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Objectives The COVID-19 pandemic and associated lockdowns disrupted health care worldwide. High-income countries observed a decrease in preterm births during lockdowns, but maternal pregnancy–related outcomes were also likely affected. This study investigates the effect of the first COVID-19 lockdown (March–June 2020) on provision of maternity care and maternal pregnancy–related outcomes in the Netherlands. Study design National quasi-experimental study. Methods Multiple linked national registries were used, and all births from a gestational age of 24+0 weeks in 2010–2020 were included. In births starting in midwife-led primary care, we assessed the effect of lockdown on provision of care. In the general pregnant population, the impact on characteristics of labour and maternal morbidity was assessed. A difference-in-regression-discontinuity design was used to derive causal estimates for the year 2020. Results A total of 1,039,728 births were included. During the lockdown, births to women who started labour in midwife-led primary care (49%) more often ended at home (27% pre-lockdown, +10% [95% confidence interval: +7%, +13%]). A small decrease was seen in referrals towards obstetrician-led care during labour (46%, −3% [−5%,−0%]). In the overall group, no significant change was seen in induction of labour (27%, +1% [−1%, +3%]). We found no significant changes in the incidence of emergency caesarean section (9%, −1% [−2%, +0%]), obstetric anal sphincter injury (2%, +0% [−0%, +1%]), episiotomy (21%, −0% [−2%, +1%]), or post-partum haemorrhage: >1000 ml (6%, −0% [−1%, +1%]). Conclusions During the first COVID-19 lockdown in the Netherlands, a substantial increase in homebirths was seen. There was no evidence for changed available maternal outcomes, suggesting that a maternity care system with a strong midwife-led primary care system may flexibly and safely adapt to external disruptions.

OBJECTIVES:The COVID-19 pandemic and associated lockdowns disrupted health care worldwide. High-income countries observed a decrease in preterm births during lockdowns, but maternal pregnancy-related outcomes were also likely affected. This study investigates the effect of the first COVID-19 lockdown (March-June 2020) on provision of maternity care and maternal pregnancy-related outcomes in the Netherlands.STUDY DESIGN: National quasi-experimental study.METHODS: Multiple linked national registries were used, and all births from a gestational age of 24+0 weeks in 2010-2020 were included. In births starting in midwife-led primary care, we assessed the effect of lockdown on provision of care. In the general pregnant population, the impact on characteristics of labour and maternal morbidity was assessed. A difference-in-regression-discontinuity design was used to derive causal estimates for the year 2020.RESULTS: A total of 1,039,728 births were included. During the lockdown, births to women who started labour in midwife-led primary care (49%) more often ended at home (27% pre-lockdown, +10% [95% confidence interval: +7%, +13%]). A small decrease was seen in referrals towards obstetrician-led care during labour (46%, -3% [-5%,-0%]). In the overall group, no significant change was seen in induction of labour (27%, +1% [-1%, +3%]). We found no significant changes in the incidence of emergency caesarean section (9%, -1% [-2%, +0%]), obstetric anal sphincter injury (2%, +0% [-0%, +1%]), episiotomy (21%, -0% [-2%, +1%]), or post-partum haemorrhage: >1000 ml (6%, -0% [-1%, +1%]).CONCLUSIONS: During the first COVID-19 lockdown in the Netherlands, a substantial increase in homebirths was seen. There was no evidence for changed available maternal outcomes, suggesting that a maternity care system with a strong midwife-led primary care system may flexibly and safely adapt to ext

This scoping review focuses on the essential role of models for causal inference in shaping actionable artificial intelligence (AI) designed to aid clinicians in decision-making. The objective was to identify and evaluate the reporting quality of studies introducing models for causal inference in intensive care units (ICUs), and to provide recommendations to improve the future landscape of research practices in this domain. To achieve this, we searched various databases including Embase, MEDLINE ALL, Web of Science Core Collection, Google Scholar, medRxiv, bioRxiv, arXiv, and the ACM Digital Library. Studies involving models for causal inference addressing time-varying treatments in the adult ICU were reviewed. Data extraction encompassed the study settings and methodologies applied. Furthermore, we assessed reporting quality of target trial components (i.e., eligibility criteria, treatment strategies, follow-up period, outcome, and analysis plan) and main causal assumptions (i.e., conditional exchangeability, positivity, and consistency). Among the 2184 titles screened, 79 studies met the inclusion criteria. The methodologies used were G methods (61%) and reinforcement learning methods (39%). Studies considered both static (51%) and dynamic treatment regimes (49%). Only 30 (38%) of the studies reported all five target trial components, and only seven (9%) studies mentioned all three causal assumptions. To achieve actionable AI in the ICU, we advocate careful consideration of the causal question of interest, describing this research question as a target trial emulation, usage of appropriate causal inference methods, and acknowledgement (and examination of potential violations of) the causal assumptions., Pattern Recognition and Bioinformatics

This scoping review focuses on the essential role of models for causal inference in shaping actionable artificial intelligence (AI) designed to aid clinicians in decision-making. The objective was to identify and evaluate the reporting quality of studies introducing models for causal inference in intensive care units (ICUs), and to provide recommendations to improve the future landscape of research practices in this domain. To achieve this, we searched various databases including Embase, MEDLINE ALL, Web of Science Core Collection, Google Scholar, medRxiv, bioRxiv, arXiv, and the ACM Digital Library. Studies involving models for causal inference addressing time-varying treatments in the adult ICU were reviewed. Data extraction encompassed the study settings and methodologies applied. Furthermore, we assessed reporting quality of target trial components (i.e., eligibility criteria, treatment strategies, follow-up period, outcome, and analysis plan) and main causal assumptions (i.e., conditional exchangeability, positivity, and consistency). Among the 2184 titles screened, 79 studies met the inclusion criteria. The methodologies used were G methods (61%) and reinforcement learning methods (39%). Studies considered both static (51%) and dynamic treatment regimes (49%). Only 30 (38%) of the studies reported all five target trial components, and only seven (9%) studies mentioned all three causal assumptions. To achieve actionable AI in the ICU, we advocate careful consideration of the causal question of interest, describing this research question as a target trial emulation, usage of appropriate causal inference methods, and acknowledgement (and examination of potential violations of) the causal assumptions.

Artificial intelligence (AI) research in the intensive care unit (ICU) mainly focuses on developing models (from linear regression to deep learning) to predict out- comes, such as mortality or sepsis [1, 2]. However, there is another important aspect of AI that is typically not framed as AI (although it may be more worthy of the name), which is the prediction of patient outcomes or events that would result from different actions, known as causal inference [3, 4]. This aspect of AI is crucial for decision-making in the ICU. To emphasize the impor- tance of causal inference, we propose to refer to any data- driven model used for causal inference tasks as ‘action- able AI’, as opposed to ‘predictive AI’, and discuss how these models could provide meaningful decision support in the ICU., Pattern Recognition and Bioinformatics, Biomechanical Engineering

Background and aims: Studies have indicated that maternal prenatal substance use may be associated with offspring attention deficit hyperactivity disorder (ADHD) via intrauterine effects. We measured associations between prenatal smoking, alcohol and caffeine consumption with childhood ADHD symptoms accounting for shared familial factors. Design: First, we used a negative control design comparing maternal and paternal substance use. Three models were used for negative control analyses: unadjusted (without confounders), adjusted (including confounders) and mutually adjusted (including confounders and partner's substance use). The results were meta-analysed across the cohorts. Secondly, we used polygenic risk scores (PRS) as proxies for exposures. Maternal PRS for smoking, alcohol and coffee consumption were regressed against ADHD symptoms. We triangulated the results across the two approaches to infer causality. Setting: We used data from three longitudinal pregnancy cohorts: Avon Longitudinal Study of Parents and Children (ALSPAC) in the United Kingdom, Generation R study (GenR) in the Netherlands and Norwegian Mother, Father and Child Cohort study (MoBa) in Norway. Participants: Phenotype data available for children were: N ALSPAC = 5455–7751; N GENR = 1537–3119; N MOBA = 28 053–42 206. Genotype data available for mothers was: N ALSPAC = 7074; N MOBA = 14 583. Measurements: A measure of offspring ADHD symptoms at age 7–8 years was derived by dichotomizing scores from questionnaires and parental self-reported prenatal substance use was measured at the second pregnancy trimester. Findings: The pooled estimate for maternal prenatal substance use showed an association with total ADHD symptoms [odds ratio (OR) SMOKING = 1.11, 95% confidence interval (CI) = 1.00–1.23; OR ALCOHOL = 1.27, 95% CI = 1.08–1.49; OR CAFFEINE = 1.05, 95% CI = 1.00–1.11], while not for fathers (OR SMOK

The COVID-19 pandemic has introduced a myriad of challenges to the social life and care of people with Parkinson’s disease (PD), which could potentially worsen mental health problems. We used baseline data of the PRIME-NL study (N = 844) to examine whether the association between COVID-19 stressors and mental health is disproportionately large in specific subgroups of people with PD and to explore effects of hypothetical reductions in COVID-19 stressors on mental health and quality of life. The mean (SD) age of the study population was 70.3 (7.8) years and 321 (38.0%) were women. The linear regression effect estimate of the association of COVID-19 stressors with mental health was most pronounced in women, highly educated people, people with advanced PD and people prone to distancing or seeking social support. Smaller effect estimates were found in people scoring high on confrontive coping or planful problem solving. The parametric G-formula method was used to calculate the effects of hypothetical interventions on COVID-19 stressors. An intervention reducing stressors with 50% in people with above median MDS-UPDRS-II decreased the Beck Depression Inventory in this group from 14.7 to 10.6, the State-Trait Anxiety Inventory from 81.6 to 73.1 and the Parkinson’s Disease Quality of Life Questionnaire from 35.0 to 24.3. Insights from this cross-sectional study help to inform tailored care interventions to subgroups of people with PD most vulnerable to the impact of COVID-19 on mental health and quality of life.

BACKGROUND: Investigators often support the validity of Mendelian randomization (MR) studies, an instrumental variable approach proposing genetic variants as instruments, via. subject matter knowledge. However, the instrumental variable model implies certain inequalities, offering an empirical method of falsifying (but not verifying) the underlying assumptions. Although these inequalities are said to detect only extreme assumption