Reliable interpretation of cardiac ultrasound images is essential for accurate clinical diagnosis and assessment. Self-supervised learning has shown promise in medical imaging by leveraging large unlabelled datasets to learn meaningful representations. In this study, we evaluate and compare two self-supervised learning frameworks, USF-MAE, developed by our team, and MoCo v3, on the recently introduced CACTUS dataset (37,736 images) for automated simulated cardiac view (A4C, PL, PSAV, PSMV, Random, and SC) classification. Both models used 5-fold cross-validation, enabling robust assessment of generalization performance across multiple random splits. The CACTUS dataset provides expert-annotated cardiac ultrasound images with diverse views. We adopt an identical training protocol for both models to ensure a fair comparison. Both models are configured with a learning rate of 0.0001 and a weight decay of 0.01. For each fold, we record performance metrics including ROC-AUC, accuracy, F1-score, and recall. Our results indicate that USF-MAE consistently outperforms MoCo v3 across metrics. The average testing AUC for USF-MAE is 99.99

The rate of gestational diabetes mellitus (GDM) has increased over the past decades, but it’s unclear whether associations with maternal outcomes have changed. We aimed to describe rates of adverse maternal outcomes following deliveries with and without GDM over time and assess risks in these outcomes for GDM by delivery period. This population-based retrospective cohort study was conducted using provincial birth registry linked with health administrative databases in Ontario, Canada. All singleton hospital deliveries between April 1, 2012 and March 31, 2020 were included. We assessed the trends of adverse maternal outcomes among GDM and non-GDM pregnancies and used modified Poisson regression to estimate associations between GDM and adverse maternal outcomes, using crude and adjusted relative risk (aRR) and risk difference (aRD) with 95

Pre-eclampsia is a multisystem disorder that arises in at least 2–4% of pregnancies and is characterised by hypertension and new-onset organ dysfunction. The pathophysiology of pre-eclampsia is diverse but mostly involves a mismatch between fetoplacental demands and uteroplacental supply, combined with a maternal predisposition to cardiometabolic disease. While our understanding of the mechanisms underlying pre-eclampsia has advanced in recent years, maternal and perinatal mortality remains high in low-income and middle-income countries (LMICs), where pre-eclampsia incidence is higher. Risk factors include maternal chronic hypertension, diabetes, obesity, and previous pre-eclampsia. Preventive strategies include structured behavioural changes focused on diet quality and moderate-intensity physical activity for women with metabolic risk factors, low-dose aspirin for women at high risk, and risk-stratified timed birth at term. To date, the only recourse for pre-eclampsia is delivery of the placenta, and many discrepancies in care remain between high-income countries and LMICs. Current efforts primarily focus on the prediction, accurate diagnosis, and prevention of pre-eclampsia.

Pre-eclampsia is a multisystem disorder that arises in at least 2-4% of pregnancies and is characterised by hypertension and new-onset organ dysfunction. The pathophysiology of pre-eclampsia is diverse but mostly involves a mismatch between fetoplacental demands and uteroplacental supply, combined with a maternal predisposition to cardiometabolic disease. While our understanding of the mechanisms underlying pre-eclampsia has advanced in recent years, maternal and perinatal mortality remains high in low-income and middle-income countries (LMICs), where pre-eclampsia incidence is higher. Risk factors include maternal chronic hypertension, diabetes, obesity, and previous pre-eclampsia. Preventive strategies include structured behavioural changes focused on diet quality and moderate-intensity physical activity for women with metabolic risk factors, low-dose aspirin for women at high risk, and risk-stratified timed birth at term. To date, the only recourse for pre-eclampsia is delivery of the placenta, and many discrepancies in care remain between high-income countries and LMICs. Current efforts primarily focus on the prediction, accurate diagnosis, and prevention of pre-eclampsia.

Cesarean delivery-associated surgical site infections (CD-SSIs) significantly increase maternal morbidity but have not been systematically reported. Surveillance strategies have been inconsistent in assessing CD-SSIs. This study aimed to compare the different methods of assessing CD-SSI incidence. Two surveillance strategies have been evaluated: electronic medical record (EMR) review and postpartum phone call. The difference in the CD-SSI incidence assessed through EMR review (4.7%) and postpartum phone call (8.9%) was statistically significant (P < 0.001); this suggests that EMR review alone may underestimate the incidence of CD-SSIs and that phone call surveillance is likely more effective for detecting cases of CD-SSIs.

Ultrasound imaging is a diagnostic modality that provides real-time, radiation-free evaluation in many clinical areas. Due to noise, operator reliance, and restricted field of vision, ultrasound images are difficult to interpret, resulting in inter-observer variability. Due to the lack of labelled datasets and the domain gap between general and sonographic images, Deep Learning models pre-trained on non-medical data are limited in transferability. To address these challenges, we introduce the Ultrasound Self-Supervised Foundation Model with Masked Autoencoding (USF-MAE), the first large-scale self-supervised MAE framework pre-trained exclusively on ultrasound data. The model was pre-trained on similar to 370,000 2D and 3D ultrasound images from 46 open-source datasets (OpenUS-46), covering over 20 anatomical regions. This curated dataset has been made publicly available. Using an encoder-decoder architecture, USF-MAE reconstructs masked image patches, enabling it to learn representations directly from unlabelled data. The pre-trained encoder was fine-tuned on three public downstream classification benchmarks: BUS-BRA, MMOTU-2D, and GIST514-DB. USF-MAE outperformed CNN and ViT baselines in all tasks, attaining F1-scores of 81.6%, 79.6%, and 82.4%, respectively. Without labels during pre-training, USF-MAE approached the supervised foundation model UltraSam on breast cancer classification and outperformed it on other tasks, showing cross-anatomical generalization. In addition, USF-MAE showed strong performance on ovarian tumour segmentation using the MMOTU-2D dataset, achieving an mAP of 51.0% and mAP@50 of 77.9%. These findings establish USF-MAE as a scalable and label-efficient ultrasound foundation model. Its ultrasonic representation learning approach supports data-efficient clinical and research applications by continually pre-training on future unlabelled public or institutional datasets without human annotation.

OBJECTIVE:To develop and evaluate a deep learning model capable of detecting ventriculomegaly on prenatal ultrasound images using a foundation model pre-trained specifically on ultrasound data. METHODS:A vision transformer-based ultrasound self-supervised foundation model with masked autoencoding (USF-MAE) was fine-tuned for binary classification of fetal brain ultrasound images as normal or ventriculomegaly. The encoder had been pre-trained on more than 370,000 ultrasound images from the OpenUS-46 corpus. For this study, it was adapted and fine-tuned on a curated data set of fetal brain images. Model performance was evaluated through fivefold cross-validation as well as an independent test cohort. Accuracy, precision, recall, specificity, F1-score and area under the receiver operating characteristic and precision-recall curves were reported as the performance metrics. Eigen-CAM and Grad-CAM were used to visualize model attention. RESULTS:USF-MAE reported an F1-score of 91.76% for the cross-validation set and 91.78% for the test set. The model outperformed all the baseline models, which included VGG-19, ResNet-50, ViT-B/16 and MoCo v3. The model reported a mean test precision of 94.47% and an accuracy of 97.24%. Activation maps indicated that the model consistently focused on the ventricular region when identifying ventriculomegaly. CONCLUSION:Pre-training on a large, ultrasound-specific corpus improved classification performance and generalization for ventriculomegaly detection. The USF-MAE framework provided strong accuracy, reliability and explainability, showing potential as a robust tool for the assessment of fetal brain structures on prenatal ultrasound.

Prenatal ultrasound is the cornerstone for detecting congenital anomalies of the kidneys and urinary tract, but diagnosis is limited by operator dependence and suboptimal imaging conditions. We sought to assess the performance of a self-supervised ultrasound foundation model for automated fetal renal anomaly classification using a curated dataset of 969 two-dimensional ultrasound images. A pretrained Ultrasound Self-Supervised Foundation Model with Masked Autoencoding (USF-MAE) was fine-tuned for binary and multi-class classification of normal kidneys, urinary tract dilation, and multicystic dysplastic kidney. Models were compared with a DenseNet-169 convolutional baseline using cross-validation and an independent test set. USF-MAE consistently improved upon the baseline across all evaluation metrics in both binary and multi-class settings. USF-MAE achieved an improvement of about 1.87

Congenital heart disease remains the most common congenital anomaly and a leading cause of neonatal morbidity and mortality. Although first-trimester fetal echocardiography offers an opportunity for earlier detection, automated analysis at this stage is challenging due to small cardiac structures, low signal-to-noise ratio, and substantial inter-operator variability. In this work, we evaluate a self-supervised ultrasound foundation model, USF-MAE, for first-trimester fetal heart view classification. USF-MAE is pretrained using masked autoencoding modelling on more than 370,000 unlabelled ultrasound images spanning over 40 anatomical regions and is subsequently fine-tuned for downstream classification. As a proof of concept, the pretrained Vision Transformer encoder was fine-tuned on an open-source dataset of 6,720 first-trimester fetal echocardiography images to classify five categories: aorta, atrioventricular flows, V sign, X sign, and Other. Model performance was benchmarked against supervised convolutional neural network baselines (ResNet-18 and ResNet-50) and a Vision Transformer (ViT-B/16) model pretrained on natural images (ImageNet-1k). All models were trained and evaluated using identical preprocessing, data splits, and optimization protocols. On an independent test set, USF-MAE achieved the highest performance across all evaluation metrics, with 90.57

OBJECTIVE:In recent years, the exponential growth of artificial intelligence (AI) has fuelled extensive research and high expectations for its potential applications. Generative AI has shown promise in streamlining clinical work. This study aimed to evaluate the ability of ChatGPT 3.5, a publicly available generative AI tool, to generate high-quality patient information materials for gestational diabetes mellitus, a common pregnancy complication. METHODS:This was an anonymized, within-subjects survey study comparing how obstetrical health care workers rated the understandability and actionability of 2 patient information sheets: the current standard version used at The Ottawa Hospital and an AI-derived version created using ChatGPT 3.5. Eligible obstetrical health care workers participated in the survey, reviewing the 2 versions of the patient education sheet without labels to identify which sheet was which. An adapted version of the Patient Education Materials Assessment Tool (PEMAT) was used to evaluate and score both versions on their understandability and actionability. We also collected information on the respondents' opinions on and exposure to the use of AI in health care to help describe their level of AI-familiarity. RESULTS:A total of 70 complete responses were received and included in the analysis. Survey respondents consisted of nurses (52.9%), resident or fellow physicians (28.6%), and practising physicians (14.3%). Respondents rated the standard and AI-generated materials similarly in understandability. Although the AI-generated version scored slightly lower in actionability, both instruments achieved scores above the 70% threshold, which is generally accepted as evidence that patient education materials are sufficiently understandable and actionable. CONCLUSIONS:These findings suggest that AI can produce patient education materials of similar quality to those currently used at The Ottawa Hospital. The actionability of the AI-generated patient education materials could be improved via prompt generation, thereby improving AI applications in clinical work and patient care.

Early diagnosis and access to resources, support and therapy are critical for improving long-term outcomes for children with autism spectrum disorder (ASD). ASD is typically detected using a case-finding approach based on symptoms and family history, resulting in many delayed or missed diagnoses. While population-based screening would be ideal for early identification, available screening tools have limited accuracy. This study aims to determine whether machine learning models applied to health administrative and birth registry data can identify young children (aged 18 months to 5 years) who are at increased likelihood of developing ASD. We assembled the study cohort using individually linked maternal-newborn data from the Better Outcomes Registry and Network (BORN) Ontario database. The cohort included all live births in Ontario, Canada between April 1st, 2006, and March 31st, 2018, linked to datasets from Newborn Screening Ontario (NSO), Prenatal Screening Ontario (PSO), and Canadian Institute for Health Information (CIHI) (Discharge Abstract Database (DAD) and National Ambulatory Care Reporting System (NACRS)). The NSO and PSO datasets provided screening biomarker values and outcomes, while DAD and NACRS contained diagnosis codes and intervention codes for mothers and offspring. Extreme Gradient Boosting models and large-scale ensembled Transformer deep learning models were developed to predict ASD diagnosis between 18 and 60 months of age. Leveraging explainable artificial intelligence methods, we determined the impactful factors that contribute to increased likelihood of ASD at both an individual- and population-level. The final study cohort included 707,274 mother-offspring pairs, with 10,956 identified cases of ASD. The best-performing ensemble of Transformer models achieved an area under the receiver operating characteristic curve of 69.6% for predicting ASD diagnosis, a sensitivity of 70.9%, a specificity of 56.9%. We determine that our model can be used to identify an enriched pool of children with the greatest likelihood of developing ASD, demonstrating the feasibility of this approach.This study highlights the feasibility of employing machine learning models and routinely collected health data to systematically identify young children at high likelihood of developing ASD. Ensemble transformer models applied to health administrative and birth registry data offer a promising avenue for universal ASD screening. Such early detection enables targeted and formal assessment for timely diagnosis and early access to resources, support, or therapy.

OBJECTIVES:This study aimed to describe the trends in cesarean delivery (CD) rates in Ontario using the modified Robson classification system, and identify the most common indications for CD. METHODS:We conducted a population-based retrospective cross-sectional study using data from the Better Outcomes Registry & Network, a comprehensive maternal-child registry in Ontario. The analysis included all pregnant persons who delivered a live- or stillborn infant weighing ≥500 g at ≥200 weeks gestation between April 1, 2012 and March 31, 2019. RESULTS:A total of 952 567 pregnant persons gave birth in Ontario, Canada, during the study period. Our findings demonstrated a slight increase in the overall CD rate over 7 fiscal years from 2012-2013 to 2018-2019. Robson group 5 (term, singleton, cephalic pregnancy with previous CD), groups 1 and 2 (nulliparous, term, singleton, cephalic pregnancy and no labour, induced labour, or spontaneous labour), and group 6 (nulliparous pregnancy with breech presentation) made the largest contributions to the overall CD rate over the study period. The top 5 primary indications for CD across all years included previous CD, atypical or abnormal fetal surveillance, malposition/malpresentation, non-progressive first stage of labour, and non-progressive second stage of labour. CONCLUSIONS:The results enhance our understanding of the key drivers of CD rates. These findings will help to inform practice improvement, support policy change, and identify areas where future research is needed.

INTRODUCTION:Although numerous studies have documented no association between COVID-19 vaccination during pregnancy and maternal and fetal health outcomes, fewer studies have evaluated fetal health effects after COVID-19 vaccination around the time of conception and early pregnancy, a time when maternal exposures may affect early placentation and the subsequent risk of placenta-mediated adverse pregnancy outcomes. MATERIAL AND METHODS:We used province-wide databases in Ontario to conduct a population-based cohort study including all live and stillbirths ≥20 weeks' gestation with a last menstrual period (LMP) between April 1 and December 31, 2021. We deterministically linked birth registry data to the vaccine registry for all 80 253 eligible pregnancies; 31 209 (38.9%) received ≥1 dose of the COVID-19 vaccine around the time of conception or first trimester. Using Cox regression, we estimated propensity score weighted hazard ratios (aHR) and 95% confidence intervals (CI) for associations between ≥1 dose of mRNA COVID-19 vaccine during the periconceptional/first trimester exposure window (28 days before the LMP to the end of first trimester) and study outcomes: hypertensive disorders (gestational hypertension, preeclampsia, eclampsia), placental abruption, preterm birth (<37 weeks), small-for-gestational-age (SGA) birth (<10th percentile), and stillbirth. RESULTS:COVID-19 vaccination around the time of conception or first trimester was associated with a small increased risk of hypertensive disorders in pregnancy in exposed versus unexposed individuals (7.4% vs. 6.1%; aHR: 1.10, 95% CI: 1.03-1.17), mostly attributed to gestational hypertension (5.0% vs. 4.1%; aHR 1.13, 95% CI: 1.05-1.22). There was no increased risk of preeclampsia (1.8% vs. 1.5%; aHR 1.08, 95% CI: 0.95-1.22), eclampsia (0.1% vs. 0.1%; aHR: 1.12, 95% CI 0.65-1.95), placental abruption (0.8% vs. 1.0%; aHR: 0.77, 95%CI: 0.65-0.91), preterm birth (8.0% vs. 8.9%; aHR: 0.92, 95%CI: 0.87-0.97), SGA birth (8.9% vs. 9.3%; aHR: 1.00, 95%CI: 0.95-1.06), or stillbirth (0.4% vs. 0.6%; aHR: 0.66, 95%CI: 0.52-0.82). CONCLUSIONS:This population-based Canadian study provides additional evidence evaluating COVID-19 vaccine administration around the start of pregnancy. While we identified no association with most placenta-mediated outcomes, we report a slight increase in the rate of gestational hypertension. This could be a true association or attributed to residual confounding. Further research is needed to verify.

OBJECTIVE:To assess the impact of high-dose folic acid supplementation (4.0-5.1 mg), started between 8+0 and 16+6 weeks of gestation and continued until delivery, on social impairments associated with Autism Spectrum Disorders, deficiencies in executive function, and emotional and behavioural problems in children. DESIGN:FACT 4 Child is a follow-up of mothers and their children born during the Folic Acid Clinical Trial (FACT), an international multi-centre double-blinded randomised trial to assess the effect of high-dose folic acid supplementation on preventing preeclampsia in women with increased risk. SETTING:Multi-centre international follow-up study. POPULATION:Mothers and their children enrolled in FACT, among them 664 completed the follow-up. METHODS:Mothers reported on social and executive function and emotional and behavioural problems in their children aged 4-9 years using standardised, validated questionnaires. MAIN OUTCOME:The proportion of children with at least one score > 1.5 SD above expected mean. RESULTS:Among 319 children in the intervention group, 43 (13.5%) had a score in the elevated range, compared with 51/345 (14.8%) in the placebo group (RR = 0.91; 95% CI: 0.63 to 1.33; p = 0.63). CONCLUSION:In children born to women at risk for preeclampsia, rates of neurodevelopmental outcomes were not different between high-dose folic acid and control groups in this study. Our finding suggests that a high dose of folic acid supplementation may not be needed in pregnant women with increased risk. A larger-scale study is needed to determine neurodevelopmental outcomes associated with different dosages and timing of folic acid supplementation during pregnancy.

BACKGROUND:Research on the impact of the COVID-19 pandemic on mothers/childbearing parents has mainly been cross-sectional and focused on psychological symptoms. This study examined the impact on function using ongoing, systematic screening of a representative Ontario sample. METHODS:An interrupted time series analysis of repeated cross-sectional data from a province-wide screening program using the Healthy Babies Healthy Children (HBHC) tool assessed changes associated with the pandemic at the time of postpartum discharge from hospital. Postal codes were used to link to neighborhood-level data. The ability to parent or care for the baby/child and other psychosocial and behavioral outcomes were assessed. RESULTS:The co-primary outcomes of inability to parent or care for the baby/child were infrequently observed in the pre-pandemic (March 9, 2019-March 15, 2020) and initial pandemic periods (March 16, 2020-March 23, 2021) (parent 209/63,006 (0.33%)-177/56,117 (0.32%), care 537/62,955 (0.85%)-324/56,086 (0.58%)). Changes after pandemic onset were not observed for either outcome although a significant (p = 0.02) increase in slope was observed for inability to parent (with questionable clinical significance). For secondary outcomes, worsening was only seen for reported complications during labor/delivery. Significant improvements were observed in the likelihood of being unable to identify a support person to assist with care, need of newcomer support, and concerns about money over time. CONCLUSIONS:There were no substantive changes in concerns about ability to parent or care for children. Adverse impacts of the pandemic may have been mitigated by accommodations for remote work and social safety net policies.

ImportanceUltrasonographic measurement of fetal nuchal translucency is used in prenatal screening for trisomies 21 and 18 and other conditions. A cutoff of 3.5 mm or greater is commonly used to offer follow-up investigations, such as prenatal cell-free DNA (cfDNA) screening or cytogenetic testing. Recent studies showed a possible association with chromosomal anomalies for levels less than 3.5 mm, but extant evidence has limitations.ObjectiveTo evaluate the association between different nuchal translucency measurements and cytogenetic outcomes on a population level.Design, Setting, and ParticipantsThis population-based retrospective cohort study used data from the Better Outcomes Registry & Network, the perinatal registry for Ontario, Canada. All singleton pregnancies with an estimated date of delivery from September 1, 2016, to March 31, 2021, were included. Data were analyzed from March 17 to August 14, 2023.ExposuresNuchal translucency measurements were identified through multiple-marker screening results.Main Outcomes and MeasuresChromosomal anomalies were identified through all Ontario laboratory-generated prenatal and postnatal cytogenetic tests. Cytogenetic testing results, supplemented with information from cfDNA screening and clinical examination at birth, were used to identify pregnancies without chromosomal anomalies. Multivariable modified Poisson regression with robust variance estimation and adjustment for gestational age was used to compare cytogenetic outcomes for pregnancies with varying nuchal translucency measurement categories and a reference group with nuchal translucency less than 2.0 mm.ResultsOf 414 268 pregnancies included in the study (mean [SD] maternal age at estimated delivery date, 31.5 [4.7] years), 359 807 (86.9%) had a nuchal translucency less than 2.0 mm; the prevalence of chromosomal anomalies in this group was 0.5%. An increased risk of chromosomal anomalies was associated with increasing nuchal translucency measurements, with an adjusted risk ratio (ARR) of 20.33 (95% CI, 17.58-23.52) and adjusted risk difference (ARD) of 9.94% (95% CI, 8.49%-11.39%) for pregnancies with measurements of 3.0 to less than 3.5 mm. The ARR was 4.97 (95% CI, 3.45-7.17) and the ARD was 1.40% (95% CI, 0.77%-2.04%) when restricted to chromosomal anomalies beyond the commonly screened aneuploidies (excluding trisomies 21, 18, and 13 and sex chromosome aneuploidies).Conclusions and RelevanceIn this cohort study of 414 268 singleton pregnancies, those with nuchal translucency measurements less than 2.0 mm were at the lowest risk of chromosomal anomalies. Risk increased with increasing measurements, including measurements less than 3.5 mm and anomalies not routinely screened by many prenatal genetic screening programs.

Background: Maternal obesity is associated with stillbirth, but uncertainty persists around the effects of higher obesity classes. We sought to compare the risk of stillbirth associated with maternal obesity alone versus maternal obesity and additional or undiagnosed factors contributing to high-risk pregnancy.Methods: We conducted a retrospective cohort study using the Better Outcomes Registry and Network (BORN) for singleton hospital births in Ontario between 2012 and 2018. We used multivariable Cox proportional hazard regression and logistic regression to evaluate the relationship between prepregnancy maternal body mass index (BMI) class and stillbirth (reference was normal BMI). We treated maternal characteristics and obstetrical complications as independent covariates. We performed mediator analyses to measure the direct and indirect effects of BMI on stillbirth through major common-pathway complications. We used fully adjusted and partially adjusted models, representing the impact of maternal obesity alone and maternal obesity with other risk factors on stillbirth, respectively.Results: We analyzed data on 681 178 births between 2012 and 2018, of which 1956 were stillbirths. Class I obesity was associated with an increased incidence of stillbirth (adjusted hazard ratio [HR] 1.55, 95% confidence interval [CI] 1.35-1.78). This association was stronger for class III obesity (adjusted HR 1.80, 95% CI 1.44-2.24), and strongest for class II obesity (adjusted HR 2.17, 95% CI 1.83-2.57). Plotting point estimates for odds ratios, stratified by gestational age, showed a marked increase in the relative odds for stillbirth beyond 37 weeks' gestation for those with obesity with and without other risk factors, compared with those with normal BMI. The impact of potential mediators was minimal.Interpretation: Maternal obesity alone and obesity with other risk factors are associated with an increased risk of stillbirth. This risk increases with gestational age, especially at term.

BackgroundThis study was designed to investigate patterns and risk factors for substance use among obstetrical patients who gave birth during the early period of the pandemic, and their partners.MethodsCross-sectional survey of obstetrical patients between March 17th and June 16th, 2020, at The Ottawa Hospital, Ottawa, Canada. Substance use was a composite measure of any alcohol, tobacco, or cannabis use since COVID-19 began. Four outcomes included: any participant substance use or increase in substance use, any partner substance use or increase in substance use. Adjusted risk ratios (ARR) and 95% confidence intervals (CI) are presented.FindingsOf 216 participants, 113 (52.3%) and 15 (6.9%) obstetrical patients reported substance use and increased use, respectively. Those born in Canada (ARR: 2.03; 95% CI: 1.27-3.23) and those with lower household income (ARR: 1.38; 95% CI: 1.04-1.85) had higher risk of substance use. Those with postpartum depression (ARR: 5.78; 95%CI: 2.22-15.05) had the highest risk of increased substance use. Families affected by school/daycare closure reported a higher risk of increased partner substance use (ARR: 2.46; 95% CI:1.38-4.39).ConclusionThis study found that risk factors for substance use included demographics (i.e., being born in Canada, income), mental health (postpartum depression), and school/childcare closures.