Assessing disability progression using the WHODAS 2.0 in multiple sclerosis: Investigating clinical and socio-demographic factors in a large longitudinal cohort study (TONiC-MS).

Background: Identifying influences on disability accumulation in multiple sclerosis (MS), including modifiable factors other than the core features of disease itself, is vital for clinical care, but has often relied on instruments with acknowledged psychometric shortcomings. We model MS disability using the WHO Disability Assessment Schedule (WHODAS) 2.0, a validated measure based on the WHO's biopsychosocial model and sensitive to the breadth of disability-related domains important to people, to investigate the factors associated with its trajectory after diagnosis.

Evaluating causal associations of chronotype with pregnancy and perinatal outcomes and its interactions with insomnia and sleep duration: a mendelian randomization study.

Background: Observational studies suggested chronotype was associated with pregnancy and perinatal outcomes. Whether these associations are causal is unclear. Our aims are to use Mendelian randomization (MR) to explore (1) associations of evening preference with stillbirth, miscarriage, gestational diabetes, hypertensive disorders of pregnancy, perinatal depression, preterm birth and offspring birthweight; and (2) differences in associations of insomnia and sleep duration with those outcomes between chronotype preferences.

A framework for conducting GWAS using repeated measures data with an application to childhood BMI.

Genetic effects on changes in human traits over time are understudied and may have important pathophysiological impact. We propose a framework that enables data quality control, implements mixed models to evaluate trajectories of change in traits, and estimates phenotypes to identify age-varying genetic effects in GWAS. Using childhood BMI as an example trait, we included 71,336 participants from six cohorts and estimated the slope and area under the BMI curve within four time periods (infancy, early childhood, late childhood and adolescence) for each participant, in addition to the age and BMI at the adiposity peak and the adiposity rebound.

Accounting for bias due to outcome data missing not at random: comparison and illustration of two approaches to probabilistic bias analysis: a simulation study.

Background: Bias from data missing not at random (MNAR) is a persistent concern in health-related research. A bias analysis quantitatively assesses how conclusions change under different assumptions about missingness using bias parameters that govern the magnitude and direction of the bias. Probabilistic bias analysis specifies a prior distribution for these parameters, explicitly incorporating available information and uncertainty about their true values.

Investigating the Potential Short-term Adverse Effects of the Quadrivalent Human Papillomavirus Vaccine: A Novel Regression Discontinuity Analysis.

Background: Human papillomavirus (HPV) vaccination has been offered in over a hundred countries worldwide (including the United Kingdom, since September 2008). Controversy around adverse effects persists, with inconsistent evidence from follow-up of randomized controlled trials and confounding by indication limiting the conclusions drawn from larger-scale observational studies. This study aims to estimate the association between receiving a quadrivalent HPV vaccine and the reporting of short-term adverse effects and to demonstrate the utility of regression discontinuity design for examining side effects in routine data.

Challenges in using data on fathers/partners to study prenatal exposures and offspring health.

Paternal exposures (and other non-maternal factors) around pregnancy could have important effects on offspring health. One challenge is that data on partners are usually from a subgroup of mothers with data, potentially introducing selection bias, limiting generalisability of findings. We aimed to investigate the potential for selection bias in studies using partner data.

Multiple imputation using auxiliary imputation variables that only predict missingness can increase bias due to data missing not at random.

Background: Epidemiological and clinical studies often have missing data, frequently analysed using multiple imputation (MI). In general, MI estimates will be biased if data are missing not at random (MNAR). Bias due to data MNAR can be reduced by including other variables ("auxiliary variables") in imputation models, in addition to those required for the substantive analysis.

Adiposity and mortality among intensive care patients with COVID-19 and non-COVID-19 respiratory conditions: a cross-context comparison study in the UK.

Background: Adiposity shows opposing associations with mortality within COVID-19 versus non-COVID-19 respiratory conditions. We assessed the likely causality of adiposity for mortality among intensive care patients with COVID-19 versus non-COVID-19 by examining the consistency of associations across temporal and geographical contexts where biases vary.

Methods: We used data from 297 intensive care units (ICUs) in England, Wales, and Northern Ireland (Intensive Care National Audit and Research Centre Case Mix Programme).

A Hypothesis-Free Approach to Identifying Potential Effects of Relative Age in School Year: an Instrumental Variable Phenome-Wide Association Study in the UK Biobank.

A child's relative age within their school year ('relative age') is associated with educational attainment and mental health. However, hypothesis driven studies often re-examine the same outcomes and exposure, potentially leading to confirmation and reporting biases, and missing unknown effects. Hypothesis-free outcome-wide analyses can potentially overcome these limitations.

Analyses Using Multiple Imputation Need to Consider Missing Data in Auxiliary Variables.

Auxiliary variables are used in multiple imputation (MI) to reduce bias and increase efficiency. These variables may often themselves be incomplete. We explored how missing data in auxiliary variables influenced estimates obtained from MI.

Investigating the neurodevelopmental correlates of early adolescent-onset emotional problems.

Background: Emotional problems (EPs) increase sharply after mid-adolescence. Earlier EPs are associated with poorer long-term outcomes, and their underlying mechanisms may differ to later-onset EPs. Given an established relationship between ADHD, autism, and later depression, we aimed to examine associations between neurodevelopmental conditions and correlates and early adolescent-onset EPs.

The impact of targeted local outreach clinics to improve COVID-19 vaccine uptake: controlled interrupted time series in South West England.

Background: Outreach clinics were part of efforts to maximise uptake in COVID-19 vaccination.

Methods: We used controlled interrupted time series, matching on age, sex, deprivation and vaccination eligibility date, to determine the effect of outreach clinics on time to first COVID-19 vaccine, using a population-based electronic health record database of 914,478 people, from December 2020 to December 2021; people living within 1 mile of each outreach clinics were exposed.

Results: 50% of 288,473 exposed citizens were white British, and 71% were aged 0-49 years.

Evaluation and comparison of nine growth and development-based measures of pubertal timing.

Background: Pubertal timing is heritable, varies between individuals, and has implications for life-course health. There are many different indicators of pubertal timing, and how they relate to each other is unclear. Our aim was to quantitatively compare nine indicators of pubertal timing.

Use of genetic correlations to examine selection bias.

Observational studies are rarely representative of their target population because there are known and unknown factors that affect an individual's choice to participate (the selection mechanism). Selection can cause bias in a given analysis if the outcome is related to selection (conditional on the other variables in the model). Detecting and adjusting for selection bias in practice typically requires access to data on nonselected individuals.

Epigenetic Aging and Racialized, Economic, and Environmental Injustice: NIMHD Social Epigenomics Program.

Importance: Epigenetic age acceleration is associated with exposure to social and economic adversity and may increase the risk of premature morbidity and mortality. However, no studies have included measures of structural racism, and few have compared estimates within or across the first and second generation of epigenetic clocks.

Objective: To determine whether epigenetic age acceleration is positively associated with exposures to diverse measures of racialized, economic, and environmental injustice measured at different levels and time periods.

Using instruments for selection to adjust for selection bias in Mendelian randomization.

Selection bias is a common concern in epidemiologic studies. In the literature, selection bias is often viewed as a missing data problem. Popular approaches to adjust for bias due to missing data, such as inverse probability weighting, rely on the assumption that data are missing at random and can yield biased results if this assumption is violated.

Protein Identification for Stroke Progression via Mendelian Randomization in Million Veteran Program and UK Biobank.

Background: Individuals who have experienced a stroke, or transient ischemic attack, face a heightened risk of future cardiovascular events. Identification of genetic and molecular risk factors for subsequent cardiovascular outcomes may identify effective therapeutic targets to improve prognosis after an incident stroke.

Methods: We performed genome-wide association studies for subsequent major adverse cardiovascular events (MACE; n=51 929; n=39 980) and subsequent arterial ischemic stroke (AIS; n=45 120; n=46 789) after the first incident stroke within the Million Veteran Program and UK Biobank.

Counterfactual Mediation Analysis with a Latent Class Exposure.

Latent classes are a useful tool in developmental research, however there are challenges associated with embedding them within a counterfactual mediation model. We develop and test a new method "updated pseudo class draws (uPCD)" to examine the association between a latent class exposure and distal outcome that could easily be extended to allow the use of any counterfactual mediation method. UPCD extends an existing group of methods (based on pseudo class draws) that assume that the true values of the latent class variable are missing, and need to be multiply imputed using class membership probabilities.

Depressive symptoms in adolescence and adult educational and employment outcomes: a structured life course analysis.

Background: Depression is a common mental health disorder that often starts during adolescence, with potentially important future consequences including 'Not in Education, Employment or Training' (NEET) status.

Methods: We took a structured life course modeling approach to examine how depressive symptoms during adolescence might be associated with later NEET status, using a high-quality longitudinal data resource. We considered four plausible life course models: (1) an where depressive symptoms in early adolescence are more associated with later NEET status relative to exposure at other stages; (2) a where depressive symptoms during the transition from compulsory education to adult life might be more deleterious regarding NEET status; (3) a , meaning that depressive symptoms around the time when most adults have completed their education and started their careers are the most strongly associated with NEET status; and (4) an which highlights the importance of chronicity of symptoms.

Non-linear Mendelian randomization: detection of biases using negative controls with a focus on BMI, Vitamin D and LDL cholesterol.

Mendelian randomisation (MR) is an established technique in epidemiological investigation, using the principle of random allocation of genetic variants at conception to estimate the causal linear effect of an exposure on an outcome. Extensions to this technique include non-linear approaches that allow for differential effects of the exposure on the outcome depending on the level of the exposure. A widely used non-linear method is the residual approach, which estimates the causal effect within different strata of the non-genetically predicted exposure (i.

Emotional problems across development: examining measurement invariance across childhood, adolescence and early adulthood.

Emotional problems (anxiety, depression) are prevalent in children, adolescents and young adults with varying ages at onset. Studying developmental changes in emotional problems requires repeated assessments using the same or equivalent measures. The parent-rated Strengths and Difficulties Questionnaire is commonly used to assess emotional problems in childhood and adolescence, but there is limited research about whether it captures a similar construct across these developmental periods.

Regression discontinuity design for the study of health effects of exposures acting early in life.

Regression discontinuity design (RDD) is a quasi-experimental approach to study the causal effect of an exposure on later outcomes by exploiting the discontinuity in the exposure probability at an assignment variable cut-off. With the intent of facilitating the use of RDD in the Developmental Origins of Health and Disease (DOHaD) research, we describe the main aspects of the study design and review the studies, assignment variables and exposures that have been investigated to identify short- and long-term health effects of early life exposures. We also provide a brief overview of some of the methodological considerations for the RDD identification using an example of a DOHaD study.