How predictive of future healthcare utilisation and mortality is data-driven population segmentation based on healthcare utilisation and chronic condition comorbidity?

BACKGROUND: In recent years data-driven population segmentation using cluster analyses of mainly health care utilisation data has been used as a proxy of future health care need. Chronic conditions patterns tended to be examined after segmentation but may be useful as a segmentation variable which, in combination with utilisation could indicate severity. These could further be of practical use to target specific clinical groups including for prevention. This study aimed to assess the ability of data-driven segmentation based on health care utilisation and comorbidities to predict future outcomes: Emergency admission, A&E attendance, GP practice contacts, and mortality. METHODS: We analysed record-linked data for 412,997 patients registered with GP practices in 2018-19 in Cwm Taf Morgannwg University Health Board (CTM UHB) area within the Secure Anonymised Information Linkage (SAIL) Databank. We created 10 segments using k-means clustering based on utilisation (GP practice contacts, prescriptions, emergency and elective admissions, A&E and outpatients) and chronic condition counts for 2018 using different variable compositions to denote need. We assessed the characteristics of the segments. We employed a train/test scheme (80% training set) to compare logistic regression model predictions with observed outcomes on follow-up in 2019. We assessed the area under the ROC curve (AUC) for models with demographic variables, with and without the segments, as well as between segmentation implementations (with/without comorbidity and primary care data). RESULTS: Adding the segments to the model with demographic covariates improved the prediction for all outcomes. For emergency admissions this increased discrimination from AUC 0.65 (CI 0.64-0.65) to 0.73 (CI 0.73-0.74). Models with the segments only performed nearly as well as the full models. Excluding comorbidity showed reduced predictive ability for mortality (similar otherwise) but most pronounced reduction when excluding all primary care variables. CONCLUSIONS: This shows that the segments have satisfactory predictive ability, even for varied outcomes and a broad range of events and conditions used in the segmentation. It suggests that the segments can be a useful tool in helping to identify specific groups of need to target with anticipatory care. Identification may be refined with selected diagnoses or more specialised tools such as risk stratification.

Non-thermal disruption of ß-adrenergic receptor-activated Ca2+ signalling and apoptosis in human ES-derived cardiomyocytes by microwave electric fields at 2.4 GHz.

The ubiquity of wireless electronic-device connectivity has seen microwaves emerge as one of the fastest growing forms of electromagnetic exposure. A growing evidence-base refutes the claim that wireless technologies pose no risk to human health at current safety levels designed to limit thermal (heating) effects. The potential impact of non-thermal effects of microwave exposure, especially in electrically-excitable tissues (e.g., heart), remains controversial. We exposed human embryonic stem-cell derived cardiomyocytes (CM), under baseline and beta-adrenergic receptor (ß-AR)-stimulated conditions, to microwaves at 2.4 GHz, a frequency used extensively in wireless communication (e.g., 4G, Bluetooth™ and WiFi). To control for any effect of sample heating, experiments were done in CM subjected to matched rates of direct heating or CM maintained at 37 °C. Detailed profiling of the temporal and amplitude features of Ca2+ signalling in CM under these experimental conditions was reconciled with the extent and spatial clustering of apoptosis. The data show that exposure of CM to 2.4 GHz EMF eliminated the normal Ca2+ signalling response to ß-AR stimulation and provoked spatially-clustered apoptosis. This is first evidence that non-thermal effects of 2.4 GHz microwaves might have profound effects on human CM function, responsiveness to activation, and survival.

Adversity profiles of children receiving care and support from social services: A latent-class analysis of school-aged children in Wales.

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.

Using Negative Control Outcomes and Difference-in-Differences Analysis to Estimate Treatment Effects in an Entirely Treated Cohort: The Effect of Ivacaftor in Cystic Fibrosis.

When an entire cohort of patients receives a treatment, it is difficult to estimate the treatment effect in the treated because there are no directly comparable untreated patients. Attempts can be made to find a suitable control group (e.g., historical controls), but underlying differences between the treated and untreated can result in bias. Here we show how negative control outcomes combined with difference-in-differences analysis can be used to assess bias in treatment effect estimates and obtain unbiased estimates under certain assumptions. Causal diagrams and potential outcomes are used to explain the methods and assumptions. We apply the methods to UK Cystic Fibrosis Registry data to investigate the effect of ivacaftor, introduced in 2012 for a subset of the cystic fibrosis population with a particular genotype, on lung function and annual rate (days/year) of receiving intravenous (IV) antibiotics (i.e., IV days). We consider 2 negative control outcomes: outcomes measured in the pre-ivacaftor period and outcomes among persons ineligible for ivacaftor because of their genotype. Ivacaftor was found to improve lung function in year 1 (an approximately 6.5-percentage-point increase in ppFEV1), was associated with reduced lung function decline (an approximately 0.5-percentage-point decrease in annual ppFEV1 decline, though confidence intervals included 0), and reduced the annual rate of IV days (approximately 60% over 3 years).

Mediation of the total effect of cystic fibrosis-related diabetes on mortality: A UK Cystic Fibrosis Registry cohort study.

AIM: To investigate whether the effect of cystic fibrosis-related diabetes (CFRD) on the composite outcome of mortality or transplant could act through lung function, pulmonary exacerbations and/or nutritional status. METHODS: A retrospective cohort of adult cystic fibrosis (CF) patients who had not been diagnosed with CFRD were identified from the UK Cystic Fibrosis Registry (n = 2750). Rate of death or transplant was compared between patients who did and did not develop CFRD (with insulin use) during follow-up using Poisson regression, separately by sex. Causal mediation methods were used to investigate whether lung function, pulmonary exacerbations and nutritional status lie on the causal pathway between insulin-treated CFRD and mortality/transplant. RESULTS: At all ages, the mortality/transplant rate was higher in both men and women diagnosed with CFRD. Pulmonary exacerbations were the strongest mediator of the effect of CFRD on mortality/transplant, with an estimated 15% [95% CI: 7%, 28%] of the effect at 2 years post-CFRD diagnosis attributed to exacerbations, growing to 24% [95% CI: 9%, 46%] at 4 years post-diagnosis. Neither lung function nor nutritional status were found to be significant mediators of this effect. Estimates were similar but with wider confidence intervals in a cohort that additionally included people with CFRD but not using insulin. CONCLUSION: There is evidence that pulmonary exacerbations mediate the effect of CFRD on mortality but, as they are estimated to mediate less than one-quarter of the total effect, the mechanism through which CFRD influences survival may involve other factors.

SARS-CoV-2 sero-prevalence in the workforces of three large workplaces in South Wales: a sero-epidemiological study.

BACKGROUND: Sero-prevalence studies quantify the proportion of a population that has antibodies against SARS-CoV-2, and can be used to identify the extent of the COVID-19 pandemic at a population level. The aim of the study was to assess the sero-prevalence of SARS-CoV-2 antibodies in the workforce at three workplaces: a food factory, non-food factory and call-centre. METHODS: Nine hundred ninety-three participants were recruited from three workplaces in South Wales. Participants completed a questionnaire and had a lateral flow point-of-care SARS-CoV-2 antibody test administered by a healthcare professional. The data were analysed using multivariable logistic regression, both using complete records only and following multiple imputation. RESULTS: The sero-prevalence of SARS-CoV-2 antibodies ranged from 4% (n = 17/402) in the non-food factory to 10% (n = 28/281) in the food factory (OR 2.93; 95% CI 1.26 to 6.81). After taking account of confounding factors evidence of a difference remained (cOR comparing food factory to call centre (2.93; 95% CI 1.26 to 6.81) and non-food factory (3.99; 95% CI 1.97 to 8.08) respectively). The SARS-CoV-2 antibody prevalence also varied between roles within workplaces. People working in office based roles had a 2.23 times greater conditional odds (95% CI 1.02 to 4.87) of being positive for SARS-CoV-2 antibodies than those working on the factory floor. CONCLUSION: The sero-prevalence of SARS-CoV-2 antibodies varied by workplace and work role. Whilst it is not possible to state whether these differences are due to COVID-19 transmission within the workplaces, it highlights the importance of considering COVID-19 transmission in a range of workplaces and work roles.

Covid-19 Coping Survey: an In-depth Qualitative Analysis of Free-Text Responses from People With and Without Existing Health Conditions in the UK.

BACKGROUND: There is currently a lack of qualitative research exploring how cognitive and emotional reactions to the threat of SARS-CoV-2 affected the health behaviours of people living with and without pre-existing mental and physical health conditions. We aimed to investigate how the threat of SARS-CoV-2 influenced the thoughts, feelings and health behaviours of people with and without pre-existing health conditions in the UK. METHODS: A cross-sectional online survey of UK adults (aged 18 and over). Free-text responses were analysed using a qualitative framework approach guided by the Common-Sense Model of Self-Regulation. RESULTS: Of the 9110 respondents, 2763 participants provided at least one free-text response. Three main themes were derived from the data. Theme one, locus of control, reports on the extent to which people felt in control during the first wave of the pandemic. Theme two, emotional impact, conveys how individuals felt and how people's personal circumstances made them more vulnerable to experiencing negative emotions during the pandemic. Theme three, coping strategies, describes common health-protective and health-threatening behaviours performed by individuals, as well as the importance of social connectedness, the social context and the need for collective action during the first national lockdown. CONCLUSION: Complex psychological interventions including behaviour change are required to mitigate the psychological burden of the SARS-CoV-2 pandemic and increase autonomy in people with and without pre-existing conditions during this highly uncertain time. Behavioural scientists can support governments and public health agencies to develop evidence-based communication and behaviour change strategies that support people to address unhelpful beliefs and emotions and strengthen coping abilities as the UK moves through and beyond the SARS-CoV-2 pandemic.

Adverse childhood experiences and child mental health: an electronic birth cohort study.

BACKGROUND: Adverse childhood experiences (ACEs) are negatively associated with a range of child health outcomes. In this study, we explored associations between five individual ACEs and child mental health diagnoses or symptoms. ACEs included living with someone who had an alcohol-related problem, common mental health disorder or serious mental illness, or experienced victimisation or death of a household member. METHODS: We analysed data from a population-level electronic cohort of children in Wales, UK, (N = 191,035) between the years of 1998 and 2012. We used Cox regression with discrete time-varying exposure variables to model time to child mental health diagnosis during the first 15 years of life. Child mental health diagnoses include five categories: (i) externalising symptoms (anti-social behaviour), (ii) internalising symptoms (stress, anxiety, depression), (iii) developmental delay (e.g. learning disability), (iv) other (e.g. eating disorder, personality disorders), and (v) any mental health diagnosis, which was created by combining externalising symptoms, internalising symptoms and other. Our analyses were adjusted for social deprivation and perinatal risk factors. RESULTS: There were strong univariable associations between the five individual ACEs, sociodemographic and perinatal factors (e.g. gestational weight at birth) and an increased risk of child mental health diagnoses. After adjusting for sociodemographic and perinatal aspects, there was a remaining conditional increased risk of any child mental health diagnosis, associated with victimisation (conditional hazard ratio (cHR) 1.90, CI 95% 1.34-2.69), and living with an adult with a common mental health diagnosis (cHR 1.63, CI 95% 1.52-1.75). Coefficients of product terms between ACEs and deprivation were not statistically significant. CONCLUSION: The increased risk of child mental health diagnosis associated with victimisation, or exposure to common mental health diagnoses, and alcohol problems in the household supports the need for policy measures and intervention strategies for children and their families.

Making apples from oranges: Comparing noncollapsible effect estimators and their standard errors after adjustment for different covariate sets.

We revisit the well-known but often misunderstood issue of (non)collapsibility of effect measures in regression models for binary and time-to-event outcomes. We describe an existing simple but largely ignored procedure for marginalizing estimates of conditional odds ratios and propose a similar procedure for marginalizing estimates of conditional hazard ratios (allowing for right censoring), demonstrating its performance in simulation studies and in a reanalysis of data from a small randomized trial in primary biliary cirrhosis patients. In addition, we aim to provide an educational summary of issues surrounding (non)collapsibility from a causal inference perspective and to promote the idea that the words conditional and adjusted (likewise marginal and unadjusted) should not be used interchangeably.

Demographic and socioeconomic patterns in the risk of alcohol-related hospital admission in children and young adults with childhood onset type-1 diabetes from a record-linked longitudinal population cohort study in Wales.

BACKGROUND: Little is known about alcohol-related harm in children and young adults with type 1 diabetes (T1D). Education on managing alcohol intake is provided to teenagers with T1D in paediatric clinics in Wales, but its effectiveness is unknown. We compared the patterns in risk of alcohol-related hospital admissions (ARHA) between individuals with and without childhood-onset T1D. METHODS: We extracted data for 1 791 577 individuals born during 1979 to 2014 with a general practitioner registration in Wales, and record-linked the demographic data to ARHA between 1998 and June 2016 within the Secure Anonymised Information Linkage Databank (SAIL). Linkage to a national T1D register (Brecon Cohort) identified 3575 children diagnosed aged <15 years since 1995. We estimated hazard ratios (HRs) with 95% confidence intervals (95% CIs) for the risk of ARHA using recurrent-event models, including interaction terms. RESULTS: Individuals with T1D had a higher riskof ARHA (HR: 1.78; 95% CI: 1.60-1.98), adjusted for age group, sex, and deprivation. The risk in people with diabetes was highest aged 14 to 17 years, around three times higher than the peak in non-T1D aged 18 to 22. Females with diabetes had a lower risk generally. The association between deprivation and ARHA was weaker in the T1D group. CONCLUSION: Young people with T1D had increased risks of ARHA, particularly at school age, and smaller socioeconomic inequalities in ARHA. A review of interventions to reduce alcohol-related harm in T1D is needed, perhaps including modification of current education and guidance for teenagers on managing alcohol consumption and reviewing criteria for hospital admission.

Time-dependent mediators in survival analysis: Modeling direct and indirect effects with the additive hazards model.

We discuss causal mediation analyses for survival data and propose a new approach based on the additive hazards model. The emphasis is on a dynamic point of view, that is, understanding how the direct and indirect effects develop over time. Hence, importantly, we allow for a time varying mediator. To define direct and indirect effects in such a longitudinal survival setting we take an interventional approach (Didelez, 2018) where treatment is separated into one aspect affecting the mediator and a different aspect affecting survival. In general, this leads to a version of the nonparametric g-formula (Robins, 1986). In the present paper, we demonstrate that combining the g-formula with the additive hazards model and a sequential linear model for the mediator process results in simple and interpretable expressions for direct and indirect effects in terms of relative survival as well as cumulative hazards. Our results generalize and formalize the method of dynamic path analysis (Fosen, Ferkingstad, Borgan, & Aalen, 2006; Strohmaier et al., 2015). An application to data from a clinical trial on blood pressure medication is given.

Mild-to-moderate renal pelvis dilatation identified during pregnancy and hospital admissions in childhood: An electronic birth cohort study in Wales, UK.

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.

Analysis of Longitudinal Studies With Repeated Outcome Measures: Adjusting for Time-Dependent Confounding Using Conventional Methods.

Estimation of causal effects of time-varying exposures using longitudinal data is a common problem in epidemiology. When there are time-varying confounders, which may include past outcomes, affected by prior exposure, standard regression methods can lead to bias. Methods such as inverse probability weighted estimation of marginal structural models have been developed to address this problem. However, in this paper we show how standard regression methods can be used, even in the presence of time-dependent confounding, to estimate the total effect of an exposure on a subsequent outcome by controlling appropriately for prior exposures, outcomes, and time-varying covariates. We refer to the resulting estimation approach as sequential conditional mean models (SCMMs), which can be fitted using generalized estimating equations. We outline this approach and describe how including propensity score adjustment is advantageous. We compare the causal effects being estimated using SCMMs and marginal structural models, and we compare the two approaches using simulations. SCMMs enable more precise inferences, with greater robustness against model misspecification via propensity score adjustment, and easily accommodate continuous exposures and interactions. A new test for direct effects of past exposures on a subsequent outcome is described.

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.

Estimating long-term treatment effects in observational data: A comparison of the performance of different methods under real-world uncertainty.

In the presence of time-dependent confounding, there are several methods available to estimate treatment effects. With correctly specified models and appropriate structural assumptions, any of these methods could provide consistent effect estimates, but with real-world data, all models will be misspecified and it is difficult to know if assumptions are violated. In this paper, we investigate five methods: inverse probability weighting of marginal structural models, history-adjusted marginal structural models, sequential conditional mean models, g-computation formula, and g-estimation of structural nested models. This work is motivated by an investigation of the effects of treatments in cystic fibrosis using the UK Cystic Fibrosis Registry data focussing on two outcomes: lung function (continuous outcome) and annual number of days receiving intravenous antibiotics (count outcome). We identified five features of this data that may affect the performance of the methods: misspecification of the causal null, long-term treatment effects, effect modification by time-varying covariates, misspecification of the direction of causal pathways, and censoring. In simulation studies, under ideal settings, all five methods provide consistent estimates of the treatment effect with little difference between methods. However, all methods performed poorly under some settings, highlighting the importance of using appropriate methods based on the data available. Furthermore, with the count outcome, the issue of non-collapsibility makes comparison between methods delivering marginal and conditional effects difficult. In many situations, we would recommend using more than one of the available methods for analysis, as if the effect estimates are very different, this would indicate potential issues with the analyses.

Socioeconomic determinants of growth in a longitudinal study in Nepal.

Socioeconomic status (SES) is associated with childhood anthropometry, but little is known about how it is associated with tissue growth and body composition. To investigate this, we looked at components of SES at birth with growth in early and mid-childhood, and body composition in a longitudinal study in Nepal. The exposure variables (material assets, land ownership, and maternal education) were quantified from questionnaire data before birth. Anthropometry data at birth, 2.5 and 8.5 years, were normalized using WHO reference ranges and conditional growth calculated. Associations with child growth and body composition were explored using multiple regression analysis. Complete anthropometry data were available for 793 children. There was a positive association between SES and height-for-age and weight-for-age, and a reduction in odds of stunting and underweight for each increase in rank of SES variable. Associations tended to be significant when moving from the lower to the upper asset score, from none to secondary education, and no land to >30 dhur (~500 m2 ). The strongest associations were for maternal secondary education, showing an increase of 0.6-0.7 z scores in height-for-age and weight-for-age at 2.5 and 8.5 years and 0.3 kg/m2 in fat and lean mass compared to no education. There was a positive association with conditional growth in the highest asset score group and secondary maternal education, and generally no association with land ownership. Our results show that SES at birth is important for the growth of children, with a greater association with fat mass. The greatest influence was maternal secondary education.

Interventional Effects for Mediation Analysis with Multiple Mediators.

The mediation formula for the identification of natural (in)direct effects has facilitated mediation analyses that better respect the nature of the data, with greater consideration of the need for confounding control. The default assumptions on which it relies are strong, however. In particular, they are known to be violated when confounders of the mediator-outcome association are affected by the exposure. This complicates extensions of counterfactual-based mediation analysis to settings that involve repeatedly measured mediators, or multiple correlated mediators. VanderWeele, Vansteelandt, and Robins introduced so-called interventional (in)direct effects. These can be identified under much weaker conditions than natural (in)direct effects, but have the drawback of not adding up to the total effect. In this article, we adapt their proposal to achieve an exact decomposition of the total effect, and extend it to the multiple mediator setting. Interestingly, the proposed effects capture the path-specific effects of an exposure on an outcome that are mediated by distinct mediators, even when-as often-the structural dependence between the multiple mediators is unknown, for instance, when the direction of the causal effects between the mediators is unknown, or there may be unmeasured common causes of the mediators.

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).

A comparison of methods to adjust for continuous covariates in the analysis of randomised trials.

BACKGROUND: Although covariate adjustment in the analysis of randomised trials can be beneficial, adjustment for continuous covariates is complicated by the fact that the association between covariate and outcome must be specified. Misspecification of this association can lead to reduced power, and potentially incorrect conclusions regarding treatment efficacy. METHODS: We compared several methods of adjustment to determine which is best when the association between covariate and outcome is unknown. We assessed (a) dichotomisation or categorisation; (b) assuming a linear association with outcome; (c) using fractional polynomials with one (FP1) or two (FP2) polynomial terms; and (d) using restricted cubic splines with 3 or 5 knots. We evaluated each method using simulation and through a re-analysis of trial datasets. RESULTS: Methods which kept covariates as continuous typically had higher power than methods which used categorisation. Dichotomisation, categorisation, and assuming a linear association all led to large reductions in power when the true association was non-linear. FP2 models and restricted cubic splines with 3 or 5 knots performed best overall. CONCLUSIONS: For the analysis of randomised trials we recommend (1) adjusting for continuous covariates even if their association with outcome is unknown; (2) keeping covariates as continuous; and (3) using fractional polynomials with two polynomial terms or restricted cubic splines with 3 to 5 knots when a linear association is in doubt.

How much do tumor stage and treatment explain socioeconomic inequalities in breast cancer survival? Applying causal mediation analysis to population-based data.

Substantial socioeconomic inequalities in breast cancer survival persist in England, possibly due to more advanced cancer at diagnosis and differential access to treatment. We aim to disentangle the contributions of differential stage at diagnosis and differential treatment to the socioeconomic inequalities in cancer survival. Information on 36,793 women diagnosed with breast cancer during 2000-2007 was routinely collected by an English population-based cancer registry. Deprivation was determined for each patient according to her area of residence at the time of diagnosis. A parametric implementation of the mediation formula using Monte Carlo simulation was used to estimate the proportion of the effect of deprivation on survival mediated by stage and by treatment. One-third (35 % [23-48 %]) of the higher mortality experienced by most deprived patients at 6 months after diagnosis, and one tenth (14 % [-3 to 31 %]) at 5 years, was mediated by adverse stage distribution. We initially found no evidence of mediation via differential surgical treatment. However, sensitivity analyses testing some of our study limitations showed in particular that up to thirty per cent of the higher mortality in most deprived patients could be mediated by differential surgical treatment. This study illustrates the importance of using causal inference methods with routine medical data and the need for testing key assumptions through sensitivity analyses. Our results suggest that, although effort for earlier diagnosis is important, this would reduce the cancer survival inequalities only by a third. Because of data limitations, role of differential surgical treatment may have been under-estimated.