettler N, Reiss R, Smith K. Associations of consumption of fruits and vegetables during pregnancy with infant birth weight or small for gestational age births: a systematic review of the literature. Int J Womens Health. 2014;6:899.
Yonezawa Y, Obara T, Yamashita T, Sugawara J, Ishikuro M, Murakami K, et al. Fruit and vegetable consumption before and during pregnancy and birth weight of new-borns in Japan: the Tohoku medical megabank project birth and three-generation cohort study. Nutr J. 2020;19(1):80.
Salcedo-Bellido I, Martínez-Galiano J, Olmedo-Requena R, Mozas-Moreno J, Bueno-Cavanillas A, Jimenez-Moleon J, et al. Association between vitamin intake during pregnancy and risk of small for gestational age. Nutrients. 2017;9(12):1277.
Zerfu TA, Pinto E, Baye K. Consumption of dairy, fruits and dark green leafy vegetables is associated with lower risk of adverse pregnancy outcomes (APO): a prospective cohort study in rural Ethiopia. Nutr Diabetes. 2018;8(1):52.
Melnik BC, John S, Schmitz G. Milk consumption during pregnancy increases birth weight, a risk factor for the development of diseases of civilization. J Transl Med. 2015;13(1):13.
Bedu-Addo K, Ephraim RKD, Tanoe-Blay C, Ahenkorah-Fondjo L, Osei-Darkwah K, Ephraim M, et al. Prevalence and associated factors of fetal macrosomia in a rural community in Ghana. Cogent Med. 2020;7(1):1746602.
Bray Madoue G, NgueleSile S, Lhagadang F, Saleh A. Foetal macrosomia: risk factors, maternal and foetal outcome in N’Djamena mother and child hospital, Chad. Obstet Gynecol Int J. 2018;9(3):40.
Nkwabong E, NzalliTangho GR. Risk factors for macrosomia. J Obstet Gynecol India. 2015;65(4):226–9.
Jasim SK, Al-Momen H, Majeed BA, Hussein MJ. Rate of Fetal Macrosomia with maternal and early neonatal complications in internally moved people affected by violence. Int J Med Res Health Sci. 2018;7(7):141–6.
Osaikhuwuomwan J, Osemwenkha A, Orukpe G. Macrosomic births in a tertiary public hospital: a survey of maternal characteristics and fetal outcome. Ethiop J Health Sci. 2016;26(1):31.
Korrida A. Rates and risk factors in macrosomic newborns among non-diabetic parturients at Tiznit City, Morocco: a case-control study. OALib. 2017;04(12):1–17.
The Author's sincere gratitude goes to the data collectors, supervisors and study participants. The authors also thank Nextgenediting for editorial assistance as part of their Global Initiative.
Ethics approval and consent to participate
Ethical approval was obtained from the ethical clearance board of Hawassa University with reference number RPGC/445/2021, according to the standardized principle and procedure, which is in line with national and WHO guidelines.
The participants were informed about the purpose of the study, and oral consent was obtained from each study participant.
Consent for publication
The authors declare that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Additional file 1.
Legend; Supplementary data of all data generated for neonatal macrosomia among mothers delivered at hospitals in Wolaita Sodo town, 2021.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
About this article
Cite this article
Woltamo, D.D., Meskele, M., Workie, S.B. et al. Determinants of fetal macrosomia among live births in southern Ethiopia: a matched case–control study. BMC Pregnancy Childbirth 22, 465 (2022). https://doi.org/10.1186/s12884-022-04734-8
- Fetal macrosomia
- Wolaita Sodo town
- Southern Ethiopia
- Maternal age