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In causal inference, parametric models are usually employed to address causal questions estimating the effect of interest. However, parametric models rely on the correct model specification assumption that, if not met, leads to biased effect estimates. Correct model specification is challenging, especially in high-dimensional settings. Incorporating Machine Learning (ML) into causal analyses may reduce the bias arising from model misspecification, since ML methods do not require the specification of a functional form of the relationship between variables. However, when ML predictions are directly plugged in a predefined formula of the effect of interest, there is the risk of introducing a “plug-in bias” in the effect measure. To overcome this problem and to achieve useful asymptotic properties, new estimators that combine the predictive potential of ML and the ability of traditional statistical methods to make inference about population parameters have been proposed. For epidemiologists interested in taking advantage of ML for causal inference investigations, we provide an overview of three estimators that represent the current state-of-art, namely Targeted Maximum Likelihood Estimation (TMLE), Augmented Inverse Probability Weighting (AIPW) and Double/Debiased Machine Learning (DML).

Background The delivery of income support interventions at an early age has positive short-term health impacts on children. However, less is known about whether those effects are sustained later in life. We addressed this question by systematically reviewing the literature on long-term (i.e., assessed after 5 + years) health impacts of income support interventions delivered in preschool age (in utero to 5 years). Methods We focused only on experimental or quasi-experimental studies, without country restrictions. We retrieved studies from subject-specific databases for general, mental health, and economics, and from citation searching. All the retrieved literature was double-screened at the title, abstract, and full-text stages. We performed a data extraction of the relevant information from the eligible studies and synthesised them via a narrative synthesis approach. Results Nine studies, eight quasi-experimental and one randomised control trial, were deemed eligible, all conducted in high or middle-income countries. These studies assessed several health outcomes, including overall mortality, cause-specific hospitalisation, mental health, and anthropometrics. Consistent long-term health improvements were observed from early income support interventions exposure across all the health dimensions assessed. Conclusions Despite knowledge gaps, especially in low-income countries, our results suggest that implementing income support interventions during preschool age can have a prolonged positive effect across several health dimensions. Implementing such policies and strategies would prove beneficial for health, alongside their main goal of reducing childhood poverty and health inequalities.

Early life is an important window of opportunity to improve health across the full lifecycle. An accumulating body of evidence suggests that exposure to adverse stressors during early life leads to developmental adaptations, which subsequently affect disease risk in later life. Also, geographical, socio-economic, and ethnic differences are related to health inequalities from early life onwards. To address these important public health challenges, many European pregnancy and childhood cohorts have been established over the last 30 years. The enormous wealth of data of these cohorts has led to important new biological insights and important impact for health from early life onwards. The impact of these cohorts and their data could be further increased by combining data from different cohorts. Combining data will lead to the possibility of identifying smaller effect estimates, and the opportunity to better identify risk groups and risk factors leading to disease across the lifecycle across countries. Also, it enables research on better causal understanding and modelling of life course health trajectories. The EU Child Cohort Network, established by the Horizon2020-funded LifeCycle Project, brings together nineteen pregnancy and childhood cohorts, together including more than 250,000 children and their parents. A large set of variables has been harmonised and standardized across these cohorts. The harmonized data are kept within each institution and can be accessed by external researchers through a shared federated data analysis platform using the R-based platform DataSHIELD, which takes relevant national and international data regulations into account. The EU Child Cohort Network has an open character. All protocols for data harmonization and setting up the data analysis platform are available online. The EU Child Cohort Network creates great opportunities for researchers to use data from different cohorts, during and beyond the LifeCycle Project duration. It also provides a novel model for collaborative research in large research infrastructures with individual-level data. The LifeCycle Project will translate results from research using the EU Child Cohort Network into recommendations for targeted prevention strategies to improve health trajectories for current and future generations by optimizing their earliest phases of life.

Maternal occupational exposures during early pregnancy can be detrimental to foetus health and have short- and long-term health effects on the child. This study examined their association with adverse birth outcomes. The study included 3938 nulliparous women from the Italian NINFEA mother-child cohort. Their occupational exposures during the first trimester of pregnancy were assessed through prospectively collected questionnaire information and job-exposure matrices. Associations between maternal exposures and birthweight, preterm birth, and delivery by caesarean section were analysed by multivariable linear and logistic regression models. An exploratory factor analysis was carried out to explore co-exposure profiles in association with birth outcomes. Women exposed to passive smoking at work and those who reduced their working hours during pregnancy were found to have an increased likelihood of all analysed birth outcomes. Children of mothers performing a demanding work were less likely to be born preterm [OR 0.72 (95% CI 0.54 to 0.95)] and more likely to have a higher birthweight [β = 40.4 g (95% CI 7.5 to 73.4)]. Maternal exposures to heat and dust were associated with a lower birthweight [β = -160.1 g (95% CI -299.6 to -20.7)] and increased odds of caesarean section [OR 6.99 (95% CI 2.36 to 25.47)], respectively. This study provides some evidence of the selection of healthy population into the workforce and of association between work-related passive smoking, heat and dust and adverse birth outcomes.

Background In high income countries one in five children still lives in poverty, which is known to adversely shape the life course health trajectory of these children. However, much less is understood on whether social and fiscal policies have the capacity to reverse this damage, which intervention is likely to be most effective and when these interventions should be delivered to maximise their impact. This systematic review attempts to address these questions by looking at the impact of income-support interventions, delivered during the first 1,000 days of life, on cardiovascular, metabolic, respiratory and mental health outcomes. Methods The review was restricted to experimental or quasi experimental studies conducted in high income countries. Studies were retrieved from multidisciplinary databases as well as health, economic, social sciences-specific literature browsers. All papers retrieved through the search strategy were double screened at title, abstract and full text stage. Relevant data of the selected studies were extracted and collected in tables, then summarised via narrative synthesis approach. Robustness of findings was assessed by tabulating impact by health outcome, type of intervention and study design. Results Overall, 16 relevant papers were identified, including 15 quasi-experimental studies and one randomized control trial (RCT). Income-support interventions included were unconditional/conditional cash transfers, income tax credit and minimum wage salary policies. Most studies were conducted in United States and Canada. Overall, the evidence suggested limited effect on mental health indicators but a positive, albeit small, effect of most policies on birth weight outcomes. Despite this, according to few studies that tried to extrapolate the results into public health terms, the potential number of negative outcomes averted might be consistent. Conclusions Income-support interventions can positively affect some of the health outcomes of interest in this review, including birth weight and mental health. Given the large number of people targeted by these programs, one could infer that – despite small – the observed effect may be still relevant at population level. Nonetheless, the limited generalisability of the evidence gathered hampers firm conclusions. For the future, the breadth and scope of this literature need to be broadened to fully exploit the potential of these interventions and understand how their public health impact can be maximised.

Prenatal exposure to air pollution has been associated with childhood respiratory disease and other adverse outcomes. Epigenetics is a suggested link between exposures and health outcomes. We aimed to investigate associations between prenatal exposure to particulate matter (PM) with diameter [Formula: see text] ([Formula: see text]) or [Formula: see text] ([Formula: see text]) and DNA methylation in newborns and children. We meta-analyzed associations between exposure to [Formula: see text] ([Formula: see text]) and [Formula: see text] ([Formula: see text]) at maternal home addresses during pregnancy and newborn DNA methylation assessed by Illumina Infinium HumanMethylation450K BeadChip in nine European and American studies, with replication in 688 independent newborns and look-up analyses in 2,118 older children. We used two approaches, one focusing on single cytosine-phosphate-guanine (CpG) sites and another on differentially methylated regions (DMRs). We also related PM exposures to blood mRNA expression. Six CpGs were significantly associated [false discovery rate (FDR) [Formula: see text]] with prenatal [Formula: see text] and 14 with [Formula: see text] exposure. Two of the [Formula: see text] CpGs mapped to FAM13A (cg00905156) and NOTCH4 (cg06849931) previously associated with lung function and asthma. Although these associations did not replicate in the smaller newborn sample, both CpGs were significant ([Formula: see text]) in 7- to 9-y-olds. For cg06849931, however, the direction of the association was inconsistent. Concurrent [Formula: see text] exposure was associated with a significantly higher NOTCH4 expression at age 16 y. We also identified several DMRs associated with either prenatal [Formula: see text] and or [Formula: see text] exposure, of which two [Formula: see text] DMRs, including H19 and MARCH11, replicated in newborns. Several differentially methylated CpGs and DMRs associated with prenatal PM exposure were identified in newborns, with annotation to genes previously implicated in lung-related outcomes. https://doi.org/10.1289/EHP4522.

Background Extracorporeal membrane oxygenation (ECMO) has become an established rescue therapy for severe acute respiratory distress syndrome (ARDS) in several etiologies including influenza A H1N1 pneumonia. The benefit of receiving ECMO in coronavirus disease 2019 (COVID-19) is still uncertain. The aim of this analysis was to compare the outcome of patients who received veno-venous ECMO for COVID-19 and Influenza A H1N1 associated ARDS. Methods This was a multicenter retrospective cohort study including adults with ARDS, receiving ECMO for COVID-19 and influenza A H1N1 pneumonia between 2009 and 2021 in seven Italian ICU. The primary outcome was any-cause mortality at 60 days after ECMO initiation. We used a multivariable Cox model to estimate the difference in mortality accounting for patients’ characteristics and treatment factors before ECMO was started. Secondary outcomes were mortality at 90 days, ICU and hospital length of stay and ECMO associated complications. Results Data from 308 patients with COVID-19 ( N  = 146) and H1N1 ( N  = 162) associated ARDS who had received ECMO support were included. The estimated cumulative mortality at 60 days after initiating ECMO was higher in COVID-19 (46%) than H1N1 (27%) patients (hazard ratio 1.76, 95% CI 1.17–2.46). When adjusting for confounders, specifically age and hospital length of stay before ECMO support, the hazard ratio decreased to 1.39, 95% CI 0.78–2.47. ICU and hospital length of stay, duration of ECMO and invasive mechanical ventilation and ECMO-associated hemorrhagic complications were higher in COVID-19 than H1N1 patients. Conclusion In patients with ARDS who received ECMO, the observed unadjusted 60-day mortality was higher in cases of COVID-19 than H1N1 pneumonia. This difference in mortality was not significant after multivariable adjustment; older age and longer hospital length of stay before ECMO emerged as important covariates that could explain the observed difference. Trial registration number : NCT05080933 , retrospectively registered.

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There is debate as to whether cohort studies are valid when they are based on a source population that is non-representative of a given general population. This baseline selection may introduce collider bias if the exposure of interest and some other outcome risk factors affect the probability of being in the source population, thus altering the associations between the exposure and those risk factors. We argue that this mechanism is not specific to ‘selected cohorts’ and also occurs in ‘representative cohorts’ due to the selection processes that occur in any population. These selection processes are for example linked to the life status, immigration and emigration, which, in turn, may be affected by environmental and social determinants, lifestyles and genetics. We provide real-world examples of this phenomenon using data on the population of the Piedmont region, Italy. In addition to well-recognised mechanisms, such as shared common causes, the associations between the exposure of interest and the risk factors for the outcome of interest in any source population are potentially shaped by collider bias due to the underlying selection processes. We conclude that, when conducting a cohort study, different source populations, whether ‘selected’ or ‘representative’, may lead to different exposure–outcome risk factor associations, and thus different degrees of lack of exchangeability, but that one approach is not inherently more or less biased than the other. The key issue is whether the relevant risk factors can be identified and controlled.

Background: The fetal period is critical for neurodevelopment, with maternal diet emerging as a key environmental factor influencing long-term child health. This study investigated the associations between maternal dietary patterns during pregnancy and neurocognitive and behavioral outcomes in 4-year-old children, with a particular focus on sex-related differences. Methods: We used data from the Piccolipiù Italian birth cohort, including 2006 mother/child pairs. Maternal dietary intake during pregnancy was assessed via a questionnaire and categorized into distinct patterns using Principal Component Analysis (PCA). Child neurodevelopment was evaluated at age 4 using the Wechsler Preschool and Primary Scale of Intelligence (WPPSI) and the Child Behavior Checklist (CBCL 1.5–5). Linear and logistic regression models were employed, adjusting for potential confounders and stratifying by child sex. Results: Two major maternal dietary patterns were identified: “Processed and high-fat foods” and “Fresh foods and fish”. Higher maternal adherence to the “Processed and high-fat foods” pattern was associated with increased externalizing behaviors in offspring (β = 0.88; 95%CI 0.28–1.49; p = 0.004). In males, this pattern was associated with an increased clinical risk of Attention Deficit Hyperactivity Disorder (ADHD) (OR (Odds Ratio) = 1.13; 95%CI: 1.02–1.26; p = 0.021). Conclusions: Our findings indicate that maternal consumption of a diet rich in processed and high-fat foods during pregnancy is associated with increased behavioral problems in children, with sex-specific vulnerabilities: slightly higher externalizing behaviors in girls and an increased risk of ADHD in boys. These results underscore the importance of promoting healthy maternal dietary patterns during pregnancy as a targeted early prevention strategy for supporting child neurodevelopment.