Survival, by birth weight and gestational age, in individuals with congenital heart disease: a population-based study

BACKGROUND: Congenital heart disease (CHD) survival estimates are important to understand prognosis and evaluate health and social care needs. Few studies have reported CHD survival estimates according to maternal and fetal characteristics. This study aimed to identify predictors of CHD survival and report conditional survival estimates. METHODS AND RESULTS: Cases of CHD (n=5070) born during 1985–2003 and notified to the Northern Congenital Abnormality Survey (NorCAS) were matched to national mortality information in 2008. Royston–Parmar regression was performed to identify predictors of survival. Five‐year survival estimates conditional on gestational age at delivery, birth weight, and year of birth were produced for isolated CHD (ie, CHD without extracardiac anomalies). Year of birth, gestational age, birth weight, and extracardiac anomalies were independently associated with mortality (all P≤0.001). Five‐year survival for children born at term (37–41 weeks) in 2003 with average birth weight (within 1 SD of the mean) was 96.3% (95% CI, 95.6–97.0). Survival was most optimistic for high‐birth‐weight children (>1 SD from the mean) born post‐term (≥42 weeks; 97.9%; 95% CI, 96.8–99.1%) and least optimistic for very preterm (<32 weeks) low‐birth‐weight (<1 SD from mean) children (78.8%; 95% CI, 72.8–99.1). CONCLUSIONS: Five‐year CHD survival is highly influenced by gestational age and birth weight. For prenatal counseling, conditional survival estimates provide best‐ and worst‐case scenarios, depending on final gestational age and birth weight. For postnatal diagnoses, they can provide parents with more‐accurate predictions based on their baby's birth weight and gestational age

Risk and Recurrence of Serious Adverse Outcomes in the First and Second Pregnancies of Women With Preexisting Diabetes

OBJECTIVE: Women with preexisting (type 1 or type 2) diabetes experience an increased risk of serious adverse pregnancy outcomes. It is not known, however, how these risks change between the first and second pregnancy andwhether there is an increased risk of recurrence. This study describes the absolute risks and recurrence of serious adverse pregnancy outcomes in 220 women with preexisting diabetes. RESEARCH DESIGN AND METHODS: A total of 440 pregnancies occurring in 220 women with preexisting diabetes who delivered successive singleton pregnancies in the North of England during 1996-2008 were identified fromtheNorthern Diabetes in Pregnancy Survey (NorDIP). Predictors of serious adverse outcome were estimated by competing-risks regression. RESULTS: Sixty-seven first pregnancies (30.5%) ended in serious adverse outcome, including 14 (6.4%) with congenital anomalies and 53 (24.1%) additional fetal or infant deaths. Thirty-seven second pregnancies (16.8%) ended in serious adverse outcomedhalf the rate among first pregnancies (P = 0.0004)dincluding 21 (9.5%) with congenital anomalies and 16 (7.3%) additional fetal or infant deaths. Serious adverse outcomes in the second pregnancy occurred twice as frequently in women who experienced a previous adverse outcome than in those who did not (26.9% vs. 12.4%, P = 0.004), but previous adverse outcome was not associated with preparation for the following pregnancy. CONCLUSIONS: Serious adverse outcomes are less common in the second pregnancies of women with preexisting diabetes, although the risk is comparable in those whose first pregnancy ends in adverse outcome. Reducing the risk of recurrence may require more support in the immediate period after an adverse pregnancy outcome

Pre-existing diabetes, maternal glycated haemoglobin, and the risks of fetal and infant death: A population-based study

Aims/hypothesis: Pre-existing diabetes is associated with an increased risk of stillbirth, but few studies have excluded the effect of congenital anomalies. This study used data from a long-standing population-based survey of women with pre-existing diabetes to investigate the risks of fetal and infant death and quantify the contribution of glycaemic control. Methods: All normally formed singleton offspring of women with pre-existing diabetes (1,206 with type 1 diabetes and 342 with type 2 diabetes) in the North of England during 1996-2008 were identified from the Northern Diabetes in Pregnancy Survey. RRs of fetal death (≥20 weeks of gestation) and infant death were estimated by comparison with population data from the Northern Perinatal Morbidity and Mortality Survey. Predictors of fetal and infant death in women with pre-existing diabetes were examined by logistic regression. Results: The prevalence of fetal death in women with diabetes was over four times greater than in those without (RR 4.56 [95% CI 3.42, 6.07], p < 0.0001), and for infant death it was nearly doubled (RR 1.86 [95% CI 1.00, 3.46], p = 0.046). There was no difference in the prevalence of fetal death (p = 0.51) or infant death (p = 0.70) between women with type 1 diabetes and women with type 2 diabetes. There was no evidence that the RR of fetal and infant death had changed over time (p = 0.95). Increasing periconception HbA1c concentration above 49 mmol/mol (6.6%) (adjusted odds ratio [aOR] 1.02 [95% CI 1.00, 1.04], p = 0.01), prepregnancy retinopathy (aOR 2.05 [95% CI 1.04, 4.05], p = 0.04) and lack of prepregnancy folic acid consumption (aOR 2.52 [95% CI 1.12, 5.65], p = 0.03) were all independently associated with increased odds of fetal and infant death. Conclusions/interpretation: Pre-existing diabetes is associated with a substantially increased risk of fetal and infant death in normally formed offspring, the effect of which is largely moderated by glycaemic control

Advanced Modelling Strategies: Challenges and pitfalls in robust causal inference with observational data

Advanced Modelling Strategies: Challenges and pitfalls in robust causal inference with observational data summarises the lecture notes prepared for a four-day workshop sponsored by the Society for Social Medicine and hosted by the Leeds Institute for Data Analytics (LIDA) at the University of Leeds on 17th-20th July 2017

Theory and performance of substitution models for estimating relative causal effects in nutritional epidemiology.

BACKGROUND: Estimating relative causal effects (i.e., "substitution effects") is a common aim of nutritional research. In observational data, this is usually attempted using 1 of 2 statistical modeling approaches: the leave-one-out model and the energy partition model. Despite their widespread use, there are concerns that neither approach is well understood in practice. OBJECTIVES: We aimed to explore and illustrate the theory and performance of the leave-one-out and energy partition models for estimating substitution effects in nutritional epidemiology. METHODS: Monte Carlo data simulations were used to illustrate the theory and performance of both the leave-one-out model and energy partition model, by considering 3 broad types of causal effect estimands: 1) direct substitutions of the exposure with a single component, 2) inadvertent substitutions of the exposure with several components, and 3) average relative causal effects of the exposure instead of all other dietary sources. Models containing macronutrients, foods measured in calories, and foods measured in grams were all examined. RESULTS: The leave-one-out and energy partition models both performed equally well when the target estimand involved substituting a single exposure with a single component, provided all variables were measured in the same units. Bias occurred when the substitution involved >1 substituting component. Leave-one-out models that examined foods in mass while adjusting for total energy intake evaluated obscure estimands. CONCLUSIONS: Regardless of the approach, substitution models need to be constructed from clearly defined causal effect estimands. Estimands involving a single exposure and a single substituting component are typically estimated more accurately than estimands involving more complex substitutions. The practice of examining foods measured in grams or portions while adjusting for total energy intake is likely to deliver obscure relative effect estimands with unclear interpretations

COVID-19 and the epistemology of epidemiological models at the dawn of AI

The models used to estimate disease transmission, susceptibility and severity determine what epidemiology can (and cannot tell) us about COVID-19. These include: ‘model organisms’ chosen for their phylogenetic/aetiological similarities; multivariable statistical models to estimate the strength/direction of (potentially causal) relationships between variables (through ‘causal inference’), and the (past/future) value of unmeasured variables (through ‘classification/prediction’); and a range of modelling techniques to predict beyond the available data (through ‘extrapolation’), compare different hypothetical scenarios (through ‘simulation’), and estimate key features of dynamic processes (through ‘projection’). Each of these models: address different questions using different techniques; involve assumptions that require careful assessment; and are vulnerable to generic and specific biases that can undermine the validity and interpretation of their findings. It is therefore necessary that the models used: can actually address the questions posed; and have been competently applied. In this regard, it is important to stress that extrapolation, simulation and projection cannot offer accurate predictions of future events when the underlying mechanisms (and the contexts involved) are poorly understood and subject to change. Given the importance of understanding such mechanisms/contexts, and the limited opportunity for experimentation during outbreaks of novel diseases, the use of multivariable statistical models to estimate the strength/direction of potentially causal relationships between two variables (and the biases incurred through their misapplication/misinterpretation) warrant particular attention. Such models must be carefully designed to address: ‘selection-collider bias’, ‘unadjusted confounding bias’ and ‘inferential mediator adjustment bias’ – all of which can introduce effects capable of enhancing, masking or reversing the estimated (true) causal relationship between the two variables examined. Selection-collider bias occurs when these two variables independently cause a third (the ‘collider’), and when this collider determines/reflects the basis for selection in the analysis. It is likely to affect all incompletely representative samples, although its effects will be most pronounced wherever selection is constrained (e.g. analyses focusing on infected/hospitalised individuals). Unadjusted confounding bias disrupts the estimated (true) causal relationship between two variables when: these share one (or more) common cause(s); and when the effects of these causes have not been adjusted for in the analyses (e.g. whenever confounders are unknown/unmeasured). Inferentially similar biases can occur when: one (or more) variable(s) (or ‘mediators’) fall on the causal path between the two variables examined (i.e. when such mediators are caused by one of the variables and are causes of the other); and when these mediators are adjusted for in the analysis. Such adjustment is commonplace when: mediators are mistaken for confounders; prediction models are mistakenly repurposed for causal inference; or mediator adjustment is used to estimate direct and indirect causal relationships (in a mistaken attempt at ‘mediation analysis’). These three biases are central to ongoing and unresolved epistemological tensions within epidemiology. All have substantive implications for our understanding of COVID-19, and the future application of artificial intelligence to ‘data-driven’ modelling of similar phenomena. Nonetheless, competently applied and carefully interpreted, multivariable statistical models may yet provide sufficient insight into mechanisms and contexts to permit more accurate projections of future disease outbreaks. 1. These biases, and the terminology involved, may be challenging to readers who are unfamiliar with the use of causal path diagrams (such as Directed Acyclic Graphs; DAGs) which have been instrumental in identifying the different roles that variables can play in causal processes (whether as ‘exposures’, ‘outcomes’, ‘confounders’, ‘mediators’, ‘colliders’, ‘competing exposures’ or ‘consequences of the outcome’) and revealing hitherto under-acknowledged sources of bias in analyses designed to support causal inference. For what we hoped might offer accessible introductions to DAGs (and how [not] to use these) please see: Ellison (2020); and Tennant et al. (2019). For more technical detail on ‘collider bias’, ‘unadjusted confounding bias’ and ‘inferential mediator adjustment bias’ (and its related concern, the ‘Table 2 fallacy’), please refer to: Cook and Ranstam 2017; Munafò et al. (2018); Tennant et al. (2017); VanderWeele and Arah (2011); and Westreich and Greenland (2013)

The Authors Respond

Although the studies highlighted in Kinlen and Peto’s letter describe situations they take to be “national in scope”, none of these adopted the ‘region-wide’ analysis we recommend. Rather, these studies have focussed on rural areas with small populations experiencing extreme levels of inward-migration that had been selected from larger regions/nation states. To definitively avoid bias, our study points to the need for comparisons of areas with varying levels of inward migration, either by comparing all areas within an entire region/nation state or random subsets thereof

Reflection on modern methods: generalized linear models for prognosis and intervention—theory, practice and implications for machine learning

Prediction and causal explanation are fundamentally distinct tasks of data analysis. In health applications, this difference can be understood in terms of the difference between prognosis (prediction) and prevention/treatment (causal explanation). Nevertheless, these two concepts are often conflated in practice. We use the framework of generalized linear models (GLMs) to illustrate that predictive and causal queries require distinct processes for their application and subsequent interpretation of results. In particular, we identify five primary ways in which GLMs for prediction differ from GLMs for causal inference: (i) the covariates that should be considered for inclusion in (and possibly exclusion from) the model; (ii) how a suitable set of covariates to include in the model is determined; (iii) which covariates are ultimately selected and what functional form (i.e. parameterization) they take; (iv) how the model is evaluated; and (v) how the model is interpreted. We outline some of the potential consequences of failing to acknowledge and respect these differences, and additionally consider the implications for machine learning (ML) methods. We then conclude with three recommendations that we hope will help ensure that both prediction and causal modelling are used appropriately and to greatest effect in health research

Randomized placebo-controlled trial on azithromycin to reduce the morbidity of bronchiolitis in Indigenous Australian infants: rationale and protocol

Background: Acute lower respiratory infections are the commonest cause of morbidity and potentially preventable mortality in Indigenous infants. Infancy is also a critical time for post-natal lung growth and development. Severe or repeated lower airway injury in very young children likely increases the likelihood of chronic pulmonary disorders later in life. Globally, bronchiolitis is the most common form of acute lower respiratory infections during infancy. Compared with non-Indigenous Australian infants, Indigenous infants have greater bacterial density in their upper airways and more severe bronchiolitis episodes. Our study tests the hypothesis that the anti-microbial and anti-inflammatory properties of azithromycin, improve the clinical outcomes of Indigenous Australian infants hospitalised with bronchiolitis.Methods: We are conducting a dual centre, randomised, double-blind, placebo-controlled, parallel group trial in northern Australia. Indigenous infants (aged ≤ 24-months, expected number = 200) admitted to one of two regional hospitals (Darwin, Northern Territory and Townsville, Queensland) with a clinical diagnosis of bronchiolitis and fulfilling inclusion criteria are randomised (allocation concealed) to either azithromycin (30 mg/kg/dose) or placebo administered once weekly for three doses. Clinical data are recorded twice daily and nasopharyngeal swab are collected at enrolment and at the time of discharge from hospital. Primary outcomes are 'length of oxygen requirement' and 'duration of stay,' the latter based upon being judged as 'ready for respiratory discharge'. The main secondary outcome is readmission for a respiratory illness within 6-months of leaving hospital. Descriptive virological and bacteriological (including development of antibiotic resistance) data from nasopharyngeal samples will also be reported.Discussion: Two published studies, both involving different patient populations and settings, as well as different macrolide antibiotics and treatment duration, have produced conflicting results. Our randomised, placebo-controlled trial of azithromycin in Indigenous infants hospitalised with bronchiolitis is designed to determine whether it can reduce short-term (and potentially long-term) morbidity from respiratory illness in Australian Indigenous infants who are at high risk of developing chronic respiratory illness. If azithromycin is efficacious in reducing the morbidly of Indigenous infants hospitalised with bronchiolitis, the intervention would lead to improved short term (and possibly long term) health benefits. Trial registration: Australia and New Zealand Clinical Trials Register (ANZCTR): ACTRN12610000326099

Adjustment for time-invariant and time-varying confounders in ‘unexplained residuals’ models for longitudinal data within a causal framework and associated challenges

‘Unexplained residuals’ models have been used within lifecourse epidemiology to model an exposure measured longitudinally at several time points in relation to a distal outcome. It has been claimed that these models have several advantages, including: the ability to estimate multiple total causal effects in a single model, and additional insight into the effect on the outcome of greater-than-expected increases in the exposure compared to traditional regression methods. We evaluate these properties and prove mathematically how adjustment for confounding variables must be made within this modelling framework. Importantly, we explicitly place unexplained residual models in a causal framework using directed acyclic graphs. This allows for theoretical justification of appropriate confounder adjustment and provides a framework for extending our results to more complex scenarios than those examined in this paper. We also discuss several interpretational issues relating to unexplained residual models within a causal framework. We argue that unexplained residual models offer no additional insights compared to traditional regression methods, and, in fact, are more challenging to implement; moreover, they artificially reduce estimated standard errors. Consequently, we conclude that unexplained residual models, if used, must be implemented with great care