Trends in Maternal Body Mass Index, Macrosomia and Caesarean Section in First-Time Mothers during the pandemic: a Multicentre Retrospective Cohort Study of 12 Melbourne Public Hospitals.

Trends in Maternal Body Mass Index, Macrosomia and Caesarean Section in First-Time Mothers during the pandemic: a Multicentre Retrospective Cohort Study of 12 Melbourne Public Hospitals. | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Trends in Maternal Body Mass Index, Macrosomia and Caesarean Section in First-Time Mothers during the pandemic: a Multicentre Retrospective Cohort Study of 12 Melbourne Public Hospitals. Andrew Goldsack, Melvin Marzan, Daniel Rolnik, Anthea Lindquist, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4412944/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Oct, 2024 Read the published version in BMC Pregnancy and Childbirth → Version 1 posted 4 You are reading this latest preprint version Abstract Objective: To compare specific perinatal outcomes in nulliparas with a singleton infant in cephalic presentation at term, with and without exposure to the COVID-19 pandemic during pregnancy. We hypothesised that the pandemic conditions in Melbourne may have been an independent contributor to trends in maternal Body Mass Index ≥25kg/m 2 , macrosomia and caesarean section. Design: Multi-centre retrospective cohort study with interrupted time-series analysis. Setting: Metropolitan Melbourne, Victoria. Population: Singleton infants ≥20 weeks gestational age born between 1 January 2019 and 31 March 2022. Main outcome measures: Rates of maternal Body Mass Index ≥25kg/m 2 , macrosomia (birthweight > 4000g) and caesarean section. Results: 25 897 individuals gave birth for the first time to a singleton infant in cephalic presentation at term in the pre-pandemic cohort, and 25 298 in the pandemic-exposed cohort. Compared with the pre-pandemic cohort, the rate of maternal Body Mass Index ≥25kg/m 2 (45.82% vs 44.57%, p=0.005), the rate of caesarean section (33.09% vs 30.80%, p<0.001) and the rate macrosomia (8.55% vs 7.99%, p=0.1) were higher among the pandemic-exposed cohort. Interrupted time-series analysis demonstrated no significant additional effect of the pandemic on pre-existing upward trends in maternal Body Mass Index ≥25kg/m 2 , caesarean section and macrosomia. Conclusions: Rates of Body Mass Index ≥25kg/m 2 and caesarean section among nulliparous individuals during pregnancy were higher following the pandemic in Melbourne. However, this appears to be a continuation of pre-existing upward trends, with no significant independent contribution from the pandemic. These trends are forecast to continue, with long term implications for population health. obesity COVID-19 pregnancy birth weight pregnancy complications Figures Figure 1 Figure 2 Figure 3 INTRODUCTION The obesity and overweight epidemic in Australia is a major public health priority for our healthcare system.[ 1 ] Australian studies have reported that the COVID-19 pandemic may have influenced the weight of some populations, with overweight and obesity (BMI ≥ 25kg/m 2 ) being more common among all age groups except the elderly.[ 2 , 3 ] A multicentre study of all births in Melbourne public hospitals showed an increase in the proportion of pregnant individuals with a BMI ≥ 25kg/m 2 during the pandemic.[ 4 ] Higher maternal weight during pregnancy is associated with higher rates of delivery by caesarean section (CS) and confers increased risks of obstetrics complications such as gestational diabetes, and macrosomic birth weight > 4000g. Maternal weight also has implications for the next generation through epigenetic modification of the infant,[ 5 ] and has been associated with childhood obesity and adverse metabolic profiles.[ 6 – 8 ] Over the past 25 years, a greater proportion of people giving birth have had a BMI ≥ 25kg/m 2 . Prevention of the associated adverse maternal and childhood outcomes through strategies such as optimisation of pre-pregnancy maternal weight is an important public health priority.[ 9 ] CS has important implications for subsequent pregnancies, including the risk of placental adhesive disorder and uterine rupture, health systems and resources as well as impacting newborn health.[ 6 , 10 ] Safely mitigating the rise in CS has therefore become a global focus in obstetric care.[ 11 ] Individuals giving birth for the first time (nulliparas) are considered a high priority group for addressing the rising CS rate, as CS in a first pregnancy makes subsequent deliveries by CS more likely.[ 12 ] Authors[ 13 ] have described the relationship between the COVID-19 pandemic and obesity by noting the psychological impact, difficulty accessing healthcare and limitations to physical activity as contributors to the global obesity crisis. Metropolitan Melbourne had 18 months of government mandates restricting movement of people during the COVID-19 pandemic, accompanied by abrupt changes in the provision of routine antenatal care. These factors may have influenced maternal weight and associated obstetric complications during 2020–2021.[ 13 ] The objective of this study was to analyse trends of maternal BMI ≥ 25kg/m 2 , macrosomia and CS before and during the pandemic. We hypothesised that the pandemic and the associated lockdown restrictions made an independent contribution to the rates of maternal BMI ≥ 25kg/m 2 , macrosomia and CS. METHODS Study design We conducted a multi-centre retrospective cohort analysis of perinatal data in two parts: (i) summary statistics (n and %) of maternal BMI > 25kg/m 2 , macrosomia, and CS births in groups and Poisson regression analysis comparing cohorts with and without exposure to the pandemic and, (ii) an interrupted time-series analysis (ITSA) of perinatal outcomes, with a forecast based on pre-exposure trends. Macrosomia is defined as birthweight >4000g, rather than by centile for GA, as our population of interest is first-time mothers delivering at or after 37 weeks GA. Study population and data sources Non-identifiable data was obtained from routine birth collections system with a waiver of consent. This analysis includes all births of ≥20 weeks gestational age (GA) from all 12 public maternity hospitals in Melbourne from 1 January 2018 to 31 March 2022. Data from private maternity hospitals in Melbourne were not available for this study. The twelve hospitals capture approximately 78% of all births from Melbourne and include all four tertiary maternity units.[14] Inclusion and exclusion criteria Singleton births of infants at ≥20 weeks GA and classifiable in the Robson classification system were included.[15, 16] Exclusions were: congenital abnormalities, deliveries <20 weeks GA or with unknown GA, terminations of pregnancy, non-Victorian residents and births with missing or contradictory information in the variables needed for Robson classification.[17] Definition of Robson 1, 2A and 2B The Robson classification system is a global standard for describing birth cohorts to facilitate standardised comparison of CS rates within and between healthcare systems.[18] In this study, we focussed on Robson groups 1, 2A and 2B as these represent individuals for whom averting a CS has the highest potential benefit for individual health outcomes and healthcare systems. Robson 1: Nulliparous, singleton, cephalic presentation, ≥37 week GA pregnancies where labour commenced spontaneously. This group includes individuals who received oxytocin or had an amniotomy for augmentation of labour. Robson 2A: Nulliparous, singleton, cephalic presentation, ≥37 week GA pregnancies for whom labour was induced. Robson 2B: Nulliparous, singleton, cephalic presentation, ≥37 week GA pregnancies that delivered pre-labour (i.e. by elective CS).[15, 16] Outcomes Our primary outcomes are reported as frequency and rates; Proportion of mothers with BMI > 25kg/m 2 Proportion of infants delivered with birthweight >4000g (macrosomia) Proportion of infants delivered by caesarean section Proportion of infants with birthweight >4000g (macrosomia), delivered by caesarean section Pandemic exposure definitions Gestational exposure to pandemic conditions is a time-dependent exposure. To avoid the fixed cohort bias[19] that arises from using calendar dates to define study cohorts with time-dependent exposures, we used ‘calculated week of last menstrual period’ (cLMP) rather than week of birth, as previously described,[20] to define the pandemic-exposed cohort and ensure all pregnancies in the pandemic cohort had an equivalent duration of exposure (Figure 1). For privacy protection, hospital data managers converted the actual infant dates of birth into the ordinal calendar week of birth (i.e. 1 to 52 for each calendar week). To generate the cLMP, we used the week-of-birth and gestational age in completed weeks at delivery. The formula used was: First day of the week¬‑of‑cLMP=week‑of‑birth-[GA(in completed weeks)×7] Using this cLMP, we defined a ‘pandemic-exposed’ cohort comprising women for whom weeks 20-40 of gestation would have occurred during the lockdown period. (Figure 1) With a defined pandemic-exposure from 23 March 2020 (Monday of the week of suspension of elective surgery due to COVID-19 in Victoria) to 28 March 2022 (which formed a 2 year period of pandemic exposure), this included women whose cLMP occurred during the 85 weeks from 4 November 2019 to 21 June 2021 inclusive.[16] The pre-pandemic control group comprised of women who had their cLMP during the corresponding calendar weeks commencing two years prior to the start of the exposed cohort (births with cLMPs in weeks commencing 30 October 2017 to 3 June 2019). Statistical analysis (i) Cohort analysis of the proportions of maternal BMI >25kg/m 2 , macrosomia and CS births in Robson groups 1 and 2 before and during the pandemic. We present the primary outcomes as frequency (n) and percentage/proportion (%) in accordance with the World Health Organisation’s Robson Manual recommendations.[15, 16] Statistical significance was assessed using independent samples t-tests or Chi-square tests as appropriate. The analyses were conducted in Stata version 18[21], with two-tailed p-values below 0.05 considered statistically significant. To compare the pandemic cohort with the pre-pandemic cohort, we independently employed Poisson regression, adjusting for covariates such as maternal age, maternal BMI, maternal region of birth, smoking status, socioeconomic status, and requirement for an interpreter. Covariates were selected based on a priori and subject matter expertise. The effect estimates were reported as adjusted relative risk (aRR) with 95% confidence intervals (CI). We accounted for the observations with missing data on covariates by using the multiple imputation by chained equation (MICE) using the “mi impute chained” command in Stata 18.[22] (ii) Interrupted time-series analysis (ITSA) of weekly rates of maternal BMI > 25kg/m 2 , macrosomia and CS births in Robson groups 1 and 2 We conducted ITSA using the cLMP from 30 October 2017 to 28 March 2022. Our intervention period started from cLMP of 4 November 2019. We employed ITSA using the “itsa” suite of commands in Stata 18.[23] This approach used the Prais-Winstein generalized least-squares regression, accounting for autocorrelation of the residuals.[23] We used sine and cosine functions to correct for seasonality.[23] (Supplemental File 1). Ethical approval Ethical approval was obtained from Austin Health (HREC/64722/Austin-2020) and Mercy Health Ethics Committees (ref. 2020-031). RESULTS Table 1. Characteristics of control and pandemic cohorts (Robsons 1 & 2) Characteristic Pre-pandemic period (20 Oct ’17 – 3 Jun ’19) Pandemic-exposed period (4 Nov ’19 – 21 Jun ’21) Total Births (%) 25 897 (50.6) 25 298 (49.4) Maternal Weight in Kg, mean (SD)*** 68.6 (16.5) 69.2 (69.0) Maternal Height in cm, mean (SD) 163.3 (7.0) 163.7 (7.2) Birth Weight in Kg, mean (SD)** 3352.3 (461.9) 3365.8 (490.0) Gestational Age at Birth days, mean (SD)** 39.49 (1.2) 39.53 (1.2) Maternal age group (years)*** 18–24 4 767 (18.4) 3 995 (15.8) 25–29 7 321 (28.3) 6 746 (26.7) 30–34 9 647 (37.3) 10 122 (40.1) 35–39 3 479 (13.4) 3 726 (14.8) 40 or older 683 (2.6) 652 (2.6) BMI Categories* 40 686 (2.7) 665 (2.7) Socio-economic status (IRSAD quintile)* 1 (most disadvantaged) 5 147 (20.0) 4 925 (19.5) 2 3 847 (15.0) 3 705 (14.7) 3 5 668 (21.9) 5 788 (22.9) 4 5 962 (23.0) 5 937 (23.5) 5 (most advantaged) 5 219 (20.2) 4 943 (19.5) Region of birth*** Australia and associated territories 13 429 (52.1) 14 068 (56.0) Americas 441 (1.7) 519 (2.1) North Africa and the Middle East 886 (3.4) 700 (2.8) North-East Asia 1 294 (5.0) 933 (3.7) North-West Europe 894 (3.5) 933 (3.7) Oceania including New Zealand 816 (3.2) 778 (3.1) South-East Asia 2 181 (8.5) 1 911 (7.6) Southern and Central Asia 4 756 (18.4) 4 316 (17.2) Southern and Eastern Europe 558 (2.2) 483 (1.9) Sub-Saharan Africa 545 (2.1) 480 (1.9) Smoking** Yes 1 121 (4.3) 955 (3.8) Gestational age at birth*** 37 - 41 25 763 (99.5) 25 055 (99.0) 42+ 134 (0.5) 243 (1.0) IRSAD = Index of Relative Socio‐economic Advantage and Disadvantage Statistical Significance * < 0.05, **<0.01, ***<0.001 Table 2. Outcomes by Robson group Group Outcomes by Robson groups N (%) Pre-pandemic period (20 Oct ’17 – 3 Jun ’19) Pandemic-exposed period (4 Nov ’19 – 21 Jun ’21) Adjusted relative risk (ARR)† (95% Confidence Interval) All Robson Groups Number of pregnancies 66 906 66 466 Macrosomic infants, n (%) 6 207 (9.28) 6 671 (10.04) 1.07 (1.04 to 1.11)*** CS births 22 456 (33.56) 23,523 (35.39) 1.04 (1.01 to 1.06)*** Robson 1 & 2 Robson 1&2 births, n (% all Robson groups) 25 897 (38.71) 25 298 (38.06) 0.98 (0.96 to 0.999)* Macrosomic infants, n (% Robson 1&2) 2 070 (7.99) 2 162 (8.55) 1.05 (.99 to 1.12) CS births, n (% Robson 1&2) 7 977 (30.80) 8 372 (33.09) 1.07 (1.03 to 1.10)*** CS rate among macrosomic infants, n (% Robson 1&2) 923 (44.59) 1 062 (49.12) 1.16 (1.06 to 1.27)** Maternal BMI ≥ 25 (%) 11 446 (44.58) 11 169 (45.82) 1.02 (1.00 to 1.03)* Robson 1 Robson 1 births, n (% all Robson groups) 10 813 (16.16) 10 496 (16.47) 1.06 (1.04 to 1.09)*** Macrosomic infants, n (% Robson 1) 794 (7.34) 838 (7.66) 1.02 (.92 to 1.13) CS births, n (% Robson 1) 1 815 (16.79) 1 855 (16.95) 1.00 (.94 to 1.07) CS rate among macrosomic infants, n (% Robson 1) 210 (26.45) 252 (30.07) 1.18 (.98 to 1.43) Robson 2A Robson 2A births, n (% all Robson groups) 13 843 (20.69) 12 763 (19.20) .96 (.94 to .99)** Macrosomic infants, n (% Robson 2A) 1 147 (8.29) 1 161 (9.10) 1.08 (.99 to 1.17) CS births, n (% Robson 2A) 4 921 (35.55) 4 928 (38.61) 1.08 (1.04 to 1.12)*** CS rate among macrosomic infants, n (% Robson 2A) 584 (50.92) 647 (55.73) 1.18 (1.04 to 1.32)*** Robson 2B Robson 2B births, n (% all Robson groups) 1 241 (1.85) 1 589 (2.39) 1.27 (1.18 to 1.38)*** Macrosomic infants, n (% Robson 2B) 129 (10.39) 163 (10.26) .97 (.76 to 1.24) CS births, n (% Robson 2B) 1 241 (100.00) 1 589 (100.00) - CS rate among macrosomic infants, n (% Robson 2B) 129 (100.00) 163 (100.00) - Missing data was accounted by multiple imputation by chained equation (MICE) Statistical Significance * < 0.05, **<0.01, ***<0.001 † - adjusted for maternal country of birth, maternal smoking, socioeconomic status, baby sex, pertussis vaccination, and interpreter requirement Robson 1 & 2: Nulliparas with singleton, term, cephalic births Robson 1: Nulliparas with singleton, term, cephalic births after spontaneous onset of labour Robson 2A: Nulliparas with singleton, term, cephalic births following induction of labour Robson 2B: Nulliparas with singleton, term, cephalic births without labour (i) Cohort analysis All Robson groups There were a total of 66 906 births in the pre-pandemic cohort and 66 466 births in the pandemic-exposed cohort after exclusions and cohort selection criteria were applied (Figure 2). The maternal and neonatal characteristics of each cohort are shown in Supplemental Table 1. The proportion of all mothers with BMI ≥25kg/m 2 was significantly higher in the pandemic cohort, although the absolute differences were small (51.7% vs 51.1%, p<0.001). The rate of macrosomia in the overall obstetric population was higher among the pandemic-exposed cohort than in the pre-pandemic cohort (10.04% vs 9.23%, p<0.001) as was the overall CS rate (35.39% vs 33.56%, p<0.005). (Table 2). Nulliparas with a term, singleton cephalic fetus (Robson groups 1 & 2) Pre-pandemic and pandemic-exposed nulliparas with term, cephalic, singleton fetuses (Robson groups 1 and 2) were compared. There were 25 897 (50.6%) in the pre-pandemic cohort and 25 298 (49.4%) in the pandemic-exposed cohort (Figure 2); baseline characteristics are provided in Table 1. The maternal and neonatal characteristics of Robson groups 1 and 2 were similar to those of the overall study population. Table 2 shows outcomes of interest by Robson group. Similar to the overall study population, the rate of maternal BMI ≥25kg/m 2 was significantly higher among term nulliparas with a cephalic singleton fetus in the pandemic-exposed cohort compared with the pre-pandemic cohort (45.82% vs 44.58%, p<0.005). The rate of CS was also higher in the pandemic cohort compared with controls (33.09% vs 30.80%, p<0.005). However, the rate of macrosomia did not significantly differ between the pandemic and control cohorts (8.55% and 7.99%, p=0.12). There was no significant change in the proportion of birth by CS following spontaneous onset of labour in the pandemic cohort (Robson 1). There was a significantly higher proportion of births by CS following induction of labour (Robson 2A) in the pandemic cohort compared with controls (38.61% vs 35.55%, p<0.005). There was also a greater proportion of pre-labour CS for nulliparas with term, singleton, and cephalic fetuses (Robson 2B) in the pandemic cohort compared with the control group (2.39% vs 1.85%, p 25, macrosomia and CS among Robson groups 1 and 2 in the control and pandemic cohorts. Slope in per cent (95% confidence interval) Variable Pre-pandemic period (30 Oct 2017 – 28 Oct 2019 ) Pandemic-exposed period (4 Nov 2019 – 21 Jun 2021) Overweight (BMI > 25) 0.04 (0.01 to 0.06) 0.01 (–0.02 to 0.04) Macrosomia 0.004 (–0.01 to 0.01) 0.001 (–0.02 to 0.02) Caesarean section 0.02 (-0.01 to 0.04) –0.03 (–0.07 to 0.02) The ITSA results are shown in Figure 3 and Table 3. Importantly, there was a pre-existing upward trend in the rate of maternal BMI ≥25kg/m 2 prior to the onset of the pandemic at 0.04% per week (95% CI: 0.01% to 0.06%). Pandemic exposure was not associated with a significant change in the rate of rise in pregnant individuals with BMI > 25kg/m 2 (Figure 3 and Table 3). Similarly, the pre-existing uptrends in macrosomia and CS did not change significantly following the onset of the pandemic (Figure 3 and Table 3). DISCUSSION Our analysis of Melbourne-wide public hospital data demonstrated that the COVID-19 pandemic was associated with a greater proportion of maternal BMI ≥25kg/m 2 and CS among first-time mothers compared with pre-pandemic controls. However, these changes appear to be continuations of pre-pandemic trends, that were not accelerated during the pandemic and Melbourne lockdowns. The proportion of maternal BMI > 25kg/m 2 in first-time mothers suddenly reduced at the onset of the pandemic but has continued to increase since, and at a lower rate compared to the pre-pandemic period (Figure 3). This longstanding uptrend in maternal BMI > 25kg/m 2 portends higher rates of obstetric, neonatal and childhood consequences. Maternal weight is a modifiable risk factor, so promotion of a healthy diet and regular exercise, ideally before conception, is an important approach to addressing these outcomes. A trial of preconception weight optimization is currently underway in NSW, which may provide evidence for future interventions to reduce the perinatal and childhood morbidity associated with higher maternal BMI.[24] Despite a higher proportion of maternal BMI > 25kg/m 2 during the pandemic, fetal macrosomia was not significantly more common in first-time mothers, regardless of mode of delivery. Caesarean section was significantly more common in first-time mothers. Subgroup analysis indicates this increase was confined to individuals who were induced or undergoing pre-labour CS, suggesting that the decision threshold to deliver by CS following IOL or offering elective CS may have altered during the pandemic. Should the proportion of BMI > 25kg/m 2 during pregnancy continue to rise in first-time mothers, a compounding of the overall CS rate may be expected as success rates of a trial of labour after previous CS are lower for individuals with a high BMI.[25] Our analysis showed that macrosomia was associated with a significantly higher rate of CS in first-time mothers in both pre-pandemic and pandemic-exposed cohorts. Recognising and responding to risk factors for macrosomia other than maternal BMI > 25kg/m 2 during pregnancy, such as gestational diabetes and excess gestational weight gain, will also likely play have a role in reducing the primary CS rate. Future research should examine individuals birthing preferences, clinician decision making and health service factors and driving this increase in CS among nulliparas in Australia. Our study is one of few examining the relationship between the pandemic, BMI and obstetric outcomes. Anderson et al. [26] reported that the global prevalence of obesity in the general adult population increased by 1% during the pandemic. A systematic review and meta-analysis[27] of the impact of the COVID-19 pandemic on perinatal outcomes made no comment on maternal BMI or macrosomia. There are conflicting reports on the rates of CS during the pandemic, with some concluding that there was no difference in high-income countries[27], while others observed a significant reduction in primary CS.[28] A major strength of our study is the large multicentre dataset, capturing public hospital births from all 12 public maternity hospitals in Metropolitan Melbourne. We collected a complete birth cohort in a unique setting of strict COVID-19 lockdown restrictions but low maternal COVID-19 case-load. Our use of cLMP to define the pandemic-exposed cohort overcomes major methodological challenges of analysing time-dependent exposures. Another strength was the use of ITSA to inform our single metric outcomes. By comparing a forecasted trends in perinatal outcomes based on pre-pandemic trends, we could determine with greater confidence that the changes identified were associated with, rather than directly caused by, the pandemic. Not all births within Metropolitan Melbourne were included in our analysis, as private hospital birth data were not available. There are major differences in maternal sociodemographic and obstetric factors between the public and private hospitals settings. In particular, CS rates are consistently higher in private hospitals compared with public hospitals.[14] We were not able to include gestational diabetes in our analysis due to coding inconsistencies in the source data. Furthermore, changes in the screening and treatment of gestational diabetes during the pandemic would have likely confounded this analysis. CONCLUSION The pandemic period was associated with a greater proportion of maternal BMI ≥25kg/m 2 and CS in first-time mothers compared with during the pre-pandemic period. Although the increase in maternal BMI > 25kg/m 2 contributed to macrosomia and a higher CS rate during the pandemic, these changes were continuations of pre-existing trends and were not accelerated by the pandemic. These trends are not likely to abate with the cessation of pandemic restrictions and have significant long-term implications for population health. Declarations Ethical approval Ethical approval was obtained from Austin Health (HREC/64722/Austin-2020) and Mercy Health Ethics Committees (ref. 2020-031). ACKNOLEDGEMENTS: We thank the following hospital staff for their assistance in setting up the data collection within their respective health services: Tania Fletcher (Mercy Health), Michelle Knight (Monash Health), Lynne Rigg & Julia Lay (Royal Women’s Hospital), Abby Monaghan (Northern Health), Lee-Anne Lynch (Western Health), Pauline Hamilton, Carolyn Gower, Therese McCarthy (Eastern Health), and Roshanee Perera (Peninsula Health). FUNDING: This study was funded by the Norman Beischer Medical Research Foundation and The University of Melbourne Department of Obstetrics, Gynaecology and Newborn Health. Melvin Marzan receives salary support from the Generation Victoria (GenV) Fellowship from the Murdoch Children’s Research Institute. Lisa Hui receives salary support from the Medical Research Future Fund (#1196010) and the University of Melbourne. Author Contribution AJG and MBM contributed equally to this work and are joint first authors. LH, SPW, DLR, JMS, KRP, SP, CLW and BWM: Conception of original collaboration. LH: Project administration, supervision and funding acquisition. LH, SPW, DLR, JMS, KRP, PMS, SP, NP, CLW, JF, BWM, MBM and AJG: Approval of analysis plan. LH, SPW, DLR, JMS, PMS, CLW, JF and MBM: Collection of primary data. AJG, MBM and LH: Literature review, methodology, data analysis and visualisation, writing - original draft preparation. ACL: Writing - preliminary draft review and edit. All authors: Writing - final review and edit. Acknowledgement We thank the following hospital staff for their assistance in setting up the data collection within their respective health services: Tania Fletcher (Mercy Health), Michelle Knight (Monash Health), Lynne Rigg & Julia Lay (Royal Women’s Hospital), Abby Monaghan (Northern Health), Lee-Anne Lynch (Western Health), Pauline Hamilton, Carolyn Gower, Therese McCarthy (Eastern Health), and Roshanee Perera (Peninsula Health).The findings of our study were presented at the Royal Australian and New Zealand College of Obstetricians and Gynaecologists (RANZCOG) Annual Scientific Meeting October-November 2023 Perth, Australia as a poster presentation. Data Availability Data is available upon reasonable request subject to HREC approval. References Health, A.I.o. and Welfare, Overweight and obesity . 2023, AIHW: Canberra. 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Heslehurst, N., et al., The association between maternal body mass index and child obesity: A systematic review and meta-analysis. PLoS Med, 2019. 16 (6): p. e1002817. Cheney, K., et al., Population attributable fractions of perinatal outcomes for nulliparous women associated with overweight and obesity, 1990–2014. Medical Journal of Australia, 2018. 208 (3): p. 119-125. Keag, O.E., J.E. Norman, and S.J. Stock, Long-term risks and benefits associated with cesarean delivery for mother, baby, and subsequent pregnancies: Systematic review and meta-analysis. PLoS Med, 2018. 15 (1): p. e1002494. (SRH), S.a.R.H.R., WHO statement on caesarean section rates . 2015, World Health Organisation Health, A.I.o. and Welfare, National Core Maternity Indicators . 2023, AIHW: Canberra. Kapoor, N., et al., The Dual Pandemics of COVID-19 and Obesity: Bidirectional Impact. Diabetes Ther, 2022. 13 (10): p. 1723-1736. Victoria, S.C., Victorian perinatal services performance indicators 2020-21 . 2022, Victorian Government: Melbourne, Australia. Robson, M.S., Classification of caesarean sections. Fetal and Maternal Medicine Review, 2001. 12 (1): p. 23-39. Organisation, W.H., Robson Classification: Implementation Manual . 2017, World Health Organisation: Geneva. Victoria, S.C., Extreme prematurity guideline . 2022, Victorian Government: Melbourne, Australia. World Health Organization, W., Robson classification: implementation manual. 2017. Barnett, A.G., Time-dependent exposures and the fixed-cohort bias. Environ Health Perspect, 2011. 119 (10): p. A422-3; author reply A423. Hui, L., et al., Increase in preterm stillbirths in association with reduction in iatrogenic preterm births during COVID-19 lockdown in Australia: a multicenter cohort study. Am J Obstet Gynecol, 2022. 227 (3): p. 491.e1-491.e17. StataCorp, L., Stata statistical software: Release 18 (2023). College Station, TX: StataCorp LP, 2023. StataCorp, L., Stata multiple-imputation reference manual . 2023. A, L., ITSA: Stata module to perform interrupted time series analysis for single and multiple groups . Statistical Software Components S457793. 2014: Boston College Department of Economics. PreBabe. PreBabe Research Study . 2021; Available from: https://prebabe.com.au/. Durnwald, C.P., H.M. Ehrenberg, and B.M. Mercer, The impact of maternal obesity and weight gain on vaginal birth after cesarean section success. Am J Obstet Gynecol, 2004. 191 (3): p. 954-7. Anderson, L.N., et al., Obesity and weight change during the COVID-19 pandemic in children and adults: A systematic review and meta-analysis. Obes Rev, 2023. 24 (5): p. e13550. Chmielewska, B., et al., Effects of the COVID-19 pandemic on maternal and perinatal outcomes: a systematic review and meta-analysis. Lancet Glob Health, 2021. 9 (6): p. e759-e772. Sinnott, C.M., et al., Investigating Decreased Rates of Nulliparous Cesarean Deliveries during the COVID-19 Pandemic. Am J Perinatol, 2021. 38 (12): p. 1231-1235. Additional Declarations No competing interests reported. Supplementary Files BMCSupplementaryInformationFINAL.docx Cite Share Download PDF Status: Published Journal Publication published 28 Oct, 2024 Read the published version in BMC Pregnancy and Childbirth → Version 1 posted Editorial decision: Revision requested 20 May, 2024 Submission checks completed at journal 17 May, 2024 Editor assigned by journal 17 May, 2024 First submitted to journal 13 May, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4412944","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":304427317,"identity":"0a9d8050-3245-4283-a8b9-0c7e1a7a5ccb","order_by":0,"name":"Andrew Goldsack","email":"","orcid":"","institution":"Melbourne Medical School, The University of Melbourne","correspondingAuthor":false,"prefix":"","firstName":"Andrew","middleName":"","lastName":"Goldsack","suffix":""},{"id":304427318,"identity":"6ee8a4f0-e600-421f-b71d-c83ea3f7b2e1","order_by":1,"name":"Melvin Marzan","email":"","orcid":"","institution":"Department of Obstetrics and Gynaecology, Melbourne Medical School, The University of Melbourne","correspondingAuthor":false,"prefix":"","firstName":"Melvin","middleName":"","lastName":"Marzan","suffix":""},{"id":304427319,"identity":"e4de91fd-5e09-401e-8999-0f1b2da81664","order_by":2,"name":"Daniel Rolnik","email":"","orcid":"","institution":"Department of Obstetrics and Gynaecology, Monash Health","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"","lastName":"Rolnik","suffix":""},{"id":304427320,"identity":"b369199c-1164-4f86-9999-78c06d596b2d","order_by":3,"name":"Anthea Lindquist","email":"","orcid":"","institution":"Department of Obstetrics and Gynaecology, Melbourne Medical School, The University of Melbourne","correspondingAuthor":false,"prefix":"","firstName":"Anthea","middleName":"","lastName":"Lindquist","suffix":""},{"id":304427321,"identity":"4040adcc-0590-4fe7-b4c0-69f2f80b4d07","order_by":4,"name":"Joanne Said","email":"","orcid":"","institution":"Department of Obstetrics and Gynaecology, Melbourne Medical School, The University of Melbourne","correspondingAuthor":false,"prefix":"","firstName":"Joanne","middleName":"","lastName":"Said","suffix":""},{"id":304427322,"identity":"6f03e85e-5411-461f-8502-08076863dac3","order_by":5,"name":"Kirsten Palmer","email":"","orcid":"","institution":"Department of Obstetrics and Gynaecology, Monash Health","correspondingAuthor":false,"prefix":"","firstName":"Kirsten","middleName":"","lastName":"Palmer","suffix":""},{"id":304427323,"identity":"c5602f8f-9af5-495d-8882-df4fdd77d947","order_by":6,"name":"Penelope Sheehan","email":"","orcid":"","institution":"Department of Obstetrics and Gynaecology, Monash Health","correspondingAuthor":false,"prefix":"","firstName":"Penelope","middleName":"","lastName":"Sheehan","suffix":""},{"id":304427324,"identity":"81361484-403a-4311-849f-1c334050ef48","order_by":7,"name":"Stephanie Potenza","email":"","orcid":"","institution":"Department of Obstetrics and Gynaecology, Mercy Hospital for Women, Mercy Health","correspondingAuthor":false,"prefix":"","firstName":"Stephanie","middleName":"","lastName":"Potenza","suffix":""},{"id":304427325,"identity":"501998ba-0ee6-48c9-b52d-6cca23faa340","order_by":8,"name":"Natasha Pritchard","email":"","orcid":"","institution":"Department of Obstetrics and Gynaecology, Melbourne Medical School, The University of Melbourne","correspondingAuthor":false,"prefix":"","firstName":"Natasha","middleName":"","lastName":"Pritchard","suffix":""},{"id":304427326,"identity":"8d6f0679-6845-442c-9389-2fcee0614d87","order_by":9,"name":"Clare Whitehead","email":"","orcid":"","institution":"Department of Obstetrics and Gynaecology, Melbourne Medical School, The University of Melbourne","correspondingAuthor":false,"prefix":"","firstName":"Clare","middleName":"","lastName":"Whitehead","suffix":""},{"id":304427327,"identity":"e7e7816e-1fb5-4b4d-9984-a2e4cdf671c3","order_by":10,"name":"Jolyon Ford","email":"","orcid":"","institution":"Department of Obstetrics and Gynaecology, Peninsula Health","correspondingAuthor":false,"prefix":"","firstName":"Jolyon","middleName":"","lastName":"Ford","suffix":""},{"id":304427328,"identity":"27ffc210-c13e-4008-89d7-8895d840f661","order_by":11,"name":"Ben Mol","email":"","orcid":"","institution":"Department of Obstetrics and Gynaecology, Monash Health","correspondingAuthor":false,"prefix":"","firstName":"Ben","middleName":"","lastName":"Mol","suffix":""},{"id":304427329,"identity":"1d9c0111-5bb4-4123-b1d2-287e5fd461fa","order_by":12,"name":"Susan Walker","email":"","orcid":"","institution":"Department of Obstetrics and Gynaecology, Melbourne Medical School, The University of Melbourne","correspondingAuthor":false,"prefix":"","firstName":"Susan","middleName":"","lastName":"Walker","suffix":""},{"id":304427330,"identity":"8cd6ea98-5cdd-448e-81ba-6e1a29e42778","order_by":13,"name":"Lisa Hui","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIie3OMYvCMBTA8VeEZglmfYLgVwgUSsEDv0q76BK7FI4bHAoHvc3Zr3GLOKqBuvQDZBVXB+XgcBKTiIMcBt1uyH9ISODHewA+3z+s3dJHUAJl+l4CJKm+uZOEN9IpLcEnCFwJ8KV9P0MI2R6ChexGarxaqw/MgXzOESbSsRiNMGgkjVWeStFgAbR+R6idBDCoDBFcjivMShQxQugiZHcyJJoZctakt9fk7CIQ2ykcDSnNFGp/XIvFSVaNKDZ7LkWNRUiHRZJNRw8JY5udOlb9AfsS0Y+YvOWMyG91+O0/JLb0bu6fH5/P5/O93AU+dErYDv/x/QAAAABJRU5ErkJggg==","orcid":"","institution":"Department of Obstetrics and Gynaecology, Melbourne Medical School, The University of Melbourne","correspondingAuthor":true,"prefix":"","firstName":"Lisa","middleName":"","lastName":"Hui","suffix":""}],"badges":[],"createdAt":"2024-05-13 11:31:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4412944/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4412944/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12884-024-06908-y","type":"published","date":"2024-10-28T16:20:10+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":57630254,"identity":"ae34d849-d2a0-47f4-b39e-41bdd5268450","added_by":"auto","created_at":"2024-06-03 14:44:53","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":35516,"visible":true,"origin":"","legend":"\u003cp\u003ePandemic exposed group timeline\u003c/p\u003e\n\u003cp\u003ecLMP = calculated week of last menstrual period\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4412944/v1/1492c4c119cb247171485f2d.jpg"},{"id":57629582,"identity":"df5aeb1f-c9df-4e82-ac3f-3c5bbbb5cad4","added_by":"auto","created_at":"2024-06-03 14:36:53","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":80528,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis flow-chart\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4412944/v1/4f04879ae2f1f53cbad2039d.jpg"},{"id":57630255,"identity":"d9d2203b-fc8d-4051-b89a-5c52c47e42a0","added_by":"auto","created_at":"2024-06-03 14:44:53","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":93939,"visible":true,"origin":"","legend":"\u003cp\u003eMaternal BMI \u003cu\u003e\u0026gt;\u003c/u\u003e25 \u003cstrong\u003e(A)\u003c/strong\u003e, fetal macrosomia \u003cstrong\u003e(B)\u003c/strong\u003e and caesarean sections \u003cstrong\u003e(C)\u003c/strong\u003e for Robsons 1 and 2 from October 30 2017 to June 21 2021 time series analysis by cLMP.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4412944/v1/e1277e007c694724b3406e71.jpg"},{"id":68207209,"identity":"194cefc8-e2b7-4791-ad49-080b026d77a2","added_by":"auto","created_at":"2024-11-04 16:35:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":842630,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4412944/v1/4e713f3e-8926-4643-9979-66fe24208c9b.pdf"},{"id":57629584,"identity":"fb2276b1-2d17-4686-80a0-fe4ab688503b","added_by":"auto","created_at":"2024-06-03 14:36:54","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":25872,"visible":true,"origin":"","legend":"","description":"","filename":"BMCSupplementaryInformationFINAL.docx","url":"https://assets-eu.researchsquare.com/files/rs-4412944/v1/ab69bf27f1473890512235e0.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Trends in Maternal Body Mass Index, Macrosomia and Caesarean Section in First-Time Mothers during the pandemic: a Multicentre Retrospective Cohort Study of 12 Melbourne Public Hospitals.","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eThe obesity and overweight epidemic in Australia is a major public health priority for our healthcare system.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] Australian studies have reported that the COVID-19 pandemic may have influenced the weight of some populations, with overweight and obesity (BMI\u0026thinsp;\u0026ge;\u0026thinsp;25kg/m\u003csup\u003e2\u003c/sup\u003e) being more common among all age groups except the elderly.[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] A multicentre study of all births in Melbourne public hospitals showed an increase in the proportion of pregnant individuals with a BMI\u0026thinsp;\u0026ge;\u0026thinsp;25kg/m\u003csup\u003e2\u003c/sup\u003e during the pandemic.[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] Higher maternal weight during pregnancy is associated with higher rates of delivery by caesarean section (CS) and confers increased risks of obstetrics complications such as gestational diabetes, and macrosomic birth weight\u0026thinsp;\u0026gt;\u0026thinsp;4000g. Maternal weight also has implications for the next generation through epigenetic modification of the infant,[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] and has been associated with childhood obesity and adverse metabolic profiles.[\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] Over the past 25 years, a greater proportion of people giving birth have had a BMI\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;25kg/m\u003csup\u003e2\u003c/sup\u003e. Prevention of the associated adverse maternal and childhood outcomes through strategies such as optimisation of pre-pregnancy maternal weight is an important public health priority.[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eCS has important implications for subsequent pregnancies, including the risk of placental adhesive disorder and uterine rupture, health systems and resources as well as impacting newborn health.[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] Safely mitigating the rise in CS has therefore become a global focus in obstetric care.[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] Individuals giving birth for the first time (nulliparas) are considered a high priority group for addressing the rising CS rate, as CS in a first pregnancy makes subsequent deliveries by CS more likely.[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eAuthors[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] have described the relationship between the COVID-19 pandemic and obesity by noting the psychological impact, difficulty accessing healthcare and limitations to physical activity as contributors to the global obesity crisis. Metropolitan Melbourne had 18 months of government mandates restricting movement of people during the COVID-19 pandemic, accompanied by abrupt changes in the provision of routine antenatal care. These factors may have influenced maternal weight and associated obstetric complications during 2020\u0026ndash;2021.[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThe objective of this study was to analyse trends of maternal BMI\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;25kg/m\u003csup\u003e2\u003c/sup\u003e, macrosomia and CS before and during the pandemic. We hypothesised that the pandemic and the associated lockdown restrictions made an independent contribution to the rates of maternal BMI\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;25kg/m\u003csup\u003e2\u003c/sup\u003e, macrosomia and CS.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e\u003cstrong\u003eStudy design\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe conducted a multi-centre retrospective cohort analysis of perinatal data in two parts: (i) summary statistics (n and %) of maternal BMI \u003cu\u003e\u0026gt;\u003c/u\u003e25kg/m\u003csup\u003e2\u003c/sup\u003e, macrosomia, and CS births in groups and Poisson regression analysis comparing cohorts with and without exposure to the pandemic and, (ii) an interrupted time-series analysis (ITSA) of perinatal outcomes, with a forecast based on pre-exposure trends. Macrosomia is defined as birthweight \u0026gt;4000g, rather than by centile for GA, as our population of interest is first-time mothers delivering at or after 37 weeks GA. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy population and data sources\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNon-identifiable data was obtained from routine birth collections system with a waiver of consent. This analysis includes all births of \u0026ge;20 weeks gestational age (GA) from all 12 public maternity hospitals in Melbourne from 1 January 2018 to 31 March 2022. Data from private maternity hospitals in Melbourne were not available for this study. The twelve hospitals capture approximately 78% of all births from Melbourne and include all four tertiary maternity units.[14]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInclusion and exclusion criteria\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSingleton births of infants at \u0026ge;20 weeks GA and classifiable in the Robson classification system were included.[15, 16]\u0026nbsp;Exclusions were: congenital abnormalities, deliveries \u0026lt;20 weeks GA or with unknown GA, terminations of pregnancy, non-Victorian residents and births with missing or contradictory information in the variables needed for Robson classification.[17]\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDefinition of Robson 1, 2A and 2B\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Robson classification system is a global standard for describing birth cohorts to facilitate standardised comparison of CS rates within and between healthcare systems.[18]\u0026nbsp;In this study, we focussed on Robson groups 1, 2A and 2B as these represent individuals for whom averting a CS has the highest potential benefit for individual health outcomes and healthcare systems.\u0026nbsp;\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eRobson 1:\u003c/strong\u003e Nulliparous, singleton, cephalic presentation, \u0026ge;37 week GA pregnancies where labour commenced spontaneously. This group includes individuals who received oxytocin or had an amniotomy for augmentation of labour.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eRobson 2A:\u003c/strong\u003e Nulliparous, singleton, cephalic presentation, \u0026ge;37 week GA pregnancies for whom labour was induced.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eRobson 2B:\u0026nbsp;\u003c/strong\u003eNulliparous, singleton, cephalic presentation, \u0026ge;37 week GA pregnancies that delivered pre-labour (i.e. by elective CS).[15, 16]\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eOutcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur primary outcomes are reported as frequency and rates;\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eProportion of mothers with BMI \u003cu\u003e\u0026gt;\u003c/u\u003e25kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/li\u003e\n \u003cli\u003eProportion of infants delivered with birthweight \u0026gt;4000g (macrosomia)\u003c/li\u003e\n \u003cli\u003eProportion of infants delivered by caesarean section\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eProportion of infants with birthweight \u0026gt;4000g (macrosomia), delivered by caesarean section\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003ePandemic exposure definitions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGestational exposure to pandemic conditions is a time-dependent exposure. To avoid the fixed cohort bias[19] that arises from using calendar dates to define study cohorts with time-dependent exposures, we used \u0026lsquo;calculated week of last menstrual period\u0026rsquo; (cLMP) rather than week of birth, as previously described,[20] to define the pandemic-exposed cohort and ensure all pregnancies in the pandemic cohort had an equivalent duration of exposure (Figure 1).\u003c/p\u003e\n\u003cp\u003eFor privacy protection, hospital data managers converted the actual infant dates of birth into the ordinal calendar week of birth (i.e. 1 to 52 for each calendar week). To generate the cLMP, we used the week-of-birth and gestational age in completed weeks at delivery. The formula used was:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFirst day of the week\u0026not;‑of‑cLMP=week‑of‑birth-[GA(in completed weeks)\u0026times;7]\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eUsing this cLMP, we defined a \u0026lsquo;pandemic-exposed\u0026rsquo; cohort comprising women for whom weeks 20-40 of gestation would have occurred during the lockdown period. (Figure 1) With a defined pandemic-exposure from 23 March 2020 (Monday of the week of suspension of elective surgery due to COVID-19 in Victoria) to 28 March 2022 (which formed a 2 year period of pandemic exposure), this included women whose cLMP occurred during the 85 weeks from 4 November 2019 to 21 June 2021 inclusive.[16] The pre-pandemic control group comprised of women who had their cLMP during the corresponding calendar weeks commencing two years prior to the start of the exposed cohort (births with cLMPs in weeks commencing 30 October 2017 to 3 June 2019).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e(i) Cohort analysis\u0026nbsp;\u003c/em\u003e\u003cem\u003eof the proportions of maternal BMI \u0026gt;25kg/m\u003csup\u003e2\u003c/sup\u003e, macrosomia and CS births in Robson groups 1 and 2 before and during the pandemic.\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe present the primary outcomes as frequency (n) and percentage/proportion (%) in accordance with the World Health Organisation\u0026rsquo;s Robson Manual recommendations.[15, 16] Statistical significance was assessed using independent samples t-tests or Chi-square tests as appropriate. The analyses were conducted in Stata version 18[21], with two-tailed p-values below 0.05 considered statistically significant. To compare the pandemic cohort with the pre-pandemic cohort, we independently employed Poisson regression, adjusting for covariates such as maternal age, maternal BMI, maternal region of birth, smoking status, socioeconomic status, and requirement for an interpreter. Covariates were selected based on \u003cem\u003ea priori\u003c/em\u003e and subject matter expertise. The effect estimates were reported as adjusted relative risk (aRR) with 95% confidence intervals (CI). We accounted for the observations with missing data on covariates by using the multiple imputation by chained equation (MICE) using the \u0026ldquo;mi impute chained\u0026rdquo; command in Stata 18.[22]\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e(ii) Interrupted time-series analysis (ITSA) of weekly rates of maternal BMI \u003cu\u003e\u0026gt;\u003c/u\u003e25kg/m\u003csup\u003e2\u003c/sup\u003e, macrosomia and CS births in Robson groups 1 and 2 \u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe conducted ITSA using the cLMP from 30 October 2017 to 28 March 2022. Our intervention period started from cLMP of 4 November 2019. We employed ITSA using the \u0026ldquo;itsa\u0026rdquo; suite of commands in Stata 18.[23] This approach used the Prais-Winstein generalized least-squares regression, accounting for autocorrelation of the residuals.[23] We used sine and cosine functions to correct for seasonality.[23] (Supplemental File 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was obtained from Austin Health (HREC/64722/Austin-2020) and Mercy Health Ethics Committees (ref. 2020-031).\u0026nbsp;\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Characteristics of control and pandemic cohorts (Robsons 1 \u0026amp; 2)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"579\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.76856649395509%\" valign=\"bottom\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.46113989637306%\" valign=\"bottom\"\u003e\n \u003cp\u003ePre-pandemic period\u003cbr\u003e\u0026nbsp;(20 Oct \u0026rsquo;17 \u0026ndash; 3 Jun \u0026rsquo;19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.77029360967185%\" valign=\"bottom\"\u003e\n \u003cp\u003ePandemic-exposed period\u003cbr\u003e\u0026nbsp;(4 Nov \u0026rsquo;19 \u0026ndash; 21 Jun \u0026rsquo;21)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.76856649395509%\" valign=\"top\"\u003e\n \u003cp\u003eTotal Births (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.46113989637306%\"\u003e\n \u003cp\u003e25 897 (50.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.77029360967185%\" valign=\"top\"\u003e\n \u003cp\u003e25 298 (49.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.76856649395509%\" valign=\"top\"\u003e\n \u003cp\u003eMaternal Weight in Kg, mean (SD)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.46113989637306%\" valign=\"top\"\u003e\n \u003cp\u003e68.6 (16.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.77029360967185%\" valign=\"top\"\u003e\n \u003cp\u003e69.2 (69.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.76856649395509%\" valign=\"top\"\u003e\n \u003cp\u003eMaternal Height in cm, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.46113989637306%\" valign=\"top\"\u003e\n \u003cp\u003e163.3 (7.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.77029360967185%\" valign=\"top\"\u003e\n \u003cp\u003e163.7 (7.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.76856649395509%\" valign=\"top\"\u003e\n \u003cp\u003eBirth Weight in Kg, mean (SD)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.46113989637306%\" valign=\"top\"\u003e\n \u003cp\u003e3352.3 (461.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.77029360967185%\" valign=\"top\"\u003e\n \u003cp\u003e3365.8 (490.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.76856649395509%\" valign=\"top\"\u003e\n \u003cp\u003eGestational Age at Birth days, mean (SD)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.46113989637306%\" valign=\"top\"\u003e\n \u003cp\u003e39.49 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.77029360967185%\" valign=\"top\"\u003e\n \u003cp\u003e39.53 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.76856649395509%\"\u003e\n \u003cp\u003eMaternal age group (years)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.46113989637306%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.77029360967185%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.76856649395509%\"\u003e\n \u003cp\u003e18\u0026ndash;24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.46113989637306%\"\u003e\n \u003cp\u003e4 767 (18.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.77029360967185%\"\u003e\n \u003cp\u003e3 995 (15.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.76856649395509%\"\u003e\n \u003cp\u003e25\u0026ndash;29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.46113989637306%\"\u003e\n \u003cp\u003e7 321 (28.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.77029360967185%\"\u003e\n \u003cp\u003e6 746 (26.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.76856649395509%\"\u003e\n \u003cp\u003e30\u0026ndash;34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.46113989637306%\"\u003e\n \u003cp\u003e9 647 (37.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.77029360967185%\"\u003e\n \u003cp\u003e10 122 (40.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.76856649395509%\"\u003e\n \u003cp\u003e35\u0026ndash;39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.46113989637306%\"\u003e\n \u003cp\u003e3 479 (13.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.77029360967185%\"\u003e\n \u003cp\u003e3 726 (14.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.76856649395509%\"\u003e\n \u003cp\u003e40 or older\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.46113989637306%\"\u003e\n \u003cp\u003e683 (2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.77029360967185%\"\u003e\n \u003cp\u003e652 (2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.76856649395509%\"\u003e\n \u003cp\u003eBMI Categories*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.46113989637306%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.77029360967185%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.76856649395509%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;\u0026lt;18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.46113989637306%\" valign=\"top\"\u003e\n \u003cp\u003e429 (1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.77029360967185%\" valign=\"top\"\u003e\n \u003cp\u003e390 (1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.76856649395509%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 18\u0026ndash;24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.46113989637306%\" valign=\"top\"\u003e\n \u003cp\u003e13 807 (53.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.77029360967185%\" valign=\"top\"\u003e\n \u003cp\u003e12 818 (52.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.76856649395509%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;25\u0026ndash;29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.46113989637306%\" valign=\"top\"\u003e\n \u003cp\u003e7 035 (27.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.77029360967185%\" valign=\"top\"\u003e\n \u003cp\u003e6 748 (27.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.76856649395509%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;30\u0026ndash;34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.46113989637306%\" valign=\"top\"\u003e\n \u003cp\u003e2 625 (10.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.77029360967185%\" valign=\"top\"\u003e\n \u003cp\u003e2 676 (11.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.76856649395509%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;35\u0026ndash;39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.46113989637306%\" valign=\"top\"\u003e\n \u003cp\u003e1 099 (4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.77029360967185%\" valign=\"top\"\u003e\n \u003cp\u003e1 080 (4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.76856649395509%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u003cu\u003e\u0026gt;\u003c/u\u003e40\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.46113989637306%\" valign=\"top\"\u003e\n \u003cp\u003e686 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.77029360967185%\" valign=\"top\"\u003e\n \u003cp\u003e665 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.76856649395509%\" valign=\"top\"\u003e\n \u003cp\u003eSocio-economic status (IRSAD quintile)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.46113989637306%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.77029360967185%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.76856649395509%\"\u003e\n \u003cp\u003e1 (most disadvantaged)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.46113989637306%\"\u003e\n \u003cp\u003e5 147 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.77029360967185%\"\u003e\n \u003cp\u003e4 925 (19.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.76856649395509%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.46113989637306%\"\u003e\n \u003cp\u003e3 847 (15.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.77029360967185%\"\u003e\n \u003cp\u003e3 705 (14.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.76856649395509%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.46113989637306%\"\u003e\n \u003cp\u003e5 668 (21.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.77029360967185%\"\u003e\n \u003cp\u003e5 788 (22.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.76856649395509%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.46113989637306%\"\u003e\n \u003cp\u003e5 962 (23.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.77029360967185%\"\u003e\n \u003cp\u003e5 937 (23.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.76856649395509%\"\u003e\n \u003cp\u003e5 (most advantaged)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.46113989637306%\"\u003e\n \u003cp\u003e5 219 (20.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.77029360967185%\"\u003e\n \u003cp\u003e4 943 (19.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.76856649395509%\"\u003e\n \u003cp\u003eRegion of birth***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.46113989637306%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.77029360967185%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.76856649395509%\"\u003e\n \u003cp\u003eAustralia and associated territories\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.46113989637306%\"\u003e\n \u003cp\u003e13 429 (52.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.77029360967185%\"\u003e\n \u003cp\u003e14 068 (56.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.76856649395509%\"\u003e\n \u003cp\u003eAmericas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.46113989637306%\"\u003e\n \u003cp\u003e441 (1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.77029360967185%\"\u003e\n \u003cp\u003e519 (2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.76856649395509%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; North Africa and the Middle East\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.46113989637306%\" valign=\"top\"\u003e\n \u003cp\u003e886 (3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.77029360967185%\" valign=\"top\"\u003e\n \u003cp\u003e700 (2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.76856649395509%\"\u003e\n \u003cp\u003eNorth-East Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.46113989637306%\"\u003e\n \u003cp\u003e1 294 (5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.77029360967185%\"\u003e\n \u003cp\u003e933 (3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.76856649395509%\"\u003e\n \u003cp\u003eNorth-West Europe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.46113989637306%\"\u003e\n \u003cp\u003e894 (3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.77029360967185%\"\u003e\n \u003cp\u003e933 (3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.76856649395509%\"\u003e\n \u003cp\u003eOceania including New Zealand\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.46113989637306%\"\u003e\n \u003cp\u003e816 (3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.77029360967185%\"\u003e\n \u003cp\u003e778 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.76856649395509%\"\u003e\n \u003cp\u003eSouth-East Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.46113989637306%\"\u003e\n \u003cp\u003e2 181 (8.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.77029360967185%\"\u003e\n \u003cp\u003e1 911 (7.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.76856649395509%\"\u003e\n \u003cp\u003eSouthern and Central Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.46113989637306%\"\u003e\n \u003cp\u003e4 756 (18.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.77029360967185%\"\u003e\n \u003cp\u003e4 316 (17.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.76856649395509%\"\u003e\n \u003cp\u003eSouthern and Eastern Europe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.46113989637306%\"\u003e\n \u003cp\u003e558 (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.77029360967185%\"\u003e\n \u003cp\u003e483 (1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.76856649395509%\"\u003e\n \u003cp\u003eSub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.46113989637306%\"\u003e\n \u003cp\u003e545 (2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.77029360967185%\"\u003e\n \u003cp\u003e480 (1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.76856649395509%\" valign=\"top\"\u003e\n \u003cp\u003eSmoking**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.46113989637306%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.77029360967185%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.76856649395509%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.46113989637306%\" valign=\"top\"\u003e\n \u003cp\u003e1 121 (4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.77029360967185%\" valign=\"top\"\u003e\n \u003cp\u003e955 (3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.76856649395509%\" valign=\"top\"\u003e\n \u003cp\u003eGestational age at birth***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.46113989637306%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.77029360967185%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.76856649395509%\" valign=\"top\"\u003e\n \u003cp\u003e37 - 41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.46113989637306%\" valign=\"top\"\u003e\n \u003cp\u003e25 763 (99.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.77029360967185%\" valign=\"top\"\u003e\n \u003cp\u003e25 055 (99.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.76856649395509%\" valign=\"top\"\u003e\n \u003cp\u003e42+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.46113989637306%\" valign=\"top\"\u003e\n \u003cp\u003e134 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.77029360967185%\" valign=\"top\"\u003e\n \u003cp\u003e243 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eIRSAD = Index of Relative Socio‐economic Advantage and Disadvantage\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStatistical Significance * \u0026lt; 0.05, **\u0026lt;0.01, ***\u0026lt;0.001\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e\u0026nbsp; \u0026nbsp;Outcomes by Robson group\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"694\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11815561959654%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.5821325648415%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29971181556196%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11815561959654%\" valign=\"bottom\"\u003e\n \u003cp\u003eOutcomes by Robson groups\u003c/p\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.587896253602306%\" valign=\"top\"\u003e\n \u003cp\u003ePre-pandemic period\u003cbr\u003e\u0026nbsp;(20 Oct \u0026rsquo;17 \u0026ndash; 3 Jun \u0026rsquo;19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.994236311239193%\" valign=\"top\"\u003e\n \u003cp\u003ePandemic-exposed period\u003cbr\u003e\u0026nbsp;(4 Nov \u0026rsquo;19 \u0026ndash; 21 Jun \u0026rsquo;21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29971181556196%\" valign=\"top\"\u003e\n \u003cp\u003eAdjusted relative risk (ARR)\u0026dagger;\u003c/p\u003e\n \u003cp\u003e(95% Confidence Interval)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11815561959654%\" valign=\"top\"\u003e\n \u003cp\u003eAll Robson Groups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.587896253602306%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.994236311239193%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29971181556196%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11815561959654%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Number of pregnancies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.587896253602306%\" valign=\"top\"\u003e\n \u003cp\u003e66 906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.994236311239193%\" valign=\"top\"\u003e\n \u003cp\u003e66 466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29971181556196%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11815561959654%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Macrosomic infants, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.587896253602306%\" valign=\"top\"\u003e\n \u003cp\u003e6 207 (9.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.994236311239193%\" valign=\"top\"\u003e\n \u003cp\u003e6 671 (10.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29971181556196%\" valign=\"top\"\u003e\n \u003cp\u003e1.07 (1.04 to 1.11)***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11815561959654%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;CS births\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.587896253602306%\" valign=\"top\"\u003e\n \u003cp\u003e22 456 (33.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.994236311239193%\" valign=\"top\"\u003e\n \u003cp\u003e23,523 (35.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29971181556196%\" valign=\"top\"\u003e\n \u003cp\u003e1.04 (1.01 to 1.06)***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11815561959654%\" valign=\"top\"\u003e\n \u003cp\u003eRobson 1 \u0026amp; 2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.587896253602306%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.994236311239193%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29971181556196%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11815561959654%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Robson 1\u0026amp;2 births, n (% all Robson groups)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.587896253602306%\" valign=\"top\"\u003e\n \u003cp\u003e25 897 (38.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.994236311239193%\" valign=\"top\"\u003e\n \u003cp\u003e25 298 (38.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29971181556196%\" valign=\"top\"\u003e\n \u003cp\u003e0.98 (0.96 to 0.999)*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11815561959654%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Macrosomic infants, n (% Robson 1\u0026amp;2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.587896253602306%\" valign=\"top\"\u003e\n \u003cp\u003e2 070 (7.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.994236311239193%\" valign=\"top\"\u003e\n \u003cp\u003e2 162 (8.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29971181556196%\" valign=\"top\"\u003e\n \u003cp\u003e1.05 (.99 to 1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11815561959654%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;CS births, n (% Robson 1\u0026amp;2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.587896253602306%\" valign=\"top\"\u003e\n \u003cp\u003e7 977 (30.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.994236311239193%\" valign=\"top\"\u003e\n \u003cp\u003e8 372 (33.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29971181556196%\" valign=\"top\"\u003e\n \u003cp\u003e1.07 (1.03 to 1.10)***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11815561959654%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;CS rate among macrosomic infants, n (% Robson 1\u0026amp;2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.587896253602306%\" valign=\"top\"\u003e\n \u003cp\u003e923 (44.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.994236311239193%\" valign=\"top\"\u003e\n \u003cp\u003e1 062 (49.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29971181556196%\" valign=\"top\"\u003e\n \u003cp\u003e1.16 (1.06 to 1.27)**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11815561959654%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Maternal BMI \u0026ge; 25 (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.587896253602306%\" valign=\"top\"\u003e\n \u003cp\u003e11 446 (44.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.994236311239193%\" valign=\"top\"\u003e\n \u003cp\u003e11 169 (45.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29971181556196%\" valign=\"top\"\u003e\n \u003cp\u003e1.02 (1.00 to 1.03)*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11815561959654%\" valign=\"top\"\u003e\n \u003cp\u003eRobson 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.587896253602306%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.994236311239193%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29971181556196%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11815561959654%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Robson 1 births, n (% all Robson groups)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.587896253602306%\" valign=\"top\"\u003e\n \u003cp\u003e10 813 (16.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.994236311239193%\" valign=\"top\"\u003e\n \u003cp\u003e10 496 (16.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29971181556196%\" valign=\"top\"\u003e\n \u003cp\u003e1.06 (1.04 to 1.09)***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11815561959654%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Macrosomic infants, n (% Robson 1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.587896253602306%\" valign=\"top\"\u003e\n \u003cp\u003e794 (7.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.994236311239193%\" valign=\"top\"\u003e\n \u003cp\u003e838 (7.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29971181556196%\" valign=\"top\"\u003e\n \u003cp\u003e1.02 (.92 to 1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11815561959654%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;CS births, n (% Robson 1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.587896253602306%\" valign=\"top\"\u003e\n \u003cp\u003e1 815 (16.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.994236311239193%\" valign=\"top\"\u003e\n \u003cp\u003e1 855 (16.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29971181556196%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (.94 to 1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11815561959654%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;CS rate among macrosomic infants, n (% Robson 1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.587896253602306%\" valign=\"top\"\u003e\n \u003cp\u003e210 (26.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.994236311239193%\" valign=\"top\"\u003e\n \u003cp\u003e252 (30.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29971181556196%\" valign=\"top\"\u003e\n \u003cp\u003e1.18 (.98 to 1.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11815561959654%\" valign=\"top\"\u003e\n \u003cp\u003eRobson 2A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.587896253602306%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.994236311239193%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29971181556196%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11815561959654%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Robson 2A births, n (% all Robson groups)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.587896253602306%\" valign=\"top\"\u003e\n \u003cp\u003e13 843 (20.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.994236311239193%\" valign=\"top\"\u003e\n \u003cp\u003e12 763 (19.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29971181556196%\" valign=\"top\"\u003e\n \u003cp\u003e.96 (.94 to .99)**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11815561959654%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Macrosomic infants, n (% Robson 2A)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.587896253602306%\" valign=\"top\"\u003e\n \u003cp\u003e1 147 (8.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.994236311239193%\" valign=\"top\"\u003e\n \u003cp\u003e1 161 (9.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29971181556196%\" valign=\"top\"\u003e\n \u003cp\u003e1.08 (.99 to 1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11815561959654%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; CS births, n (% Robson 2A)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.587896253602306%\" valign=\"top\"\u003e\n \u003cp\u003e4 921 (35.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.994236311239193%\" valign=\"top\"\u003e\n \u003cp\u003e4 928 (38.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29971181556196%\" valign=\"top\"\u003e\n \u003cp\u003e1.08 (1.04 to 1.12)***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11815561959654%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; CS rate among macrosomic infants, n (% Robson 2A)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.587896253602306%\" valign=\"top\"\u003e\n \u003cp\u003e584 (50.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.994236311239193%\" valign=\"top\"\u003e\n \u003cp\u003e647 (55.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29971181556196%\" valign=\"top\"\u003e\n \u003cp\u003e1.18 (1.04 to 1.32)***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11815561959654%\" valign=\"top\"\u003e\n \u003cp\u003eRobson 2B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.587896253602306%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.994236311239193%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29971181556196%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11815561959654%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Robson 2B births, n (% all Robson groups)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.587896253602306%\" valign=\"top\"\u003e\n \u003cp\u003e1 241 (1.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.994236311239193%\" valign=\"top\"\u003e\n \u003cp\u003e1 589 (2.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29971181556196%\" valign=\"top\"\u003e\n \u003cp\u003e1.27 (1.18 to 1.38)***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11815561959654%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Macrosomic infants, n (% Robson 2B)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.587896253602306%\" valign=\"top\"\u003e\n \u003cp\u003e129 (10.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.994236311239193%\" valign=\"top\"\u003e\n \u003cp\u003e163 (10.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29971181556196%\" valign=\"top\"\u003e\n \u003cp\u003e.97 (.76 to 1.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11815561959654%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;CS births, n (% Robson 2B)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.587896253602306%\" valign=\"top\"\u003e\n \u003cp\u003e1 241 (100.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.994236311239193%\" valign=\"top\"\u003e\n \u003cp\u003e1 589 (100.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29971181556196%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.11815561959654%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;CS rate among macrosomic infants, n (% Robson 2B)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.587896253602306%\" valign=\"top\"\u003e\n \u003cp\u003e129 (100.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.994236311239193%\" valign=\"top\"\u003e\n \u003cp\u003e163 (100.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29971181556196%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eMissing data was accounted by multiple imputation by chained equation (MICE)\u003c/p\u003e\n\u003cp\u003eStatistical Significance * \u0026lt; 0.05, **\u0026lt;0.01, ***\u0026lt;0.001\u003c/p\u003e\n\u003cp\u003e\u0026dagger; - adjusted for maternal country of birth, maternal smoking, socioeconomic status, baby sex, pertussis vaccination, and interpreter requirement\u003c/p\u003e\n\u003cp\u003eRobson 1 \u0026amp; 2: Nulliparas with singleton, term, cephalic births\u003c/p\u003e\n\u003cp\u003eRobson 1: Nulliparas with singleton, term, cephalic births after spontaneous onset of labour\u003c/p\u003e\n\u003cp\u003eRobson 2A: Nulliparas with singleton, term, cephalic births following induction of labour\u003c/p\u003e\n\u003cp\u003eRobson 2B: Nulliparas with singleton, term, cephalic births without labour\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e(i) Cohort analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAll Robson groups\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThere were a total of 66 906 births in the pre-pandemic cohort and 66 466 births in the pandemic-exposed cohort after exclusions and cohort selection criteria were applied (Figure 2). The maternal and neonatal characteristics of each cohort are shown in Supplemental Table 1. The proportion of all mothers with BMI \u0026ge;25kg/m\u003csup\u003e2\u003c/sup\u003e was significantly higher in the pandemic cohort, although the absolute differences were small (51.7% vs 51.1%, p\u0026lt;0.001).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe rate of macrosomia in the overall obstetric population was higher among the pandemic-exposed cohort than in the pre-pandemic cohort (10.04% vs 9.23%, p\u0026lt;0.001) as was the overall CS rate (35.39% vs 33.56%, p\u0026lt;0.005). (Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNulliparas with a term, singleton cephalic fetus (Robson groups 1 \u0026amp; 2)\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ePre-pandemic and pandemic-exposed nulliparas with term, cephalic, singleton fetuses (Robson groups 1 and 2) were compared. There were 25 897 (50.6%) in the pre-pandemic cohort and 25 298 (49.4%) in the pandemic-exposed cohort (Figure 2); baseline characteristics are provided in Table 1. The maternal and neonatal characteristics of Robson groups 1 and 2 were similar to those of the overall study population.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2 shows outcomes of interest by Robson group. Similar to the overall study population, the rate of maternal BMI \u0026ge;25kg/m\u003csup\u003e2\u003c/sup\u003e was significantly higher among term nulliparas with a cephalic singleton fetus in the pandemic-exposed cohort compared with the pre-pandemic cohort (45.82% vs 44.58%, p\u0026lt;0.005). The rate of CS was also higher in the pandemic cohort compared with controls (33.09% vs 30.80%, p\u0026lt;0.005). However, the rate of macrosomia did not significantly differ between the pandemic and control cohorts (8.55% and 7.99%, p=0.12).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThere was no significant change in the proportion of birth by CS following spontaneous onset of labour in the pandemic cohort (Robson 1). There was a significantly higher proportion of births by CS following induction of labour (Robson 2A) in the pandemic cohort compared with controls (38.61% vs 35.55%, p\u0026lt;0.005). There was also a greater proportion of pre-labour CS for nulliparas with term, singleton, and cephalic fetuses (Robson 2B) in the pandemic cohort compared with the control group (2.39% vs 1.85%, p\u0026lt;0.005).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e(iii) Interrupted time-series analysis (ITSA) of Robson groups 1 and 2\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eTable 3:\u003c/strong\u003e Trends in BMI \u003cu\u003e\u0026gt;\u003c/u\u003e 25, macrosomia and CS among Robson groups 1 and 2 in the control and pandemic cohorts.\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"680\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.12334801762115%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"63.87665198237885%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eSlope in per cent (95% confidence interval)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.1764705882353%\" valign=\"bottom\"\u003e\n \u003cp\u003eVariable\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.91176470588235%\" valign=\"bottom\"\u003e\n \u003cp\u003ePre-pandemic period\u003cbr\u003e\u0026nbsp;(30 Oct 2017 \u0026ndash; 28 Oct 2019 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.91176470588235%\" valign=\"bottom\"\u003e\n \u003cp\u003ePandemic-exposed period\u003cbr\u003e\u0026nbsp;(4 Nov 2019 \u0026ndash; 21 Jun 2021)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.1764705882353%\" valign=\"top\"\u003e\n \u003cp\u003eOverweight (BMI \u003cu\u003e\u0026gt;\u003c/u\u003e25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.91176470588235%\" valign=\"top\"\u003e\n \u003cp\u003e0.04 (0.01 to 0.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.91176470588235%\" valign=\"top\"\u003e\n \u003cp\u003e0.01 (\u0026ndash;0.02 to 0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.1764705882353%\" valign=\"top\"\u003e\n \u003cp\u003eMacrosomia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.91176470588235%\" valign=\"top\"\u003e\n \u003cp\u003e0.004 (\u0026ndash;0.01 to 0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.91176470588235%\" valign=\"top\"\u003e\n \u003cp\u003e0.001 (\u0026ndash;0.02 to 0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.1764705882353%\" valign=\"top\"\u003e\n \u003cp\u003eCaesarean section\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.91176470588235%\" valign=\"top\"\u003e\n \u003cp\u003e0.02 (-0.01 to 0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.91176470588235%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026ndash;0.03 (\u0026ndash;0.07 to 0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe ITSA results are shown in Figure 3 and Table 3. Importantly, there was a pre-existing upward trend in the rate of maternal BMI \u0026ge;25kg/m\u003csup\u003e2\u003c/sup\u003e prior to the onset of the pandemic at 0.04% per week (95% CI: 0.01% to 0.06%). Pandemic exposure was not associated with a significant change in the rate of rise in pregnant individuals with BMI \u003cu\u003e\u0026gt;\u003c/u\u003e25kg/m\u003csup\u003e2\u003c/sup\u003e (Figure 3 and Table 3). Similarly, the pre-existing uptrends in macrosomia and CS did not change significantly following the onset of the pandemic (Figure 3 and Table 3).\u0026nbsp;\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eOur analysis of Melbourne-wide public hospital data demonstrated that the COVID-19 pandemic was associated with a greater proportion of maternal BMI \u0026ge;25kg/m\u003csup\u003e2\u003c/sup\u003e and CS among first-time mothers compared with pre-pandemic controls. However, these changes appear to be continuations of pre-pandemic trends, that were not accelerated during the pandemic and Melbourne lockdowns.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe proportion of maternal BMI \u003cu\u003e\u0026gt;\u003c/u\u003e25kg/m\u003csup\u003e2\u003c/sup\u003e in first-time mothers suddenly reduced at the onset of the pandemic but has continued to increase since, and at a lower rate compared to the pre-pandemic period (Figure 3). This longstanding uptrend in maternal BMI \u003cu\u003e\u0026gt;\u003c/u\u003e25kg/m\u003csup\u003e2\u003c/sup\u003e portends higher rates of obstetric, neonatal and childhood consequences. Maternal weight is a modifiable risk factor, so promotion of a healthy diet and regular exercise, ideally before conception, is an important approach to addressing these outcomes. A trial of preconception weight optimization is currently underway in NSW, which may provide evidence for future interventions to reduce the perinatal and childhood morbidity associated with higher maternal BMI.[24]\u003c/p\u003e\n\u003cp\u003eDespite a higher proportion of maternal BMI \u003cu\u003e\u0026gt;\u003c/u\u003e25kg/m\u003csup\u003e2\u003c/sup\u003e during the pandemic, fetal macrosomia was not significantly more common in first-time mothers, regardless of mode of delivery. Caesarean section was significantly more common in first-time mothers. Subgroup analysis indicates this increase was confined to individuals who were induced or undergoing pre-labour CS, suggesting that the decision threshold to deliver by CS following IOL or offering elective CS may have altered during the pandemic. Should the proportion of BMI \u003cu\u003e\u0026gt;\u003c/u\u003e25kg/m\u003csup\u003e2\u003c/sup\u003e during pregnancy continue to rise in first-time mothers, a compounding of the overall CS rate may be expected as success rates of a trial of labour after previous CS are lower for individuals with a high BMI.[25]\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur analysis showed that macrosomia was associated with a significantly higher rate of CS in first-time mothers in both pre-pandemic and pandemic-exposed cohorts. Recognising and responding to risk factors for macrosomia other than maternal BMI \u003cu\u003e\u0026gt;\u003c/u\u003e25kg/m\u003csup\u003e2\u003c/sup\u003e during pregnancy, such as gestational diabetes and excess gestational weight gain, will also likely play have a role in reducing the primary CS rate. Future research should examine individuals birthing preferences, clinician decision making and health service factors and driving this increase in CS among nulliparas in Australia.\u003c/p\u003e\n\u003cp\u003eOur study is one of few examining the relationship between the pandemic, BMI and obstetric outcomes. Anderson \u003cem\u003eet al.\u003c/em\u003e[26]\u0026nbsp;reported that the global prevalence of obesity in the general adult population increased by 1% during the pandemic. A systematic review and meta-analysis[27]\u0026nbsp;of the impact of the COVID-19 pandemic on perinatal outcomes made no comment on maternal BMI or macrosomia. There are conflicting reports on the rates of CS during the pandemic, with some concluding that there was no difference in high-income countries[27], while others observed a significant reduction in primary CS.[28]\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA major strength of our study is the large multicentre dataset, capturing public hospital births from all 12 public maternity hospitals in Metropolitan Melbourne. We collected a complete birth cohort in a unique setting of strict COVID-19 lockdown restrictions but low maternal COVID-19 case-load. Our use of cLMP to define the pandemic-exposed cohort overcomes major methodological challenges of analysing time-dependent exposures. Another strength was the use of ITSA to inform our single metric outcomes. By comparing a forecasted trends in perinatal outcomes based on pre-pandemic trends, we could determine with greater confidence that the changes identified were associated with, rather than directly caused by, the pandemic.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot all births within Metropolitan Melbourne were included in our analysis, as private hospital birth data were not available. There are major differences in maternal sociodemographic and obstetric factors between the public and private hospitals settings. In particular, CS rates are consistently higher in private hospitals compared with public hospitals.[14] We were not able to include gestational diabetes in our analysis due to coding inconsistencies in the source data. Furthermore, changes in the screening and treatment of gestational diabetes during the pandemic would have likely confounded this analysis.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThe pandemic period was associated with a greater proportion of maternal BMI \u0026ge;25kg/m\u003csup\u003e2\u003c/sup\u003e and CS in first-time mothers compared with during the pre-pandemic period. Although the increase in maternal BMI \u003cu\u003e\u0026gt;\u003c/u\u003e25kg/m\u003csup\u003e2\u003c/sup\u003e contributed to macrosomia and a higher CS rate during the pandemic, these changes were continuations of pre-existing trends and were not accelerated by the pandemic. These trends are not likely to abate with the cessation of pandemic restrictions and have significant long-term implications for population health.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthical approval\u003c/h2\u003e \u003cp\u003eEthical approval was obtained from Austin Health (HREC/64722/Austin-2020) and Mercy Health Ethics Committees (ref. 2020-031).\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eACKNOLEDGEMENTS:\u003c/h2\u003e \u003cp\u003eWe thank the following hospital staff for their assistance in setting up the data collection within their respective health services: Tania Fletcher (Mercy Health), Michelle Knight (Monash Health), Lynne Rigg \u0026amp; Julia Lay (Royal Women\u0026rsquo;s Hospital), Abby Monaghan (Northern Health), Lee-Anne Lynch (Western Health), Pauline Hamilton, Carolyn Gower, Therese McCarthy (Eastern Health), and Roshanee Perera (Peninsula Health).\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFUNDING:\u003c/h2\u003e \u003cp\u003eThis study was funded by the Norman Beischer Medical Research Foundation and The University of Melbourne Department of Obstetrics, Gynaecology and Newborn Health. Melvin Marzan receives salary support from the Generation Victoria (GenV) Fellowship from the Murdoch Children\u0026rsquo;s Research Institute. Lisa Hui receives salary support from the Medical Research Future Fund (#1196010) and the University of Melbourne.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAJG and MBM contributed equally to this work and are joint first authors. LH, SPW, DLR, JMS, KRP, SP, CLW and BWM: Conception of original collaboration. LH: Project administration, supervision and funding acquisition. LH, SPW, DLR, JMS, KRP, PMS, SP, NP, CLW, JF, BWM, MBM and AJG: Approval of analysis plan. LH, SPW, DLR, JMS, PMS, CLW, JF and MBM: Collection of primary data. AJG, MBM and LH: Literature review, methodology, data analysis and visualisation, writing - original draft preparation. ACL: Writing - preliminary draft review and edit. All authors: Writing - final review and edit.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank the following hospital staff for their assistance in setting up the data collection within their respective health services: Tania Fletcher (Mercy Health), Michelle Knight (Monash Health), Lynne Rigg \u0026amp; Julia Lay (Royal Women\u0026rsquo;s Hospital), Abby Monaghan (Northern Health), Lee-Anne Lynch (Western Health), Pauline Hamilton, Carolyn Gower, Therese McCarthy (Eastern Health), and Roshanee Perera (Peninsula Health).The findings of our study were presented at the Royal Australian and New Zealand College of Obstetricians and Gynaecologists (RANZCOG) Annual Scientific Meeting October-November 2023 Perth, Australia as a poster presentation.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData is available upon reasonable request subject to HREC approval.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHealth, A.I.o. and Welfare, \u003cem\u003eOverweight and obesity\u003c/em\u003e. 2023, AIHW: Canberra.\u003c/li\u003e\n\u003cli\u003eHannebery P, W.N., Streets F and Fay S, \u003cem\u003eCanary in the mine: A unique analysis of the impact of the COVID-19 pandemic on the physical and mental health of Australians\u003c/em\u003e. 2021, SiSU Health.\u003c/li\u003e\n\u003cli\u003eHealth, A.I.o. and Welfare, \u003cem\u003eAustralia\u0026rsquo;s health 2022: data insights\u003c/em\u003e. 2022, AIHW: Canberra.\u003c/li\u003e\n\u003cli\u003eHui L, W.C., Palmer K, et al., \u003cem\u003eCollaborative Maternity and Newborn Dashboard for the COVID-19 pandemic. 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Stock, \u003cem\u003eLong-term risks and benefits associated with cesarean delivery for mother, baby, and subsequent pregnancies: Systematic review and meta-analysis.\u003c/em\u003e PLoS Med, 2018. \u003cstrong\u003e15\u003c/strong\u003e(1): p. e1002494.\u003c/li\u003e\n\u003cli\u003e(SRH), S.a.R.H.R., \u003cem\u003eWHO statement on caesarean section rates\u003c/em\u003e. 2015, World Health Organisation \u003c/li\u003e\n\u003cli\u003eHealth, A.I.o. and Welfare, \u003cem\u003eNational Core Maternity Indicators\u003c/em\u003e. 2023, AIHW: Canberra.\u003c/li\u003e\n\u003cli\u003eKapoor, N., et al., \u003cem\u003eThe Dual Pandemics of COVID-19 and Obesity: Bidirectional Impact.\u003c/em\u003e Diabetes Ther, 2022. \u003cstrong\u003e13\u003c/strong\u003e(10): p. 1723-1736.\u003c/li\u003e\n\u003cli\u003eVictoria, S.C., \u003cem\u003eVictorian perinatal services performance indicators 2020-21\u003c/em\u003e. 2022, Victorian Government: Melbourne, Australia.\u003c/li\u003e\n\u003cli\u003eRobson, M.S., \u003cem\u003eClassification of caesarean sections.\u003c/em\u003e Fetal and Maternal Medicine Review, 2001. \u003cstrong\u003e12\u003c/strong\u003e(1): p. 23-39.\u003c/li\u003e\n\u003cli\u003eOrganisation, W.H., \u003cem\u003eRobson Classification: Implementation Manual\u003c/em\u003e. 2017, World Health Organisation: Geneva.\u003c/li\u003e\n\u003cli\u003eVictoria, S.C., \u003cem\u003eExtreme prematurity guideline\u003c/em\u003e. 2022, Victorian Government: Melbourne, Australia.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization, W., \u003cem\u003eRobson classification: implementation manual.\u003c/em\u003e 2017.\u003c/li\u003e\n\u003cli\u003eBarnett, A.G., \u003cem\u003eTime-dependent exposures and the fixed-cohort bias.\u003c/em\u003e Environ Health Perspect, 2011. \u003cstrong\u003e119\u003c/strong\u003e(10): p. A422-3; author reply A423.\u003c/li\u003e\n\u003cli\u003eHui, L., et al., \u003cem\u003eIncrease in preterm stillbirths in association with reduction in iatrogenic preterm births during COVID-19 lockdown in Australia: a multicenter cohort study.\u003c/em\u003e Am J Obstet Gynecol, 2022. \u003cstrong\u003e227\u003c/strong\u003e(3): p. 491.e1-491.e17.\u003c/li\u003e\n\u003cli\u003eStataCorp, L., \u003cem\u003eStata statistical software: Release 18 (2023).\u003c/em\u003e College Station, TX: StataCorp LP, 2023.\u003c/li\u003e\n\u003cli\u003eStataCorp, L., \u003cem\u003eStata multiple-imputation reference manual\u003c/em\u003e. 2023.\u003c/li\u003e\n\u003cli\u003eA, L., \u003cem\u003eITSA: Stata module to perform interrupted time series analysis for single and multiple groups\u003c/em\u003e. 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Mercer, \u003cem\u003eThe impact of maternal obesity and weight gain on vaginal birth after cesarean section success.\u003c/em\u003e Am J Obstet Gynecol, 2004. \u003cstrong\u003e191\u003c/strong\u003e(3): p. 954-7.\u003c/li\u003e\n\u003cli\u003eAnderson, L.N., et al., \u003cem\u003eObesity and weight change during the COVID-19 pandemic in children and adults: A systematic review and meta-analysis.\u003c/em\u003e Obes Rev, 2023. \u003cstrong\u003e24\u003c/strong\u003e(5): p. e13550.\u003c/li\u003e\n\u003cli\u003eChmielewska, B., et al., \u003cem\u003eEffects of the COVID-19 pandemic on maternal and perinatal outcomes: a systematic review and meta-analysis.\u003c/em\u003e Lancet Glob Health, 2021. \u003cstrong\u003e9\u003c/strong\u003e(6): p. e759-e772.\u003c/li\u003e\n\u003cli\u003eSinnott, C.M., et al., \u003cem\u003eInvestigating Decreased Rates of Nulliparous Cesarean Deliveries during the COVID-19 Pandemic.\u003c/em\u003e Am J Perinatol, 2021. \u003cstrong\u003e38\u003c/strong\u003e(12): p. 1231-1235.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pregnancy-and-childbirth","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prch","sideBox":"Learn more about [BMC Pregnancy and Childbirth](http://bmcpregnancychildbirth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/prch/default.aspx","title":"BMC Pregnancy and Childbirth","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"obesity, COVID-19, pregnancy, birth weight, pregnancy complications","lastPublishedDoi":"10.21203/rs.3.rs-4412944/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4412944/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e To compare specific perinatal outcomes in nulliparas with a singleton infant in cephalic presentation at term, with and without exposure to the COVID-19 pandemic during pregnancy. We hypothesised that the pandemic conditions in Melbourne may have been an independent contributor to trends in maternal Body Mass Index ≥25kg/m\u003csup\u003e2\u003c/sup\u003e, macrosomia and caesarean section.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDesign:\u003c/strong\u003e Multi-centre retrospective cohort study with interrupted time-series analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSetting:\u003c/strong\u003e Metropolitan Melbourne, Victoria.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePopulation:\u003c/strong\u003e Singleton infants ≥20 weeks gestational age born between 1 January 2019 and 31 March 2022.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMain outcome measures:\u003c/strong\u003e Rates of maternal Body Mass Index ≥25kg/m\u003csup\u003e2\u003c/sup\u003e, macrosomia (birthweight \u003cu\u003e\u0026gt;\u003c/u\u003e4000g) and caesarean section.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e 25 897 individuals gave birth for the first time to a singleton infant in cephalic presentation at term in the pre-pandemic cohort, and 25 298 in the pandemic-exposed cohort. Compared with the pre-pandemic cohort, the rate of maternal Body Mass Index ≥25kg/m\u003csup\u003e2\u003c/sup\u003e (45.82% vs 44.57%, p=0.005), the rate of caesarean section (33.09% vs 30.80%, p\u0026lt;0.001) and the rate macrosomia (8.55% vs 7.99%, p=0.1) were higher among the pandemic-exposed cohort. Interrupted time-series analysis demonstrated no significant additional effect of the pandemic on pre-existing upward trends in maternal Body Mass Index ≥25kg/m\u003csup\u003e2\u003c/sup\u003e, caesarean section and macrosomia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e Rates of Body Mass Index ≥25kg/m\u003csup\u003e2\u003c/sup\u003e and caesarean section among nulliparous individuals during pregnancy were higher following the pandemic in Melbourne. However, this appears to be a continuation of pre-existing upward trends, with no significant independent contribution from the pandemic. These trends are forecast to continue, with long term implications for population health.\u003c/p\u003e","manuscriptTitle":"Trends in Maternal Body Mass Index, Macrosomia and Caesarean Section in First-Time Mothers during the pandemic: a Multicentre Retrospective Cohort Study of 12 Melbourne Public Hospitals.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-03 14:36:49","doi":"10.21203/rs.3.rs-4412944/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-05-20T08:20:14+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-17T08:31:27+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-17T08:31:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pregnancy and Childbirth","date":"2024-05-13T11:30:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pregnancy-and-childbirth","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prch","sideBox":"Learn more about [BMC Pregnancy and Childbirth](http://bmcpregnancychildbirth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/prch/default.aspx","title":"BMC Pregnancy and Childbirth","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9c8b435f-1c86-4e5d-96a3-a9061fd08958","owner":[],"postedDate":"June 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-11-04T16:25:59+00:00","versionOfRecord":{"articleIdentity":"rs-4412944","link":"https://doi.org/10.1186/s12884-024-06908-y","journal":{"identity":"bmc-pregnancy-and-childbirth","isVorOnly":false,"title":"BMC Pregnancy and Childbirth"},"publishedOn":"2024-10-28 16:20:10","publishedOnDateReadable":"October 28th, 2024"},"versionCreatedAt":"2024-06-03 14:36:49","video":"","vorDoi":"10.1186/s12884-024-06908-y","vorDoiUrl":"https://doi.org/10.1186/s12884-024-06908-y","workflowStages":[]},"version":"v1","identity":"rs-4412944","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4412944","identity":"rs-4412944","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}