Global trends in prevalence of maternal overweight and obesity: A systematic review and meta-analysis of routinely collected data retrospective cohorts

Main Article Content

Lisa Kent
School of Medicine, Dentistry and Biomedical Sciences, Centre for Public Health, Queen's University Belfast, UK, BT12 6BA
Meabh McGirr
School of Medicine, Dentistry and Biomedical Sciences, Centre for Public Health, Queen's University Belfast, UK, BT12 6BA
Kelly-Ann Eastwood
School of Medicine, Dentistry and Biomedical Sciences, Centre for Public Health, Queen's University Belfast, UK, BT12 6BA; Fetal Medicine Unit, St Michaels Hospital, University Hospitals Bristol and Weston NHS Foundation Trust, Bristol, UK, BS2 8EG

Abstract

Pregnant women with obesity are at greater risk of complications during pregnancy, peripartum and post-partum, compared to women with healthy BMI. Worldwide data demonstrating the changes in trends of maternal overweight and obesity prevalence informs service development to address maternal obesity, while directing resources to areas of greatest need. This systematic review and meta-analysis of population level data sought to evaluate global temporal changes in prevalence of maternal obesity and overweight/obesity, and compare trends between regions.


Pooled prevalence of obesity and overweight/obesity was estimated using random effects meta-analysis. Temporal and geographical trends in prevalence of obesity and overweight/obesity were examined using linear regression.


From 11,684 publications, 94 met inclusion criteria representing 121 study cohorts (Europe n = 71; North America n = 23; Australia/Oceania n = 10; Asia n = 5; South America n = 12), totalling 49,009,168 pregnancies. No studies from Africa met the inclusion criteria. Eighty studies (85.1%) were evaluated as having a low risk of bias and 14 studies (14.9%) moderate. In the most recent full decade (2010-2019), global prevalence of maternal obesity was estimated as 16.3% (95% confidence interval (CI): 15.1-17.5%), or approximately one in six pregnancies. Combined overweight/obesity in pregnancy had a pooled prevalence of 43.8% (95%CI: 42.2-45.4%), approaching half of all pregnancies. In each continent, an upward trend similar to the global trend was observed. North America demonstrated the highest prevalence (obesity: 18.7% (95%CI: 15.0-23.2%)); overweight/obesity: 47.0% (95%CI: 45.7-48.3%)) and Asia demonstrated the lowest prevalence (obesity: 10.8% (95%CI: 7.0-16.5%)); overweight/obesity: 28.5% (95%CI: 18.3-41.5%)). Both maternal obesity and combined overweight/obesity prevalence increased annually by 0.34% and 0.64% (p < 0.001), respectively. Our linear regression model estimates current global prevalence of maternal obesity as 20.9% (95%CI 18.6-23.1%) and projects that this will increase to 23.3% (95%CI 20.3-26.2%) by 2030.


Globally, maternal obesity and overweight/obesity prevalence is high and increasing, but varies greatly between regions, being highest in North America and lower in Asia. Maternity services across the globe should be adequately resourced to cope with the complexity of needs of pregnant women living with obesity. Future public health interventions should focus on reversing the high prevalence of maternal obesity observed across the globe. The availability of population-level data and research varies between regions, with more data required to understand the needs of maternal populations in the continents of Africa and Asia. Globally, there is a need for improved harmonisation and publication of data for monitoring and improvement of maternal inequalities.

Introduction

Globally, rates of overweight and obesity prevalence have been increasing steadily, rising from 9.8% in 2006 to 13.2% in 2016 and are now reaching epidemic proportions worldwide [1-3]. Currently more than 50% of all adult women are overweight or obese, and this trend is reflected in women of child-bearing age [1].

There is high level of evidence that maternal obesity is associated with a range of adverse pregnancy complications and perinatal outcomes and is recognised as an important maternal risk factor by health authorities around the globe [4–8]. Outcomes of concern in pregnant women with obesity include hypertensive disorders of pregnancy (pre-eclampsia and gestational hypertension), gestational diabetes, large for gestational age fetuses, premature birth, stillbirth and infant mortality, induction of labour, emergency caesarean section, postpartum haemorrhage and shoulder dystocia [4–8].

Body mass index (BMI) is a commonly used indicator of healthy weight, calculated as an individual’s weight in kilograms divided by the square of height in metres (kg/m2) [1]. The World Health Organization (WHO) classification categorises adults (>20 years) into the following: underweight (BMI <18.5 kg/m2), normal weight (BMI 18.5–24.9 kg/m2), overweight (BMI 25–29.9 kg/m2), obesity class I (BMI 30–34.9 kg/m2), obesity class II (BMI 35–39.9 kg/m2) and obesity class III (BMI >40 kg/m2) [1].

There is a lack of conclusive worldwide data demonstrating the changes in trends of maternal overweight and obesity. Until recently, no study had explored the changing trends in prevalence across the globe [9]. Our review utilises population level data to provide a comprehensive update to the publication from Martínez-Hortelano et al. published in 2020, extending the inclusion and exclusion criteria to select cohorts that more closely reflect the populations studied [9]. Several longitudinal studies have investigated maternal obesity, however, these are not without their limitations - excluding pregnancies by gestational age cut offs [10] and including only those which resulted in live births [11–14]. Given the association between increased BMI, preterm birth and stillbirth, these studies could result in systematic bias and therefore underestimate the true prevalence of overweight and obesity.

To better understand trends in the prevalence of overweight and obesity irrespective of maternal characteristics and birth outcomes, this systematic review aims to explore population-level trends in prevalence of maternal obesity and overweight/obesity over time and across regions of the world.

Methods

Sources

Our systematic review was carried out in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (Supplementary Material 1) [15]. A comprehensive search strategy was developed by two authors (LK, KAE). After discussion with specialist subject librarians, the agreed search terms were:

  1. “pregnan$” OR “matern$” OR “obstetric$” OR “mother” OR “expectant” OR “gestation$”
  2. “body mass index” OR “BMI” OR “weight” OR “overweight” OR “obesity” OR “obese”
  3. 1. AND 2.

An extensive literature search was carried out by LK in March 2020 (and updated in February 2021 and February 2023) using Ovid MEDLINE, EMBASE and CINAHL Plus searching databases from inception. Searches were restricted to full-text, human studies reported in the English language.

Study selection

Inclusion and exclusion criteria were developed to target population level studies that used administrative and routinely collected healthcare data. Studies were eligible for inclusion if (i) they presented obesity prevalence data for pregnant women; (ii) BMI was recorded pre-pregnancy (for cases of singleton or multiple births) or recorded in early pregnancy (for singleton pregnancies) [16] and (iii) the study used the WHO classification boundaries for overweight and obesity, or alternative boundaries for classification specific to race/ethnicity of the cohort studied. Studies were excluded if they had exclusion criteria relating to (i) age; (ii) race; (iii) maternal health status; (iv) birth outcome; (v) gestation at birth; or if they had (vi) reported fewer than one hundred cases; (vii) reported only livebirths; or (viii) required voluntary participation. Exclusion criteria (i) to (v) and (vii) were chosen as these characteristics are associated with BMI and have the potential to bias estimates of prevalence. The exclusion criterion of fewer than 100 cases was used as smaller studies may not reflect populations being studied. Studies that required voluntary participation were also excluded as women who are self-conscious of their BMI status may not volunteer or consent to studies which require this measurement. After removing duplicates, screening of all titles and abstracts was performed by two authors (LK & KAE March 2020 and February 2021; MMcG & LK February 2023), and full texts obtained for studies identified by all authors at this stage. Lastly, hand searching of full text papers was performed (LK) to identify any additional publications suitable for inclusion. Full text copies of the remaining articles were assessed for inclusion by three authors (LK, KAE, MMcG) with each receiving an allocation and all assessments being reviewed by the full team. Consensus decisions were made as a team on inclusion or exclusion where necessary. Additional studies were excluded if they had the same study population from the same curated data set as another included study.

Data extraction

A detailed data extraction form was developed (LK, KAE, MMcG) (Supplementary Material 2). In addition to inclusion and exclusion criteria, these forms required details of the country and region, participant characteristics, the BMI classification used, and the number and percentage of pregnant women recorded within each BMI category. Each author extracted data for a proportion of the included studies, with 10% of papers randomly selected for independent verification by one other author. Study authors were contacted via email for clarification of inconsistencies or missing data.

Risk of bias assessment

The Hoy Risk of Bias tool was used to assess the quality and validity of measured data, and the risk of bias of included studies (LK, KAE, MMcG) (Supplementary Material 3) [17]. This validated tool for assessing the risk of bias in prevalence studies is comprised of ten questions, assessing four domains of bias. Questions 1 to 4 focus on the external validity of studies (assessing selection bias and nonresponse bias); and questions 5 to 10 consider the internal validity of studies (evaluating measurement bias and analysis-related bias). If criteria were fulfilled and the answer to the question was ‘Yes’ (low risk), a score of 0 was assigned. If the criteria were not met (answering ‘No’ or ‘Unclear’ to the question, meaning high risk), a score of 1 was assigned. To ensure harmonisation between authors when assessing each criterion, additional guidance was developed to reflect the context of this systematic review of population-based studies reporting prevalence of maternal overweight and obesity (Supplementary Material 3). If the overall score was 0–3, 4–6 or 7–10, the risk of bias was classified respectively as ‘Low’, ‘Moderate’ or ‘High’, applying the same approach as previous studies [18, 19].

Development of database

A comprehensive study database was designed (LK, MMcG) to capture study characteristics and reported prevalence of each BMI category. The data was entered by MMcG, with a random 10% sample checked for quality assurance by a second author (LK). If separate study populations were examined in one paper, each were added into the database separately, and noted as Study ID A, B, etc. to ensure there was no overlap between these study populations.

Data synthesis and analysis

All data preparation and analysis was performed using R [20]. For each included study, the midpoint date was calculated. Prevalence was calculated as the percentage of total pregnancies classed as obese (BMI ≥30 kg/m2), and overweight/obese (BMI ≥25 kg/m2).

Random effects meta-analysis was conducted on untransformed prevalence data using the DerSimonian and Laird [21] method to summarise estimates and 95% confidence intervals for the pooled prevalence of obesity and overweight/obesity. The percentage of total variability due to between-study heterogeneity was estimated using the I2 statistic, however this was expected to be large due to anticipated temporal and regional differences in maternal obesity from study cohorts spanning different time periods. In the first instance this was performed for all study cohorts reflecting global prevalence over the full time-period covered by the included studies. This process was then repeated for each region of the world and each decade to describe the prevalence in different geographical regions over time. The pooled prevalence for obesity and overweight/obesity were then visualised as bar charts with 95% confidence intervals.

The relationship between prevalence and time was visualised for (i) obesity, and (ii) overweight/obesity using scatterplots of individual study cohorts. For overall global trends, a linear fitted line with 95% confidence intervals was created from all study cohorts. To visually compare trends between different geographical regions, linear fitted lines were created for maternal obesity prevalence estimates grouped by area of the world.

Linear regression was then used to model the trends in global prevalence over time, first using the full dataset containing all study cohorts. The midpoint year of the study period for each included study was used as the independent variable, and prevalence of i) obesity and ii) overweight and obesity was used as the dependent variable. As a sensitivity analysis, further linear regression models were fitted using data from i) 1980 onwards, ii) 1990 onwards, iii) 2000 onwards and iv) 2010 onwards. To compare prevalence between areas of the world, linear models were then fitted using midpoint year of study and area of the world as independent variables, and Europe, as the category with the most studies, selected as the reference. The estimated differences in prevalence and 95% confidence interval were calculated for each model and p values considered statistically significant at ≤0.05.

Registration

The study protocol was registered with the Research Registry (UIN: reviewregistry998) on 23rd September 2020.

Results

Eligibility of studies

After screening 11,635 papers, 94 studies met the inclusion criteria (PRISMA Flow Diagram, Figure 1). Nine of the included articles reported data from multiple cohorts [22-30] resulting in 121 study cohorts.

Figure 1: PRISMA flow diagram. Reasons for full-text articles to be excluded does not sum to 229 as many studies had more than one reason for exclusion.

Characteristics of included studies

Overall, data from 49,009,168 pregnancies were included. Characteristics of included studies are presented in Table 1 (n = 94). Of the 121 study cohorts present in the included studies, the majority were European populations (n = 71), with 23 cohorts in North America, 12 cohorts in South America, ten from Australia and Oceania, and five from Asia. No studies were found representing populations from Africa. Included study cohorts represent the populations of 25 different countries, with all but two studies having taken place in high income countries [31, 32], according to the World Bank definition [33]. Fifty-one of the study cohorts were countrywide studies, using data from national databases, whilst 70 were regional studies which used data from databases that covered a smaller geographical region within a country, or in some cases a single hospital or group of hospitals. The years covered by the included studies span from 1959 to 2022. Midpoint dates of included studies ranged from 1962 to 2021. Seventy-four study cohorts included only women who delivered singleton births and 36 included both singleton and multiple births, whilst eleven were unclear as to the order of the included pregnancies. Maternal BMI was measured pre-pregnancy for women in 57 study cohorts, in early pregnancy for 38 cohorts, and a combination of both pre-pregnancy and early pregnancy measurement in five study populations. Timing of BMI measurement was unclear in 21 study cohorts. BMI was categorised according to the WHO classification in the majority of study cohorts (n=95); three studies adjusted BMI classification according to ethnicity. In the remaining study cohorts that did not use the WHO BMI classification or ethnicity-specific classification, only the boundary between underweight and healthy differed, and therefore did not affect the current analysis of prevalence of overweight and obesity.

Study ID Start date End date Country Region Countrywide or regional Singleton or multiple births Time of measurement parity cases
Asia
Jin 2021 [ 34 ] Jan-13 Dec-19 Japan Fukushima Regional Singleton & Multiple Pre-pregnancy Nulli & Multi 3610
Leung 2008 [ 31 ] Jan-95 Dec-05 China Hong Kong Regional Singleton Early pregnancy Nulli & Multi 29303
Ma 2016 [ 32 ] Jan-02 Dec-12 China Zhangqiu Regional Singleton Pre-pregnancy Nulli & Multi 28256
Michlin 2000 [ 35 ] Aug-95 Nov-95 Israel Nahariya Regional Singleton & Multiple Unclear Nulli & Multi 887
Morikawa 2012 [ 36 ] Jan-07 Dec-09 Japan – Regional Singleton Pre-pregnancy Nulli & Multi 138530
Australia / Oceania
Callaway 2006 [ 37 ] Jan-98 Dec-02 Australia Brisbane Regional Singleton Pre-pregnancy Nulli & Multi 14230
Cheney 2018 [ 38 ] Jan-90 Dec-14 Australia Sydney Regional Singleton Early pregnancy Nulliparous 42582
Cunningham 2013 [ 39 ] Jan-05 Dec-10 Australia North-East Victoria Regional Singleton Early pregnancy Nulli & Multi 6138
Davey 2020 [ 40 ] Jan-09 Dec-13 Australia Victoria Regional Singleton & Multiple Unclear Nulli & Multi 325763
Dodd 2011 [ 41 ] Jan-08 Dec-08 Australia South Australia Regional Singleton Early pregnancy Nulli & Multi 11233
Donald 2020 [ 42 ] Jan-05 Dec-15 New Zealand – Countrywide Singleton & Multiple Unclear Nulli & Multi 455010
Knight-Agarwal 2016 [ 43 ] Jan-08 Dec-13 Australia Australian Capital Territory Regional Singleton Early pregnancy Nulli & Multi 14857
McIntyre 2012 [ 44 ] Jan-98 Dec-09 Australia Brisbane Regional Singleton Pre-pregnancy Nulli & Multi 75432
San Martin Porter 2021 [ 45 ] Jul-15 Dec-15 Australia Queensland Regional Singleton Pre-pregnancy Nulli & Multi 27817
Watson 2013 [ 46 ] Jan-08 Dec-08 Australia Queensland Regional Singleton Unclear Nulli & Multi 37912
Europe
Abayomi 2009 [ 47 ] Jul-04 Jun-05 England Liverpool Regional Unclear Unclear Nulli & Multi 6913
Arrowsmith 2011 [ 48 ] Jan-04 Dec-08 England Liverpool Regional Singleton Early pregnancy Nulli & Multi 29224
Bak 2016 [ 49 ] Jan-08 Dec-12 Denmark – Countrywide Singleton Pre- & Early Nulli & Multi 187486
Baker 2012 [ 50 ] Apr-07 Mar-08 England London Regional Unclear Unclear Nulli & Multi 4221
Bastola 2020 [ 51 ] Jan-04 Dec-14 Finland – Countrywide Singleton Pre-pregnancy Nulli & Multi 364678
Bhattacharya 2007 [ 7 ] Jan-76 Dec-05 Scotland Aberdeen Regional Singleton Unclear Nulliparous 24241
Blomberg 2010 [ 52 ] Jan-95 Dec-07 Sweden – Countrywide Unclear Pre-pregnancy Nulli & Multi 1049582
Blomberg 2011 [ 53 ] Jan-97 Dec-08 Sweden – Countrywide Singleton Pre-pregnancy Nulli & Multi 959469
Briese 2011 [ 54 ] Jan-98 Dec-00 Germany – Countrywide Singleton Unclear Nulliparous 243571
Cedergren 2007 [ 55 ] Jan-94 Dec-04 Sweden – Countrywide Singleton Pre-pregnancy Nulli & Multi 298948
Cedergren 2008a [ 24 ] Jul-95 Dec-03 Sweden – Countrywide Singleton Pre- & Early Nulli & Multi 636141
Cedergren 2008b [ 24 ] Jan-92 Dec-01 Sweden – Countrywide Singleton Pre- & Early Nulli & Multi 781725
Cnattingius 1998 [ 56 ] Jan-92 Dec-93 Sweden – Countrywide Singleton Pre-pregnancy Nulli & Multi 167750
Collier 2017 [ 57 ] Jan-81 Dec-12 Scotland – Countrywide Singleton & Multiple Unclear Nulli & Multi 47290
Denison 2014 [ 58 ] Jan-03 Feb-10 Scotland – Countrywide Singleton Early pregnancy Nulli & Multi 124280
Deruelle 2017 [ 59 ] Jan-99 Dec-09 France – Countrywide Singleton & Multiple Pre-pregnancy Nulli & Multi 346617
Erjavek 2016a [ 25 ] Jan-10 Dec-10 Croatia – Countrywide Singleton & Multiple Pre-pregnancy Unclear 42656
Erjavek 2016b [ 25 Jan-14 Dec-14 Croatia – Countrywide Singleton & Multiple Pre-pregnancy Unclear 39092
Farah 2009 [ 60 ] Jan-07 Dec-07 Ireland Dublin Regional Singleton Early pregnancy Nulli & Multi 5824
Frischknecht 2009a [ 26 ] Jan-86 Dec-86 Switzerland Munsterlingen Regional Singleton Early pregnancy Nulli & Multi 690
Frischknecht 2009b [ 26 ] Jan-04 Dec-04 Switzerland Munsterlingen Regional Singleton Early pregnancy Nulli & Multi 668
Gardosi 2009 [ 61 ] Jan-92 Dec-95 Sweden – Countrywide Singleton Early pregnancy Nulli & Multi 354205
Hedegaard 2014 [ 62 ] Jan-03 Dec-12 Denmark – Countrywide Singleton & Multiple Unclear Nulli & Multi 498770
Henriksson 2020 [ 63 ] Jan-10 Dec-18 Sweden – Countrywide Singleton Early pregnancy Nulli & Multi 535609
Heslehurst 2007 [ 64 ] Jan-90 Dec-04 England Middlesbrough Regional Unclear Pre- & Early Nulli & Multi 36361
Heslehurst 2012 [ 65 ] Jan-95 Dec-07 England – Countrywide Unclear Unclear Nulli & Multi 502474
Huisman 2013 [ 66 ] Aug-04 Aug-06 Netherlands – Countrywide Singleton & Multiple Unclear Nulli & Multi
Johansson 2017 [ 67 ] Jul-08 Oct-14 Sweden Stockholm-Gotland Regional Singleton Early pregnancy Nulli & Multi 160560
Kanagalingham 2005a [ 27 ] Jan-90 Dec-90 Scotland Glasgow Regional Singleton Early pregnancy Nulli & Multi 203
Kanagalingham 2005b [ 27 ] Jan-02 Dec-04 Scotland Glasgow Regional Singlet on Early pregnancy Nulli & Multi 312
Kent 2021a [ 28 ] Jan-10 Dec-10 Northern Ireland – Countrywide Singleton Early pregnancy Nulli & Multi 9995
Kent 2021b [ 28 ] Jan-11 Dec-11 Northern Ireland – Countrywide Singleton Early pregnancy Nulli & Multi 22478
Kent 2021c [ 28 ] Jan-12 Dec-12 Northern Ireland – Countrywide Singleton Early pregnancy Nulli & Multi 22177
Kent 2021d [ 28 ] Jan-13 Dec-13 Northern Ireland – Countrywide Singleton Early pregnancy Nulli & Multi 22184
Kent 2021e [ 28 ] Jan-14 Dec-14 Northern Ireland – Countrywide Singleton Early pregnancy Nulli & Multi 22150
Kent 2021f [ 28 ] Jan-15 Dec-15 Northern Ireland – Countrywide Singleton Early pregnancy Nulli & Multi 22182
Kent 2021g [ 28 ] Jan-16 Dec-16 Northern Ireland – Countrywide Singleton Early pregnancy Nulli & Multi 21644
Kent 2021h [ 28 ] Jan-17 Dec-17 Northern Ireland – Countrywide Singleton Early pregnancy Nulli & Multi 10150
Khashan 2009 [ 68 ] Jan-04 Dec-06 England North West Regional Singleton Early pregnancy Nulli & Multi 99403
Kristensen 2005 [ 69 ] Jan-89 Dec-96 Denmark Aarhus Regional Singleton Pre-pregnancy Nulli & Multi 24505
Le Ray 2015a [ 29 ] Jan-03 Dec-03 France – Countrywide Singleton & Multiple Pre-pregnancy Nulli & Multi 13605
Le Ray 2015b [ 29 ] Jan-10 Dec-10 France – Countrywide Singleton & Multiple Pre-pregnancy Nulli & Multi 13644
Lindholm 2015 [ 70 ] Jan-06 Dec-08 Sweden – Countrywide Singleton Early pregnancy Nulliparous 71638
Lucovnik 2018 [ 71 ] Jan-02 Dec-15 Slovenia – Countrywide Singleton Pre-pregnancy Nulli & Multi 271913
Maier 2016 [ 72 ] Jan-14 Dec-14 Germany Berlin Regional Unclear Pre-pregnancy Unclear 591
Mantakas 2010 [ 73 ] Jan-01 Nov-08 England Sheffield Regional Singleton Early pregnancy Nulliparous 6509
McKeating 2015 [ 74 ] Jan-09 Dec-13 Ireland Dublin Regional Singleton Early pregnancy Nulli & Multi 41135
Melchor 2019 [ 75 ] Jan-13 Dec-17 Spain Vizkcaya Regional Singleton Pre-pregnancy Nulli & Multi 16609
Mogren 2018 [ 76 ] Jan-11 Dec-16 Sweden – Countrywide Singleton Early pregnancy Nulli & Multi 536267
Nishikawa 2017 [ 77 ] Jan-04 Dec-12 England London Regional Singleton Early pregnancy Nulli & Multi 42591
Nohr 2012 [ 78 ] Jan-92 Dec-06 Sweden – Countrywide Singleton Pre-pregnancy Nulli & Multi 1199328
Oteng-Ntim 2013 [ 79 ] Jan-04 Dec-08 England London Regional Singleton Early pregnancy Nulli & Multi 17910
Ovesen 2011 [ 80 ] Jan-04 Jun-10 Denmark – Countrywide Singleton Pre-pregnancy Nulli & Multi 369347
Penn 2014 [ 81 ] Jan-04 May-12 England South London Regional Singleton Early pregnancy Nulli & Multi 42678
Premru-Srsen 2019 [ 82 ] Jan-13 Dec-17 Slovenia – Countrywide Singleton Pre-pregnancy Nulli & Multi 98820
Raja 2012 [ 83 ] Jan-02 Dec-07 England London Harrow Regional Unclear Early pregnancy Nulli & Multi 24632
Ramoniene 2017 [ 84 ] Jan-10 Dec-10 Lithuania Kaunas Regional Singleton Pre-pregnancy Nulli & Multi 3371
Rankin 2010 [ 85 ] Jan-03 Dec-05 England North East Regional Singleton Unclear Unclear 30703
Rantakallio 1995a [ 30 ] Jan-66 Dec-66 Finland Northern Regional Unclear Pre-pregnancy Nulli & Multi 10969
Rantakallio 1995b [ 30 ] Jul-85 Jun-86 Finland Northern Regional Unclear Pre-pregnancy Nulli & Multi 9128
Reynolds 2019 [ 86 ] Jan-10 Dec-17 Ireland Dublin Regional Unclear Unclear Nulli & Multi 67349
Reynolds 2020 [ 87 ] Jan-09 Dec-17 Ireland Dublin Regional Singleton Early pregnancy Nulliparous 9724
Scott-Pillai 2015 [ 8 ] Jan-04 Dec-11 Northern Ireland Belfast Regional Singleton Early pregnancy Nulli & Multi 30298
Sebire 2001 [ 88 ] Jan-89 Dec-97 England London North West Thames Regional Singleton Early pregnancy Nulli & Multi 325395
Stepan 2006 [ 89 ] Jan-01 Dec-04 Germany Leipzig Regional Singleton Pre-pregnancy Unclear 5067
Villamor 2006 [ 90 ] Jan-92 Dec-01 Sweden – Countrywide Singleton Pre-pregnancy Nulliparous 151025
North America
Abenhaim 2007 [ 91 ] Apr-87 Mar-97 Canada Montreal Regional Unclear Pre-pregnancy Nulli & Multi 18643
Berendzen 2013 [ 92 ] Jan-09 Dec-09 USA Tennessee (Knoxville) Regional Singleton Pre-pregnancy Nulli & Multi 2235
Butwick 2018 [ 93 ] Jan-08 Dec-12 USA California Regional Singleton & Multiple Pre-pregnancy Nulli & Multi 2176673
Chen 2004 [ 94 ] Feb-93 Jun-01 USA Florida (Gainsville) Regional Singleton Pre-pregnancy Nulliparous 3355
Crane 2009 [ 95 ] Apr-01 Mar-07 Canada Newfoundland and Labrador Regional Singleton Pre-pregnancy Nulli & Multi 5377
Declerq 2016 [ 96 ] Jan-12 Dec-13 USA 38 States and District of Columbia Regional Singleton Pre- & Early Nulli & Multi 6178824
Doyle 2022 [ 97 ] Mar-20 Apr-21 USA Florida Regional Singleton & Multiple Pre-pregnancy Nulli & Multi 234492
Ehrenthal 2011 [ 98 ] Jun-03 Dec-06 USA Delaware (Newark) Regional Singleton Pre-pregnancy Nulli & Multi 16582
Eick 2019 [ 99 ] Jan-05 Dec-12 Puerto Rico Countrywide Singleton Pre-pregnancy Unclear 330296
Gonzales-Rios 2022 [ 100 ] May-08 Dec-12 USA Pennsylvania Regional Singleton & Multiple Pre-pregnancy Nulli & Multi 8749
Kabiru 2004 [ 101 ] Jan-99 Dec-02 USA Atlanta, Georgia Regional Singleton Early pregnancy Nulli & Multi 5529
Leonard 2020 [ 102 ] Jan-07 Dec-12 USA California Regional Singleton & Multiple Pre-pregnancy Nulli & Multi 2650182
Lutsiv 2015 [ 103 ] Apr-07 Mar-10 Canada Ontario Regional Singleton Pre-pregnancy Nulli & Multi
Lynch 2014 [ 104 ] Oct-05 Oct-10 USA Denver Regional Singleton Pre-pregnancy Nulli & Multi 11726
Magann 2011 [ 105 ] Jan-07 Jul-08 USA Virginia and Mississippi Regional Singleton Early pregnancy Nulli & Multi 4500
Miao 2021 [ 106 ] Apr-12 Mar-18 Canada Ontario Regional Singleton Pre-pregnancy Unclear 706017
Mills 2020 [ 107 ] Jan-04 Dec-14 USA – Countrywide Singleton & Multiple Unclear Nulli & Multi 9096788
Naeye 1990 [ 108 ] Jan-59 Dec-66 USA Several regions Regional Singleton & Multiple Pre-pregnancy Nulli & Multi 56857
Pasko 2016 [ 109 ] Jan-06 Jun-14 USA Alabama (Birmingham) Regional Singleton Early pregnancy Unclear 15313
Seaton 2023 [ 110 ] Mar-20 Feb-22 USA New York Regional Singleton & Multiple Unclear Nulli & Multi 8983
Thompson 2019 [ 111 ] Jan-14 Dec-16 USA – Countrywide Singleton Pre-pregnancy Nulli & Multi 10811496
Young 2016 [ 112 ] Jan-03 Apr-14 USA Pennsylvania (Pittsburgh) Regional Singleton Pre-pregnancy Nulliparous 28361
South America
Carilho 2022a [ 22 ] Jan-08 Dec-08 Brazil – Countrywide Singleton & Multiple Pre-pregnancy Nulli & Multi 69413
Carilho 2022b [ 22 ] Jan-09 Dec-09 Brazil – Countrywide Singleton & Multiple Pre-pregnancy Nulli & Multi 157680
Carilho 2022c [ 22 ] Jan-10 Dec-10 Brazil – Countrywide Singleton & Multiple Pre-pregnancy Nulli & Multi 178033
Carilho 2022d [ 22 ] Jan-11 Dec-11 Brazil – Countrywide Singleton & Multiple Pre-pregnancy Nulli & Multi 232617
Carilho 2022e [ 22 ] Jan-12 Dec-12 Brazil – Countrywide Singleton & Multiple Pre-pregnancy Nulli & Multi 266640
Carilho 2022f [ 22 ] Jan-13 Dec-13 Brazil – Countrywide Singleton & Multiple Pre-pregnancy Nulli & Multi 250386
Carilho 2022g [ 22 ] Jan-14 Dec-14 Brazil – Countrywide Singleton & Multiple Pre-pregnancy Nulli & Multi 218177
Carilho 2022h [ 22 ] Jan-15 Dec-15 Brazil – Countrywide Singleton & Multiple Pre-pregnancy Nulli & Multi 230838
Carilho 2022i [ 22 ] Jan-16 Dec-16 Brazil – Countrywide Singleton & Multiple Pre-pregnancy Nulli & Multi 219411
Carilho 2022j [ 22 ] Jan-17 Dec-17 Brazil – Countrywide Singleton & Multiple Pre-pregnancy Nulli & Multi 193962
Carilho 2022k [ 22 ] Jan-18 Dec-18 Brazil – Countrywide Singleton & Multiple Pre-pregnancy Nulli & Multi 70614
Conde-Agudelo 2000 [ 113 ] Jan-85 Dec-97 Uruguay Montevideo Regional Singleton & Multiple Pre-pregnancy Nulli & Multi 878680
Table 1: Characteristics of included population-level, retrospective studies.

Risk of bias

Quality assessment of the 94 included studies is reported (Supplementary Material 4). Eighty studies (85.1%) were evaluated as having a low risk of bias and 14 studies (14.9%) were judged as moderate. The more common reasons for receiving a higher score were greater than 2% missing values reported or that weight or BMI was ascertained through self-report. Given that the assessed risk of bias of included studies was at highest moderate, this was not thought likely to affect the interpretation of our results.

Synthesis of results (Tables 2 and 3, Figures 2 and 3)

The prevalence for each BMI category is presented separately for all included study cohorts in Table 2 and visualised via box plots in Figures 2, 3. Pooled prevalence, synthesised by area of the world and decade, is presented in Table 3 and visualised in Supplementary Materials 5, 6. Globally, prevalence of obesity (BMI ≥30 kg/m2) in pregnancy has more than tripled from 4.7% (95% CI 2.6–8.6) pre-1990 to 16.3% (95% CI: 15.1–17.5), or one in approximately six pregnancies, in the decade 2010 to 2019 (Figures 2 and Supplementary Material 5). Only two studies reported prevalence of obesity since 2020, both from North America, but these appear to show a continuation of the upward trend, with a pooled prevalence of obesity estimated as 24.5% (95% CI: 21.4–27.8), or one in every four pregnancies. Prevalence of combined overweight & obesity (BMI ≥25 kg/m2) in pregnancy has also increased globally, more than doubling from 20.6% (95% CI: 16.4-25.6) pre-1990 to 43.8% (95% CI: 42.2-45.4) in the decade 2010 to 2019 (Figures 3 and Supplementary Material 6). Asia did not have sufficient study cohorts to allow for a pooled estimate of prevalence to be estimated for each decade. Across the remaining areas, only North America and Europe had sufficient results to stratify by at least three decades (1990s, 2000s, 2010s). Pooled prevalence was possible for 2000s and 2010s for Australia & Oceania and South America. Across each area of the world, an upward trend similar to the global trend was observed, with North America demonstrating the highest prevalence in obesity and overweight/obesity in each decade. Percentage of total variability due to between-study heterogeneity, as measured by the I2 statistic, was large in all meta-analyses, ranging from 98.0 to 100%.

Study ID Overweight (%) Obese I (%) Obese II (%) Obese III (%) Obese all (%) Overweight & obese (%)
Asia
Jin 2021 12.1 . . . 19.7 31.8
Leung 2008 13.5 . . . 2.3 15.8
Ma 2016 18.7 8.6 . . 13.0 31.7
Michlin 2000 19.7 . . . 18.8 38.6
Morikawa 2012 . . . . . 11.7
Australia/ Oceania
Callaway 2006 20.3 . . 1.7 13.5 33.8
Cheney 2018 14.8 4.2 1.3 0.7 6.2 21.0
Cunningham 2013 33.0 18.6 8.3 5.7 32.6 65.6
Davey 2020 26.8 . . . 19.9 46.7
Dodd 2011 26.2 13.6 6.1 4.0 23.7 49.9
Donald 2020 28.1 . . . 23.5 51.6
Knight-Agarwal 2016 25.2 10.8 5.0 4.0 19.7 44.9
McIntyre 2012 20.1 7.6 2.8 1.5 11.9 32.0
San Martin Porter 2021 22.8 . . . 19.6 42.4
Watson 2013 26.8 14.0 6.1 3.6 23.7 50.5
Europe
Abayomi 2009 26.7 . . . 17.2 43.9
Arrowsmith 2011 27.3 11.3 4.3 2.1 17.7 45.0
Bak 2016 21.8 8.1 2.9 1.3 12.2 34.0
Baker 2012 19.0 . . . 11.0 30.0
Bastola 2020 22.6 . . . 13.0 35.7
Bhattacharya 2007 21.9 7.7 . . 8.3 30.2
Blomberg 2010 24.4 7.3 2.1 0.7 10.2 34.5
Blomberg 2011 24.7 7.6 2.3 0.8 10.7 35.3
Briese 2011 19.0 . . . 7.9 26.9
Carillo-Aguirre 2020 A 33.9 13.6 4.0 1.4 19.0 52.9
Carillo-Aguirre 2020 B 33.4 13.6 4.0 1.4 19.0 52.4
Carillo-Aguirre 2020 C 33.3 13.8 4.1 1.4 19.2 52.6
Carillo-Aguirre 2020 D 33.0 14.5 4.2 1.5 20.3 53.3
Carillo-Aguirre 2020 D 33.6 14.9 4.6 1.5 21.0 54.5
Cedergren 2007 23.7 . . . 9.4 33.1
Cedergren 2008 A 24.2 . . . 9.6 33.8
Cedergren 2008 B 22.5 . . . 8.1 30.7
Cnattingius 1998 19.9 . . . 6.2 26.1
Collier 2017 27.6 . . . 21.1 48.8
Denison 2014 28.2 . . 2.2 19.6 47.8
Deruelle 2017 16.2 . . 0.8 8.3 24.5
Erjavek 2016 A . 5.2 1.4 0.4 7.0 .
Erjavek 2016 B . 5.8 1.5 0.5 7.8 .
Farah 2009 28.2 . . 1.6 15.0 43.2
Frischknecht 2009 A 12.5 . . . 3.5 15.9
Frischknecht 2009 B 21.1 . . . 9.0 30.1
Gardosi 2009 . . . . 6.7 .
Hedegaard 2014 33.1 12.0 . . 16.1 49.2
Henriksson 2020 25.0 . . . 12.8 37.8
Heslehurst 2007 23.7 . . . 10.8 34.5
Heslehurst 2012 25.9 . . . 14.8 40.7
Johansson 2017 21.4 6.4 1.7 0.5 8.6 30.0
Kanagalingham 2005 A . . . . 9.4 .
Kanagalingham 2005 B . . . . 18.9 .
Kent 2021 A 28.6 11.9 4.2 2.3 18.4 47.0
Kent 2021 B 29.1 12.1 4.6 2 18.7 47.8
Kent 2021 C 29.3 12 4.8 2.3 19.1 48.4
Kent 2021 D 29.5 12.1 4.9 2.2 19.2 48.7
Kent 2021 E 29.1 12.5 5.3 2.5 20.3 49.4
Kent 2021 F 30.2 12.6 5.3 2.5 20.4 50.7
Kent 2021 G 30.0 13.5 5.9 2.9 22.3 52.3
Kent 2021 H 29.9 13.3 6.4 2.8 22.5 52.4
Khashan 2009 28.4 . . 1.8 17.9 46.4
Kristensen 2015 10.5 . . . 3.9 14.4
Le Ray 2015 A 15.4 . . . 7.5 22.9
Le Ray 2015 B 17.3 . . . 9.9 27.2
Lindholm 2015 22.4 . . 2.9 9.8 32.1
Lucovnik 2018 17.8 . . . 8.2 26.1
Maier 2016 17.9 . . . 11.7 29.6
Mantakas 2010 26.5 . . . 14.5 41.0
McKeating 2015 29.3 11.3 3.8 1.5 16.6 45.9
Melchor 2019 25.1 9.0 3.2 1.1 13.3 38.4
Mogren 2018 25.4 9.2 2.9 1.0 13.1 38.6
Nishikawa 2017 24.6 9.8 3.5 1.5 14.7 39.4
Nohr 2012 23.3 6.7 . . 9.1 32.5
Oteng-Ntim 2013 24.3 9.2 3.3 1.5 13.9 38.2
Ovesen 2011 20.9 7.7 . . 11.7 32.6
Penn 2014 24.6 . . . 14.9 39.5
Premru-Srsen 2019 19.2 . . . 9.8 29.0
Raja 2012 29.8 . . 1.1 15.8 45.6
Ramoniene 2017 3.1 2.8 . . 4.2 7.3
Rankin 2010 26.3 . . . 16.4 42.7
Rantakallio 1995 A 18.7 3.4 . . 3.9 22.6
Rantakallio 1995 B 13.7 3.1 . . 3.9 17.6
Reynolds 2019 29.1 11.2 4.1 1.7 17.0 46.1
Reynolds 2020 26.3 8.1 2.7 0.8 11.6 37.9
Scott-Pillai 2015 27.8 11.0 3.9 1.9 16.9 44.6
Sebire 2001 24.3 . . . 9.6 33.9
Stepan 2006 31.0 13.3 3.9 1.6 18.8 49.8
Villamor 2006 19.0 . . . 5.4 24.4
North America
Abenhaim 2007 16.5 . . 0.6 6.7 23.1
Berendzen 2013 22.6 . . 5.8 26.4 48.9
Butwick 2018 25.9 12.7 5.2 3.1 21.0 47.0
Chen 2004 21.0 . . 2.0 14.8 35.8
Crane 2009 26.4 . . 4.3 24.4 50.7
Declerq 2016 25.5 13.4 6.3 4.3 23.9 49.4
Ehrenthal 2011 24.9 11.8 5.8 3.6 21.2 46.1
Eick 2019 24.7 . . . 17.8 42.5
Gonzales-Rios 2022 48.7 14.5 63.2
Kabiru 2004 31.0 17.0 8.0 5.9 30.9 61.9
Leonard 2020 25.8 12.6 5.2 3.0 20.8 46.6
Lisonkova 2017 25.8 13.0 6.2 4.2 23.5 49.3
Lutsiv 2015 . . . . . 48.2
Lynch 2014 24.7 10.7 4.0 2.7 17.3 42.0
Magann 2011 23.8 . . . 26.3 50.1
Miao 2021 . . . . 17.4 .
Mills 2020 . . . . 3.6 .
Naeye 1990 17.9 . . . 9.2 27.0
Pasko 2016 24.8 18.0 10.3 10.3 38.6 63.4
Seaton 2023 . . . . 22.9 .
Thompson 2019 25.9 13.9 6.7 4.6 25.1 51.0
Young 2016 . . . . 14.5 .
South America
Carrilho 2022 A 22.6 . . . 9.8 32.4
Carrilho 2022 B 23.3 . . . 10.9 34.2
Carrilho 2022 C 24.1 . . . 11.8 35.9
Carrilho 2022 D 25.2 . . . 13.1 38.3
Carrilho 2022 E 26.1 . . . 14.2 40.3
Carrilho 2022 F 26.4 . . . 14.5 40.9
Carrilho 2022 G 27.1 . . . 15.9 43
Carrilho 2022 H 27.6 . . . 17 44.6
Carrilho 2022 I 28 . . . 18 46
Carrilho 2022 J 28.6 . . . 19 47.6
Carrilho 2022 K 28.8 . . . 19.8 48.6
Conde-Agudelo 2000 7.0 . . . 5.8 12.8
Table 2: Prevalence of Maternal Overweight and Obesity: Individual Study Cohort Results.
Obesity Overweight/Obesity
Cohorts (n) Total individuals (n) Estimated prevalence [95% CI] Cohorts (n) Total individuals (n) Estimated prevalence [95% CI]
Global 120 48,935,440 13.6 [12.4, 15.0] 110 38,235,147 38.9 [37.2, 40.6]
Asia 5 200,586 10.8 [7.0, 16.5] 4 62,056 28.5 [18.3, 41.5]
Austr. & Oceania 10 1,010,974 18.2 [15.3, 21.5] 10 1,010,974 43.4 [37.8, 49.1]
Europe 71 11,642,821 12.1 [11.2, 12.9] 65 10,847,479 36.9 [35.3, 38.5]
N. America 22 33,114,608 18.7 [15.0, 23.2] 19 23,348,187 47.0 [45.7, 48.3]
S. America 12 2,966,451 13.6 [10.8, 16.9] 12 2,966,451 37.9 [29.9, 46.6]
Stratified by decade
Global
Pre-1990 4 77,644 4.7 [2.6, 8.6] 4 77,644 20.6 [16.4, 25.6]
–1999 18 5,192,253 8.7 [7.8, 9.8] 16 4,837,845 29.1 [25.1, 33.5]
–2009 51 21,148,274 14.3 [11.9, 17.1] 47 11,599,137 39.8 [37.7, 42.0]
–2019 45 22,273,794 16.3 [15.1, 17.5] 42 21,486,029 43.8 [42.2, 45.4]
+ 2 243,475 24.5 [21.5, 27.8]a 1 234,492 –
Austr. & Oceania
–1999 – – – – – –
–2009 6 187,527 16.7 [10.7, 25.3] 6 187,527 41.4 [30.3, 53.4]
–2019 4 823,447 20.6 [18.4, 23.1] 4 823,447 46.4 [42.8, 50.1]
Europe
–1999 14 4,290,688 8.4 [7.4, 9.4] 12 3,936,280 30.2 [28.1, 32.3]
–2009 28 5,465,686 13.3 [12.1, 14.6] 26 5,106,500 38.0 [35.3, 40.7]
–2019 26 1,865,660 14.6 [13.1, 16.3] 24 1,783,912 42.0 [39.0, 45.1]
N. America
–1999 2 21,998 10 [4.4, 21.0] 2 21998 29.0 [18.3, 42.8]
–2009 12 15,071,879 19.1 [11.9, 29.2] 11 6020458 48.3 [47.2, 49.4]
–2019 5 17,720,399 23.1 [21.2, 25.1] 4 17014382 56.5 [55.1, 57.8]
+ 2 243,475 24.6 [21.4, 27.7]a 1 234,492 –
S. America
–1999 – – – – – –
–2009 2 227,093 10.3 [9.3, 11.5] 2 227,093 33.3 [31.6, 35.1]
–2019 9 1,860,678 15.7 [14.2, 17.4] 9 1,860,678 42.7 [40.2, 45.3]
Table 3: Pooled prevalence of obesity and overweight/obesity in pregnancy. aBoth studies conducted in N. America, therefore not reflective of global prevalence.

Figure 2: Prevalence of obesity (BMI ≥30 kg/m2) in pregnancy.

Figure 3: Prevalence of overweight/obesity (BMI ≥25 kg/m2) in pregnancy.

Temporal trends in prevalence of maternal overweight and obesity (Table 4, Figure 4)

Figure 4: Temporal trends in prevalence of obesity (BMI ≥30 kg/m2) and overweight/obesity (BMI ≥25 kg/m2) in pregnancy.

Obesity
Model Estimated increase in prevalence (%) per year
% [95%CI] p
Full time period 0.34 [0.23, 0.45] <0.001
1980+ 0.42 [0.28, 0.56] <0.001
1990+ 0.41 [0.26, 0.56] <0.001
2000+ 0.32 [0.07, 0.57] 0.0141
2010+ 0.63 [0.03, 1.23] 0.0435
Overweight and Obesity
Model Estimated increase in prevalence (%) per year
% [95%CI] p
Full time period 0.64 [0.44, 0.84] <0.001
1980+ 0.82 [0.57, 1.07] <0.001
1999+ 0.77 [0.50, 1.04] <0.001
2000+ 0.60 [0.17, 1.03] 0.0073
2010+ 0.82 [−0.36, 2.00] 0.1845
Table 4: Rate* of increase per year in prevalence of maternal overweight and obesity. *Estimated from linear regression of prevalence versus midpoint year of study.

Figure 4 further demonstrates the upward trend of prevalence of both obesity, and overweight/obesity in pregnancy. This trend is apparent globally (Figure 4a,b) and for each area of the world (Figure 4c,d). North America demonstrates some of the highest prevalence rates for obesity and overweight/obesity, with Australia and Oceania appearing to increase at a greater rate and reaching similar rates as North America by 2010.

Linear regression performed on all studies reporting (i) obesity prevalence (n = 120 study cohorts) and (ii) combined overweight and obesity prevalence (n = 110 study cohorts), both demonstrated a statistically significant increase over time (p < 0.001), with prevalence increasing by (i) 0.34% (95% CI: 0.23–0.45) and (ii) 0.64% (95% CI: 0.44–0.84), respectively, with each year.

Sensitivity analysis

Additional analysis was carried out on all studies from 1980, 1990, 2000 and 2010 onwards. The range in estimated yearly increase in prevalence of maternal obesity was 0.32–0.63% (each model p < 0.05), and 0.60– 0.82% for overweight/obesity (each model p < 0.05) (Table 4).

Comparison of prevalence of maternal overweight and obesity between areas of the world (Table 5)

Obesity
Model Estimated difference in prevalence (%) between continents
% [95%CI] p
Europe Reference –
N. America 6.90 [4.45, 9.35] <0.001
Austr. & Oceania 5.59 [2.20, 8.98] 0.002
Asia 0.02 [−4.62, 4.65] 0.995
S. America −0.97 [−4.16, 2.22] 0.552
Overweight and Obesity
Model Estimated increase in prevalence (%) between continents
% [95%CI] p
Europe Reference –
N. America 9.83 [5.30, 14.36] <0.001
Austr. & Oceania 4.68 [−1.23, 10.60] 0.124
Asia −7.74 [−16.69, 1.22] 0.093
S. America −2.98 [−8.54, 2.59] 0.297
Table 5: Differences* in prevalence of maternal overweight and obesity across continents. *Estimated from linear regression of prevalence versus continent adjusted for midpoint year of study.

Using Europe as a reference and adjusting for midpoint year of study, obesity prevalence was compared across areas of the world. A significantly higher prevalence was observed for study cohorts from North America (6.90%, p < 0.001) and Australia & Oceania (5.59%, p = 0.002). Analysis of combined overweight and obesity rates demonstrated a significantly greater prevalence in North America (9.83%, p < 0.001).

Discussion

Main findings

The scale of this systematic review, along with the strict inclusion and exclusion criteria applied, provides a comprehensive insight into the current state of global and continental maternal overweight and obesity prevalence. Our findings demonstrate a significant increase in maternal obesity and overweight/obesity prevalence over time, with our linear regression analysis estimating a 0.34% and 0.64% increase in obesity prevalence and combined overweight and obesity prevalence, respectively, with every passing year. Pooled analysis from the most recent studies (post 2010) indicates that globally, one in six pregnancies are now complicated by obesity. In North America, since 2020, one in four pregnancies are complicated by obesity.

Comparisons with existing literature

In comparison to a previous review of pre-pregnancy BMI from Martínez-Hortelano et al, global obesity prevalence found in our study was slightly lower for the full time period (13.6% v. 16.3%) [9]. This may be due to inclusion of earlier studies, with lower prevalence, in our study. However, when results were stratified by continent, our pooled prevalence of maternal obesity was higher, and particularly striking when the analysis was limited to the decade 2010–2019: North America (23.1% vs. 17.6%), Europe (14.6% vs. 9.1%), Australia and Oceania (20.6% vs. 11.3%). Our findings likely reflect the more recent studies included in our review, and our strict inclusion/exclusion criteria which ensured that results were reflective of population-based prevalence and not biased by including studies that may have unintentionally excluded pregnant women with higher BMI’s.

The estimated global trend of increasing overweight and obesity prevalence in pregnant women mirrors that seen in the general female population [114]. It is estimated that mean BMI has increased by 0.59 kg/m2 per decade from 1975 to 2014 [114], and 0.5 kg/m2 per decade from 1980 to 2008 [115]. In 2014, obesity prevalence in the general female population was estimated to be 14.9% [114], reflected by WHO estimates in 2016 (15%) [2]. However, a much higher prevalence has been estimated for the general female population in high income countries; 25.3% in 2014, and 26.2% in 2016 [2]. Our linear regression analysis suggests that global obesity prevalence in the pregnant population was 17.8% in 2014, and 18.4% in 2016. Using our model, the current global prevalence of maternal obesity is 20.9% (95% CI 18.6 to 23.1%), and is projected to reach 23.3% (95% CI 20.3 to 26.2%) by 2030.

The highest prevalence of maternal obesity is found in North America and Australia/Oceania, who also demonstrate the highest prevalence of combined overweight/obesity. These findings are also recognised in the general female population; with national bodies including, the National Academy of Medicine (USA) [116], Health Canada [117], the National Health and Medical Research Council (Australia) [118] and the National Institute for Health and Care Excellence (UK) [6], recognising the urgent need for strategies to support weight management, including pre-conception care [119]. A steeper increase in obesity and overweight/obesity prevalence was seen in studies carried out in Australia/Oceania. Our findings are similar to those reported by Finucane et al., who noted greatest increase in BMI in the general population of females in Australia/Oceania (1980–2008) [115].

Challenges in conducting world-wide meta-analyses of prevalence of maternal obesity

Comparing prevalence of maternal overweight and obesity between areas of the world is challenging. Studies included in this review ranged significantly in the type of data used (e.g. routinely collected healthcare data versus vital statistics) and scale of the population covered (e.g. hospital database versus region or countrywide datasets), making it difficult to assess comparable studies. In Nordic countries, reporting of all births to national organisations is required [120]. As a result, the number of population wide studies included in our review from Sweden (n = 12) was higher than other countries, which may have skewed the results for Europe. Many studies carried out in USA were excluded as much of the published data currently relies on information obtained from birth certificates, meaning stillbirths are often excluded. As a result, North America contributed only three countrywide studies [99, 107, 111]. A further challenge was the ability to reliably compare trends on a global scale, as the number of studies per geographical region varied greatly. As previously stated, no studies undertaken in Africa met the inclusion criteria, mainly due to the lack of routinely collected data from retrospective studies. The lack of data available for analysis may reflect local constraints in collecting healthcare information, including lack of digitised healthcare records, late or non-presentation of women for antenatal care, and lower levels of tertiary education resulting in poor self-reporting of pre-pregnancy weight [121]. Of the five Asian studies that were included, only two used the WHO recommended BMI classification specific to the Asian population [34, 36] (BMI of 23–24.9 kg/m2 considered overweight and ≥25 kg/m2 obese) [122]. This may have resulted in possible misclassification of at-risk overweight and obese Asian women. Furthermore, pre-pregnancy BMI may be influenced by country income [114]; only two studies included in this review took place outside a high-income country [31, 32], due to limited publication of routinely collected data from low- and middle-income countries. Again, this publication bias may reflect the paucity of standardised national databases and digitised clinical records [123]. Maternal BMI is also linked to ethnicity [124, 125] and parity [126] however included studies had insufficient data to enable us to explore this association further.

There is also a paucity of guidance in the field of evidence synthesis of prevalence, which has been previously recognised [127]. This has led to variability in approaches to the methods, risk of bias assessment, data synthesis and reporting of such reviews. We echo the Prevalence Estimates Review-Systematic Review Methodology Group (PERSyst) call to action to develop guidance and reporting standards for these types of review [127].

Strengths and limitations

The use of strict inclusion and exclusion criteria ensured high quality studies of population level data representative of all births were included. In addition, use of an appropriate risk of bias tool for studies investigating prevalence [17] supports the interpretation and generalisation of results in this review. No studies were judged to have a high risk of bias, ensuring that our results were not unduly influenced by selection bias, sampling bias, non-response bias or data-collection bias, and that they were suitably representative of population-level data.

A key limitation is that our review mainly included studies from countries categorised as high income at the time of review, this may have been in part due to the exclusion of reports not written in English. Translation of non-English language reports was not within the scope of this review due to funding constraints. A further limitation is that the income status of countries where specific studies took place was not assessed at the time of their study period but instead when the review was undertaken, as a result some income status’ may have differed. Additionally, several study populations were included that had overlapping data, for example, studies using national data from the Swedish Medical Birth Registry. Instead of withdrawing studies and risking exclusion of valid data for certain years, we attempted to ameliorate these effects by calculating and reporting midpoint year of studies. Further MeSH terms, such as “antenatal” or “perinatal$” may have yielded additional studies. The review did not evaluate underweight or undernutrition, which we acknowledge can also be linked to adverse pregnancy outcomes [128, 129].

Future implications

Our findings provide key epidemiological and clinical insights. This review demonstrates an increasing trend in overweight and obesity prevalence in pregnant women across the world, with particularly high prevalence in high income countries, notably in North America, Australia/Oceania and Europe.

To improve accurate surveillance of maternal BMI, key public health stakeholders must focus their efforts on provision of better data informatics to assist in cohesive collection of healthcare data, particularly in low- and middle- income countries. A robust evidence base is required to allow comparable analysis, with this review pointing to the need for further research to be focused in Asia, South America, Africa and in low- and middle-income countries worldwide, using ethnically appropriate BMI classification boundaries. While BMI is a useful tool for population level surveillance, it has limitations at an individual level [130, 131], and there is a need for development of useful tools to aid clinical assessment of personal risk [132]. One example is the Edmonton Obesity Staging System [130, 131]. Novel tools such as this must also be evaluated for use at both an individual and population level in women of child-bearing age.

The increasing trend towards maternal obesity in pregnancy demonstrated by this review is set to provide further challenges to provision of obstetric care. Interpregnancy weight gain, independent of parity and baseline BMI, increases adverse obstetric and neonatal outcomes, including gestational diabetes, hypertensive disorders of pregnancy, increased need for operative delivery, fetal macrosomia and stillbirth [43, 133, 134]. Women with increased BMI in pregnancy are also more likely to have pre-existing multimorbidity (two or more long-term physical or mental health conditions) which is likely to further complicate their pregnancy and postpartum course [135].

Effective strategies to support optimisation of weight before conception are essential to reduce maternal, fetal and neonatal risks and to help women establish a healthy weight trajectory beyond pregnancy.

Conclusion

Our study shows high global prevalence of maternal overweight and obesity which is increasing over time. Overweight and obesity prevalence varies greatly between areas of the world, being highest in North America and lower in Asia. Maternity services across the globe should be adequately resourced to cope with the complexity of needs of pregnant women living with obesity. Future public health interventions should focus on a life-course approach to addressing the trajectory of obesity; with particular focus on the preconception and inter-pregnancy periods. At present, our research demonstrates a less apparent trend to high obesity prevalence in Asia. This may represent an opportunity for early intervention, and to reverse trends in maternal obesity at a population-level.

The availability of population-level maternity BMI data and research varies between regions, with more data required to understand the needs of maternal populations in the continents of Africa and Asia. Globally, there is a need for improved harmonisation and publication of data to enable better monitoring of obesity trends, and the opportunity to address maternal inequalities in relation to obesity.

Acknowledgements

Special thanks to Colleen Tierney, Subject Librarian, Queen’s University Belfast, for her assistance in developing a search strategy; and to Prof Christopher Cardwell, Queen’s University Belfast, for support with the statistical analysis.

Contributorship statement

LK and MMcG contributed equally to this paper and are joint first authors.

LK contributed to the Conceptualisation, Data Curation, Formal Analysis, Methodology, Visualisation, Resources, Writing - Review and Editing, Supervision and Project Administration of this systematic review.

MMcG contributed to the Data Curation, Formal Analysis, Writing - Original Draft and Visualisation of this systematic review.

LK & KAE is corresponding author and is responsible for the overall content as guarantor.

KAE contributed to the Conceptualisation, Methodology, Writing - Review and Editing, Supervision, Project Administration and Funding Acquisition of this systematic review.

Funding

This work was supported by Queen’s University Belfast School of Medicine, Dentistry and Biomedical Sciences Scholarships Committee: project code G1070CPH.

Competing interests

The authors declare no competing interests.

Ethics approval

Ethics approval was not required as this study synthesised data from published studies.

Availability of data, code and other materials

All data relevant to the study are included in the article or uploaded as Supplementary Information. Code used for the data synthesis and analysis is available at https://github.com/lisa-kent/systematic-review-maternal-obesity. Other materials (data extraction form template, risk of bias assessment questions) are available as Supplementary Information.

Registration information

Review Registry UIN: reviewregistry998

Protocol can be accessed:

https://www.researchregistry.com/browse-the-registry#registryofsystematicreviewsmeta-analyses/registryofsystematicreviewsmeta-analysesdetails/5f6b0b90d0a6c20018b0a0c4/

Changes to protocol since registration:

Initial Registered Protocol Details Changes
Inclusion criteria included control groups from interventional studies or case control studies, providing that there were no restrictions imposed regarding age, race, or health conditions. Control groups of interventional studies requiring informed consent were excluded due to the possibility of bias through participant self-selection
BMI must be measured during pregnancy BMI measurement was accepted if measured pre-pregnancy or early pregnancy
Mean and standard error or standard deviation, or median and 95% confidence interval should be reported. Prevalence (%) within each BMI category was collected
Risk of bias will be assessed using the Newcastle-Ottawa scale (http://www.ohri.ca/programs/clinical_epidemiology/oxford.asp) or the NHLBI Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies (https://www.nhlbi.nih.gov/health-topics/study-quality-assessment-tools) as appropriate depending on study design. Risk of Bias was assessed using the Hoy Risk of Bias Tool
Descriptive data analysis will be performed. Results will be presented in tables and figures and a narrative review of all studies will be included. A more robust statistical approach was employed, namely random effects meta-analysis and linear regression

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Article Details

How to Cite
Kent, L., McGirr, M. and Eastwood, K.-A. (2024) “Global trends in prevalence of maternal overweight and obesity: A systematic review and meta-analysis of routinely collected data retrospective cohorts”, International Journal of Population Data Science, 9(2). doi: 10.23889/ijpds.v9i2.2401.

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Published: Jul 15, 2024
DOI: https://doi.org/10.23889/ijpds.v9i2.2401
Keywords:
Pre-pregnancy body mass index, early pregnancy body mass index, maternal obesity, maternal overweight, BMI trends, obesity trends, overweight trends
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