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Background: Research consistently finds poorer health and educational outcomes for children who have experienced out-of-home care relative to the general population. Few studies have explored differences between those in care and those in receipt of intervention from social services but not in care. Children receiving social services interventions often experience Adverse Childhood Experiences (ACEs), and deprivation, which are known to negatively impact outcomes. We aimed to estimate the association of different social services interventions with educational outcomes and hospital admissions, while adjusting for ACEs and deprivation. Methods: We linked retrospective, routinely collected administrative records from health, education, and social care to create a cohort via the Secure Anonymised Information Linkage (SAIL) databank in Wales, UK. We analysed data for children and household members (N = 30,439) across four different groups: [1] no social care intervention; [2] children in need but not in care (CIN); [3] children on the Child Protection Register but not in care (CPR); [4] children in care - i.e. removed from the family home and looked after by the local authority (CLA). Our primary outcome was education outcomes at age 16 years. Secondary outcomes were all cause emergency hospital admissions, and emergency admissions for external causes/injuries. Results: Children in receipt of social services intervention were more likely to not attain the expected level upon leaving statutory education at age 16 after adjusting for ACEs and other characteristics (for children who had been in out-of-home care (conditional OR: 1·76, (95%CI) 1·25 − 2·48), in need (2·51, 2·00–3·15) and those at risk (i.e., on the child protection register) (4·04, 2·44 − 6·68). For all-cause emergency admissions, all social care groups were at greater risk compared to children in the general population (children in care (conditional HR: 1·31, 1·01–1·68), children in need (1·62, 1·38 − 1·90), and children at risk (1·51, 1·11 − 2·04). Conclusions: All groups receiving social service intervention experience poorer educational and health outcomes than peers in the general population. Children who remain with their home parents or caregivers but are identified as 'in need' or 'at risk' by social care practitioners require further research. Integrated support is needed from multiple sectors, including health, educational and social care. [ABSTRACT FROM AUTHOR]

Regression by composition is a new and flexible toolkit for building and understanding statistical models. Focusing here on regression models for a binary outcome conditional on a binary treatment and other covariates, we motivate the need for regression by composition. We do this first by exhibiting—using L'Abbé plots—the families of relationships between untreated and treated conditional outcome risks that emerge from generalized linear models for many different link functions. These are compared with the relationships (between untreated and treated risks) that arise from mechanistic sufficient component cause models, which are first principles causal models for binary outcomes. By considering mechanistic models that allow for non-monotone causal effects and by allowing sufficient causes to be associated, we expand upon similar discussions in the recent literature. We discuss conditions under which commonly used statistical models for binary data, such as logistic regression, arise from mechanistic models where the sufficient causes are associated in a particular way, as well as other situations in which the statistical models arising do not correspond to a generalized linear model but can be naturally expressed as a regression by composition model. [ABSTRACT FROM AUTHOR]

Controlling for too many potential confounders can lead to or aggravate problems of data sparsity or multicollinearity, particularly when the number of covariates is large in relation to the study size. As a result, methods to reduce the number of modelled covariates are often deployed. We review several traditional modelling strategies, including stepwise regression and the 'change-in-estimate' (CIE) approach to deciding which potential confounders to include in an outcome-regression model for estimating effects of a targeted exposure. We discuss their shortcomings, and then provide some basic alternatives and refinements that do not require special macros or programming. Throughout, we assume the main goal is to derive the most accurate effect estimates obtainable from the data and commercial software. Allowing that most users must stay within standard software packages, this goal can be roughly approximated using basic methods to assess, and thereby minimize, mean squared error (MSE).

Background: Children receive care and support from social services due to the risk of harm or impeded development or because of disability. This study aimed to identify typologies of adversity experienced by children receiving care and support from social services and to explore how typologies differ by sociodemographic characteristics. Methods: This is a cross‐sectional study of 'Children Receiving Care and Support' (N = 12 792) during 2017/2018 in Wales, UK. We sought to (1) examine the prevalence of household adversities experienced by children in receipt of care and support from social services; (2) identify typologies of household adversities; and (3) explore how typologies of household adversities differ by family characteristics (demographics, measures of social disadvantage, perinatal and care factors). Results: We found evidence for multiple risk factor constellations. The four‐class solution suggested four distinct classes of adversities: child disability (50.0%), low adversities (20.3%), family poor health (6.7%) and multiple risks (23.0%). Children in the 'multiple risk' class were significantly more likely to be younger, more deprived and 'looked after' by the local authority compared with those in the 'low adversities' class. Conclusions: Given the presence of different constellations of household adversities, policies and interventions that address multiple risk factors simultaneously may be more effective and have longer‐lasting benefits. [ABSTRACT FROM AUTHOR]

Background Chronic kidney disease (CKD) is a growing contributor to the global burden of noncommunicable diseases. Early diagnosis and treatment can reduce the severity of kidney damage and the need for dialysis or transplantation. It is not known whether mild-to-moderate renal pelvis dilatation (RPD) identified at 18-20 weeks gestation is an early indicator of renal pathology. The aim of this follow-up to the Welsh Study of Mothers and Babies was to assess the risk of hospital admission in children with mild-to-moderate antenatal RPD compared with children without this finding. We also examined how the natural history of the RPD (whether the dilatation persists in later pregnancy or postpartum) or its characteristics (unilateral versus bilateral) changed the risk of hospital admission. Methods/Findings This population-based cohort study included singleton babies born in Wales between January 1, 2009, and December 31, 2011 (n = 22,045). We linked ultrasound scan data to routinely available data on hospital admissions from the Patient Episode Database for Wales (PEDW). The outcome was a hospital admission for urinary tract causes (defined by an expert study steering group) in the first three years of life. We used Cox regression to model time to first hospital admission, according to whether there was evidence of RPD at the fetal anomaly scan (FAS) and/or evidence of dilatation in later investigations, adjusting for other predictors of admission. We used multiple imputation with chained equations to impute values for missing data. We included 21,239 children in the analysis. The risk of at least one hospital admission was seven times greater in those with RPD (n = 138) compared with those without (n = 21,101, conditional hazard ratio [cHR] 7.23, 95% confidence interval [CI] 4.31-12.15, p < 0.001). The risk of hospital admission was higher in children with RPD at the FAS and later dilatation (cHR 25.13, 95% CI 13.26-47.64, p < 0.001) and in children without RPD at the FAS who had later dilatation (cHR 62.06, 95% CI 41.10-93.71, p < 0.001) than in children without RPD (n = 21,057). Among children with RPD at the FAS but no dilatation in later pregnancy or postpartum, we did not find an association with hospital admissions (cHR 2.16, 95% CI 0.69-6.75, p = 0.185), except when the initial dilatation was bilateral (cHR 4.77, 95% CI 1.17-19.47, p = 0.029). Limitations of the study include small numbers in subgroups (meaning that these results should be interpreted with caution), that less severe outcomes (such as urinary tract infections [UTIs] managed in the community or in outpatients) could not be included in our analysis, and that obtaining records of radiological investigations later in pregnancy and postpartum was challenging. Our conclusions were consistent after conducting sensitivity analyses to account for some of these limitations. Conclusions In this large population-based study, children with RPD at the FAS had higher rates of hospital admissions when there was persistent dilatation in later pregnancy or postpartum. Our results can be used to improve counselling of parents and develop care pathways for antenatal screening programmes, including protocols for reporting and further investigation of RPD., Author(s): Lisa Hurt 1, Melissa Wright 2, Joanne Demmler 3, Judith VanDerVoort 4, Susan Morris 4, Fiona Brook 5, David Tucker 6, Maria Chapman 7, Nick A. Francis 1, Rhian [...]

The ICH E9 addendum introduces the term intercurrent event to refer to events that happen after treatment initiation and that can either preclude observation of the outcome of interest or affect its interpretation. It proposes five strategies for handling intercurrent events to form an estimand but does not suggest statistical methods for estimation. In this article we focus on the hypothetical strategy, where the treatment effect is defined under the hypothetical scenario in which the intercurrent event is prevented. For its estimation, we consider causal inference and missing data methods. We establish that certain "causal inference estimators" are identical to certain "missing data estimators." These links may help those familiar with one set of methods but not the other. Moreover, using potential outcome notation allows us to state more clearly the assumptions on which missing data methods rely to estimate hypothetical estimands. This helps to indicate whether estimating a hypothetical estimand is reasonable, and what data should be used in the analysis. We show that hypothetical estimands can be estimated by exploiting data after intercurrent event occurrence, which is typically not used. for this article are available online. [ABSTRACT FROM AUTHOR]