Trends in cardiometabolic conditions and pregnancy outcomes: a retrospective cohort study in South-Eastern Melbourne

Abstract

Objectives

To examine trends in preconception and pregnancy cardiometabolic risk factors and conditions, pregnancy and birth complications, obstetric interventions, and the impact of COVID-19, and to forecast future disease burden.

Design

A multi-centre retrospective cohort study.

Setting

A large hospital network with three maternity hospitals serving ethnically diverse populations in Melbourne, Australia.

Participants

Pregnant women who gave birth between 2016 and 2022.

Outcome measures

Trends in cardiometabolic conditions, birth complications and obstetric interventions.

Results

Over 7 years, 63 232 women were included, of whom 40% were nulliparous, and 60.9% were born overseas from 167 countries. From 2016–2022, maternal age (30.2–31.3 years), obesity (21.0%–26.2%), gestational diabetes mellitus (GDM) (15.9%–28.1%) and caesarean delivery (28.5%–37.6%) increased, while average gestational weight gain, premature births and special care admissions declined from 12.6–11.6 kg, 6.3%–4.9% and 24.2%–14.1%, respectively; and was statistically significant (p<0.05). Hypertensive disorders of pregnancy remained stable, fluctuating slightly (6.5% in 2016, 7.6% in 2020, 6.9% in 2022). During the COVID-19 lockdown period, the odds of GDM and induced birth increased by 8.0%, whereas the odds of caesarean section decreased by 5.0%. GDM is forecast to reach 43.0% by 2028.

Conclusions

Prepregnancy and pregnancy cardiometabolic risk factors and conditions, pregnancy and birth complications, and obstetric interventions increased markedly over 7 years. Despite this, offspring complications, including special care admissions, stillbirths and prematurity, decreased, while pregnancy complications peaked during COVID-19. GDM is forecasted to increase to 43.0% by 2028, posing an unsustainable health and economic burden that necessitates urgent public health initiatives.

STRENGTHS AND LIMITATIONS OF THIS STUDY.

• This study analysed a diverse population in a universal free healthcare system using routinely collected data.

• Population diversity strengthens generalisability, though the birth country may not fully reflect ethnicity.

• Routine data collection ensures consistency and reliability, allowing large-scale analysis.

• Medical history may not capture all conditions due to reporting variations and recall bias.

• Gestational diabetes mellitus incidence forecasts are limited by future behavioural, environmental and healthcare system changes.

Introduction

In recent decades, prepregnancy cardiometabolic risk factors have increased, including advanced maternal age and higher prepregnancy body mass index (BMI).^{1 2} In most countries, the average age at birth is 30 years or above,¹ with 2021 figures of 31.1 in Australia² and 30.9 in the UK.³ Globally, 38.9 and 14.6 million pregnant women were overweight and obese in 2014, respectively,⁴ with 50.6% and 57.2% of birthing mothers in 2020 having a BMI >25 kg/m² in Australia² and the US,⁵ respectively. Advanced age and high BMI are associated with prepregnancy cardiometabolic conditions (type 2 diabetes mellitus (T2DM) and hypertension (HTN)).⁶ Prepregnancy BMI status is additionally associated with unhealthy gestational weight gain (GWG).⁷ Globally, 39.3% of women exceed the National Academy of Medicine recommendations for healthy GWG.⁸ Recent quarantine restrictions caused by the COVID-19 pandemic further impacted physical activity and eating behaviours⁹ and may have further impacted prepregnancy BMI and GWG.¹⁰

In turn, prepregnancy cardiometabolic risk factors and medical conditions, and excess GWG, increase cardiometabolic hypertensive disorders in pregnancy (HDP) and gestational diabetes mellitus (GDM).^{10 11} GDM is new-onset hyperglycaemia in pregnancy and is the most common cardiometabolic disorder of pregnancy, with a global standardised prevalence of 14%.¹² HDP represents a spectrum of conditions, including chronic HTN, gestational HTN and pre-eclampsia, affecting 18.08 million women globally in 2019.¹³ GDM and HDP have shared pathophysiological pathways, including obesity and insulin resistance,¹⁴ and both are exacerbated by rising obesity.

Prepregnancy and pregnancy cardiometabolic conditions can increase complications for mother and baby,¹⁵ including increasing future risk of obesity, insulin resistance, T2DM and cardiovascular disease in the mother and offspring.^{16 17} This is associated with a dramatic rise in healthcare interventions and costs.¹⁸ In this context, the USA preventive services task force recommends implementing lifestyle modifications during pregnancy to mitigate pregnancy complications and future cardiometabolic risk.¹⁹ These lifestyle modifications include physical activity and dietary interventions, which can reduce excess GWG and lower the risk of GDM and HDP,^{20 21} improving maternal and newborn outcomes.

To contextualise the changing landscape of maternal cardiometabolic health, this study aims to explore trends in prepregnancy maternal cardiometabolic risk factors and conditions, GWG, cardiometabolic complications in pregnancy, birth interventions and maternal and neonatal complications over 7 years. The study is set within one of Australia’s largest hospital networks, with trends likely applicable to other urban, well-resourced healthcare settings worldwide. We also explore the impact of COVID-19 and forecast future cardiometabolic disease burden in pregnancy.

Methods

Setting

This is a multi-centre, retrospective cohort study using routinely collected data from the Birthing Outcomes System (BOS) V.6.04, Management Consultants and Technology Services, Caulfield, Victoria, Australia. The BOS dataset was collected based on the Australian clinical practice guidelines: pregnancy care, 2014 Module II edition and 2020 edition.²² The study dataset represents an ethnically diverse population of singleton pregnant women attending Monash Health public maternity hospitals (two secondary and one tertiary), spanning conception to delivery over 7 years (2016–2022), located in southeast Melbourne, Australia.

In the Australian healthcare system, public hospitals provide a publicly funded universal healthcare insurance scheme. Australia’s healthcare was ranked third by healthcare system performance scores among eleven high-income countries by the Commonwealth Fund (New York).²³ An extended strengthening the reporting of observational studies in epidemiology statement, designed for observational studies using routinely collected health data, known as the REporting of studies conducted using observational routinely-collected health data statement, was used to guide the reporting in this manuscript.

Data preparation

The BOS dataset consists of routinely collected data encompassing socio-demographic, antenatal, natal and postnatal information. Key demographic factors, including age, weight and BMI, were available in a structured format. In contrast, other variables were recorded in text format, including maternal pre-existing medical conditions, including HTN and T2DM, obstetric complications, labour complications, interventions and immediate postpartum complications. These variables were subsequently recoded into a structured format manually with validation to facilitate further analysis.

Key study variables encompassed maternal demographic and prepregnancy data (age, BMI, country of birth, and residential Socio-Economic Indexes for Areas—Index of Relative Socio-Economic Disadvantage (SEIFA-IRSD), year of delivery, parity, gravidity, a past GDM history for multiparous women), pre-existing conditions (HTN, T2DM), pregnancy complications (GDM, HDP including pre-eclampsia/eclampsia), interventions and birth and neonatal outcome data. Age was categorised into five groups based on the WHO criteria (<20, 20–24, 25–29, 30–34, 35–39, and 40 years and older), while BMI was calculated using the weight and height recorded by the midwife during the initial antenatal visit. Country of birth was based on self-report, categorised into six groups geographically: ‘Australasian and Oceanian’, ‘Southern and Southeast Asian’, ‘Central and Northeast Asian’, ‘Middle Eastern’, ‘North African and Sub-Saharan African’, ‘European’ and ‘Peoples of the Americas’. Socio-economic status was determined using the SEIFA-IRSD derived from the Australian Census and using the residential postcode.²⁴ The index was further segmented into five quintile categories according to score distribution within the sample.

Weight and gestational age were recorded at antenatal visits. GWG was calculated by subtracting the baseline weight measurement from the recorded weight at each visit between six and 42 weeks of gestation. Overall, GWG was calculated by subtracting the baseline weight from the weight at the last recorded antenatal visit, 37 weeks to 42 weeks of gestation. According to guidelines issued by the US Institute of Medicine, healthy GWG is defined as a weight gain of 12.5–18 kg for women with an underweight BMI (<18.5 kg/m²), 11.5–16 kg for those with a normal BMI (18.5 and 24.9 kg/m²), 7–11.5 kg for those with an overweight BMI (25 and 29.9 kg/m²) and 5–9 kg for those with an obese BMI (>30 kg/m²). GWG, which falls outside these specified ranges, is considered unhealthy and categorised as either inadequate or excessive.²⁵

As a common cardiometabolic disease during pregnancy, the prevalence of GDM was determined within subgroups according to country of birth and BMI. GDM was diagnosed according to the recommendation of the International Association of the Diabetes in Pregnancy Study Groups.^{26 27} We defined HDP per the Monash Health Society of Obstetric Medicine of Australia and New Zealand/National Institute for Health and Care Excellence-aligned guideline: HTN in pregnancy is SBP ≥140 mm Hg and/or DBP ≥90 mm Hg on two readings ≥4 hours apart; HDP comprises chronic HTN, gestational HTN, pre-eclampsia/eclampsia and pre-eclampsia superimposed on chronic HTN; pre-eclampsia is ≥20 weeks with HTN plus maternal organ involvement and/or fetoplacental compromise; significant proteinuria is PCR ≥30 mg/mmol.^{13 28} Low birth weight was defined as <2500 g regardless of the gestational age, and macrosomia was defined as >4000 g regardless of the gestational age. Fetal growth restriction (FGR) is a condition in which a fetus does not achieve its genetically determined growth potential due to underlying pathological processes²⁹ and is reported in the dataset.

Statistical analysis

Socio-demographic characteristics of participants were summarised using descriptive statistics. These include means and frequencies of variables, analysed through descriptive longitudinal analysis and visualised over 7 years using the ‘plotly’ interactive graphing library for python V.2.7 (available at http://www.python.org/).

Before regression and trend analyses, all variables were prepared in the following format: prepregnancy conditions (BMI (underweight, normal, overweight, obesity, maternal age (<20, 20–24, 25–29, 30–34, 35–39, >=40 years), pre-existing diabetes mellitus (1/0), HTN (1/0), pregnancy conditions (GWG (excess, normal, low) and GWG based on prepregnancy BMI categories, GDM (1/0), HDP (1/0), pre-eclampsia or eclampsia (1/0), birth complications (induced birth (1/0), caesarean section (1/0), obstructed labour (1/0), fetal and neonatal complications (FGR(1/0), prematurity(1/0), special care nursery or neonatal intensive care unit (1/0), low birth weight(1/0)) were prepared cautiously. The period from March 2020 to October 2021 was used to indicate the impact of the COVID-19 quarantine restrictions on these trends because this area in Melbourne, Australia, had endured the world’s longest number of lockdown days, with severe government health orders and travel restrictions in place for extended periods during this time.³⁰ The missing BMI values, constituting 0.7% of the dataset, were imputed using the ‘IterativeImputer’ library with a random forest regressor as the estimator. The imputation model included maternal age, height, parity and ethnicity as predictors, based on their clinical relevance and completeness.

Annual prevalence (%) of each variable was calculated by birth year from 2016 to 2022. For categorical variables with more than two levels (eg, BMI, maternal age, GWG), linear regression was used to assess temporal trends by modelling yearly percentages as the outcome, with birth year as a continuous predictor. For binary variables, the proportion of cases per year was calculated, and linear regression was similarly applied to test for unadjusted temporal trends in these percentages. A p value<0.05 was considered statistically significant. Trend results were visualised using interactive line plots (Plotly), with annotations marking key public health events such as national COVID-19 lockdowns to contextualise observed changes. Measures of variability around the estimated rates using 95% CIs and sensitivity analysis, including only the first pregnancy, are presented in online supplemental files.

To evaluate adjusted temporal trends, multivariable logistic regression was performed for all binary maternal, pregnancy, birth and neonatal outcomes. Each model included birth year as a continuous independent variable to capture temporal change and was adjusted for maternal age and BMI to account for potential confounding. Adjusted yearly prevalence estimates were obtained by predicting outcome probabilities for each year while holding covariates at their mean values. A p value<0.05 for the birth year coefficient was considered statistically significant. Results were visualised using interactive line plots (Plotly), with major COVID-19 lockdown periods annotated to contextualise changes over time.

To assess the impact of the COVID-19 quarantine period, multivariable logistic regression models were fitted for each condition, including a binary variable (quarantine time, coded as 1 else 0) alongside birth year as a continuous predictor. All models were adjusted for maternal age and BMI. The independent effect of the COVID-19 period was quantified using adjusted (aORs) with 95% CIs and corresponding p values. Adjusted prevalence estimates were generated for each year by holding covariates constant, and results were visualised using interactive line plots (Plotly), highlighting significant temporal trends and COVID-19-related effects across key maternal, birth and neonatal conditions.

To forecast the future burden of GDM, the seasonal autoregressive integrated moving average with exogenous regressors model was used. The past 7 year trend of GDM prevalence was used to forecast the subsequent burden until 2028. GDM was designated the endogenous variable, and BMI and age were treated as exogenous variables.

Results

Socio-demographic variables

During the past 7 years, 63 232 women gave birth at the study sites, 40% were nulliparious at first antenatal visit. Of these women, 60.9% were born overseas in a diaspora of 167 countries, with 32.6% overall from Southern or Southeast Asia. Participants had an average age of 30.7±5.2 years, and 29.4% and 23.5% of them were classified as overweight or obese, respectively. About four per cent were underweight (table 1).

Table 1. Sociodemographic characteristics of the study population (n=63 232).

Characteristics	Total
Age, years (mean, SD)	30.7 (5.2)
Country of birth (n, %)
Australasia and Oceania	27 947 (44.2)
Southern and Southeast Asian	20 602 (32.6)
Central and Northeast Asian	7073 (11.2)
Middle Eastern, North African and Sub-Saharan African	3778 (6)
European	2533 (4)
Peoples of the Americas	606 (1)
Not stated	693(1)
Parity (n, %)
0	25 265 (40)
≥ 1	37 967 (60)
Year of birth (n, %)
2019	9177 (14.5)
2017	9157 (14.5)
2021	9122 (14.4)
2016	9118 (14.4)
2018	9028 (14.3)
2020	8925 (14.1)
2022	8705 (13.8)
Marital status (n, %)
Married	44 103 (69.8)
De facto	11 351 (18)
Single	7095 (11.2)
Other*	683 (1)
BMI
Underweight	2484 (3.9)
Normal weight	27 294 (43.2)
Overweight	18 568 (29.4)
Obesity	14 886 (23.5)

^*Separated, divorced, not stated/inadequately described, widowed.

BMI, body mass index.

Prepregnancy cardiometabolic risk factors and diseases

Age and BMI both increased over the study period. Pregnant women who were aged 40 years and older accounted for 4.0% in 2016 and increased to 5.9% by 2022, and this difference was statistically significant. In 2016, 28.1% of pregnant women were overweight, and 21.0% were obese. In 2022, this increased to 31.2% and 26.2%, respectively (figure 1). The trends of BMI categories remain statistically significant before and after adjustment. Pre-existing T2DM and HTN cases increased from 1.4%–1.9% and from 0.9%–1.2%, respectively and were statistically significant (figure 2).

Figure 1. Trends of prepregnancy maternal body mass index categories and mean maternal age and categories. BMI, body mass index.

Figure 2. Trends in the prevalence of common comorbid medical conditions during pregnancy. DM, diabetes mellitus; HTN, hypertension.

Cardiometabolic risk factors and diseases in pregnancy

The average GWG was 12.6 kg in 2016, which decreased to 12.4 kg in 2021, and 11.6 kg in 2022. According to prepregnancy BMI categories, individuals with obesity tended to have lower GWG but were more likely to exceed BMI-stratified GWG recommendations. The trend of excessive GWG began to rise in 2019 until 2021 and started to decline afterwards. Healthy GWG has been decreasing since 2017 (figure 3).

Figure 3. Trends of gestational weight gain across BMI categories during pregnancy over 7 years. BMI, body mass index; GWG, gestational weight gain.

The incidence of GDM significantly rose from 15.9% in 2016 to 28.1% in 2022, peaking at 29.5% during the COVID-19 quarantine period in 2021, and it was statistically significant. Despite minor fluctuations, HDP, including pre-eclampsia and eclampsia, prevalence remained stable, with rates of 6.5% in 2016, 7.6% in 2020, and 6.9% in 2022, showing no significant upward trend (figure 4).

Figure 4. Trends in prevalence of common cardiometabolic conditions during pregnancy. GDM, gestational diabetes mellitus; HDP, hypertensive disorders of pregnancy.

Obstetric interventions and birth complications

Induction of labour climbed from 32.5% in 2016 to 40.5% in 2020 before a minor decline to 36.9% in 2022. Caesarean delivery increased from 28.5% to 37.6% from 2016 to 2022. The prevalence of obstructed labour rose from 1.7% to 3.0%. All obstetric interventions and birth complications trends were statistically significant (figure 5).

Figure 5. Trends in prevalence of obstetric interventions and birth complications over time.

Foetal and neonatal complications

As shown in figure 6, the prevalence of FGR decreased significantly from 8.5% in 2016 to 6.3% in 2022 while preterm births declined from 6.3% to 4.9%. Admission of newborns to special and intensive care units also decreased significantly from 24.2% in 2016 to 14.1% in 2022. Low birth weight decreased from 9.1% to 8.6%. All fetal and neonatal complications trends were statistically significant.

Figure 6. Trends in prevalence of fetal and neonatal complications. FGR, fetal growth restriction; SCN/NICU, special care nursery and neonatal intensive care unit.

All longitudinal trends of cardiometabolic conditions and obstetric complications, with 95% CIs (2016–2022), are summarised in online supplemental table S1 of the supplementary files.

Adjusted trends and COVID-19 impact

In models adjusted for maternal age, the direction and significance of BMI–category trends were unchanged: obesity and overweight increased, whereas normal weight and underweight decreased; all trends remained statistically significant. After adjustment for maternal age and BMI, prepregnancy DM continued to increase but was not statistically significant, while chronic HTN increased and remained statistically significant. With the same adjustment, healthy GWG decreased, and excess and insufficient GWG increased, although these increases were not statistically significant. GDM increased (p<0.05), whereas HDP and pre-eclampsia/eclampsia decreased with nonsignificant trends. Finally, birth interventions and maternal complications increased and remained statistically significant, and fetal and neonatal complications decreased and remained statistically significant (see online supplemental figures S1–S6).

The odds of GDM were aOR 1.08; 95% CI 1.03–1.13 and induced birth were aOR 1.08; 95%CI 1.04–1.13, increased by 8% during COVID-19, whereas the odds of caesarean section birth were aOR 0.95, 95% CI 0.91–0.99, decreased by 5%. COVID-19 had no statistically significant impact on the trends of prepregnancy BMI, obstructed labour, fetal and neonatal complications (see online supplemental figures S7–S10, table S2).

Sensitivity analyses of all respective trends, including only the first pregnancy, are also presented in online supplemental table S3 and figures S11–S21 of supplementary files.

Expected future burden of GDM in pregnancy

The average annual forecast GDM prevalence is estimated at 33.0% in 2023, 34.0% in 2024, 37.0% in 2025, 39.0% in 2026, 42.0% in 2027, and 43.0% in 2028 (figure 7).

Figure 7. Actual and forecasted prevalence of GDM at Monash Health, including historical GDM prevalence and the forecast of prevalence with 95% CIs—analysed with a SARIMA model until 2028. GDM, gestational diabetes mellitus.

Discussion

This multi-centre study in a diverse population revealed an increasing burden of prepregnancy and pregnancy cardiometabolic risk factors and diseases. Maternal BMI and mean age, as well as pre-existing diabetes and HTN, increased from 2016 to 2022. Although GWG was not increasing, common cardiometabolic diseases during pregnancy—such as GDM and HDP, including pre-eclampsia—were increasing. Similarly, increasing rates of common birth interventions and complications, including induction of labour, caesarean delivery and obstructed labour, were observed. Despite these increased risk factors and pregnancy disorders, newborn outcomes, such as admission to special care and intensive care units and preterm births, decreased during the study period.

There has been a concerning risk in obesity and overweight in the general population, mirrored by an increasing trend in overweight and obesity rates among pregnant women. Our data are in line with global reports of a 10-year trend demonstrating a sharp increase in the number of overweight and obese pregnant women.⁴ Obesity has been deemed a chronic medical condition and not only reduces fertility but also increases adverse health outcomes in pregnancy. In addition, pregnancies among older individuals are becoming more common.³¹ Together, these contribute to higher-risk pregnancies, which are more likely to be complicated by conditions such as HTN and T2DM, in turn increasing healthcare costs. As a result, both advanced maternal age and increasing BMI present major public health concerns mandating public health interventions, yet investment in prevention remains far more limited than the economic impact of adverse outcomes.

We have shown that an upward shift in prepregnancy and pregnancy cardiometabolic risk factors coincided with a significant rise in cardiometabolic complications during pregnancy, mainly GDM. The prevalence of GDM increased from 16% in 2016 to 28% in 2022. The period between 2020 and 2021 marked a notable secondary peak, during which a considerable 4.2% increase occurred. This finding aligns with other studies in various countries reporting an increased GDM during the COVID-19 pandemic.^{11 32} The rise in this condition is a significant health and economic burden and, again, requires population-level prevention strategies. Recently, the implementation of a sugar tax in the US has seen significant product reformulation and major reductions in sugar intake, including pregnancy and population-level reductions in GWG and GDM.³³ These public health interventions are critical to reducing the health burden demonstrated here.

Similar to prepregnancy and pregnancy cardiometabolic conditions, an increased prevalence of obstetric interventions was also observed, including high rates of induction of labour and caesarean sections. The increase in induced births from 32.5% in 2016 to 40.5% in 2020 and in caesarean section deliveries from 28.5% to 37.7% by 2022 indicates a substantial increase in interventions during childbirth. The observed increase in caesarean section rates, despite a slight decline in GDM prevalence last year, may be driven by additional factors, underscoring further investigation. Although population caesarean delivery rates of up to 10% are associated with decreased maternal and neonatal mortality rates, caesarean delivery rates higher than 10% have not been proven to reduce maternal and newborn mortality.³⁴ However, this was before the rising complications in pregnancy. In addition to the significant health system costs attributed to interventions, which we recently demonstrated were unsustainable and sit at around 50% in public pregnancy and birth costs over a similar period. Rising rates of caesareans may also increase adverse health outcomes such as obesity and asthma among children, and in future pregnancies, uterine rupture and placenta accreta spectrum.³⁵ According to the WHO, approximately US$2.32 billion in global healthcare expenditure could be avoided if medically unnecessary caesareans were not performed.³⁶ However, this has not been evaluated in the context of rapidly rising complex pregnancies, and clearly, ongoing research is needed.

Despite the rising rates of maternal obstetric interventions and complications, notable declines were observed in fetal and newborn complications. This finding is supported by a recent study that shows a continuously decreasing trend in neonatal, infant and child mortality.³⁷ Factors that may have contributed to these declining trends include advances in antenatal care, changes in healthcare policies and practices such as updated guidelines for obstetric and neonatal care,^{22 38} implementation of evidence-based interventions and investments in perinatal healthcare infrastructure. With rising delivery interventions, as noted above, the necessity and role of rising birth interventions remain controversial in the face of this rising public health crisis, which needs further exploration.

Finally, the rapidly rising prevalence of GDM detected in the present study will likely continue to increase. In this population, the prevalence of GDM is projected to be 33% in 2023 and 43% by 2028. This research underscores the role of rising maternal age, obesity and increased GWG in propagating the prevalence of GDM. Additionally, rising GDM-related intervention rates, adverse health outcomes for both mothers and infants and cardiometabolic disorders are exacerbating the cardiometabolic public health crisis.¹⁵ However, the ongoing transition to new diagnostic criteria for GDM³⁹ may add complexity, as these changes may substantially reduce the proportion of women diagnosed with the condition. Given this context, risk prediction and prevention are critical to address the anticipated growth in GDM, such as healthy lifestyle prevention interventions in routine pregnancy care and the sugar tax, as noted above.

Strengths and limitations

This study analysed a routinely collected health service dataset, notably representing an ethnically diverse population in a universal free healthcare system. Population diversity is a significant strength, as it enhances the generalisability of the findings across demographic groups, although we acknowledge that the country of birth does not necessarily reflect ethnicity. Additionally, the routine nature of the data collection implies a consistent and systematic approach to gathering information, further bolstering the reliability of the dataset and enabling a large population to be studied. However, the study has its limitations. The medical history may not capture all clinical conditions, as individuals’ perceptions and reporting accuracy can vary, leading to recall bias, and some conditions may be underreported. This study, conducted across three hospitals under Monash Health, may not fully account for subtle site-specific variations in patient demographics or clinical practices within the unified health service. The observed rise in caesarean sections cannot be specifically attributed to obstructed labour from shoulder dystocia or macrosomia, as this analysis focuses on trend rather than causality, and further research is needed to determine underlying factors. Furthermore, our forecast for the incidence of GDM is limited by variability in future population behaviour and the potential for unforeseen changes in environmental, social or healthcare dynamics, such as the ongoing transition to new diagnostic criteria.

Conclusion

There is an increasing number of pre-existing and pregnancy-related cardiometabolic risk factors and diseases, such as maternal age, prepregnancy BMI, excessive GWG and GDM. GDM is forecasted to affect more than 40% of Australian pregnancies by 2028. There is also a notable increase in birth complications and interventions. However, common fetal and neonatal complications, including neonatal unit admissions, stillbirths and prematurity, are decreasing. This study provides evidence of the type and scale of the burgeoning crisis of cardiometabolic conditions in pregnancy, which is relevant to other urban, well-resourced healthcare settings worldwide. As maternal cardiometabolic risk continues to rise, alongside dramatically escalating costs and failed investment and implementation of public health interventions, projected health and cost burdens are likely to become unsustainable. Optimal birth intervention rates require ongoing assessment as these cardiometabolic risks increase, and innovative approaches to risk prediction and effective implementation of public health interventions are increasingly critical.

Data availability statement

Data may be obtained from a third party and are not publicly available.