Developing survey weights to ensure representativeness in a national, matched cohort study: results from the children and young people with Long Covid (CLoCk) study.

BACKGROUND: Findings from studies assessing Long Covid in children and young people (CYP) need to be assessed in light of their methodological limitations. For example, if non-response and/or attrition over time systematically differ by sub-groups of CYP, findings could be biased and any generalisation limited. The present study aimed to (i) construct survey weights for the Children and young people with Long Covid (CLoCk) study, and (ii) apply them to published CLoCk findings showing the prevalence of shortness of breath and tiredness increased over time from baseline to 12-months post-baseline in both SARS-CoV-2 Positive and Negative CYP. METHODS: Logistic regression models were fitted to compute the probability of (i) Responding given envisioned to take part, (ii) Responding timely given responded, and (iii) (Re)infection given timely response. Response, timely response and (re)infection weights were generated as the reciprocal of the corresponding probability, with an overall 'envisioned population' survey weight derived as the product of these weights. Survey weights were trimmed, and an interactive tool developed to re-calibrate target population survey weights to the general population using data from the 2021 UK Census. RESULTS: Flexible survey weights for the CLoCk study were successfully developed. In the illustrative example, re-weighted results (when accounting for selection in response, attrition, and (re)infection) were consistent with published findings. CONCLUSIONS: Flexible survey weights to address potential bias and selection issues were created for and used in the CLoCk study. Previously reported prospective findings from CLoCk are generalisable to the wider population of CYP in England. This study highlights the importance of considering selection into a sample and attrition over time when considering generalisability of findings.

Using linked administrative data to aid the handling of non-response and restore sample representativeness in cohort studies: the 1958 national child development study and hospital episode statistics data.

BACKGROUND: There is growing interest in whether linked administrative data have the potential to aid analyses subject to missing data in cohort studies. METHODS: Using linked 1958 National Child Development Study (NCDS; British cohort born in 1958, n = 18,558) and Hospital Episode Statistics (HES) data, we applied a LASSO variable selection approach to identify HES variables which are predictive of non-response at the age 55 sweep of NCDS. We then included these variables as auxiliary variables in multiple imputation (MI) analyses to explore the extent to which they helped restore sample representativeness of the respondents together with the imputed non-respondents in terms of early life variables (father's social class at birth, cognitive ability at age 7) and relative to external population benchmarks (educational qualifications and marital status at age 55). RESULTS: We identified 10 HES variables that were predictive of non-response at age 55 in NCDS. For example, cohort members who had been treated for adult mental illness had more than 70% greater odds of bring non-respondents (odds ratio 1.73; 95% confidence interval 1.17, 2.51). Inclusion of these HES variables in MI analyses only helped to restore sample representativeness to a limited extent. Furthermore, there was essentially no additional gain in sample representativeness relative to analyses using only previously identified survey predictors of non-response (i.e. NCDS rather than HES variables). CONCLUSIONS: Inclusion of HES variables only aided missing data handling in NCDS to a limited extent. However, these findings may not generalise to other analyses, cohorts or linked administrative datasets. This work provides a demonstration of the use of linked administrative data for the handling of missing cohort data which we hope will act as template for others.

Congenital diaphragmatic hernia subtypes: Comparing birth prevalence, occurrence by maternal age, and mortality in a national birth cohort.

BACKGROUND: Population-based administrative data have rarely been used to compare the birth prevalence, risk factors for occurrence, and mortality of congenital diaphragmatic hernia (CDH) subtypes. OBJECTIVES: We used a national birth cohort to identify CDH subtypes and compared their birth prevalence, relationship with maternal age after accounting for sociodemographic factors, and 1-year mortality rates. METHODS: Linked hospital admission and death records were used to identify isolated and complex CDH cases (involving additional anomalies) among singleton livebirths in England between 2002 and 2018. The prevalence of each CDH subtype per 10,000 livebirths was estimated overall and by infant, birth and maternal characteristics. The relationship between maternal age and each subtype relative to no CDH was examined using multivariable log-binomial regression to estimate risk ratios (RRs). One-year mortality rates were examined using Kaplan-Meier curves and the hazard ratio (HR) of complex versus isolated CDH was calculated using Cox regression. RESULTS: Among 9.5 million livebirths, we identified 1285 with isolated CDH and 1150 with complex CDH. The overall prevalence of isolated and complex CDH was 1.4 (95% confidence interval [CI] 1.3, 1.4) and 1.2 (95% CI 1.1, 1.3) per 10,000 livebirths, respectively. Only complex CDH was associated with maternal age. Compared with maternal age 25-34 years, complex CDH risk was elevated for maternal age < 20 years (RR 1.31, 95% CI 1.00, 1.72). Risk was highest for maternal age ≥ 40 years (RR 1.61, 95% CI 1.21, 2.15) although accounting for chromosomal anomalies attenuated the risk (RR 1.39, 95% CI 1.00, 1.92). The 1-year mortality rate for complex CDH (33.1%, 95% CI 30.5, 35.9) was slightly higher than for isolated CDH (29.7%, 95% CI 27.3, 32.3) (HR 1.10, 95% CI 0.96, 1.27). CONCLUSIONS: Mechanisms of occurrence differed between and within CDH subtypes and 1-year mortality of complex CDH was slightly higher than for isolated CDH.

Could interventions on physical activity mitigate genomic liability for obesity? Applying the health disparity framework in genetically informed studies.

Polygenic scores (PGS) are now commonly available in longitudinal cohort studies, leading to their integration into epidemiological research. In this work, our aim is to explore how polygenic scores can be used as exposures in causal inference-based methods, specifically mediation analyses. We propose to estimate the extent to which the association of a polygenic score indexing genetic liability to an outcome could be mitigated by a potential intervention on a mediator. To do this this, we use the interventional disparity measure approach, which allows us to compare the adjusted total effect of an exposure on an outcome, with the association that would remain had we intervened on a potentially modifiable mediator. As an example, we analyse data from two UK cohorts, the Millennium Cohort Study (MCS, N = 2575) and the Avon Longitudinal Study of Parents and Children (ALSPAC, N = 3347). In both, the exposure is genetic liability for obesity (indicated by a PGS for BMI), the outcome is late childhood/early adolescent BMI, and the mediator and potential intervention target is physical activity, measured between exposure and outcome. Our results suggest that a potential intervention on child physical activity can mitigate some of the genetic liability for childhood obesity. We propose that including PGSs in a health disparity measure approach, and causal inference-based methods more broadly, is a valuable addition to the study of gene-environment interplay in complex health outcomes.

Factors associated with excess all-cause mortality in the first wave of the COVID-19 pandemic in the UK: A time series analysis using the Clinical Practice Research Datalink.

BACKGROUND: Excess mortality captures the total effect of the Coronavirus Disease 2019 (COVID-19) pandemic on mortality and is not affected by misspecification of cause of death. We aimed to describe how health and demographic factors were associated with excess mortality during, compared to before, the pandemic. METHODS AND FINDINGS: We analysed a time series dataset including 9,635,613 adults (≥40 years old) registered at United Kingdom general practices contributing to the Clinical Practice Research Datalink. We extracted weekly numbers of deaths and numbers at risk between March 2015 and July 2020, stratified by individual-level factors. Excess mortality during Wave 1 of the UK pandemic (5 March to 27 May 2020) compared to the prepandemic period was estimated using seasonally adjusted negative binomial regression models. Relative rates (RRs) of death for a range of factors were estimated before and during Wave 1 by including interaction terms. We found that all-cause mortality increased by 43% (95% CI 40% to 47%) during Wave 1 compared with prepandemic. Changes to the RR of death associated with most sociodemographic and clinical characteristics were small during Wave 1 compared with prepandemic. However, the mortality RR associated with dementia markedly increased (RR for dementia versus no dementia prepandemic: 3.5, 95% CI 3.4 to 3.5; RR during Wave 1: 5.1, 4.9 to 5.3); a similar pattern was seen for learning disabilities (RR prepandemic: 3.6, 3.4 to 3.5; during Wave 1: 4.8, 4.4 to 5.3), for black or South Asian ethnicity compared to white, and for London compared to other regions. Relative risks for morbidities were stable in multiple sensitivity analyses. However, a limitation of the study is that we cannot assume that the risks observed during Wave 1 would apply to other waves due to changes in population behaviour, virus transmission, and risk perception. CONCLUSIONS: The first wave of the UK COVID-19 pandemic appeared to amplify baseline mortality risk to approximately the same relative degree for most population subgroups. However, disproportionate increases in mortality were seen for those with dementia, learning disabilities, non-white ethnicity, or living in London.

Predictive model for long COVID in children 3 months after a SARS-CoV-2 PCR test.

BACKGROUND: To update and internally validate a model to predict children and young people (CYP) most likely to experience long COVID (i.e. at least one impairing symptom) 3 months after SARS-CoV-2 PCR testing and to determine whether the impact of predictors differed by SARS-CoV-2 status. METHODS: Data from a nationally matched cohort of SARS-CoV-2 test-positive and test-negative CYP aged 11-17 years was used. The main outcome measure, long COVID, was defined as one or more impairing symptoms 3 months after PCR testing. Potential pre-specified predictors included SARS-CoV-2 status, sex, age, ethnicity, deprivation, quality of life/functioning (five EQ-5D-Y items), physical and mental health and loneliness (prior to testing) and number of symptoms at testing. The model was developed using logistic regression; performance was assessed using calibration and discrimination measures; internal validation was performed via bootstrapping and the final model was adjusted for overfitting. RESULTS: A total of 7139 (3246 test-positives, 3893 test-negatives) completing a questionnaire 3 months post-test were included. 25.2% (817/3246) of SARS-CoV-2 PCR-positives and 18.5% (719/3893) of SARS-CoV-2 PCR-negatives had one or more impairing symptoms 3 months post-test. The final model contained SARS-CoV-2 status, number of symptoms at testing, sex, age, ethnicity, physical and mental health, loneliness and four EQ-5D-Y items before testing. Internal validation showed minimal overfitting with excellent calibration and discrimination measures (optimism-adjusted calibration slope: 0.96575; C-statistic: 0.83130). CONCLUSIONS: We updated a risk prediction equation to identify those most at risk of long COVID 3 months after a SARS-CoV-2 PCR test which could serve as a useful triage and management tool for CYP during the ongoing pandemic. External validation is required before large-scale implementation.

Parental feeding and childhood genetic risk for obesity: exploring hypothetical interventions with causal inference methods.

BACKGROUND: Parental-feeding behaviors are common intervention targets for childhood obesity, but often only deliver small changes. Childhood BMI is partly driven by genetic effects, and the extent to which parental-feeding interventions can mediate child genetic liability is not known. Here we aim to examine how potential interventions on parental-feeding behaviors can mitigate some of the association between child genetic liability and BMI in early adolescence, using causal inference methods. METHODS: Data from the Avon Longitudinal Study of Parents and Children were used to estimate an interventional disparity measure for a child polygenic score for BMI (PGS-BMI) on BMI at 12 years. The approach compares counterfactual outcomes for different hypothetical interventions on parental-feeding styles applied when children are 10-11 years (n = 4248). Results are presented as adjusted total association (Adj-Ta) between genetic liability (PGS-BMI) and BMI at 12 years, versus the interventional disparity measure-direct effect (IDM-DE), which represents the association that would remain, had we intervened on parental-feeding under different scenarios. RESULTS: For children in the top quintile of genetic liability, an intervention shifting parental feeding to the levels of children with lowest genetic risk, resulted in a difference of 0.81 kg/m2 in BMI at 12 years (Adj-Ta = 3.27, 95% CI: 3.04, 3.49; versus IDM-DE = 2.46, 95% CI: 2.24, 2.67). CONCLUSIONS: Findings suggest that parental-feeding interventions have the potential to buffer some of the genetic liability for childhood obesity. Further, we highlight a novel way to analyze potential interventions for health conditions only using secondary data analyses, by combining methodology from statistical genetics and social epidemiology.

Identification and prediction of novel classes of long-term disease trajectories for patients with juvenile dermatomyositis using growth mixture models.

OBJECTIVES: Uncertainty around clinical heterogeneity and outcomes for patients with JDM represents a major burden of disease and a challenge for clinical management. We sought to identify novel classes of patients having similar temporal patterns in disease activity and relate them to baseline clinical features. METHODS: Data were obtained for n = 519 patients, including baseline demographic and clinical features, baseline and follow-up records of physician's global assessment of disease (PGA), and skin disease activity (modified DAS). Growth mixture models (GMMs) were fitted to identify classes of patients with similar trajectories of these variables. Baseline predictors of class membership were identified using Lasso regression. RESULTS: GMM analysis of PGA identified two classes of patients. Patients in class 1 (89%) tended to improve, while patients in class 2 (11%) had more persistent disease. Lasso regression identified abnormal respiration, lipodystrophy and time since diagnosis as baseline predictors of class 2 membership, with estimated odds ratios, controlling for the other two variables, of 1.91 for presence of abnormal respiration, 1.92 for lipodystrophy and 1.32 for time since diagnosis. GMM analysis of modified DAS identified three classes of patients. Patients in classes 1 (16%) and 2 (12%) had higher levels of modified DAS at diagnosis that improved or remained high, respectively. Patients in class 3 (72%) began with lower DAS levels that improved more quickly. Higher proportions of patients in PGA class 2 were in DAS class 2 (19%, compared with 16 and 10%). CONCLUSION: GMM analysis identified novel JDM phenotypes based on longitudinal PGA and modified DAS.

Comprehensive analysis of the association of seasonal variability with maternal and neonatal nutrition in lowland Nepal.

OBJECTIVE: To provide a comprehensive seasonal analysis of pregnant mothers' eating behaviour and maternal/newborn nutritional status in an undernourished population from lowland rural Nepal, where weather patterns, agricultural labour, food availability and disease prevalence vary seasonally. DESIGN: Secondary analysis of cluster-randomised Low Birth Weight South Asia Trial data, applying cosinor analysis to predict seasonal patterns. OUTCOMES: Maternal mid-upper arm circumference (MUAC), BMI, dietary diversity, meals per day, eating down and food aversion in pregnancy (≥31 weeks' gestation) and neonatal z-scores of length-for-age (LAZ), weight-for-age (WAZ) and head circumference-for-age (HCAZ) and weight-for-length (WLZ). SETTING: Rural areas of Dhanusha and Mahottari districts in plains of Nepal. PARTICIPANTS: 2831 mothers aged 13-50 and 3330 neonates. RESULTS: We found seasonal patterns in newborn anthropometry and pregnant mothers' anthropometry, meal frequency, dietary diversity, food aversion and eating down. Seasonality in intake varied by food group. Offspring anthropometry broadly tracked mothers'. Annual amplitudes in mothers' MUAC and BMI were 0·27 kg/m2 and 0·22 cm, with peaks post-harvest and nadirs in October when food insecurity peaked. Annual LAZ, WAZ and WLZ amplitudes were 0·125, 0·159 and 0·411 z-scores, respectively. Neonates were the shortest but least thin (higher WLZ) in winter (December/January). In the hot season, WLZ was the lowest (May/June) while LAZ was the highest (March and August). HCAZ did not vary significantly. Food aversion and eating down peaked pre-monsoon (April/May). CONCLUSIONS: Our analyses revealed complex seasonal patterns in maternal nutrition and neonatal size. Seasonality should be accounted for when designing and evaluating public heath nutrition interventions.

Association between prenatal maternal infection and disordered eating behaviours in adolescence: a UK population-based prospective birth cohort study.

BACKGROUND: Prenatal infections have been proposed as a putative risk factor for a number of psychiatric outcomes across a continuum of severity. Evidence on eating disorders is scarce. We investigated whether exposure to prenatal maternal infections is associated with an increased risk of disordered eating and weight and shape concerns in adolescence in a large UK birth cohort. METHODS: We used data from the Avon Longitudinal Study of Parents and Children. The primary exposure was maternal experience of infections at any time in pregnancy. Study outcomes were presence of any, monthly or weekly disordered eating at 14 and 16 years of age, and weight and shape concerns at 14 years. We defined the causal effect of the exposure on these outcomes using a counterfactual framework adjusting our analyses for a number of hypothesised confounders, and imputing missing confounder data using multiple imputation. RESULTS: In total, 4884 children had complete exposure and outcome data at age 14 years, and 4124 at 16 years. Exposed children had a greater risk of reporting weekly disordered eating at both age 14 [risk difference (RD) 0.9%, 95% confidence interval (CI) -0.01 to 1.9, p = 0.08] and 16 (RD 2.3%, 95% CI 0.6-3.9, p < 0.01), though evidence of an association was weak at age 14 years. Exposed children also had greater weight and shape concerns at age 14 years (mean difference 0.15, 95% CI 0.05-0.26, p < 0.01). CONCLUSIONS: Exposure to prenatal maternal infection is associated with greater risk of disordered eating in adolescence. This association could be explained by in utero processes leading to impaired neurodevelopment or altered immunological profiles. Residual confounding cannot be excluded.

Longitudinal associations between circulating interleukin-6 and C-reactive protein in childhood, and eating disorders and disordered eating in adolescence.

OBJECTIVE: Few studies have explored the association between inflammation and eating disorders and none used a longitudinal design. We investigated the association between serum-levels of interleukin 6 (IL-6) and C-reactive protein (CRP) measured in childhood and eating disorders and related behaviours and cognitions in adolescence in a large general population sample. METHODS: We used data from the Avon Longitudinal Study of Parents and Children (ALSPAC). Our exposures were thirds of IL6 and CRP derived from serum measurements taken at age nine years, and outcomes were eating disorder diagnoses and self-reported disordered eating behaviours at ages 14, 16, and 18 years. We used univariable and multivariable multilevel logistic regression models adjusting for a number of potential confounders, including sex, fat mass, and pre-existing mental health difficulties. RESULTS: Our sample included 3480 children. Those in the top third of CRP had lower odds of binge eating (odds ratio(OR):0.62, 95% confidence interval (CI):0.39,1.00,p "equals" 0.05) and fasting (OR:0.63, 95% CI:0.38,1.07,p "equals" 0.09) after adjustment for confounders. We also observed weak associations of comparable magnitude for purging, anorexia nervosa, and bulimia nervosa. We did not find any associations between levels of IL6 and any of the outcomes under study. CONCLUSIONS: There was little evidence of an association between CRP and IL-6 and adolescent eating disorder outcomes. The inverse association observed between CRP and binge eating was unexpected, so caution is needed when interpreting it. One possible explanation is that higher CRP levels could have a protective role for disordered eating by affecting appetitive traits.

Identifying typical trajectories in longitudinal data: modelling strategies and interpretations.

Individual-level longitudinal data on biological, behavioural, and social dimensions are becoming increasingly available. Typically, these data are analysed using mixed effects models, with the result summarised in terms of an average trajectory plus measures of the individual variations around this average. However, public health investigations would benefit from finer modelling of these individual variations which identify not just one average trajectory, but several typical trajectories. If evidence of heterogeneity in the development of these variables is found, the role played by temporally preceding (explanatory) variables as well as the potential impact of differential trajectories may have on later outcomes is often of interest. A wide choice of methods for uncovering typical trajectories and relating them to precursors and later outcomes exists. However, despite their increasing use, no practical overview of these methods targeted at epidemiological applications exists. Hence we provide: (a) a review of the three most commonly used methods for the identification of latent trajectories (growth mixture models, latent class growth analysis, and longitudinal latent class analysis); and (b) recommendations for the identification and interpretation of these trajectories and of their relationship with other variables. For illustration, we use longitudinal data on childhood body mass index and parental reports of fussy eating, collected in the Avon Longitudinal Study of Parents and Children.

Steady Growth in Early Infancy Is Associated with Greater Anthropometry in Indian Children Born Low Birth Weight at Term.

BACKGROUND: Patterns of early growth are associated with later body composition and risk of adult noncommunicable disease but information from low-income countries is limited. OBJECTIVES: The aim of this study was to investigate early growth trajectories and later anthropometric and bone density outcomes among children born term low birth weight (LBW: 1.8-2.5 kg). METHODS: We used data from 902 children from the Delhi Infant Vitamin D Supplementation study of LBW term infants (which collected monthly anthropometry from birth to 6 mo) and who had height, weight, midupper arm circumference (MUAC), midupper arm muscle circumference (MUAMC), subscapular and triceps skinfold thicknesses, tibia and radius bone density measured at age 4-6 y. We investigated how growth in the first 6 mo of life, modeled using the SuperImposition by Translation and Rotation (SITAR) growth curve model, was related to these outcomes. SITAR summarizes each infant's weight and length trajectory in terms of a population mean curve and child-specific growth parameters: size, timing, and intensity. These were included as explanatory variables in linear regression models for the childhood outcomes. RESULTS: Considering the infant weight and length SITAR parameters jointly, childhood weight was strongly associated with infant length timing [estimated regression coefficient ß = 0.25 (95% CI: 0.10, 0.39)] and with weight size, timing, and intensity [ß = 9.01 (6.75, 11.27), ß = -0.25 (-0.43, -0.07), ß = 5.03 (3.22, 6.84), respectively]. Childhood height was associated only with the length parameters [ß = 0.97 (0.71, 1.23), ß = -0.43 (-0.77, -0.09), ß = 11.68 (8.60, 14.75), respectively]; childhood MUAC, MUAMC, and skinfolds with all parameters; and bone density with none. Overall, delayed and sustained growth in infant weight and length resulted in higher values of all outcomes except bone density, with the period up to 15 wk of age appearing critical for setting childhood anthropometry in this population. CONCLUSIONS: The explanation for the effects of delayed growth and length of the period in which trajectories are set is unclear; however, sustained and delayed growth in early infancy appears to be beneficial for these LBW children at least in the short-term. The trial was registered at clinicaltrials.gov as BT/PR7489/PID/20/285/2006.

Adjusting for BMI in analyses of volumetric mammographic density and breast cancer risk.

BACKGROUND: Fully automated assessment of mammographic density (MD), a biomarker of breast cancer risk, is being increasingly performed in screening settings. However, data on body mass index (BMI), a confounder of the MD-risk association, are not routinely collected at screening. We investigated whether the amount of fat in the breast, as captured by the amount of mammographic non-dense tissue seen on the mammographic image, can be used as a proxy for BMI when data on the latter are unavailable. METHODS: Data from a UK case control study (numbers of cases/controls: 414/685) and a Norwegian cohort study (numbers of cases/non-cases: 657/61059), both with volumetric MD measurements (dense volume (DV), non-dense volume (NDV) and percent density (%MD)) from screening-age women, were analysed. BMI (self-reported) and NDV were taken as measures of adiposity. Correlations between BMI and NDV, %MD and DV were examined after log-transformation and adjustment for age, menopausal status and parity. Logistic regression models were fitted to the UK study, and Cox regression models to the Norwegian study, to assess associations between MD and breast cancer risk, expressed as odds/hazard ratios per adjusted standard deviation (OPERA). Adjustments were first made for standard risk factors except BMI (minimally adjusted models) and then also for BMI or NDV. OPERA pooled relative risks (RRs) were estimated by fixed-effect models, and between-study heterogeneity was assessed by the I2 statistics. RESULTS: BMI was positively correlated with NDV (adjusted r = 0.74 in the UK study and r = 0.72 in the Norwegian study) and with DV (r = 0.33 and r = 0.25, respectively). Both %MD and DV were positively associated with breast cancer risk in minimally adjusted models (pooled OPERA RR (95% confidence interval): 1.34 (1.25, 1.43) and 1.46 (1.36, 1.56), respectively; I2 = 0%, P >0.48 for both). Further adjustment for BMI or NDV strengthened the %MD-risk association (1.51 (1.41, 1.61); I2 = 0%, P = 0.33 and 1.51 (1.41, 1.61); I2 = 0%, P = 0.32, respectively). Adjusting for BMI or NDV marginally affected the magnitude of the DV-risk association (1.44 (1.34, 1.54); I2 = 0%, P = 0.87 and 1.49 (1.40, 1.60); I2 = 0%, P = 0.36, respectively). CONCLUSIONS: When volumetric MD-breast cancer risk associations are investigated, NDV can be used as a measure of adiposity when BMI data are unavailable.

Maternal Prepregnancy Weight Status and Adolescent Eating Disorder Behaviors: A Longitudinal Study of Risk Pathways.

BACKGROUND: Maternal characteristics and childhood growth have been identified as risk factors for eating disorders. Most studies to date have been unable to investigate these factors prospectively while accounting for their interdependencies. We address this by investigating whether the association of maternal prepregnancy body mass index (ppBMI) with adolescent eating disorder behaviors can be explained by childhood growth and/or a concurrent environmental pathway captured by maternal eating habits. METHODS: We analyzed data from girls participating in the Avon Longitudinal Study of Parents and Children (ALSPAC), a prospective UK cohort. The study had information on parentally and self-reported eating disorder behaviors at age 13/14 years (n = 3,529), maternal ppBMI and eating habits at age 8, child's birth weight, BMI from age 7 to 12, pubertal development at 11, and relevant confounders. We quantified contributions of childhood growth and concomitant maternal eating habits to the association of maternal ppBMI with eating disorder behaviors in terms of interventional disparity effects for multiple mediators. RESULTS: Maternal prepregnancy underweight was negatively associated with eating disorder behaviors (-0.18; 95% confidence interval: -0.29, -0.06), whereas overweight/obesity had the opposite relationship (0.25; 0.18, 0.32). Both were nearly fully explained by childhood growth. CONCLUSIONS: Although maternal ppBMI is associated with developing eating disorders, its role needs to be understood in the context of childhood factors, in particular childhood growth. The relatively small size of the remaining associations, once growth factors are hypothetically equalized across levels of maternal ppBMI, suggests that childhood growth is a potential area for prevention.

An Assessment and Extension of the Mechanism-Based Approach to the Identification of Age-Period-Cohort Models.

Many methods have been proposed to solve the age-period-cohort (APC) linear identification problem, but most are not theoretically informed and may lead to biased estimators of APC effects. One exception is the mechanism-based approach recently proposed and based on Pearl's front-door criterion; this approach ensures consistent APC effect estimators in the presence of a complete set of intermediate variables between one of age, period, cohort, and the outcome of interest, as long as the assumed parametric models for all the relevant causal pathways are correct. Through a simulation study mimicking APC data on cardiovascular mortality, we demonstrate possible pitfalls that users of the mechanism-based approach may encounter under realistic conditions: namely, when (1) the set of available intermediate variables is incomplete, (2) intermediate variables are affected by two or more of the APC variables (while this feature is not acknowledged in the analysis), and (3) unaccounted confounding is present between intermediate variables and the outcome. Furthermore, we show how the mechanism-based approach can be extended beyond the originally proposed linear and probit regression models to incorporate all generalized linear models, as well as nonlinearities in the predictors, using Monte Carlo simulation. Based on the observed biases resulting from departures from underlying assumptions, we formulate guidelines for the application of the mechanism-based approach (extended or not).

Assessing within-woman changes in mammographic density: a comparison of fully versus semi-automated area-based approaches.

BACKGROUND: Mammographic density (MD) varies throughout a woman's life. We compared the performance of a fully automated (ImageJ-based) method to the observer-dependent Cumulus approach in the assessment of within-woman changes in MD over time. METHODS: MD was assessed in annual pre-diagnostic films (from age 40 to early 50s) from 313 breast cancer cases and 452 matched controls using Cumulus (left medio-lateral oblique (MLO) readings) and the ImageJ-based method (mean left-right MLO readings). Linear mixed models were used to compare within-woman changes in MD among controls. Associations between individual-specific MD trajectories and breast cancer were examined using conditional logistic regression. RESULTS: The age-related trajectories predicted by Cumulus and the ImageJ-based method were similar for all MD measures, except that the ImageJ-based method yielded slightly higher (by 2.54%, 95% CI 2.07%, 3.00%) estimates for percent MD. For both methods, the yearly rate of change in percent MD was twice faster after menopause than before, and higher BMI was associated with lower mean percent MD, but not associated with rate of change. Both methods yielded similar associations of individual-specific MD trajectories with breast cancer risk. CONCLUSIONS: The ImageJ-based method is a valid fully automated alternative to Cumulus for measuring within-woman changes in MD in digitized films. The Age Trial is registered as an International Standard Randomized Controlled Trial, number ISRCTN24647151.

Disability and all-cause mortality in the older population: evidence from the English Longitudinal Study of Ageing.

Despite the vast body of literature studying disability and mortality, evidence to support their association is scarce. This work investigates the role of disability in explaining all-cause mortality among individuals aged 50+ who participated in the English Longitudinal Study of Aging. The aim is to explain the gender paradox in health and mortality by analysing whether the association of disability with mortality differs between women and men. Disability was conceived following the International Classification of Functioning, Disability and Health (ICF), proposed by the WHO, that conceptualizes disability as a combination of three components: impairment, activity limitation and participation restriction. Latent variable models were used to identify domain-specific factors and general disability. The association of the latter with mortality up to 10 years after enrolment was estimated using discrete-time survival analysis. Our work confirms the validity of the ICF framework and finds that disability is strongly associated with mortality, with a time-varying effect among men, and a smaller constant effect for women. Adjusting for demographic, socioeconomic and behavioural factors attenuated the association for both sexes, but overall the effects remained high and significant. These findings confirm the existence of gender paradox by showing that, when affected by disability, women survive longer than men, although if men survive the first years they appear to become more resilient to disability. Sensitivity analyses suggested that the gender paradox cannot be solely explained by gender-specific health conditions: there must be other mechanisms acting within the pathway between disability and mortality that need to be explored.

Mediation analysis with intermediate confounding: structural equation modeling viewed through the causal inference lens.

The study of mediation has a long tradition in the social sciences and a relatively more recent one in epidemiology. The first school is linked to path analysis and structural equation models (SEMs), while the second is related mostly to methods developed within the potential outcomes approach to causal inference. By giving model-free definitions of direct and indirect effects and clear assumptions for their identification, the latter school has formalized notions intuitively developed in the former and has greatly increased the flexibility of the models involved. However, through its predominant focus on nonparametric identification, the causal inference approach to effect decomposition via natural effects is limited to settings that exclude intermediate confounders. Such confounders are naturally dealt with (albeit with the caveats of informality and modeling inflexibility) in the SEM framework. Therefore, it seems pertinent to revisit SEMs with intermediate confounders, armed with the formal definitions and (parametric) identification assumptions from causal inference. Here we investigate: 1) how identification assumptions affect the specification of SEMs, 2) whether the more restrictive SEM assumptions can be relaxed, and 3) whether existing sensitivity analyses can be extended to this setting. Data from the Avon Longitudinal Study of Parents and Children (1990-2005) are used for illustration.