Maternal overweight and obesity among pregnant women in public hospitals of eastern Ethiopia: Multicentre cross-sectional study

Abstract

Background:

Maternal obesity is a global public health concern and the most common medical disorder, affecting 30% of pregnant women globally, with low- and middle-income countries sharing 70% of the worldwide burden. There is a paucity of data relating to the epidemiology of overweight/obesity among pregnant women in Ethiopia. Hence, this study aimed to assess the prevalence and associated factors of maternal overweight/obesity among pregnant women in public hospitals in urban areas of eastern Ethiopia in 2023/2024.

Methods:

An institutional-based cross-sectional design was conducted in public hospitals in Dire Dawa, Harar, and Jigjiga, Ethiopia, from August 11, 2023 to April 24, 2024. All singleton pregnant mothers who started their antenatal care visit in the first 16 weeks of gestation were selected using one-stage cluster sampling. The total sample was proportionally distributed to all public hospitals based on the number of pregnant women per hospital. The data were collected through maternal interviews, medical record reviews, and maternal anthropometric measurements. An ordinal logistic regression analysis was used to assess factors associated with maternal overweight/obesity.

Result:

The prevalence of maternal overweight/obesity among pregnant women was 20.33% (95% confidence interval: 18.81%–21.94%). The odds of being overweight/obese were higher among pregnant women aged between 25 and 34 years (adjusted odds ratio: 1.51; 95% confidence interval: 1.23–1.84), 35–49 years (adjusted odds ratio: 2.91; 95% confidence interval: 1.99–4.25), multiparous (adjusted odds ratio: 1.09; 95% confidence interval: 1.03–1.16), history of macrosomic babies (adjusted odds ratio: 2.09; 95% confidence interval: 1.54–2.83), family history of diabetes mellitus (adjusted odds ratio: 1.51; 95% confidence interval: 1.13–2.03), watching TV (adjusted odds ratio: 1.23; 95% confidence interval: 1.01–1.50), and habit of taking dinner always (adjusted odds ratio: 2.35; 95% confidence interval: 1.16–4.80) than their counterparts.

Conclusion:

Maternal overweight/obesity is an emerging public health challenge in eastern Ethiopia, with one in five women entering pregnancy with the problem. It was associated with socio-demographic, obstetric, genetic, and lifestyle factors. Comprehensive screening services and targeted interventions focusing on at-risk women are essential to identify and address maternal overweight/obesity among pregnant women.

Keywords

Maternal overweight/obesity body mass index pregnant women eastern Ethiopia

Introduction

Obesity is a complex nutritional and metabolic disorder characterized by excessive body fat that impairs health and increases morbidity and mortality.¹ A silent pandemic of obesity is one of the most serious, rapidly growing global public health threats of the 21st century. It is a ticking time bomb that has serious physical, psychological, and social consequences and contributes to preventable deaths yearly.^2
–4 It is difficult to imagine a more important and serious challenge facing maternal and child health than maternal obesity.⁵ Currently, maternal obesity has become a global public health concern and the most common medical disorder in pregnancy that affects every aspect of the continuum of maternity care.^6,7 Approximately, half of pregnant women are either overweight or obese; the estimated pooled prevalence of overweight and obesity during pregnancy is about 43.8% (95% confidence interval (CI): 42.2%–45.4%).^8,9

The increasing prevalence of maternal overweight/obesity worldwide provides a significant challenge for obstetric practice today.^10,11 It has risen dramatically in recent years in every country in the globe, with some variations between countries; approximately one in five pregnant women is obese.^12,13 The prevalence of maternal obesity in Africa ranges between 6.5% and 50.7%.¹⁴ Over the next few decades, maternal overweight and obesity are projected to rise substantially in sub-Saharan Africa (SSA).^15,16 According to the Demographic and Health Survey data from SSA countries, the prevalence of maternal overweight and obesity ranged from 6.7% to 15.9%, and the prevalence of maternal obesity was 6.7%.^15,17 There is a lack of information about the prevalence of maternal overweight and obesity in Ethiopia. A recent meta-analysis confirms the huge research gap concerning maternal obesity in East African countries, including Ethiopia.¹⁸ A prospective cohort study in Addis Ababa showed that among pregnant women, 17.3% were overweight and 0.9% were obese.¹⁹

Overweight and obesity have a multifactorial and complex aetiology that includes the interplay of genetic predisposition, socio-economic factors, metabolic factors, behavioural factors, environmental conditions, cultural beliefs, and chronic psychosocial stress.^2,20
–22 Changes in the global diet toward high-calorie fast foods and sugar-sweetened beverages, lifestyle, physical activity, westernization, and urbanization are among the hypothesized leading contributors to obesity.^15,22

The increment in the prevalence of obesity in pregnant women can create a transgenerational cycle of obesity in the population. Given the important public health implications, there is a clear need to establish national and regional prevalence rates of maternal obesity to support the appropriate organization of maternity services and ensure that at-risk women receive adequate care. Moreover, gaining insight into the association between maternal obesity and its contributing factors could enable the development of a more targeted behavioural change intervention. It can be implemented before, during, and after pregnancy. Nevertheless, there is a dearth of evidence concerning maternal overweight/obesity and its associated factors among pregnant women in Ethiopia, including the study areas. Therefore, this study aims to assess the prevalence and associated factors of maternal overweight/obesity among pregnant women in public hospitals of urban areas in eastern Ethiopia. In line with the study objective, the following two research questions were formulated. First, what is the prevalence of maternal overweight/obesity during pregnancy in public hospitals of urban areas in eastern Ethiopia? Second, what are the factors associated with maternal overweight/obesity during pregnancy in public hospitals of urban areas in eastern Ethiopia?

Methods

Study design, area, and period

This multicentre, institution-based cross-sectional study was conducted in public hospitals across three major cities in eastern Ethiopia: Dire Dawa, Harar, and Jigjiga. Harar, the administrative city of the Harari region, is one of the ancient cities in Ethiopia. Jugal General Hospital and Haramaya University Hiwot Fana Comprehensive Hospital are the two public hospitals in the city. The two public hospitals in Dire Dawa city are the Dil Chora Referral Hospital and the Sabiyan General Hospital. Jigjiga is the capital city of the Somali region. Jigjiga Suldan Shiek Hassan Yabare University Referral Hospital and Kharamara General Hospital are the two public hospitals in the city. The study was conducted from August 11, 2023 to April 24, 2024.

Source and study population

The source population included all pregnant women attending antenatal care (ANC) services at public hospitals in Harar, Dire Dawa, and Jigjiga. The study population comprised pregnant women who initiated their ANC visit in the first 16 weeks of gestation and who attended the delivery service at the selected public hospitals during the study period.

Inclusion and exclusion criteria

All women with a singleton pregnancy who were coming to delivery services at public hospitals and who presented for ANC booking at or before 16 completed weeks of gestation were included. Pregnant women with no body mass index (BMI) in the prenatal card, two or more previous cesarean sections, and a physical deformity that affects the height and weight measurement were excluded.

Sample size determination and sampling procedure

To determine the prevalence of overweight/obesity, the sample size was calculated using the single population proportion formula:

\frac{n = {(Z_{\frac{α}{2}})}^{2} p (1 - p)}{d^{2}}

where:

n = sample size required.

$\begin{array}{l} Z_{\frac{α}{2}} = the standard normal deviation corresponding to \\ 95 % confidence level = 1.96 . \end{array}$

p = prevalence of obesity among pregnant women from previous studies.^19,23
–25

d = margin of error (half of the degrees of freedom) (1%-5%).

Finally, after comparing sample sizes across different prevalences in these previous studies, the largest sample size, 1446,²⁵ was used. Adding 1.5 design effect and 10% for non-response, the final sample was 2314.

The sample size for the factors associated with obesity was calculated using a double-population proportion formula in Epi Info Version 7.2.2.0 (Centers for Disease Control and Prevention, Atlanta, Georgia, USA), with a two-sided confidence level of 95%, a power of 80%, and a 1:4 ratio between pregnant women with and without obesity. The most significant predictors of maternal overweight/obesity among pregnant women from previous studies, such as maternal age,²⁶ place of residence,²⁶ educational status,²⁶ employment,²⁷ and wealth status,²⁷ were used. The largest sample size was 1610. Adding a 1.5 design effect and 10% for non-response. Thus, the final sample was 2576.

One-stage cluster sampling was used to select pregnant women in this study. All public hospitals in Harar, Dire Dawa, and Jigjiga cities were included in this study. The total sample size was proportionally distributed across hospitals based on the number of pregnant women in each hospital’s delivery services. The source population for each objective is estimated from the 12-month report of pregnant women attending the delivery units of the selected hospitals. Then, the average number of pregnant women attending the services per data collection period is calculated. All pregnant women who visited the delivery service during the data collection period and fulfilled the inclusion criteria were selected.

Data collection methods, tools, and procedures

The data were collected through maternal interviews, medical record reviews, and maternal anthropometric examinations. Data were collected by six trained and experienced health professionals using a standard, pre-tested, and structured questionnaire. The questionnaire has five parts: socio-demographic characteristics, obstetric history, behavioural factors, dietary diversity, and anthropometric measurements. This questionnaire was developed following an extensive literature review.^{11,20,28
–33} The data were collected for 8 months. Socio-demographic, obstetric history, behavioural data, and dietary diversity information were collected through face-to-face interviews, while early pregnancy anthropometric measurements were obtained from maternal medical records.

The study participant underwent anthropometric measurements, which included body weight and height. Each measurement was conducted twice, and the average of them was recorded. The third measurement was taken if the difference between the first two measures was >0.5 units. The weight of the pregnant woman was measured using a weighing machine (digital scale) with the subject wearing her usual clothes, shoeless, and without any objects in her pockets. The weighing machine was zeroed immediately before each session and was regularly checked using a known fixed weight. The measurement was taken with a precision of 100 g (to the nearest 0.1 kg). The pregnant woman’s height was measured without shoes by placing the feet together and centring them against a metric scale attached to the wall. The participants were asked to stand upright with their back (heels, calves, buttocks, shoulder blades, and head in contact with the wall) and their eyes directed forward. A wooden drafting triangle was placed on the person’s head, pressing against the hair. The reading was done to the nearest 0.1 cm. The same instruments were used for all the patients in each hospital.

Operational definitions

Overweight and obesity was determined based on the World Health Organization (WHO) definition using early pregnancy maternal BMI, and pregnant women were categorized into four groups: underweight (BMI < 18.5 kg/m²), normal weight (BMI 18.5–24.9 kg/m²), overweight with BMI (25–29.9 kg/m²), and obese (BMI > 30 kg/m²).^34
–36

Minimum dietary diversity: The FAO’s Minimum Dietary Diversity for Women (MDD-W), based on a previous day’s 24-h dietary recall method, was used to determine maternal dietary diversity using 10 food groups. The MDD-W was defined as adequate if the women consumed five or more food groups and inadequate if they consumed fewer than five food groups in the past 24 h.³⁷

The household wealth index was categorized into quintiles (poorest, poor, middle, rich, and richest) using easy-to-collect data on household ownership of selected assets, such as televisions, working refrigerators, bicycles, domestic animals, any agricultural land, and other characteristics of a household.

Prolonged viewing time was defined as watching TV for more than 2 h/day. Viewing times (more than 2 and <2 h/day).³⁸

Data quality control

To ensure data quality, data collectors were trained, questionnaires were pre-tested, the data collection process was supervised, and standard operating procedures (SOPs) were used. Intensive training was provided to the data collectors and supervisors over 2 days. The questionnaire was pre-tested on 128 (5%) pregnant women 2 weeks before actual data collection to check the functionality of the tools. Some modifications were made to the questionnaire based on the findings from this pre-test. After the questionnaire was translated into the local languages (Amharic, Oromiffa, and Somali), the original and translated questionnaires were compared to assess consistency. The overall data collection process was supervised by three supervisors who have relevant experience. The supervisors and the principal investigators checked the collected questionnaires daily for consistency and completeness. SOPs were developed and followed for each measurement to ensure the quality and reliability of anthropometric measurements.

Data processing and analysis

Data was entered into Epi Info Version 7 and imported into STATA Version 18.0 (StataCorp LLC, College Station, Texas, USA) for analysis. The wealth index was determined by using principal component analysis. Moreover, the proportions, patterns, and mechanisms of missing data were assessed. Accordingly, the missing-data mechanism was missing completely at random. The outcome variable, the obesity status of mothers, was measured as undernutrition, normal nutrition, and overweight/obese, which has a natural order. Hence, an ordinal logistic regression using the proportional odds model (POM) was conducted to assess the relationship between independent variables and the odds of maternal overweight/obesity. The POM, also known as the cumulative logit model, was used to model maternal obesity status because the outcome variable is defined in terms of cumulative probabilities rather than the likelihood of a single event. The cumulative probabilities are the probabilities that the response Y falls in category i or below, for each possible i, i = 1, 2,. . ., C, where C is the number of categories.

Ordinal logistic regression with 95% CIs was used to infer associations and make predictions. Initially, each variable was entered into an ordinal regression model as the only independent variable, with a dependent variable. All explanatory variables associated with the outcome variables in bivariate analyses with p ⩽ 0.20, and other clinically relevant variables, were entered into the multiple ordinal logistic regression model after checking multicollinearity. Multicollinearity was assessed by using the variance inflation factor (VIF). The VIF values exceeding 10 were considered a sign of serious multicollinearity, which may require correction. However, no variable had a VIF of more than 5. The VIF ranged from 1.03 (family history of diabetes mellitus (DM)) to 3.56 (eating dinner), with a mean VIF of 1.71.

The proportional odds assumption was tested using both the “brant test” and “omodel commands” for each variable in the final model. The proportional odds assumption was found to be insignificant (p = 0.111) at the 5% level of significance, indicating that the data satisfy the proportional odds assumption. In multivariable analysis, variables with p < 0.05 at the 95% CI were considered significantly associated with obesity among pregnant women.

Ethical considerations

This study was conducted in accordance with the international and national health research ethics principles and regulations. The research project proposal was approved and ethically cleared by the Institutional Health Research Ethics Review Committee of the College of Health and Medical Sciences, Haramaya University (ref. no. IHRERC/045/2023). Permission and voluntary, informed, written, and signed consent were obtained from the hospital head and each pregnant woman before conducting the interview and measurements. In addition, written informed consent was obtained from the legally authorized representative for pregnant women younger than 18 years. Furthermore, assent was obtained from them in accordance with ethical guidelines. For participants with no formal education, the information sheet was read aloud in the local language by a data collector in the presence of a witness (relative and healthcare professional). The witness confirmed that the participant received adequate information about the study, had the opportunity to ask questions, and voluntarily agreed to participate. Voluntary consent was documented using a thumbprint and countersigned by the witness. To ensure confidentiality, the respondent’s name was not written on the questionnaire. Moreover, no reference was made in oral or written reports that could link participants to the research (Supplemental Material).

Results

Socio-demographic characteristics

Of 2560 women with early ANC follow-up during the study period, 7 (0.04%) were excluded due to missing maternal height or weight data. A total of 2553 pregnant women participated in the study, yielding a response rate of 99.11%. The age of the pregnant women ranged from 15 to 45 with a mean of 26.13 (SD +4.91) years. The predominant (54.64%) age group was 25–34 years. Regarding educational status, 38.93% of pregnant women completed primary education, while 42.66% of their partners attended higher education. Employment data showed that 60.52% of mothers were not engaged in paid work. The average household size was 3.73 persons (SD +1.94), ranging from 1 to 17 (Table 1).

Table 1.

Socio-demographic characteristics of pregnant women attending ANC and delivery service at public hospitals of urban areas in eastern Ethiopia, 2024.

Variables	Frequency	Percentage
Age (mean ± SD = 26.13 ± 4.91)
15–24 years	963	37.72
25–34 years	1395	54.64
35–49 years	195	7.64
Marital status
Currently married	2409	94.36
Currently unmarried^a	144	5.64
Maternal educational status
No formal education	369	14.45
Primary education	994	38.93
Secondary education	652	25.54
Higher education	538	21.07
Partner’s educational status
No formal education	111	4.35
Primary education	588	23.03
Secondary education	765	29.96
Higher education	1089	42.66
Mother employment status
Government/NGO employee	417	16.33
No paid employment	1545	60.52
Self-employed	329	12.89
Merchant	190	7.44
Others^b	72	2.82
Place of residence
Urban	2403	94.12
Rural	150	5.88
Household size (mean ± SD = 3.73 ± 1.94)
One to two persons	816	31.96
Three to five persons	1365	53.47
Six or more persons	372	14.57
Wealth index
Poorest	511	20.02
Poorer	577	22.60
Middle	645	25.26
Richer	343	13.44
Richest	477	18.68

ANC: antenatal care.

Currently unmarried includes single, widowed, divorced/separated, and living together.

Farmers, students, drivers, unemployed.

Prevalence of maternal overweight/obesity

The mean early pregnancy BMI of pregnant women was 22.47 (SD ±3.73) kg/m², ranging from 11.69 to 46.67 kg/m². According to early pregnancy BMI, 11.20% (95% CI: 10.04%-12.49%) of pregnant women were underweight, 68.47% (95% CI: 66.04%-70.24%) were within the normal BMI range, and 16.29% (95% CI: 14.91%-17.78%) were classified as overweight, and 4.03% (95% CI: 3.34%-4.87%) were classified as obese. The overall prevalence of maternal overweight/obesity among pregnant women in public hospitals of urban areas in eastern Ethiopia was 20.33% (95% CI: 18.81%-21.94%; Figure 1).

Figure 1.

Prevalence of maternal overweight/obesity among pregnant women attending ANC and delivery service at public hospitals of urban areas in eastern Ethiopia, 2024 (n = 2553).

Factors associated with maternal overweight/obesity

A bivariate ordinal logistic regression analysis indicated that age, educational status, wealth index, parity, history of abortion, history of macrosomic baby, modern contraceptive use, khat chewing, alcohol consumption, physical exercise, family history of DM, TV watching, and eating dinner fulfilled the criteria for inclusion in the final model. The final multiple ordinal logistic regression model revealed that age, wealth index, parity, history of macrosomic baby, family history of DM, TV watching, and taking dinner were significantly associated with maternal obesity.

The odds of being overweight/obese versus the combined underweight and normal BMI categories were 1.51 times (adjusted odds ratio (AOR): 1.51; 95% CI: 1.23–1.84) and 2.91 (AOR: 2.91; 95% CI: 1.99–4.25) greater among pregnant women aged between 25–34 and 35–49 years, respectively, given all the other variables are held constant. The pregnant women in the poorest, poorer, richer, and richest wealth index categories had 30% (AOR: 0.70; 95% CI: 0.53–0.91), 30% (AOR: 0.70; 95% CI: 0.55–0.90), 30% (AOR: 0.70; 95% CI: 0.53–0.93), and 47% (AOR: 0.53; 95% CI: 0.41–0.69) lower odds of being overweight/obese versus the combined normal and underweight, respectively, in compared with middle-income women.

As the number of births increased by one, the odds of being overweight/obese versus the combined normal and underweight were increased by 9% (AOR: 1.09; 95% CI: 1.03–1.16). The odds of being overweight/obese versus the combined normal and underweight were two times (AOR: 2.09; 95% CI: 1.54–2.83) higher among pregnant women with a history of having macrosomic babies.

The pregnant women with a family history of DM had 51% (AOR: 1.51; 95% CI: 1.13–2.03) higher odds of being overweight/obese versus the combined underweight and normal BMI. Furthermore, those pregnant women who watched TV had 23% (AOR: 1.23; 95% CI: 1.01–1.50) higher odds of being overweight/obese versus the combined underweight and normal BMI. The odds of being overweight/obese versus the combined normal and underweight were 2.35 times (AOR: 2.35; 95% CI: 1.16–4.80) higher among pregnant women who always take dinner, given all the other variables are held constant (Table 2).

Table 2.

Bivariate and multiple ordinal logistic regression of factors associated with maternal overweight/obesity among pregnant women in public hospitals of eastern Ethiopia, 2024.

Variables	Maternal BMI status			p value	COR (95% CI)	p value	AOR (95% CI)
Variables	Underweight (n = 286)	Optimum weight (n = 1748)	Overweight/obese (n = 519)	p value	COR (95% CI)	p value	AOR (95% CI)
Maternal age
15–24 years	136	711	116		1.00		1.00
25–34 years	140	930	325	0.000	1.85 (1.55–2.22)	0.000^a	1.51 (1.23–1.84)^a
35–49 years	10	107	78	0.000	4.16 (3.03–5.72)	0.000^a	2.91 (1.99–4.25)^a
Maternal education
No formal education	45	234	90		1.00		1.00
Primary education	117	691	186	0.100	0.81 (0.63–1.04)	0.867	0.98 (0.74–1.28)
Secondary education	62	457	133	0.631	0.94 (0.71–1.23)	0.546	1.10 (0.81–1.48)
Higher education	62	366	110	0.346	0.87 (0.66–1.15)	0.541	0.91 (0.66–1.24)
Wealth index
Poorest	57	378	76	0.000	0.56 (0.44–0.72)	0.009^a	0.70 (0.53–0.91)^a
Poorer	63	392	122	0.009	0.73 (0.58–0.93)	0.005^a	0.70 (0.55–0.90)^a
Middle	59	409	177		1.00		1.00
Richer	33	246	64	0.009	0.69 (0.53–0.91)	0.013^a	0.70 (0.53–0.93)^a
Richest	74	323	80	0.009	0.51 (0.40–0.66)	0.000^a	0.53 (0.41–0.69)^a
Para (mean ± SD)	1.05 ± 1.36	1.30 ± 1.55	1.88 ± 1.98	0.000	1.21 (1.15–1.27)	0.006^a	1.09 (1.03–1.16)^a
History of abortion
No	235	1446	235		1.00		1.00
Yes	51	302	51	0.061	1.23 (0.99–1.52)	0.675	1.05 (0.84–1.31)
History of a macrosomic baby
No	280	1632	433		1.00		1.00
Yes	6	116	86	0.000	3.22 (2.42–4.28)	0.000^a	2.09 (1.54–2.83)^a
Modern contraceptives
No	183	1015	270		1.00		1.00
Yes	103	733	249	0.001	1.33 (1.13–1.58)	0.428	1.08 (0.89–1.30)
Khat chew
No	247	1508	439		1.00		1.00
Yes	39	240	80	0.384	1.11 (0.88–1.41)	0.514	0.92 (0.72–1.18)
Alcohol drink
No	251	1486	425		1.00		1.00
Yes	35	262	94	0.021	1.31 (1.04–1.64)	0.273	1.15 (0.89–1.48)
Physical exercise
No	265	1585	458		1.00		1.00
Yes	21	163	61	0.032	1.35 (1.03–1.78)	0.084	1.29 (0.97–1.73)
Family history of DM
No	261	1615	445		1.00
Yes	25	133	74	0.000	1.70 (1.28–2.26)	0.006	1.51 (1.13–2.03)^a
Watch TV daily
No	120	677	177		1.00		1.00
Yes	166	1071	342	0.019	1.23 (1.03–1.45)	0.023^a	1.23 (1.01–1.50)^a
Frequency of taking dinner
Do not eat	9	22	4		1.00		1.00
Sometimes	16	62	15	0.218	1.68 (0.74–3.85)	0.459	1.37 (0.60–3.14)
Always	261	1664	500	0.009	2.57 (1.27–5.23)	0.018^a	2.35 (1.16–4.80)

AOR: adjusted odds ratio; BMI: body mass index; CI: confidence interval; DM: diabetes mellitus; COD: crude odds ratio.

Show significant association with obesity at p < 0.05.

Discussion

The prevalence of maternal overweight/obesity has risen dramatically in recent years all over the world, mainly in low- and middle-income countries (LMICs). This study also revealed that the prevalence of maternal overweight/obesity among pregnant women in public hospitals of urban areas in eastern Ethiopia was 20.33% (95% CI: 18.81%-21.94%). The study confirmed that maternal overweight/obesity was associated with maternal age, wealth index, number of births, history of having macrosomic babies, family history of DM, watching TV, and habit of always taking dinner.

The notably high prevalence of maternal overweight/obesity observed in the current study can likely be attributed to a variety of interconnected obesogenic behaviours. It includes the shift towards an urban lifestyle, unhealthy eating habits (increased consumption of fast food, sugary beverages, and high-fat snacks), as well as social and cultural perceptions. Urban living, characterized by greater sedentary activities such as extended television viewing, use of digital devices, limited physical exercise, engagement in less labour-intensive occupations, and consumption of energy-dense foods, can be particularly influential in the development of maternal obesity.^14,15,18,22 In certain Ethiopian communities, being overweight or obese is often viewed as a symbol of wealth, prosperity, and success.³⁹

The prevalence of maternal overweight/obesity among pregnant women in the current study was consistent with studies conducted in Kazakhstan (19.7%).⁴⁰ However, this result was slightly higher than studies conducted in Malawi (17%)³² and Addis Ababa, Ethiopia (18.2%).¹⁹ On the other hand, the current finding was lower than studies in Djibouti (24.8%),⁴¹ Libya (32.0%),⁴² Bangladesh (38%),⁴³ Tanzania (38.8%),⁴⁴ Cameron (47%),²⁹ Ghana (47.5%),³⁰ Saudi Arabia (47.8%),⁴⁵ South Africa (65.2%),⁴⁶ and India (63.9%).⁴⁷ This variation might be because of differences in diagnostic criteria, weight measurement methods, inclusion criteria, and timing of anthropometric measurements.

While the current study applied the widely accepted WHO guidelines for BMI classification, the studies in Pakistan and India used the revised consensus guidelines for Asian Indians. These guidelines are lower thresholds than the WHO standards and classify overweight as a BMI of 23.0–24.9 kg/m² and obesity as a BMI above 25 kg/m². These revised criteria capture a larger proportion of pregnant women as overweight or obese than the traditional WHO thresholds.⁴⁷ Cohort studies in Cameron^29,30 and Djibouti City⁴¹ have used self-reported prepregnancy weight to classify BMI when early pregnancy weight was unavailable, which may overestimate BMI. The mean timing of the first ANC visit in Saudi Arabia was 20.63 (SD +8.8) weeks.⁴⁵ Similarly, a study in Bangladesh reported that only 19.5% of pregnant women visited their first ANC in the first trimester.⁴³

This study found that the odds of being overweight/obese were higher among pregnant women aged between 25–34 and 35–49 years in comparison with younger ones. This finding was consistent with several studies that reported the risk of developing maternal overweight/obesity increased with advancing age.^14,48 A survey in Ghana revealed that each 1-year increase in maternal age was associated with a 0.19 increase in mean BMI.⁴⁹ Ahmed et al. reported that the risk of being overweight or obese was higher among older women aged 25–34 and for 35–49 years compared to those who were younger, 15–24 years old.²⁷ This association between maternal age and the risk of overweight and obesity might be due to natural changes in body composition and the slowing of metabolism associated with ageing. As people age, metabolism slows, muscle mass decreases, and is replaced by fat, and body fat tends to accumulate and redistribute. Combined with reduced energy expenditure due to lower levels of physical activity and lifestyle changes, these factors contribute to weight gain, overweight, and obesity.^50,51

The wealth index, a summary measure of socio-economic status, is another variable, that is, significantly associated with the risk of maternal overweight and obesity. This study showed that pregnant women with both lower and higher wealth index categories had lower odds of being overweight/obese. However, controversial findings have been reported from different previous studies regarding this. Studies in the Netherlands and Malaysia have shown that mothers with a low socio-economic status have a higher risk of maternal obesity.⁶ On the other hand, studies from LMICs like India, Tanzania, and Ethiopia have indicated that wealthier households have significantly increased the risk of maternal overweight/obesity.^15,27 Still other studies also revealed no significant association between maternal obesity and women’s wealth or income status.^14,49 This variation might be associated with multiple factors. A cross-sectional study of 13 countries in Latin America and the Caribbean from 1998 to 2017 showed a shift in the burden of obesity across socio-economic strata among women in LMIC, with the highest incidence no longer attributable to the highest wealth groups.⁵² The concept of a “wealth-overweight transition” is observed in many LMICs as their economies grow. This term describes the shift in the burden of overweight/obesity from wealthier to poorer segments of the population as national economies develop over time. By 2040, it is projected that 70.2% (64.1%-76.4%) of countries will have begun or completed the wealth-overweight transition.⁵³ Traditionally, in lower-income settings, obesity was more common among wealthier individuals. However, as economies expand and urbanization increases, lifestyle changes often affect the entire population, not just the wealthier groups.

Consequently, overweight and obesity begin to disproportionately affect poorer communities. A study based on 103 LMICs’ national health surveys revealed that the prevalence of overweight increased substantially among the poorest. Moreover, there will be substantial growth in the burden of overweight in SSA countries.⁵³ This transition presents a unique public health challenge, as lower-income groups often have fewer resources to manage obesity-related health issues. Furthermore, this transition poses another challenge for policymakers in addressing both under- and over-malnutrition. They need to design and implement interventions for a different target population than in the past.

Parity has shown a positive association between maternal overweight and obesity; as the number of births increased, the odds of being overweight/obese also increased. This finding was consistent with a previous study in southern Ghana.⁴⁹ It was reported that women with a previous birth history or multiparous women had a higher chance of having a higher prepregnancy BMI or starting pregnancy with overweight/obesity than nulliparous women.^14,48 On the other hand, findings from a nationwide cross-sectional survey in India reported that maternal obesity was not significantly associated with ANC follow-up.¹⁴ A plausible explanation for this parity-related obesity might be the retention of gestational weight gain, biological factors, and lifestyle factors associated with having large families. Postpartum weight retention can predispose women to an elevated risk of obesity.^54,55 Fadzil et al. showed that 33.8% of mothers retained at least 5 kg of their preconception weight at 6 months postpartum, and higher gestational weight gain (⩾12 kg) was significantly associated with weight retention.⁵⁶ This parity-related obesity may also be linked to postpartum dietary habits and physical inactivity among mothers. In the cultural practices around high-energy food intake following childbirth can lead to increased body fat accumulation with each subsequent pregnancy.

This study also revealed an association between a history of delivering a macrosomic baby and an increased risk of maternal overweight and obesity. This association may suggest a reverse relationship, where delivering a macrosomic baby might indicate that the mother was already overweight or obese in earlier pregnancies. Various previous studies showed that maternal obesity is the most important predictor and strongest risk factor of delivering an infant with macrosomia.^57
–61 A study in Tigray, Ethiopia, found a positive association between prepregnancy BMI and the likelihood of delivering a macrosomic baby, where the odds of delivering a macrosomic baby were significantly higher among overweight and obese mothers.⁵⁷ This relationship between prepregnancy BMI and fetal macrosomia may be mediated by various biochemical factors in both the mother and fetus. Maternal obesity is associated with increased fetal glucose and insulin levels, likely because of heightened maternal insulin resistance.

In addition, elevated triglyceride levels in obese pregnant women lead to increased free fatty acids, which, through placental lipase activity, are transferred to the fetus. This combination of excess glucose, insulin, and fatty acids promotes excessive fetal growth and contributes to the risk of macrosomia.^61,62 Song et al. reported that high triglyceride levels and gestational DM have chain-mediating effects in the causal pathway between maternal prepregnancy overweight/obesity and the risk of fetal macrosomia.⁶³

A family history of DM, an indicator of a higher genetic predisposition to a disease, showed a positive association with the risk of maternal obesity and overweight. Women who have a family history of DM were at increased risk of obesity compared with women without a family history of diabetes. Previous studies also reported this intergenerational cycle of DM and obesity.^64
–66 A family history of DM is associated with a range of metabolic abnormalities, including obesity. Cederberg et al. reported that a family history of DM is a double risk factor, increasing the likelihood of being overweight.⁶⁶ This vicious intergenerational cycle of DM and obesity may reflect the complex interplay among shared genetic traits and socio-economic, cultural, environmental, and behavioural factors. These factors are closely intertwined within families, making it challenging to separate their individual effects. One critical point to consider is the possibility of underreporting a family history of disease. Reporting a family history may depend on people’s health-seeking behaviour or health literacy, the family’s economic status, the characteristics (severity, burden, and duration) of the disease, and the availability of diagnostic and treatment facilities.

In this study, behavioural factors, such as watching TV, showed a positive association with the risk of developing maternal obesity and overweight. This finding was in line with studies conducted in Myanmar,⁶⁷ Bangladesh,⁶⁸ Ghana,⁶⁹ and Tanzania.²⁷ These studies revealed that exposure to media such as watching TV, reading newspapers and magazines strongly correlated with overweight and obesity among women. This association between watching TV and the risk of overweight and obesity might be explained by prolonged inactivity and reduced levels of physical activity. Watching TV might be a good proxy indicator for spending prolonged sedentary time and a higher socio-economic status. Both Bangladesh⁶⁸ and Tanzania²⁷ studies tried to correlate this association with other habits that people usually do while watching TV, like regular consumption of alcohol and obesogenic foods such as chips and fried potatoes, which can also contribute to overweight and obesity.

This study also revealed that pregnant women with a habit of having dinner regularly had higher odds of being overweight/obese. This finding was also consistent with other studies.^70
–73 Multiple epidemiological studies found that late-night meals and later eating schedules adversely affect weight regulation and promote the development of obesity over time.^74,75 The increased risk of obesity associated with frequent nighttime eating likely results from multiple factors, including reduced metabolic efficiency, disruptions in energy intake, lower energy expenditure, dysregulation of appetite hormones, and sleep disturbance.⁷⁴ In addition to taking dinner and the timing of dinner, the type of food is a major factor that should be considered. Xiao et al. reported a clear relationship between increased nighttime energy intake and the risk of obesity. Those people who consumed carbohydrate and protein at night were at a higher risk of developing obesity.⁷⁰

The present study had some limitations. First, using early pregnancy weight instead of prepregnancy BMI could lead to under- or over-reporting of maternal overweight and obesity, as some women may experience weight loss because of nausea and vomiting, or slight weight gain during early pregnancy (gestational age (GA) < 16 weeks). Therefore, the findings may not fully reflect the true prevalence of overweight/obesity in the general obstetric population. Second, this study included only women who initiated ANC during early pregnancy, whereas most pregnant women in LMICs, including Ethiopia, typically begin ANC later in pregnancy. The inclusion of women attending urban public hospitals with early ANC booking may have introduced selection and institutional bias. Women attending private health facilities or those who do not seek early ANC may differ systematically in socio-demographic or health characteristics, which could limit the generalizability of the findings to all pregnant women in eastern Ethiopia. Third, the cross-sectional design limits the ability to determine temporal relationships between maternal overweight/obesity and associated factors, such as the habit of eating dinner and watching TV. Fourth, behavioural factors, obstetric history, and dietary diversity, mainly assessed via 24-h dietary recall, were self-reported through face-to-face interviews, which may be subject to recall and social desirability bias. Finally, although the study was multicentre and conducted in public hospitals, women from rural areas and those attending private health institutions were likely underrepresented, which may further limit the generalizability of the findings beyond urban hospital settings.

Conclusion

Maternal overweight/obesity is an emerging public health challenge in urban areas of eastern Ethiopia, with one in five pregnant women entering pregnancy with the problem. Maternal overweight/obesity was associated with socio-demographic (age, wealth status), obstetric (parity, history of macrosomic baby), genetic (family history of DM), and lifestyle (watching TV, eating dinner) factors. Healthcare programmers and healthcare professionals, mainly obstetricians, midwives, and nurses, should apply comprehensive screening services to identify pregnant women with maternal overweight and obesity. Health care providers should give special emphasis to pregnant women at the highest risk of maternal overweight and obesity, including older women, multiparous women, those with a history of macrosomic baby, a family history of DM, and a sedentary lifestyle (watching TV). It is also crucial to educate all pregnant women about the main risk factors of maternal overweight/obesity and the importance of a healthy lifestyle to improve their awareness of controlling and preventing maternal obesity.

Supplemental Material

sj-docx-1-smo-10.1177_20503121261432875 – Supplemental material for Maternal overweight and obesity among pregnant women in public hospitals of eastern Ethiopia: Multicentre cross-sectional study

Supplemental material, sj-docx-1-smo-10.1177_20503121261432875 for Maternal overweight and obesity among pregnant women in public hospitals of eastern Ethiopia: Multicentre cross-sectional study by Assefa Tola, Nega Assefa, Lemessa Oljira, Tadesse Gure Eticha and Tesfaye Gobena in SAGE Open Medicine

Footnotes

Acknowledgements

The authors would like to thank Haramaya University for providing funds to cover data collection expenses. We would like to express our heartfelt appreciation to the data collectors, supervisors, pregnant women, hospital staff working at the obstetric unit, and heads of hospitals for their willingness and unreserved contribution to this study.

ORCID iDs

Assefa Tola

Nega Assefa

Tadesse Gure Eticha

Ethical considerations

Ethical clearance was secured from the Institutional Health Research Ethics Review Committee (IHRERC) of the College of Health and Medical Sciences (CHMS), Haramaya University (ref. no. IHRERC/045/2023). This study was conducted in accordance with the international and national health research ethics principles and regulations.

Author contributions

A.T.: conceptualization, data curation, formal analysis, funding acquisition, methodology, resources, writing – original draft, writing – review and editing. N.A., L.O., T.G., and T.G.: conceptualization, funding acquisition, methodology, software, writing – original draft, writing – review and editing. All the authors read and approved the final article. All authors took responsibility for the accuracy of the analysis and the contents of the article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was suported by Haramaya University (HURG_2022_02_04_45).

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability statement

The datasets analyzed in the current study are available from the corresponding author upon reasonable request.

Supplemental material

Supplemental material for this article is available online.

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