The relationship between virtual antenatal care and pregnancy outcomes in a diverse UK inner-city population; A group-based trajectory modelling approach using routine health records

The relationship between virtual antenatal care and pregnancy outcomes in a diverse UK inner-city population; A group-based trajectory modelling approach using routine health records | 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 The relationship between virtual antenatal care and pregnancy outcomes in a diverse UK inner-city population; A group-based trajectory modelling approach using routine health records Kathryn Dalrymple, Florence Tydeman, Jeffrey Bone, Lucilla Poston, and 12 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6800101/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The COVID-19 pandemic resulted in major maternity service reconfigurations, particularly an increase in virtual antenatal care (vANC). We aimed to explore the relationship between vANC trajectories over time and pregnancy outcomes. Methods Pregnancy and birth outcome data were obtained pre-pandemic, during the pandemic with, and without lockdowns from the Born in South London (eLIXIR-BiSL) health record data linkage of a multiethnic and deprived UK inner-city population. Antenatal care was characterised by the number of outpatient contacts during six pregnancy epochs: 0–14 + 6, 15 + 0–20 + 6, 21 + 0–27 + 6, 28 + 0–32 + 6, 33 + 0–36 + 6 and ≥ 37 + 0 weeks’ gestation. In each epoch, the proportion of vANC was grouped into quartiles, and group-based trajectory modelling (GBTM) was used to extract vANC trajectories. Adjusted multinominal logistic regression was used to explore relationships between vANC trajectories and pregnancy outcomes. Results Based on 34,114 mother-child dyads (Oct-2018-Jul-2023), GBTM suggested four trajectories of vANC: ‘Trajectory-0’: Stable over pregnancy, and lowest quartile (n = 27,751 pregnancies, 81·3%); ‘Trajectory-1’: High 1st trimester vANC (n = 832, 2·4%); ‘Trajectory-2’: High 2nd trimester vANC (n = 2,410, 7·1%); and ‘Trajectory-3’: High 3rd trimester vANC (n = 3,121, 9·2%). Following adjustment, compared with Trajectory-0, Trajectory-2 had more premature births: (< 37 weeks, adjusted relative risk 1·21, 95% confidence interval 1·02 − 1·44), labour inductions (1·13, 1·02 − 1·25), breech presentation (1·92, 1·02–3·62), and postpartum haemorrhage (1·14, 1·00–1·30). Compared with Trajectory-0, Trajectory-3 had more premature births (< 37 weeks, 1·35, 1·16 − 1·58), elective (1·54, 1·38 − 1·72) or emergency (1·21, 1·01–1·34) Caesarean sections and neonatal intensive care unit admissions (1·28, 1·09 − 1·50); and less early skin-to-skin contact (0·82, 0·73 − 0·92), breastfeeding (0·90, 0·81 − 0·99), and 3rd or 4th degree vaginal tears (0·82, 0·75 − 0·90). Conclusion More vANC, as a proportion of antenatal care received, was associated with more adverse pregnancy outcomes, when women received vANC in the second or third trimesters. Trajectories virtual care antenatal care birth outcomes electronic health records Figures Figure 1 Figure 2 TWEETABLE STATEMENT More virtual antenatal care in 2 nd or 3rd trimesters was linked to higher risks of preterm birth, C-sections, NICU admissions & lower breastfeeding rates in a diverse UK cohort. #Pregnancy #MaternalHealth A. Why was this study conducted? The COVID-19 pandemic led to increased virtual antenatal care (vANC), but its impact on pregnancy outcomes remains unclear. This study examines vANC trajectories and their association with maternal and neonatal outcomes in a multiethnic, urban UK population. B. What are the key findings? More vANC in the second or third trimesters was linked to higher risks of preterm birth, labour induction, C-sections, NICU admissions, and postpartum haemorrhage. Increased vANC in later pregnancy was also associated with lower early skin-to-skin contact and breastfeeding rates. C. What does this study add to what is already known? This study provides new insights into how different vANC patterns affect pregnancy outcomes. Findings highlight the need for careful integration of virtual care in maternity services to minimize potential risks. INTRODUCTION Before the COVID-19 pandemic in the United Kingdom (UK), maternity care was almost exclusively in-person. At least 7–10 routine appointments were offered, depending on parity and multiple pregnancy status 1 . During the pandemic, routine antenatal care was modified in-line with guidance for infection control, with cancellation of many face-to-face appointments 2 . Virtual technology (i.e., remote consultations via video conferencing or telephone) was used as an alternative to in-person appointments, and out-of-office self-monitoring was implemented for some women with conditions such as pregnancy hypertension or gestational diabetes mellitus 3 . To further limit face-to-face contact, appointments were combined when possible, and there was a reduction in choice of carer and place of birth 4 . Furthermore, public health messaging to maintain social distancing and stay at home may have inadvertently influenced women to not seek care for problems arising during their pregnancy 5,6 and modifications to staffing arrangements may have affected antenatal care 7–9 . A national survey of maternity care providers highlighted the extensive impact of the pandemic on maternity services in the UK. The majority of units reported: a reduction in antenatal (70%) or postnatal (56%) appointments; a reduction in unscheduled antenatal presentations (89%); use of remote consultation (89%), and temporary suspension midwife-led unit or homebirths (59%). Also, nearly half of mental health care staff reported feeling less able to assess women, some of whom engaged poorly with virtual appointments 7,10 . While much of the virtual antenatal care (vANC) adopted in lieu of face-to-face care during the pandemic has been reversed, some continues. It is important to understand whether or not the ongoing vANC, or vANC during another health system shock, is advisable. A substantial experiential literature describes largely negative experiences of virtual care for those receiving 11,12 and those providing maternity care during the pandemic 6,8,9,13–20 . Both groups have expressed concerns about compromised quality of care, as well as access and participation. Maternity care providers highlighted enhanced convenience for some, but digital exclusion of others. However, little is known about the impact of vANC on clinical outcomes 21–23 . In Australia, these were reported to be unchanged during widespread use of telehealth in the first 14 months of the pandemic 24 . However, changes in care-seeking behaviour and care provision have potential contrary effects and prevent a definitive assessment of the impact of vANC on pregnancy outcomes 25 . While there is considerable qualitative data on women’s and health care providers’ experiences of receiving and delivering virtual care, less is known about the impact of virtual antenatal care on clinical outcomes. This analysis aimed to model data from before, and during the pandemic (with and without lockdowns) to quantify the disruption of in-person ANC to vANC using routine electronic health data and (ii) understand the relationships between vANC and birth outcomes in a UK inner-city population. METHODS Study design Data for this study were obtained from the ‘eLIXIR (Early LIfe cross-LInkage in Research) Born in South London (BiSL)’ data linkage from Oct-2018-July-2023. The linkage and governance have been described previously 26 (for further information see Supplementary File and Figure S1). Participants A pregnancy was included if information on antenatal registration, at least one antenatal appointment after registration, and birth outcomes were available for a singleton pregnancy. We excluded: (i) duplicate records, (e.g. those related to transfer of care and registration at two hospitals) identified by two or more antenatal care registration IDs with estimated delivery dates within 14 days; in this instance, the first record was included; (ii) records without either a patient or pregnancy ID; (iii) records noting multiple pregnancies (e.g. twins), as birth outcomes may differ for infants born from a multiple pregnancy; and (iv) records where women had registration and delivery data, but no outpatient antenatal care records. Data Registration for antenatal care data included demographics, past medical and obstetric history, and characteristics of current pregnancy, including intended hospital of birth, referred to as Site A or B. Antenatal care was characterised by the number of outpatient contacts, and the proportion that were virtual, during six epochs: 0–14 + 6 , 15–20 + 6 , 21–27 + 6 , 28–32 + 6 , 33–36 + 6 and ≥ 37 + 0 weeks’ gestation. An appointment was deemed to have been ‘virtual’ if: (i) maternal blood pressure, dipstick proteinuria, and fetal heart rate (after 15 weeks’ gestation) were missing from the visit record; or (ii) notes from the appointment suggested that it was virtual, (e.g. ‘virtual’, ‘telephone’, or ‘call’). Otherwise, the appointment was considered to have taken place face-to-face. Once the proportion of vANC was estimated for each of the six epochs, these were converted to quartiles: 0–25%, 26–50%, 51–75% and 76–100%; e.g. if a woman attended three appointments from 28–32 + 6 weeks’ and two appointments were virtual, then 66.6% of her appointments were virtual; and this epoch was coded as the third quartile (51–75%). We evaluated key pregnancy and delivery indictors across pandemic phases, according to the antenatal registration date: pre-pandemic (1 October 2018-22 March 2020); pandemic with lockdowns (23 Mar 2020-17 July 2021) and pandemic without lockdowns (18 July 2021-8 July 2023), categorised by delivery date of each pregnancy 27 . The key pregnancy and birth indicators assessed reflect a combination of: Organisation Performance Indicators, Clinical Quality Improvement Metrics and National Maternity Indicators (further details Table S3 ). Trajectory modelling It could not be assumed that all participants in a given timeframe would have experienced the same longitudinal changes in vANC, especially during the pandemic with serial lockdowns and easing. To address this, we undertook latent class modelling, using group-based trajectory modelling (GBTM), to identify subgroups of individuals with similar trajectories/patterns of vANC. To identify the number of latent classes/trajectories for virtual care as best describing the data, we used a forward modelling approach, and the analysis was conducted using the Guidelines for Reporting on Latent Trajectory Studies 28 (GRoLTS checklist, Table S4). After fitting the one-class model, additional classes were added incrementally. For each iteration, model adequacy was assessed using the model estimation criteria (Supplementary file). Once the model adequacy stopped improving, an additional model was fitted with one extra class to ensure the full array of possible models had been tested (Table S5). The adequacy of each model was assessed using the following fit criteria: the Bayesian Information Criterion, average posterior probability of assignments, the ratio of the odds of a correct classification, group membership, and relative entropy (for further details see Supplementary File). Statistical analysis Binary and categorical variables are presented using counts and percentages. The distributions of continuous variables were assessed and summarised by mean and standard deviation or median and interquartile range (IQR) for normally or non-normally distributed variables, respectively. To be included in the analysis, women needed data in at least one timepoint (e.g. a woman registered for antenatal care, had an antenatal appointment and the delivered within one epoch). We used a censored normal model suitable for use with scaled data (i.e., vANC percentages quartiles) 29 . The six epochs were converted to mean gestational age. Univariable and multivariable multinominal logistic regression analyses were used to assess the relationships between the virtual care trajectories and birth outcomes and are presented as adjusted relative risk ratios (aRR). To identify confounders for inclusion in multivariable models, direct acyclic graphs were created for birth outcomes ( Figure S2 ). The minimal adjustment set variables were: index of multiple deprivation (IMD), parity, time (months) and registration: gestation, hospital, smoking status, and pandemic epoch: pre-pandemic (1 Oct 2018-22 Mar 2020), first pandemic lockdown (23 Mar 2020-23 Jun 2020), first lockdown easing (24 Jun 2020-4 Nov 2020), second pandemic lockdown (5 Nov 2020-5 Jan 2021), third pandemic lockdown (6 Jan 2021-17 Jul 2021), and pandemic without lockdown (18 Jul 2021-8 July 2023); due to the correlation between pandemic epoch and time, only pandemic epoch was retained as a confounder. An interaction term was included for between IMD and ethnicity. Body mass index (BMI) was not included due to the increase in missing data for this variable during the pandemic, potentially due to lack of face-to-face appointments to measure height and weight. Given that our primary focus was on relationships during the pandemic, including BMI could have introduced bias due to its non-random missingness. Differences between number of appointments for each trajectory were modelled using negative binomial regression due to overdispersion and adjusted for parity, registration gestation and antenatal care type. We undertook several sensitivity analyses. First, we assessed the differences between those women who were included in the analysis vs those excluded; birth and delivery data were compared using chi-squared test or regression for categorical and continuous outcomes, respectively. Second, we truncated the pregnancies included at the beginning and end of the study period, to ensure that the number of births was stable, to minimise the impact of pregnancies that booked late for ANC or delivered very early; the new study period was from 1st April 2019 to 30th April 2023. Third, we removed parity as an adjustment variable, and stratified the analysis for parity. All analyses were undertaken in Stata (version 18.0). RESULTS Following removal of duplicates and multiple pregnancies (n=1,012), 58,402 unique pregnancy IDs were recorded between October 2018-July 2023, of which 34,114 were included in the analysis ( Figure 1 ). Women excluded from the analysis, differed from those included with regards to a number of registration characteristics and birth outcomes. For details see Tables S6-7 . For the 34,114 pregnancies included, 59% were at Site A. At registration, mean maternal age was 32.7 years, 17% were affected by obesity (>30kg/m 2 ), median gestational age was 9.7 weeks, 13% of the cohort registered after 16 weeks and 22.7% had midwifery only led care (i.e. deemed to be low risk). Half of the cohort were from the global majority, of which 42% and 20% were of Black of Asian ethnicities and >60% were from the two lowest IMD quintiles. 7% had difficulty understanding English. Few women reported any prior drug use (<7%) or current/in the past 12 months (<2%). 53.2% were nulliparous. Of the multiparous women, 31.9%, 1.8%, 8.8% and 7.9% reported a prior Caesarean, stillbirth, postpartum haemorrhage (PPH) or preterm birth, respectively (Table 1). The median number of antenatal and virtual appointments were 9 (IQR 7-12) and 1 (0-2), respectively (Table 2). 22.8% of women gave birth pre-pandemic and 45.6% during pandemic without lockdowns. Less than 3% were smoking at delivery, and 12.1% developed gestational hypertension. The majority of women (68.5%) had no risk factors at birth, 94.1% of pregnancies ended at term and 22.0% of births were induced, with over half of births involving intervention. Breech delivery was rare (0.3%), and 3 rd or 4 th degree vaginal tears uncommon (1.5%). PPH occurred in 10.0% of pregnancies. Stillbirth and neonatal death were rare at 3.9 and 2.3 births per 1,000, respectively. 88.3% of newborns had early skin-to-skin, 6.9% were SGA, and 5.6% were admitted to neonatal intensive care unit (NICU) (Table 2). GBTM identified four trajectories of vANC as best describing the data (Figure 2, Table S4, Figures S3-4). 81.3% of participants (Trajectory-0; n=27,751) were characterised as having had a stable and low occurrence of vANC over time. 2.4% (Trajectory-1; n=832) of participants had a high proportion of vANC during the 1 st trimester, 7.1% (Trajectory-2; n=2,410) a high proportion of vANC during the 2 nd trimester, and 9.2% (Trajectory-3; n=3,121) a high proportion of vANC during the 3 rd trimester. Characteristics at registration for antenatal care were different between trajectory groups (Table 1). Participants in: Trajectory-0 were more likely to register for antenatal care before the pandemic or during the pandemic without lockdowns and more likely to be <20 years or register late for antenatal care. Trajectory-1 were more likely to register at Site A and during pandemic without lockdowns and had a higher percentage of BMI missingness and the lowest percentage of late registrations (1.0%). Trajectory-2 were most likely to register during the first and second pandemic lockdowns and had the lowest percentage of women <20 years, were more likely to be of White ethnicity, and less likely to have difficulty understanding English. Trajectory-3 were least likely to register at Site A, most likely to register pre-pandemic, to have the highest percentage of midwifery only care, more likely to have used drugs in the previous 12 months and be nulliparous; also, multiparous women in this trajectory were more likely to have had a previous PPH or preterm birth. Compared to Trajectory-0, the rate of visits for those in Trajectories 1 and 3 decreased by a factor of 0.98 and increased by a rate of 1.04, respectively. Overall, the rate of visits was similar for the 4 trajectory groups (Table 3). In the adjusted analyses with trajectory-0 as the reference outcome, pregnancy outcomes were found to differ according to vANC trajectory, as well as in unadjusted analyses (Table 3). Pregnancies in Trajectory-1 had no significant difference in pregnancy outcomes (Table 3). Pregnancies in Trajectory-2 were less likely to be diagnosed with gestational hypertension [aRR 0.84 (95% confidence interval: 0.74, 0.96)]; more likely to experience preterm birth <37 weeks, specifically birth at 24 +0 -27 +6 weeks’ gestation [1.79 (1.10, 2.93)]; more likely to have a baby with a breech presentation [1.92 (1.02, 3.62)]; less likely to have an assisted vaginal birth [0.87 (0.76, 1.00)] and more likely to have a PPH [1.14 (1.00, 1.30)] (Table 3). Pregnancies in Trajectory-3 were less likely to be diagnosed with gestational hypertension [0.84 (0.73, 0.96)]; more likely to deliver preterm [1.35 (1.16, 1.58)] at 28 +0 -33 +6 or 34 +0 -36 +6 weeks’ gestation; more likely to give birth by elective or emergency Caesarean; less likely to have a 3 rd -4 th degree vaginal tear [0.82 (0.75, 0.90)]; less likely to have early skin-to-skin [0.82 (0.73, 0.92)] with the newborn; more likely for the newborn to be admitted to NICU [1.28 (1.09, 1.50)]; and less likely to breastfeed as the first feed [0.90 (0.81, 0.99)] (Table 3). In the first sensitivity analysis, women (n=2,174) excluded because they had registration and delivery data, but no outpatient antenatal care records, were observed to be at higher risk of adverse outcomes which were observed more often, compared with women included in the analysis (Tables S8-9). Women who were excluded (vs. those included) were more likely to have a virtual registration appointment (25.7% vs 2.5%, respectively), have higher BMI or obesity, and register late (56.5% vs 16.5%, respectively). Nearly 70% registered before the pandemic and they were less likely to be nulliparous. They were more likely to be smokers and deliver <37 weeks’ gestation, and less likely to be induced. The infants were less likely to be offered breastmilk or have early skin-to-skin, and more likely be admitted to NICU, be stillborn (3.5% vs 0.4%) or suffer a neonatal death (3.0% vs 0.2%). These women were also far more likely to have inpatient antenatal care (42.6% vs 2.5%). Second, results of the truncated analysis (n=29,434 pregnancies, Table S10) did not differ from those results presented in Table 3. Third, in the analysis stratified by parity, most point estimates had the same direction of effect and 95% CI overlapped, although for a number of outcomes in multiparous pregnancies, a number of 95% CIs crossed 1.0 (Table S10); the only exceptions were for nulliparous pregnancies in Trajectory-1 (n=425), who were more likely to be smokers at delivery, and less likely to have early skin-to-skin contact with their babies. DISCUSSION In this cohort of more than 34,000 pregnancies from a diverse, South London population, we identified four trajectories of vANC before and during the COVID-19 pandemic, with and without lockdowns. Those trajectories were associated with small proportions of high first, second, or third trimester vANC and for the majority of pregnancies, low and stable vANC throughout pregnancy. Compared with the latter, a high proportion of vANC provided during the 2nd or 3rd trimesters of pregnancy was associated with increases in adverse birth outcomes, including preterm birth, breech presentation, Caesarean, PPH, NICU admission, and fewer babies receiving early skin-to-skin contact or being breastfed as their first feed. Of note, women who had no ANC after a registration appointment and were excluded from the main analysis, were found to have more adverse outcomes For decision-makers to fully assess policy related to use of vANC, information is required on clinical outcomes and experiences. We have reported some negative associations between vANC and adverse pregnancy outcomes when vANC is part of maternity care in the second or third trimesters. An important consideration is whether the vANC is increasing the incidence of those adverse outcomes, or whether more complicated pregnancies receive more vANC as part of enhanced maternal and fetal surveillance. Our data support the former explanation, as we found no evidence of either an increased total number of appointments nor total number of risk factors at birth in the high second or third trimester trajectory groups, compared with the low and stable (predominately face-to-face care) vANC trajectory group. Further support for the association between vANC and adverse outcomes is the lower prevalence of gestational hypertension among women who had a high proportion of vANC; noting that; gestational hypertension is diagnosed by repeated measures of high blood pressure and without the latter, the diagnosis may have been missed at a vANC appointment, especially in late pregnancy (when gestational hypertension, and the more complicated hypertensive disorder, pre-eclampsia, are most likely to develop). Our results contrast with those of two large studies. An Australian study of routinely-collected data in over 27,000 births from January 2018 to April 2021 24 , used an interrupted time-series (ITS) analysis and found no differences in adverse pregnancy outcomes associated with telehealth-integrated antenatal care, including pre-eclampsia, fetal growth restriction, and perinatal mortality. A similar study of over 12,000 pregnancies in the USA, compared outcomes from May 2019 to October 2020, and found the implementation of audio-only virtual prenatal visits was not associated with changes in perinatal outcomes 30 . The differences observed between our study and these results may be due to the analytical approaches taken. ITS is a valuable approach for modelling the impact of an ‘interruption’ on outcomes over time when an RCT is not possible, GBTM is more granular, by defining distinct trajectories of vANC by trimester. Of note, when considering only the relationship between time and outcomes, most outcomes follow patterns established pre-pandemic 31 . We found no association between the vANC registration and adverse pregnancy outcomes, adjusted for gestational age at registration. This may question the current approach in England (compared with other countries) which places a distinct focus on early first trimester registration, and in contrast with the approach developed in Scotland and continued elsewhere (e.g., Canada) 32,33 , has fewer appointments later in pregnancy when complications are more likely to develop 34 . Our finding suggest that England could consider focusing on more appointments in later pregnancy rather than an emphasis on early pregnancy registration; this suggestion does need validating in our cohorts. In contrast to clinical outcomes, most publications have assessed the impact of the integration of vANC on the experiences of those receiving 11 and those providing vANC 8,13–20 . Those experiences have been predominantly negative. In a systematic review, women receiving vANC most often described concerns about disruption of care and safety, and access to adequate technology; they felt that improved access to care was needed, and they wished to participate in that care. Maternity care providers 8,13–20 have described a negative impact of vANC on quality of care, and expressed concerns about digital exclusion of certain groups. Nevertheless, some reports have described positive consequences of vANC, such as increased convenience and flexibility 11 . It has been suggested that vANC could increase access to maternity care for those living in rural locations, or who are unable to attend clinics due to childcare or other inflexible commitments 35,36 . While this relies on patients having access to the necessary infrastructure, be it mobile phone data, a computer and internet access, or even privacy to engage in virtual care 7 , a recent review of these practical considerations related to vANC found that challenges could be addressed through vANC design based on the user’s needs, technical competency, and available resources 37 . Interpretation and future implications Our results suggest that virtual care in the second and third trimesters results in poorer clinical outcomes. With predominantly negative experiences of vANC by both care providers and care seekers during the pandemic, our data sound a cautionary note about a policy of vANC in routine ANC during future health system shocks or future maternity care outside of health crises. Future work should explore whether women who are interested in some component of vANC, can be identified as very low risk and likely to benefit specifically from the provision of some vANC. While vANC may be a valuable tool for improving access to care, it may also reinforce or amplify existing health disparities, especially for disadvantaged populations; as such, future work should assess outcomes by ethnicity and deprivation, and explore why a virtual appointment took place (e.g. provider-driven, or a woman was either unable or unwilling to attend an appointment), to enhance our understanding of the impact (if any) of vANC on disparities in pregnancy outcomes. Strengths and limitations This analysis has several strengths; eLIXIR-BiSL is a population-based cohort which incorporates demographic, maternal, and neonatal health records from a multi-ethnic and socially deprived inner-city population of pregnant women. The breadth and granularity of the data enabled modelling of meaningful trajectories of vANC, to explore associations between trajectories with pregnancy outcomes, and to evaluate GBTM fit using several model adequacy criteria 38 . There are inherent limitations with the use of any routinely-collected data, due to missingness and a lack of standardisation, compared with strict research protocols. While we inferred that care was virtual when all of three key assessments were missing, we believe that this assumption is supported by the overall robustness of NHS care standards. GBTM assumes each trajectory group has a fixed shape (e.g. linear or quadratic) and within each trajectory, the slope and intercept are constant for all individuals; violating these assumptions can affect classification accuracy 39 . The data may be subject to confounding by indication, when a patient is more likely to receive a specific treatment due to their individual circumstances; although we attempted to account for this by performing sensitivity analyses and adjusting for confounders (including pandemic epoch), there remains the possibility of residual confounding. We were unable to adjust for SARS-CoV2 positivity, due to the low prevalence rate of 0.1% in this cohort and as reported by others in England 40 . Our findings may not be generalisable to those not included in this study, such as multiple pregnancies or those living in rural settings. Finally, given the exploratory nature of this study and the broad range of pregnancy outcomes examined, we have not adjusted for multiple testing. CONCLUSION Our analysis has identified distinct patterns of vANC trajectories during pregnancy in a diverse UK inner-city population. During the pandemic, vANC provided during the 2nd or 3rd trimesters of pregnancy was associated with adverse pregnancy outcomes for mothers and babies. These findings emphasise the importance of tailored vANC strategies to optimise maternal and neonatal health, and suggest that face-to-face ANC should potentially be preserved, particularly during future health system shocks. Declarations DATA STATEMENT The data accessed by eLIXIR remain within an NHS firewall and governance is provided by the eLIXIR Oversight Committee which reports to relevant information governance clinical leads. Subject to these conditions, data access is encouraged and those interested should contact the eLIXIR Chief Investigator (Professor Lucilla Poston; [email protected] ). Access can also be requested through the HDRUK Innovation Gateway (https://web.www.healthdatagateway.org/dataset/3c780d45-ed7b-4101-9c32-d50512cd9cfe). ACKNOWLEDGEMENTS We wish to thank the women, their infants, and families from all participating sites for sharing their data and supporting this programme. AUTHORS CONTRIBUTIONS The study was conceived by KVD, LAM and PvD. All authors contributed to the design and delivery of the study. The author KVD assumes responsibility for the formal analysis and completeness of data reporting, results have been validated by FT. KVD and LAM drafted the manuscript, which was revised and approved by all authors. FUNDING SOURCES This project was funded by the National Institute for Health Research (NIHR) HSDR Programme [reference number NIHR134293]. The Early Life Cross Linkage in Research, Born in South London (eLIXIR-BiSL) Partnership was developed by an MRC Partnership Grant [MR/P003060/1] and the MRC Longitudinal Population Study Grant [MR/X009742/1]. The eLIXIR-BiSL platform is also part-supported by the National Institute for Health Research (NIHR) Biomedical Research Centre at the South London and Maudsley NHS Foundation Trust and King’s College London. The funder (NIHR) played no role in study design, data acquisition, analysis, interpretation, or the decision to submit for publication. Abigail Easter, King’s College London, is supported by the National Institute for Health and Care Research (NIHR) Applied Research Collaboration South London (NIHR ARC South London) at King’s College Hospital NHS Foundation Trust. The views expressed are those of the author[s] and not necessarily those of the NIHR or the Department of Health and Social Care. Competing interests: The authors have no competing interests to declare. References National Institute for Health and Care Excellence. Antenatal care. 2021. Accessed May 15, 2024. https://www.nice.org.uk/guidance/ng201 Meaney S, Leitao S, Olander EK, Pope J, Matvienko-Sikar K. The impact of COVID-19 on pregnant womens’ experiences and perceptions of antenatal maternity care, social support, and stress-reduction strategies. Women Birth . 2022;35(3):307–316. doi:10.1016/j.wombi.2021.04.013 Coxon K, Turienzo CF, Kweekel L, et al. The impact of the coronavirus (COVID-19) pandemic on maternity care in Europe. 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Women’s experiences of maternity care in the United Kingdom during the COVID-19 pandemic: A follow-up systematic review and qualitative evidence synthesis. Women and Birth . 2024;37(3):101588. doi:10.1016/j.wombi.2024.02.004 Flaherty SJ, Delaney H, Matvienko-Sikar K, Smith V. Maternity care during COVID-19: a qualitative evidence synthesis of women’s and maternity care providers’ views and experiences. BMC Pregnancy Childbirth . 2022;22(1):438. doi:10.1186/s12884-022-04724-w Hinton L, Dakin FH, Kuberska K, et al. Quality framework for remote antenatal care: qualitative study with women, healthcare professionals and system-level stakeholders. BMJ Qual Saf . 2024;33(5):301–313. doi:10.1136/bmjqs-2021-014329 Brigante L, Morelli A, Jokinen M, Plachcinski R, Rowe R. Impact of the COVID-19 pandemic on midwifery-led service provision in the United Kingdom in 2020-21: Findings of three national surveys. Midwifery . 2022;112:103390. doi:10.1016/j.midw.2022.103390 Hanley SJ, Jones AB, Oberman J, et al. Implementation of Public Health England infection prevention and control guidance in maternity units in response to the COVID-19 pandemic. J Hosp Infect . 2022;129:219–226. doi:10.1016/j.jhin.2022.04.018 Martin-Key NA, Spadaro B, Schei TS, Bahn S. Proof-of-Concept Support for the Development and Implementation of a Digital Assessment for Perinatal Mental Health: Mixed Methods Study. J Med Internet Res . 2021;23(6):e27132. doi:10.2196/27132 Moltrecht B, de Cassan S, Rapa E, Hanna JR, Law C, Dalton LJ. Challenges and opportunities for perinatal health services in the COVID-19 pandemic: a qualitative study with perinatal healthcare professionals. BMC Health Services Research . 2022;22(1):1026. doi:10.1186/s12913-022-08427-y Wilson CA, Dalton-Locke C, Johnson S, Simpson A, Oram S, Howard LM. Challenges and opportunities of the COVID-19 pandemic for perinatal mental health care: a mixed-methods study of mental health care staff. Arch Womens Ment Health . 2021;24(5):749–757. doi:10.1007/s00737-021-01108-5 Wiseman O, Emmett L, Hickford G, et al. The challenges and opportunities for implementing group antenatal care ('Pregnancy Circles’) as part of standard NHS maternity care: A co-designed qualitative study. Midwifery . 2022;109:103333. doi:10.1016/j.midw.2022.103333 Hinton L, Kuberska K, Dakin F, et al. A qualitative study of the dynamics of access to remote antenatal care through the lens of candidacy. J Health Serv Res Policy . 2023;28(4):222–232. doi:10.1177/13558196231165361 Galle A, Semaan A, Huysmans E, et al. A double-edged sword—telemedicine for maternal care during COVID-19: findings from a global mixed-methods study of healthcare providers. BMJ Global Health . 2021;6(2):e004575. doi:10.1136/bmjgh-2020-004575 Craighead CG, Collart C, Frankel R, et al. Impact of Telehealth on the Delivery of Prenatal Care During the COVID-19 Pandemic: Mixed Methods Study of the Barriers and Opportunities to Improve Health Care Communication in Discussions About Pregnancy and Prenatal Genetic Testing. JMIR Form Res . 2022;6(12):e38821. doi:10.2196/38821 Gourevitch RA, Anyoha A, Ali MM, Novak P. Use of Prenatal Telehealth in the First Year of the COVID-19 Pandemic. JAMA Network Open . 2023;6(10):e2337978. doi:10.1001/jamanetworkopen.2023.37978 Thirugnanasundralingam K, Davies-Tuck M, Rolnik DL, et al. Effect of telehealth-integrated antenatal care on pregnancy outcomes in Australia: an interrupted time-series analysis. The Lancet Digital Health . 2023;5(11):e798-e811. doi:10.1016/S2589-7500(23)00151-6 Fernandez Turienzo C, Newburn M, Agyepong A, et al. Addressing inequities in maternal health among women living in communities of social disadvantage and ethnic diversity. BMC Public Health . 2021;21(1):176. doi:10.1186/s12889-021-10182-4 Carson LE, Azmi B, Jewell A, et al. Cohort profile: the eLIXIR Partnership—a maternity–child data linkage for life course research in South London, UK. BMJ Open . 2020;10(10):e039583. doi:10.1136/bmjopen-2020-039583 Brown J, Kirk-Wade E, Baker C, Barber S. Coronavirus: A history of English lockdown laws. UK Parliament . Published online 2021. Accessed May 15, 2024. https://commonslibrary.parliament.uk/research-briefings/cbp-9068/ van de Schoot R, Sijbrandij M, Winter SD, Depaoli S, Vermunt JK. The GRoLTS-Checklist: Guidelines for Reporting on Latent Trajectory Studies. Structural Equation Modeling: A Multidisciplinary Journal . 2017;24(3):451–467. doi:10.1080/10705511.2016.1247646 Tolles J, Lewis RJ. Time-to-Event Analysis. JAMA . 2016;315(10):1046–1047. doi:10.1001/jama.2016.1825 Duryea EL, Adhikari EH, Ambia A, Spong C, McIntire D, Nelson DB. Comparison Between In-Person and Audio-Only Virtual Prenatal Visits and Perinatal Outcomes. JAMA Netw Open . 2021;4(4):e215854-e215854. doi:10.1001/jamanetworkopen.2021.5854 Tydeman F. Pregnancy outcomes during the COVID-19 pandemic: insights from eLIXIR, Born in South-London. In: Royal Statistical Society Conference . ; 2024. NHS inform Scotland. Your antenatal care. NHS inform. Accessed June 25, 2024. https://www.nhsinform.scot/ready-steady-baby/pregnancy/your-antenatal-care/your-antenatal-care/ Canada PHA of. Care during Pregnancy: Family-Centred Maternity and Newborn Care National Guidelines .; 2021. Accessed June 25, 2024. https://www.canada.ca/en/public-health/services/publications/healthy-living/maternity-newborn-care-guidelines-chapter-3.html Overview | Antenatal care | Guidance | NICE. August 19, 2021. Accessed June 24, 2024. https://www.nice.org.uk/guidance/ng201 Gamberini C, Angeli F, Ambrosino E. Exploring solutions to improve antenatal care in resource-limited settings: an expert consultation. BMC Pregnancy and Childbirth . 2022;22(1):449. doi:10.1186/s12884-022-04778-w Atkinson J, Hastie R, Walker S, Lindquist A, Tong S. Telehealth in antenatal care: recent insights and advances. BMC Med . 2023;21:332. doi:10.1186/s12916-023-03042-y Ghimire S, Martinez S, Hartvigsen G, Gerdes M. Virtual prenatal care: A systematic review of pregnant women’s and healthcare professionals’ experiences, needs, and preferences for quality care. International Journal of Medical Informatics . 2023;170:104964. doi:10.1016/j.ijmedinf.2022.104964 Mésidor M, Rousseau MC, O’Loughlin J, Sylvestre MP. Does group-based trajectory modeling estimate spurious trajectories? BMC Medical Research Methodology . 2022;22(1):194. doi:10.1186/s12874-022-01622-9 Nagin DS. Group-Based Trajectory Modeling: An Overview. ANM . 2014;65(2–3):205–210. doi:10.1159/000360229 Gurol-Urganci I, Waite L, Webster K, et al. Obstetric interventions and pregnancy outcomes during the COVID-19 pandemic in England: A nationwide cohort study. PLOS Medicine . 2022;19(1):e1003884. doi:10.1371/journal.pmed.1003884 Tables Table 1 to 3 are available in the Supplementary Files section. Additional Declarations The authors declare no competing interests. Supplementary Files TableS.docx Virtualcaresupplementaryfile.docx Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. 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Why was this study conducted?","content":"\u003cul type=\"disc\"\u003e\n \u003cli\u003eThe COVID-19 pandemic led to increased virtual antenatal care (vANC), but its impact on pregnancy outcomes remains unclear.\u003c/li\u003e\n \u003cli\u003eThis study examines vANC trajectories and their association with maternal and neonatal outcomes in a multiethnic, urban UK population.\u003c/li\u003e\n\u003c/ul\u003e\u003cp\u003e\u003cstrong\u003eB.\u0026nbsp;\u003c/strong\u003eWhat are the key findings?\u003c/p\u003e\u003cul type=\"disc\"\u003e\n \u003cli\u003eMore vANC in the second or third trimesters was linked to higher risks of preterm birth, labour induction, C-sections, NICU admissions, and postpartum haemorrhage.\u003c/li\u003e\n \u003cli\u003eIncreased vANC in later pregnancy was also associated with lower early skin-to-skin contact and breastfeeding rates.\u003c/li\u003e\n\u003c/ul\u003e\u003cp\u003e\u003cstrong\u003eC.\u0026nbsp;\u003c/strong\u003eWhat does this study add to what is already known?\u003c/p\u003e\u003cul type=\"disc\"\u003e\n \u003cli\u003eThis study provides new insights into how different vANC patterns affect pregnancy outcomes.\u003c/li\u003e\n \u003cli\u003eFindings highlight the need for careful integration of virtual care in maternity services to minimize potential risks.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"INTRODUCTION","content":"\u003cp\u003eBefore the COVID-19 pandemic in the United Kingdom (UK), maternity care was almost exclusively in-person. At least 7\u0026ndash;10 routine appointments were offered, depending on parity and multiple pregnancy status\u003csup\u003e1\u003c/sup\u003e. During the pandemic, routine antenatal care was modified in-line with guidance for infection control, with cancellation of many face-to-face appointments\u003csup\u003e2\u003c/sup\u003e. Virtual technology (i.e., remote consultations via video conferencing or telephone) was used as an alternative to in-person appointments, and out-of-office self-monitoring was implemented for some women with conditions such as pregnancy hypertension or gestational diabetes mellitus\u003csup\u003e3\u003c/sup\u003e. To further limit face-to-face contact, appointments were combined when possible, and there was a reduction in choice of carer and place of birth\u003csup\u003e4\u003c/sup\u003e. Furthermore, public health messaging to maintain social distancing and stay at home may have inadvertently influenced women to not seek care for problems arising during their pregnancy\u003csup\u003e5,6\u003c/sup\u003e and modifications to staffing arrangements may have affected antenatal care\u003csup\u003e7\u0026ndash;9\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eA national survey of maternity care providers highlighted the extensive impact of the pandemic on maternity services in the UK. The majority of units reported: a reduction in antenatal (70%) or postnatal (56%) appointments; a reduction in unscheduled antenatal presentations (89%); use of remote consultation (89%), and temporary suspension midwife-led unit or homebirths (59%). Also, nearly half of mental health care staff reported feeling less able to assess women, some of whom engaged poorly with virtual appointments \u003csup\u003e7,10\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWhile much of the virtual antenatal care (vANC) adopted \u003cem\u003ein lieu\u003c/em\u003e of face-to-face care during the pandemic has been reversed, some continues. It is important to understand whether or not the ongoing vANC, or vANC during another health system shock, is advisable. A substantial experiential literature describes largely negative experiences of virtual care for those receiving\u003csup\u003e11,12\u003c/sup\u003e and those providing maternity care during the pandemic\u003csup\u003e6,8,9,13\u0026ndash;20\u003c/sup\u003e. Both groups have expressed concerns about compromised quality of care, as well as access and participation. Maternity care providers highlighted enhanced convenience for some, but digital exclusion of others. However, little is known about the impact of vANC on clinical outcomes\u003csup\u003e21\u0026ndash;23\u003c/sup\u003e. In Australia, these were reported to be unchanged during widespread use of telehealth in the first 14 months of the pandemic\u003csup\u003e24\u003c/sup\u003e. However, changes in care-seeking behaviour and care provision have potential contrary effects and prevent a definitive assessment of the impact of vANC on pregnancy outcomes\u003csup\u003e25\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWhile there is considerable qualitative data on women\u0026rsquo;s and health care providers\u0026rsquo; experiences of receiving and delivering virtual care, less is known about the impact of virtual antenatal care on clinical outcomes. This analysis aimed to model data from before, and during the pandemic (with and without lockdowns) to quantify the disruption of in-person ANC to vANC using routine electronic health data and (ii) understand the relationships between vANC and birth outcomes in a UK inner-city population.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eData for this study were obtained from the \u0026lsquo;eLIXIR (Early LIfe cross-LInkage in Research) Born in South London (BiSL)\u0026rsquo; data linkage from Oct-2018-July-2023. The linkage and governance have been described previously\u003csup\u003e26\u003c/sup\u003e (for further information see Supplementary File and Figure S1).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eParticipants\u003c/h3\u003e\n\u003cp\u003eA pregnancy was included if information on antenatal registration, at least one antenatal appointment after registration, and birth outcomes were available for a singleton pregnancy. We excluded: (i) duplicate records, (e.g. those related to transfer of care and registration at two hospitals) identified by two or more antenatal care registration IDs with estimated delivery dates within 14 days; in this instance, the first record was included; (ii) records without either a patient or pregnancy ID; (iii) records noting multiple pregnancies (e.g. twins), as birth outcomes may differ for infants born from a multiple pregnancy; and (iv) records where women had registration and delivery data, but no outpatient antenatal care records.\u003c/p\u003e\n\u003ch3\u003eData\u003c/h3\u003e\n\u003cp\u003eRegistration for antenatal care data included demographics, past medical and obstetric history, and characteristics of current pregnancy, including intended hospital of birth, referred to as Site A or B. Antenatal care was characterised by the number of outpatient contacts, and the proportion that were virtual, during six epochs: 0\u0026ndash;14\u003csup\u003e+\u0026thinsp;6\u003c/sup\u003e, 15\u0026ndash;20\u003csup\u003e+\u0026thinsp;6\u003c/sup\u003e, 21\u0026ndash;27\u003csup\u003e+\u0026thinsp;6\u003c/sup\u003e, 28\u0026ndash;32\u003csup\u003e+\u0026thinsp;6\u003c/sup\u003e, 33\u0026ndash;36\u003csup\u003e+\u0026thinsp;6\u003c/sup\u003e and \u0026ge;\u0026thinsp;37\u003csup\u003e+\u0026thinsp;0\u003c/sup\u003e weeks\u0026rsquo; gestation. An appointment was deemed to have been \u0026lsquo;virtual\u0026rsquo; if: (i) maternal blood pressure, dipstick proteinuria, and fetal heart rate (after 15 weeks\u0026rsquo; gestation) were missing from the visit record; or (ii) notes from the appointment suggested that it was virtual, (e.g. \u0026lsquo;virtual\u0026rsquo;, \u0026lsquo;telephone\u0026rsquo;, or \u0026lsquo;call\u0026rsquo;). Otherwise, the appointment was considered to have taken place face-to-face. Once the proportion of vANC was estimated for each of the six epochs, these were converted to quartiles: 0\u0026ndash;25%, 26\u0026ndash;50%, 51\u0026ndash;75% and 76\u0026ndash;100%; e.g. if a woman attended three appointments from 28\u0026ndash;32\u003csup\u003e+\u0026thinsp;6\u003c/sup\u003e weeks\u0026rsquo; and two appointments were virtual, then 66.6% of her appointments were virtual; and this epoch was coded as the third quartile (51\u0026ndash;75%).\u003c/p\u003e \u003cp\u003eWe evaluated key pregnancy and delivery indictors across pandemic phases, according to the antenatal registration date: pre-pandemic (1 October 2018-22 March 2020); pandemic with lockdowns (23 Mar 2020-17 July 2021) and pandemic without lockdowns (18 July 2021-8 July 2023), categorised by delivery date of each pregnancy \u003csup\u003e27\u003c/sup\u003e. The key pregnancy and birth indicators assessed reflect a combination of: Organisation Performance Indicators, Clinical Quality Improvement Metrics and National Maternity Indicators (further details \u003cb\u003eTable S3\u003c/b\u003e).\u003c/p\u003e\n\u003ch3\u003eTrajectory modelling\u003c/h3\u003e\n\u003cp\u003eIt could not be assumed that all participants in a given timeframe would have experienced the same longitudinal changes in vANC, especially during the pandemic with serial lockdowns and easing. To address this, we undertook latent class modelling, using group-based trajectory modelling (GBTM), to identify subgroups of individuals with similar trajectories/patterns of vANC.\u003c/p\u003e \u003cp\u003eTo identify the number of latent classes/trajectories for virtual care as best describing the data, we used a forward modelling approach, and the analysis was conducted using the Guidelines for Reporting on Latent Trajectory Studies\u003csup\u003e28\u003c/sup\u003e (GRoLTS checklist, Table S4). After fitting the one-class model, additional classes were added incrementally. For each iteration, model adequacy was assessed using the model estimation criteria (Supplementary file). Once the model adequacy stopped improving, an additional model was fitted with one extra class to ensure the full array of possible models had been tested (Table S5). The adequacy of each model was assessed using the following fit criteria: the Bayesian Information Criterion, average posterior probability of assignments, the ratio of the odds of a correct classification, group membership, and relative entropy (for further details see Supplementary File).\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eBinary and categorical variables are presented using counts and percentages. The distributions of continuous variables were assessed and summarised by mean and standard deviation or median and interquartile range (IQR) for normally or non-normally distributed variables, respectively. To be included in the analysis, women needed data in at least one timepoint (e.g. a woman registered for antenatal care, had an antenatal appointment and the delivered within one epoch). We used a censored normal model suitable for use with scaled data (i.e., vANC percentages quartiles)\u003csup\u003e29\u003c/sup\u003e. The six epochs were converted to mean gestational age.\u003c/p\u003e \u003cp\u003eUnivariable and multivariable multinominal logistic regression analyses were used to assess the relationships between the virtual care trajectories and birth outcomes and are presented as adjusted relative risk ratios (aRR). To identify confounders for inclusion in multivariable models, direct acyclic graphs were created for birth outcomes (\u003cb\u003eFigure S2\u003c/b\u003e). The minimal adjustment set variables were: index of multiple deprivation (IMD), parity, time (months) and registration: gestation, hospital, smoking status, and pandemic epoch: pre-pandemic (1 Oct 2018-22 Mar 2020), first pandemic lockdown (23 Mar 2020-23 Jun 2020), first lockdown easing (24 Jun 2020-4 Nov 2020), second pandemic lockdown (5 Nov 2020-5 Jan 2021), third pandemic lockdown (6 Jan 2021-17 Jul 2021), and pandemic without lockdown (18 Jul 2021-8 July 2023); due to the correlation between pandemic epoch and time, only pandemic epoch was retained as a confounder. An interaction term was included for between IMD and ethnicity. Body mass index (BMI) was not included due to the increase in missing data for this variable during the pandemic, potentially due to lack of face-to-face appointments to measure height and weight. Given that our primary focus was on relationships during the pandemic, including BMI could have introduced bias due to its non-random missingness. Differences between number of appointments for each trajectory were modelled using negative binomial regression due to overdispersion and adjusted for parity, registration gestation and antenatal care type.\u003c/p\u003e \u003cp\u003eWe undertook several sensitivity analyses. First, we assessed the differences between those women who were included in the analysis vs those excluded; birth and delivery data were compared using chi-squared test or regression for categorical and continuous outcomes, respectively. Second, we truncated the pregnancies included at the beginning and end of the study period, to ensure that the number of births was stable, to minimise the impact of pregnancies that booked late for ANC or delivered very early; the new study period was from 1st April 2019 to 30th April 2023. Third, we removed parity as an adjustment variable, and stratified the analysis for parity. All analyses were undertaken in Stata (version 18.0).\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eFollowing removal of duplicates and multiple pregnancies (n=1,012), 58,402 unique pregnancy IDs were recorded between October 2018-July 2023, of which 34,114 were included in the analysis (\u003cstrong\u003eFigure 1\u003c/strong\u003e). Women excluded from the analysis, differed from those included with regards to a number of registration characteristics and birth outcomes. For details see \u003cstrong\u003eTables S6-7\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor the 34,114 pregnancies included, 59% were at Site A. At registration, mean maternal age was 32.7 years, 17% were affected by obesity (\u0026gt;30kg/m\u003csup\u003e2\u003c/sup\u003e), median gestational age was 9.7 weeks, 13% of the cohort registered after 16 weeks and 22.7% had midwifery only led care (i.e. deemed to be low risk). Half of the cohort were from the global majority, of which 42% and 20% were of Black of Asian ethnicities and \u0026gt;60% were from the two lowest IMD quintiles. 7% had difficulty understanding English. Few women reported any prior drug use (\u0026lt;7%) or current/in the past 12 months (\u0026lt;2%). 53.2% were nulliparous. Of the multiparous women, 31.9%, 1.8%, 8.8% and 7.9% reported a prior Caesarean, stillbirth, postpartum haemorrhage (PPH) or preterm birth, respectively (Table 1). The median number of antenatal and virtual appointments were 9 (IQR 7-12) and 1 (0-2), respectively (Table 2). 22.8% of women gave birth pre-pandemic and 45.6% during pandemic without lockdowns. Less than 3% were smoking at delivery, and 12.1% developed gestational hypertension. The majority of women (68.5%) had no risk factors at birth, 94.1% of pregnancies ended at term and 22.0% of births were induced, with over half of births involving intervention. Breech delivery was rare (0.3%), and 3\u003csup\u003erd\u003c/sup\u003e or 4\u003csup\u003eth\u003c/sup\u003e degree vaginal tears uncommon (1.5%). PPH occurred in 10.0% of pregnancies. Stillbirth and neonatal death were rare at 3.9 and 2.3 births per 1,000, respectively. 88.3% of newborns had early skin-to-skin, 6.9% were SGA, and 5.6% were admitted to neonatal intensive care unit (NICU) (Table 2).\u003c/p\u003e\n\u003cp\u003eGBTM identified four trajectories of vANC as best describing the data (Figure 2, Table S4, Figures S3-4). 81.3% of participants (Trajectory-0; n=27,751) were characterised as having had a stable and low occurrence of vANC over time. 2.4% (Trajectory-1; n=832) of participants had a high proportion of vANC during the 1\u003csup\u003est\u003c/sup\u003e trimester, 7.1% (Trajectory-2; n=2,410) a high proportion of vANC during the 2\u003csup\u003end\u003c/sup\u003e trimester, and 9.2% (Trajectory-3; n=3,121) a high proportion of vANC during the 3\u003csup\u003erd\u003c/sup\u003e trimester.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCharacteristics at registration for antenatal care were different between trajectory groups (Table 1). Participants in:\u003c/p\u003e\n\u003cul class=\"decimal_type\"\u003e\n \u003cli\u003eTrajectory-0 were more likely to register for antenatal care before the pandemic or during the pandemic without lockdowns and more likely to be \u0026lt;20 years or register late for antenatal care. \u0026nbsp;\u003c/li\u003e\n \u003cli\u003eTrajectory-1 were more likely to register at Site A and during pandemic without lockdowns and had a higher percentage of BMI missingness and the lowest percentage of late registrations (1.0%).\u003c/li\u003e\n \u003cli\u003eTrajectory-2 were most likely to register during the first and second pandemic lockdowns and had the lowest percentage of women \u0026lt;20 years, were more likely to be of White ethnicity, and less likely to have difficulty understanding English.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eTrajectory-3 were least likely to register at Site A, most likely to register pre-pandemic, to have the highest percentage of midwifery only care, more likely to have used drugs in the previous 12 months and be nulliparous; also, multiparous women in this trajectory were more likely to have had a previous PPH or preterm birth.\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eCompared to Trajectory-0, the rate of visits for those in Trajectories 1 and 3 decreased by a factor of 0.98 and increased by a rate of 1.04, respectively. Overall, the rate of visits was similar for the 4 trajectory groups (Table 3).\u003c/p\u003e\n\u003cp\u003eIn the adjusted analyses with trajectory-0 as the reference outcome, pregnancy outcomes were found to differ according to vANC trajectory, as well as in unadjusted analyses (Table 3). Pregnancies in Trajectory-1 had no significant difference in pregnancy outcomes (Table 3). Pregnancies in Trajectory-2 were less likely to be diagnosed with gestational hypertension [aRR 0.84 (95% confidence interval: 0.74, 0.96)]; more likely to experience preterm birth \u0026lt;37 weeks, specifically birth at 24\u003csup\u003e+0\u003c/sup\u003e-27\u003csup\u003e+6\u003c/sup\u003e weeks\u0026rsquo; gestation [1.79 (1.10, 2.93)]; more likely to have a baby with a breech presentation [1.92 (1.02, 3.62)]; less likely to have an assisted vaginal birth [0.87 (0.76, 1.00)] and more likely to have a PPH [1.14 (1.00, 1.30)] (Table 3). Pregnancies in Trajectory-3 were less likely to be diagnosed with gestational hypertension [0.84 (0.73, 0.96)]; more likely to deliver preterm [1.35 (1.16, 1.58)] at 28\u003csup\u003e+0\u003c/sup\u003e-33\u003csup\u003e+6\u003c/sup\u003e or 34\u003csup\u003e+0\u003c/sup\u003e-36\u003csup\u003e+6\u0026nbsp;\u003c/sup\u003e weeks\u0026rsquo; gestation; more likely to give birth by elective or emergency Caesarean; less likely to have a 3\u003csup\u003erd\u003c/sup\u003e-4\u003csup\u003eth\u003c/sup\u003e degree vaginal tear [0.82 (0.75, 0.90)]; less likely to have early skin-to-skin [0.82 (0.73, 0.92)] with the newborn; more likely for the newborn to be admitted to NICU [1.28 (1.09, 1.50)]; and less likely to breastfeed as the first feed [0.90 (0.81, 0.99)] (Table 3).\u003c/p\u003e\n\u003cp\u003eIn the first sensitivity analysis, women (n=2,174) excluded because they had registration and delivery data, but no outpatient antenatal care records, were observed to be at higher risk of adverse outcomes which were observed more often, compared with women included in the analysis (Tables S8-9). Women who were excluded (vs. those included) were more likely to have a virtual registration appointment (25.7% vs 2.5%, respectively), have higher BMI or obesity, and register late (56.5% vs 16.5%, respectively). Nearly 70% registered before the pandemic and they were less likely to be nulliparous. They were more likely to be smokers and deliver \u0026lt;37 weeks\u0026rsquo; gestation, and less likely to be induced. The infants were less likely to be offered breastmilk or have early skin-to-skin, and more likely be admitted to NICU, be stillborn (3.5% vs 0.4%) or suffer a neonatal death (3.0% vs 0.2%). These women were also far more likely to have inpatient antenatal care (42.6% vs 2.5%). Second, results of the truncated analysis (n=29,434 pregnancies, Table S10) did not differ from those results presented in Table 3. Third, in the analysis stratified by parity, most point estimates had the same direction of effect and 95% CI overlapped, although for a number of outcomes in multiparous pregnancies, a number of 95% CIs crossed 1.0 (Table S10); the only exceptions were for nulliparous pregnancies in Trajectory-1 (n=425), who were more likely to be smokers at delivery, and less likely to have early skin-to-skin contact with their babies.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn this cohort of more than 34,000 pregnancies from a diverse, South London population, we identified four trajectories of vANC before and during the COVID-19 pandemic, with and without lockdowns. Those trajectories were associated with small proportions of high first, second, or third trimester vANC and for the majority of pregnancies, low and stable vANC throughout pregnancy. Compared with the latter, a high proportion of vANC provided during the 2nd or 3rd trimesters of pregnancy was associated with increases in adverse birth outcomes, including preterm birth, breech presentation, Caesarean, PPH, NICU admission, and fewer babies receiving early skin-to-skin contact or being breastfed as their first feed. Of note, women who had no ANC after a registration appointment and were excluded from the main analysis, were found to have more adverse outcomes\u003c/p\u003e \u003cp\u003eFor decision-makers to fully assess policy related to use of vANC, information is required on clinical outcomes and experiences. We have reported some negative associations between vANC and adverse pregnancy outcomes when vANC is part of maternity care in the second or third trimesters. An important consideration is whether the vANC is increasing the incidence of those adverse outcomes, or whether more complicated pregnancies receive more vANC as part of enhanced maternal and fetal surveillance. Our data support the former explanation, as we found no evidence of either an increased total number of appointments nor total number of risk factors at birth in the high second or third trimester trajectory groups, compared with the low and stable (predominately face-to-face care) vANC trajectory group. Further support for the association between vANC and adverse outcomes is the lower prevalence of gestational hypertension among women who had a high proportion of vANC; noting that; gestational hypertension is diagnosed by repeated measures of high blood pressure and without the latter, the diagnosis may have been missed at a vANC appointment, especially in late pregnancy (when gestational hypertension, and the more complicated hypertensive disorder, pre-eclampsia, are most likely to develop).\u003c/p\u003e \u003cp\u003eOur results contrast with those of two large studies. An Australian study of routinely-collected data in over 27,000 births from January 2018 to April 2021\u003csup\u003e24\u003c/sup\u003e, used an interrupted time-series (ITS) analysis and found no differences in adverse pregnancy outcomes associated with telehealth-integrated antenatal care, including pre-eclampsia, fetal growth restriction, and perinatal mortality. A similar study of over 12,000 pregnancies in the USA, compared outcomes from May 2019 to October 2020, and found the implementation of audio-only virtual prenatal visits was not associated with changes in perinatal outcomes\u003csup\u003e30\u003c/sup\u003e. The differences observed between our study and these results may be due to the analytical approaches taken. ITS is a valuable approach for modelling the impact of an \u0026lsquo;interruption\u0026rsquo; on outcomes over time when an RCT is not possible, GBTM is more granular, by defining distinct trajectories of vANC by trimester. Of note, when considering only the relationship between time and outcomes, most outcomes follow patterns established pre-pandemic \u003csup\u003e31\u003c/sup\u003e .\u003c/p\u003e \u003cp\u003eWe found no association between the vANC registration and adverse pregnancy outcomes, adjusted for gestational age at registration. This may question the current approach in England (compared with other countries) which places a distinct focus on early first trimester registration, and in contrast with the approach developed in Scotland and continued elsewhere (e.g., Canada)\u003csup\u003e32,33\u003c/sup\u003e, has fewer appointments later in pregnancy when complications are more likely to develop \u003csup\u003e34\u003c/sup\u003e. Our finding suggest that England could consider focusing on more appointments in later pregnancy rather than an emphasis on early pregnancy registration; this suggestion does need validating in our cohorts.\u003c/p\u003e \u003cp\u003eIn contrast to clinical outcomes, most publications have assessed the impact of the integration of vANC on the experiences of those receiving\u003csup\u003e11\u003c/sup\u003e and those providing vANC\u003csup\u003e8,13\u0026ndash;20\u003c/sup\u003e. Those experiences have been predominantly negative. In a systematic review, women receiving vANC most often described concerns about disruption of care and safety, and access to adequate technology; they felt that improved access to care was needed, and they wished to participate in that care. Maternity care providers\u003csup\u003e8,13\u0026ndash;20\u003c/sup\u003e have described a negative impact of vANC on quality of care, and expressed concerns about digital exclusion of certain groups. Nevertheless, some reports have described positive consequences of vANC, such as increased convenience and flexibility\u003csup\u003e11\u003c/sup\u003e. It has been suggested that vANC could increase access to maternity care for those living in rural locations, or who are unable to attend clinics due to childcare or other inflexible commitments\u003csup\u003e35,36\u003c/sup\u003e. While this relies on patients having access to the necessary infrastructure, be it mobile phone data, a computer and internet access, or even privacy to engage in virtual care\u003csup\u003e7\u003c/sup\u003e, a recent review of these practical considerations related to vANC found that challenges could be addressed through vANC design based on the user\u0026rsquo;s needs, technical competency, and available resources\u003csup\u003e37\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eInterpretation and future implications\u003c/h3\u003e\n\u003cp\u003eOur results suggest that virtual care in the second and third trimesters results in poorer clinical outcomes. With predominantly negative experiences of vANC by both care providers and care seekers during the pandemic, our data sound a cautionary note about a policy of vANC in routine ANC during future health system shocks or future maternity care outside of health crises. Future work should explore whether women who are interested in some component of vANC, can be identified as very low risk and likely to benefit specifically from the provision of some vANC. While vANC may be a valuable tool for improving access to care, it may also reinforce or amplify existing health disparities, especially for disadvantaged populations; as such, future work should assess outcomes by ethnicity and deprivation, and explore why a virtual appointment took place (e.g. provider-driven, or a woman was either unable or unwilling to attend an appointment), to enhance our understanding of the impact (if any) of vANC on disparities in pregnancy outcomes.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eThis analysis has several strengths; eLIXIR-BiSL is a population-based cohort which incorporates demographic, maternal, and neonatal health records from a multi-ethnic and socially deprived inner-city population of pregnant women. The breadth and granularity of the data enabled modelling of meaningful trajectories of vANC, to explore associations between trajectories with pregnancy outcomes, and to evaluate GBTM fit using several model adequacy criteria\u003csup\u003e38\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThere are inherent limitations with the use of any routinely-collected data, due to missingness and a lack of standardisation, compared with strict research protocols. While we inferred that care was virtual when all of three key assessments were missing, we believe that this assumption is supported by the overall robustness of NHS care standards. GBTM assumes each trajectory group has a fixed shape (e.g. linear or quadratic) and within each trajectory, the slope and intercept are constant for all individuals; violating these assumptions can affect classification accuracy\u003csup\u003e39\u003c/sup\u003e. The data may be subject to confounding by indication, when a patient is more likely to receive a specific treatment due to their individual circumstances; although we attempted to account for this by performing sensitivity analyses and adjusting for confounders (including pandemic epoch), there remains the possibility of residual confounding. We were unable to adjust for SARS-CoV2 positivity, due to the low prevalence rate of 0.1% in this cohort and as reported by others in England\u003csup\u003e40\u003c/sup\u003e. Our findings may not be generalisable to those not included in this study, such as multiple pregnancies or those living in rural settings. Finally, given the exploratory nature of this study and the broad range of pregnancy outcomes examined, we have not adjusted for multiple testing.\u003c/p\u003e \u003c/div\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eOur analysis has identified distinct patterns of vANC trajectories during pregnancy in a diverse UK inner-city population. During the pandemic, vANC provided during the 2nd or 3rd trimesters of pregnancy was associated with adverse pregnancy outcomes for mothers and babies. These findings emphasise the importance of tailored vANC strategies to optimise maternal and neonatal health, and suggest that face-to-face ANC should potentially be preserved, particularly during future health system shocks.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDATA STATEMENT\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data accessed by eLIXIR remain within an NHS firewall and governance is provided by the eLIXIR Oversight Committee which reports to relevant information governance clinical leads. Subject to these conditions, data access is encouraged and those interested should contact the eLIXIR Chief Investigator (Professor Lucilla Poston; [email protected]). Access can also be requested through the HDRUK Innovation Gateway (https://web.www.healthdatagateway.org/dataset/3c780d45-ed7b-4101-9c32-d50512cd9cfe).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe wish to thank the women, their infants, and families from all participating sites for sharing their data and supporting this programme.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHORS CONTRIBUTIONS\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conceived by KVD, LAM and PvD. All authors contributed to the design and delivery of the study. The author KVD assumes responsibility for the formal analysis and completeness of data reporting, results have been validated by FT. \u0026nbsp;KVD and LAM drafted the manuscript, which was revised and approved by all authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING SOURCES\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis project was funded by the National Institute for Health Research (NIHR) HSDR Programme [reference number NIHR134293]. The Early Life Cross Linkage in Research, Born in South London (eLIXIR-BiSL) Partnership was developed by an MRC Partnership Grant [MR/P003060/1] and the MRC Longitudinal Population Study Grant [MR/X009742/1]. The eLIXIR-BiSL platform is also part-supported by the National Institute for Health Research (NIHR) Biomedical Research Centre at the South London and Maudsley NHS Foundation Trust and King\u0026rsquo;s College London. The funder (NIHR) played no role in study design, data acquisition, analysis, interpretation, or the decision to submit for publication. Abigail Easter, King\u0026rsquo;s College London, is supported by the National Institute for Health and Care Research (NIHR) Applied Research Collaboration South London (NIHR ARC South London) at King\u0026rsquo;s College Hospital NHS Foundation Trust. The views expressed are those of the author[s] and not necessarily those of the NIHR or the Department of Health and Social Care.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThe authors have no competing interests to declare.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNational Institute for Health and Care Excellence. Antenatal care. 2021. Accessed May 15, 2024. https://www.nice.org.uk/guidance/ng201\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeaney S, Leitao S, Olander EK, Pope J, Matvienko-Sikar K. The impact of COVID-19 on pregnant womens\u0026rsquo; experiences and perceptions of antenatal maternity care, social support, and stress-reduction strategies. \u003cem\u003eWomen Birth\u003c/em\u003e. 2022;35(3):307\u0026ndash;316. doi:10.1016/j.wombi.2021.04.013\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCoxon K, Turienzo CF, Kweekel L, et al. The impact of the coronavirus (COVID-19) pandemic on maternity care in Europe. \u003cem\u003eMidwifery\u003c/em\u003e. 2020;88:102779. doi:10.1016/j.midw.2020.102779\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAydin E, Glasgow KA, Weiss SM, et al. 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Accessed June 25, 2024. https://www.nhsinform.scot/ready-steady-baby/pregnancy/your-antenatal-care/your-antenatal-care/\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCanada PHA of. \u003cem\u003eCare during Pregnancy: Family-Centred Maternity and Newborn Care National Guidelines\u003c/em\u003e.; 2021. Accessed June 25, 2024. https://www.canada.ca/en/public-health/services/publications/healthy-living/maternity-newborn-care-guidelines-chapter-3.html\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOverview | Antenatal care | Guidance | NICE. August 19, 2021. Accessed June 24, 2024. https://www.nice.org.uk/guidance/ng201\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGamberini C, Angeli F, Ambrosino E. 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Virtual prenatal care: A systematic review of pregnant women\u0026rsquo;s and healthcare professionals\u0026rsquo; experiences, needs, and preferences for quality care. \u003cem\u003eInternational Journal of Medical Informatics\u003c/em\u003e. 2023;170:104964. doi:10.1016/j.ijmedinf.2022.104964\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM\u0026eacute;sidor M, Rousseau MC, O\u0026rsquo;Loughlin J, Sylvestre MP. Does group-based trajectory modeling estimate spurious trajectories? \u003cem\u003eBMC Medical Research Methodology\u003c/em\u003e. 2022;22(1):194. doi:10.1186/s12874-022-01622-9\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNagin DS. Group-Based Trajectory Modeling: An Overview. \u003cem\u003eANM\u003c/em\u003e. 2014;65(2\u0026ndash;3):205\u0026ndash;210. doi:10.1159/000360229\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGurol-Urganci I, Waite L, Webster K, et al. Obstetric interventions and pregnancy outcomes during the COVID-19 pandemic in England: A nationwide cohort study. \u003cem\u003ePLOS Medicine\u003c/em\u003e. 2022;19(1):e1003884. doi:10.1371/journal.pmed.1003884\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 to 3 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Trajectories, virtual care, antenatal care, birth outcomes, electronic health records","lastPublishedDoi":"10.21203/rs.3.rs-6800101/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6800101/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe COVID-19 pandemic resulted in major maternity service reconfigurations, particularly an increase in virtual antenatal care (vANC). We aimed to explore the relationship between vANC trajectories over time and pregnancy outcomes.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003ePregnancy and birth outcome data were obtained pre-pandemic, during the pandemic with, and without lockdowns from the Born in South London (eLIXIR-BiSL) health record data linkage of a multiethnic and deprived UK inner-city population. Antenatal care was characterised by the number of outpatient contacts during six pregnancy epochs: 0\u0026ndash;14\u0026thinsp;+\u0026thinsp;6, 15\u0026thinsp;+\u0026thinsp;0\u0026ndash;20\u0026thinsp;+\u0026thinsp;6, 21\u0026thinsp;+\u0026thinsp;0\u0026ndash;27\u0026thinsp;+\u0026thinsp;6, 28\u0026thinsp;+\u0026thinsp;0\u0026ndash;32\u0026thinsp;+\u0026thinsp;6, 33\u0026thinsp;+\u0026thinsp;0\u0026ndash;36\u0026thinsp;+\u0026thinsp;6 and \u0026ge;\u0026thinsp;37\u0026thinsp;+\u0026thinsp;0 weeks\u0026rsquo; gestation. In each epoch, the proportion of vANC was grouped into quartiles, and group-based trajectory modelling (GBTM) was used to extract vANC trajectories. Adjusted multinominal logistic regression was used to explore relationships between vANC trajectories and pregnancy outcomes.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eBased on 34,114 mother-child dyads (Oct-2018-Jul-2023), GBTM suggested four trajectories of vANC: \u0026lsquo;Trajectory-0\u0026rsquo;: Stable over pregnancy, and lowest quartile (n\u0026thinsp;=\u0026thinsp;27,751 pregnancies, 81\u0026middot;3%); \u0026lsquo;Trajectory-1\u0026rsquo;: High 1st trimester vANC (n\u0026thinsp;=\u0026thinsp;832, 2\u0026middot;4%); \u0026lsquo;Trajectory-2\u0026rsquo;: High 2nd trimester vANC (n\u0026thinsp;=\u0026thinsp;2,410, 7\u0026middot;1%); and \u0026lsquo;Trajectory-3\u0026rsquo;: High 3rd trimester vANC (n\u0026thinsp;=\u0026thinsp;3,121, 9\u0026middot;2%). Following adjustment, compared with Trajectory-0, Trajectory-2 had more premature births: (\u0026lt;\u0026thinsp;37 weeks, adjusted relative risk 1\u0026middot;21, 95% confidence interval 1\u0026middot;02\u0026thinsp;\u0026minus;\u0026thinsp;1\u0026middot;44), labour inductions (1\u0026middot;13, 1\u0026middot;02\u0026thinsp;\u0026minus;\u0026thinsp;1\u0026middot;25), breech presentation (1\u0026middot;92, 1\u0026middot;02\u0026ndash;3\u0026middot;62), and postpartum haemorrhage (1\u0026middot;14, 1\u0026middot;00\u0026ndash;1\u0026middot;30). Compared with Trajectory-0, Trajectory-3 had more premature births (\u0026lt;\u0026thinsp;37 weeks, 1\u0026middot;35, 1\u0026middot;16\u0026thinsp;\u0026minus;\u0026thinsp;1\u0026middot;58), elective (1\u0026middot;54, 1\u0026middot;38\u0026thinsp;\u0026minus;\u0026thinsp;1\u0026middot;72) or emergency (1\u0026middot;21, 1\u0026middot;01\u0026ndash;1\u0026middot;34) Caesarean sections and neonatal intensive care unit admissions (1\u0026middot;28, 1\u0026middot;09\u0026thinsp;\u0026minus;\u0026thinsp;1\u0026middot;50); and less early skin-to-skin contact (0\u0026middot;82, 0\u0026middot;73\u0026thinsp;\u0026minus;\u0026thinsp;0\u0026middot;92), breastfeeding (0\u0026middot;90, 0\u0026middot;81\u0026thinsp;\u0026minus;\u0026thinsp;0\u0026middot;99), and 3rd or 4th degree vaginal tears (0\u0026middot;82, 0\u0026middot;75\u0026thinsp;\u0026minus;\u0026thinsp;0\u0026middot;90).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eMore vANC, as a proportion of antenatal care received, was associated with more adverse pregnancy outcomes, when women received vANC in the second or third trimesters.\u003c/p\u003e","manuscriptTitle":"The relationship between virtual antenatal care and pregnancy outcomes in a diverse UK inner-city population; A group-based trajectory modelling approach using routine health records","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-04 12:17:44","doi":"10.21203/rs.3.rs-6800101/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a52e0a5d-de65-4eac-9c3a-1ea9a810a016","owner":[],"postedDate":"June 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-04T12:17:44+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-04 12:17:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6800101","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6800101","identity":"rs-6800101","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}