The tale wagged by the DAG: broadening the scope of causal inference and explanation for epidemiology

Negligence in biomedical research: an anti-racist approach for substance use researchers

Racism is embedded in the fabric of society at structural, disciplinary, hegemonic, and interpersonal levels, working as a mechanism that drives health disparities. In particular, stigmatized views of substance use get entangled with racialization, serving as a tool to uphold oppressive systems. While national health institutions have made commitments to dismantle these systems in the United States, anti-racism has not been integrated into biomedical research practice. The ways in which substance use researchers use and interpret race data-without engaging in structural racism as a mechanism of health inequity-can only be described as inadequate. Drawing upon concepts from the Public Health Critical Race praxis, QuantCrit, and an anti-racism research framework, we recommend a set of guidelines to help biomedical researchers conceptualize and engage with race more responsibly in substance use research.

Heat, humidity and health impacts: how causal diagrams can help tell the complex story

The global health burden associated with exposure to heat is a grave concern and is projected to further increase under climate change. While physiological studies have demonstrated the role of humidity alongside temperature in exacerbating heat stress for humans, epidemiological findings remain conflicted. Understanding the intricate relationships between heat, humidity, and health outcomes is crucial to inform adaptation and drive increased global climate change mitigation efforts. This article introduces 'directed acyclic graphs' (DAGs) as causal models to elucidate the analytical complexity in observational epidemiological studies that focus on humid-heat-related health impacts. DAGs are employed to delineate implicit assumptions often overlooked in such studies, depicting humidity as a confounder, mediator, or an effect modifier. We also discuss complexities arising from using composite indices, such as wet-bulb temperature. DAGs representing the health impacts associated with wet-bulb temperature help to understand the limitations in separating the individual effect of humidity from the perceived effect of wet-bulb temperature on health. General examples for regression models corresponding to each of the causal assumptions are also discussed. Our goal is not to prioritize one causal model but to discuss the causal models suitable for representing humid-heat health impacts and highlight the implications of selecting one model over another. We anticipate that the article will pave the way for future quantitative studies on the topic and motivate researchers to explicitly characterize the assumptions underlying their models with DAGs, facilitating accurate interpretations of the findings. This methodology is applicable to similarly complex compound events.

Exploring a Potential Interaction Between the Effect of Specific Maternal Smoking Patterns and Comorbid Antenatal Depression in Causing Postpartum Depression

Purpose

To explore a potential interaction between the effect of specific maternal smoking patterns and the presence of antenatal depression, as independent exposures, in causing postpartum depression (PPD).

Methods

This case-control study of participants with singleton term births (N = 51220) was based on data from the 2017-2018 Pregnancy Risk Assessment Monitoring System. Multivariable log-binomial regression models examined the main effects of smoking patterns and self-reported symptoms of antenatal depression on the risk of PPD on the adjusted risk ratio (aRR) scale and tested a two-way interaction adjusting for covariates selected in a directed acyclic graph (DAG). The interaction effects were measured on the additive scale using relative excess risk due to interaction (RERI), the attributable proportion of interaction (AP), and the synergy index (SI). Causal effects were defined in a counterfactual framework. The E-value quantified the potential impact of unobserved/unknown covariates, conditional on observed covariates.

Results

Among 6841 women in the sample who self-reported PPD, 35.7% also reported symptoms of antenatal depression. Out of 3921 (7.7%) women who reported smoking during pregnancy, 32.6% smoked at high intensity (≥10 cigarettes/day) in all three trimesters and 36.6% had symptoms of antenatal depression. The main effect of PPD was the strongest for women who smoked at high intensity throughout pregnancy (aRR 1.65; 95% CI: 1.63, 1.68). A synergistic interaction was detected, and the effect of all maternal smoking patterns was augmented, particularly in late pregnancy for Increasers and Reducers.

Conclusion

Strong associations and interaction effects between maternal smoking patterns and co-occurring antenatal depression support smoking prevention and cessation interventions during pregnancy to lower the likelihood of PPD.

Process-Based psychotherapy personalization: considering causality with continuous-time dynamic modeling

Psychotherapy can be improved by integrating the study of mediators (how it works) and moderators (for whom it works). To demonstrate this integration, we studied the relationship between resource activation, problem-coping experiences and symptoms in cognitive-behavior therapy (CBT) for depression, to obtain preliminary insights on causal inference (which process leads to symptom improvement?) and prediction (which one for whom?).

A sample of 715 patients with depression who received CBT was analyzed. Hierarchical Bayesian continuous time dynamic modeling was used to study the temporal dynamics between the variables analyzed within the first ten sessions. Depression and self-efficacy at baseline were examined as predictors of these dynamics.

There were significant cross-effects between the processes studied. Under typical assumptions, resource activation had a significant effect on symptom improvement. Problem-coping experience had a significant effect on resource activation. Depression and self-efficacy moderated these effects. However, when system noise was considered, these effects may be affected by other processes.

Resource activation was strongly associated with symptom improvement. To the extent of inferring causality, for patients with mild-moderate depression and high self-efficacy, promoting resource activation can be recommended. For patients with severe depression and low self-efficacy, promoting problem-coping experiences can be recommended.

UK research data resources based on primary care electronic health records: review and summary for potential users

BACKGROUND

The range and scope of electronic health record (EHR) data assets in the UK has recently increased, which has been mainly in response to the COVID-19 pandemic. Summarising and comparing the large primary care resources will help researchers to choose the data resources most suited to their needs.

AIM

To describe the current landscape of UK EHR databases and considerations of access and use of these resources relevant to researchers.

DESIGN & SETTING

Narrative review of EHR databases in the UK.

METHOD

Information was collected from the Health Data Research Innovation Gateway, publicly available websites and other published data, and from key informants. The eligibility criteria were population-based open-access databases sampling EHRs across the whole population of one or more countries in the UK. Published database characteristics were extracted and summarised, and these were corroborated with resource providers. Results were synthesised narratively.

RESULTS

Nine large national primary care EHR data resources were identified and summarised. These resources are enhanced by linkage to other administrative data to a varying extent. Resources are mainly intended to support observational research, although some can support experimental studies. There is considerable overlap of populations covered. While all resources are accessible to bona fide researchers, access mechanisms, costs, timescales, and other considerations vary across databases.

CONCLUSION

Researchers are currently able to access primary care EHR data from several sources. Choice of data resource is likely to be driven by project needs and access considerations. The landscape of data resources based on primary care EHRs in the UK continues to evolve.

Redlining-associated methylation in breast tumors: the impact of contemporary structural racism on the tumor epigenome

Purpose

Place-based measures of structural racism have been associated with breast cancer mortality, which may be driven, in part, by epigenetic perturbations. We examined the association between contemporary redlining, a measure of structural racism at the neighborhood level, and DNA methylation in breast tumor tissue.

Methods

We identified 80 Black and White women diagnosed and treated for a first-primary breast cancer at Emory University Hospitals (2008-2017). Contemporary redlining was derived for census tracts using the Home Mortgage Disclosure Act database. Linear regression models were used to calculate the association between contemporary redlining and methylation in breast tumor tissue. We also examined epigenetic age acceleration for two different metrics, regressing β values for each cytosine-phosphate-guanine dinucleotide (CpG) site on redlining while adjusting for covariates. We employed multivariable Cox-proportional hazards models and 95% confidence intervals (CI) to estimate the association between aberrant methylation and mortality.

Results

Contemporary redlining was associated with 5 CpG sites after adjustment for multiple comparisons (FDR<0.10). All genes were implicated in breast carcinogenesis, including genes related to inflammation, immune function and stress response (ANGPT1, PRG4 and PRG4). Further exploration of the top 25 CpG sites, identified interaction of 2 sites (MRPS28 and cg11092048) by ER status and 1 site (GDP1) was associated with all-cause mortality. Contemporary redlining was associated with epigenetic age acceleration by the Hannum metric (β=5.35; CI 95%=0.30,10.4) and showed positive but non-significant correlation with the other clock.

Conclusion

We identified novel associations between neighborhood contemporary redlining and the breast tumor DNA methylome, suggesting that racist policies leading to inequitable social and environmental exposures, may impact the breast tumor epigenome. Additional research on the potential implications for prognosis is needed.

Occupational differences in COVID-19 incidence, severity, and mortality in the United Kingdom: Available data and framework for analyses

There are important differences in the risk of SARS-CoV-2 infection and death depending on occupation. Infections in healthcare workers have received the most attention, and there are clearly increased risks for intensive care unit workers who are caring for COVID-19 patients. However, a number of other occupations may also be at an increased risk, particularly those which involve social care or contact with the public. A large number of data sets are available with the potential to assess occupational risks of COVID-19 incidence, severity, or mortality. We are reviewing these data sets as part of the Partnership for Research in Occupational, Transport, Environmental COVID Transmission (PROTECT) initiative, which is part of the National COVID-19 Core Studies. In this report, we review the data sets available (including the key variables on occupation and potential confounders) for examining occupational differences in SARS-CoV-2 infection and COVID-19 incidence, severity and mortality. We also discuss the possible types of analyses of these data sets and the definitions of (occupational) exposure and outcomes. We conclude that none of these data sets are ideal, and all have various strengths and weaknesses. For example, mortality data suffer from problems of coding of COVID-19 deaths, and the deaths (in England and Wales) that have been referred to the coroner are unavailable. On the other hand, testing data is heavily biased in some periods (particularly the first wave) because some occupations (e.g. healthcare workers) were tested more often than the general population. Random population surveys are, in principle, ideal for estimating population prevalence and incidence, but are also affected by non-response. Thus, any analysis of the risks in a particular occupation or sector (e.g. transport), will require a careful analysis and triangulation of findings across the various available data sets.

Occupational differences in COVID-19 incidence, severity, and mortality in the United Kingdom: Available data and framework for analyses

There are important differences in the risk of SARS-CoV-2 infection and death depending on occupation. Infections in healthcare workers have received the most attention, and there are clearly increased risks for intensive care unit workers who are caring for COVID-19 patients. However, a number of other occupations may also be at an increased risk, particularly those which involve social care or contact with the public. A large number of data sets are available with the potential to assess occupational risks of COVID-19 incidence, severity, or mortality. We are reviewing these data sets as part of the Partnership for Research in Occupational, Transport, Environmental COVID Transmission (PROTECT) initiative, which is part of the National COVID-19 Core Studies. In this report, we review the data sets available (including the key variables on occupation and potential confounders) for examining occupational differences in SARS-CoV-2 infection and COVID-19 incidence, severity and mortality. We also discuss the possible types of analyses of these data sets and the definitions of (occupational) exposure and outcomes. We conclude that none of these data sets are ideal, and all have various strengths and weaknesses. For example, mortality data suffer from problems of coding of COVID-19 deaths, and the deaths (in England and Wales) that have been referred to the coroner are unavailable. On the other hand, testing data is heavily biased in some periods (particularly the first wave) because some occupations (e.g. healthcare workers) were tested more often than the general population. Random population surveys are, in principle, ideal for estimating population prevalence and incidence, but are also affected by non-response. Thus, any analysis of the risks in a particular occupation or sector (e.g. transport), will require a careful analysis and triangulation of findings across the various available data sets.

These Are Not the Effects You Are Looking for: Causality and the Within-/Between-Persons Distinction in Longitudinal Data Analysis

In psychological science, researchers often pay particular attention to the distinction between within- and between-persons relationships in longitudinal data analysis. Here, we aim to clarify the relationship between the within- and between-persons distinction and causal inference and show that the distinction is informative but does not play a decisive role in causal inference. Our main points are threefold. First, within-persons data are not necessary for causal inference; for example, between-persons experiments can inform about (average) causal effects. Second, within-persons data are not sufficient for causal inference; for example, time-varying confounders can lead to spurious within-persons associations. Finally, despite not being sufficient, within-persons data can be tremendously helpful for causal inference. We provide pointers to help readers navigate the more technical literature on longitudinal models and conclude with a call for more conceptual clarity: Instead of letting statistical models dictate which substantive questions researchers ask, researchers should start with well-defined theoretical estimands, which in turn determine both study design and data analysis.

Examining the Reciprocity Between Perceived Discrimination and Health: A Longitudinal Perspective

This study aims to fill two interrelated knowledge gaps in the extant literature on the association between perceived discrimination and health. First, potential selection bias associated with pre-existing health conditions has rarely been rigorously tested in empirical studies. Second, whether there is a reciprocal relationship between perceived discrimination and health has been underexplored. Using longitudinal data from the Americans' Changing Lives data, waves 3 to 5 (N=1,058), we test the reciprocity between perceived discrimination and health with a formal mediation analysis technique. We also use the Heckman correction to adjust for the potential selection bias associated with attrition. Our analysis indicates that perceived discrimination is associated with poor self-rated health and depressive symptoms even when previous health conditions are considered. Furthermore, net of other confounders, there is a reciprocal relationship between perceived discrimination and depressive symptoms. However, this reciprocity does not hold for self-rated health. These findings indicate that there is a vicious circle between perceived discrimination and mental health. That is, poor mental health may lead to perceived discrimination, and heightened perceived discrimination may subsequently increase depressive symptoms. Sensitivity tests suggest that this reciprocity may vary by gender and race.

Can Robots Do Epidemiology? Machine Learning, Causal Inference, and Predicting the Outcomes of Public Health Interventions

This paper argues that machine learning (ML) and epidemiology are on collision course over causation. The discipline of epidemiology lays great emphasis on causation, while ML research does not. Some epidemiologists have proposed imposing what amounts to a causal constraint on ML in epidemiology, requiring it either to engage in causal inference or restrict itself to mere projection. We whittle down the issues to the question of whether causal knowledge is necessary for underwriting predictions about the outcomes of public health interventions. While there is great plausibility to the idea that it is, conviction that something is impossible does not by itself motivate a constraint to forbid trying. We disambiguate the possible motivations for such a constraint into definitional, metaphysical, epistemological, and pragmatic considerations and argue that “Proceed with caution” (rather than “Stop!”) is the outcome of each. We then argue that there are positive reasons to proceed, albeit cautiously. Causal inference enforces existing classification schema prior to the testing of associational claims (causal or otherwise), but associations and classification schema are more plausibly discovered (rather than tested or justified) in a back-and-forth process of gaining reflective equilibrium. ML instantiates this kind of process, we argue, and thus offers the welcome prospect of uncovering meaningful new concepts in epidemiology and public health—provided it is not causally constrained.

State LGBTQ policies and binge drinking among sexual minority youth in the US: a multilevel analysis

PURPOSE

Though higher rates of binge drinking have been reported among sexual minority (lesbian, gay, and bisexual) youths compared to their heterosexual peers in the United States, questions remain about which specific structural factors drive these inequities. We test whether state LGBTQ (lesbian, gay, bisexual, transgender, queer/questioning) policy climate is associated with youth binge drinking, as well as whether that association is unique to sexual minority youth.

METHODS

We use LGBTQ policy scores compiled by the Movement Advancement Project in 2017 and analyze 2017 Youth Risk Behavior Survey data (126,432 youths living in 24 states). We fit multi-level logistic regression models to estimate odds ratios for binge drinking with state-level LGBTQ policy scores.

RESULT

More progressive state-level overall LGBTQ policies were associated with lower odds of binge drinking among sexual minority youths (interaction term OR = 0.990, CI 0.984-0.996, p = 0.0009), but not among heterosexuals (OR = 1.001, CI 0.992, 1.009, p = 0.881). For sexual minorities, living in the most LGBTQ-affirming state was associated with a probability of binge drinking 27% lower than that of those living in the least LGBTQ-affirming state. In sensitivity analyses, policies specific to LGBTQ youth (opposed to general LGBTQ people) were even more strongly related to sexual minorities' binge drinking (interaction term: state-level youth policy score × individual-level sexual identity-of OR = 0.949, CI 0.927-0.971, p = 0.00001). In sex-stratified models, associations with overall LGBTQ policy scores were significant only for girls (OR = 0.986, CI 0.979-0.993, p < 0.001), not boys (OR = 0.997, CI 0.986-1.008, p = 0.551), while associations with youth-focused LGBTQ policies were significant for both sexual minority girls and boys.

CONCLUSION

Our findings point to state-level LGBTQ policy climate as a potential social determinant of binge drinking problems among sexual minority youths.

A new era: improving use of sociodemographic constructs in the analysis of pediatric cohort study data

Given the diversity of sex, gender identity, race, ethnicity, and socioeconomic position (SEP) in children across the United States, it is incumbent upon pediatric and epidemiologic researchers to conduct their work in ways that promote inclusivity, understanding and reduction in inequities. Current child health research often utilizes an approach of "convenience" in how data related to these constructs are collected, categorized, and included in models; the field needs to be more systematic and thoughtful in its approach to understand how sociodemographics affect child health. We offer suggestions for improving the discourse around sex, gender identity, race, ethnicity, and SEP in child health research. We explain how analytic models should be driven by a conceptual framework grounding the choices of variables that are included in analyses, without the automatic "adjusting for" all sociodemographic constructs. We propose to leverage newly available data from large multi-cohort consortia as unique opportunities to improve the current standards for analyzing and reporting core sociodemographic constructs. Improving the characterization and interpretation of child health studies with regards to core sociodemographic constructs is critical for optimizing child health and reducing inequities in the health and well-being of all children across the United States. IMPACT: Current child health research often utilizes an approach of "convenience" in how data related to sex, race/ethnicity, and SEP are collected, categorized, and included in models. We offer suggestions for how scholars can improve the discourse around sex, gender identity, race, ethnicity, and SEP in child health research. We explain how analytic models should be driven by a conceptual framework grounding the choices of variables that are included in analyses. We propose to leverage newly available large cohort consortia of child health studies as opportunities to improve the current standards for analyzing and reporting core sociodemographic constructs.

Appraising clinical applicability of studies: mapping and synthesis of current frameworks, and proposal of the FrACAS framework and VICORT checklist

BACKGROUND

Not all research findings are translated to clinical practice. Reasons for lack of applicability are varied, and multiple frameworks and criteria exist to appraise the general applicability of epidemiological and clinical research. In this two-part study, we identify, map, and synthesize frameworks and criteria; we develop a framework to assist clinicians to appraise applicability specifically from a clinical perspective.

METHODS

We conducted a literature search in PubMed and Embase to identify frameworks appraising applicability of study results. Conceptual thematic analysis was used to synthesize frameworks and criteria. We carried out a framework development process integrating contemporary debates in epidemiology, findings from the literature search and synthesis, iterative pilot-testing, and brainstorming and consensus discussions to propose a concise framework to appraise clinical applicability.

RESULTS

Of the 4622 references retrieved, we identified 26 unique frameworks featuring 21 criteria. Frameworks and criteria varied by scope and level of aggregation of the evidence appraised, target user, and specific area of applicability (internal validity, clinical applicability, external validity, and system applicability). Our proposed Framework Appraising the Clinical Applicability of Studies (FrACAS) classifies studies in three domains (research, practice informing, and practice changing) by examining six criteria sequentially: Validity, Indication-informativeness, Clinical relevance, Originality, Risk-benefit comprehensiveness, and Transposability (VICORT checklist).

CONCLUSIONS

Existing frameworks to applicability vary by scope, target user, and area of applicability. We introduce FrACAS to specifically assess applicability from a clinical perspective. Our framework can be used as a tool for the design, appraisal, and interpretation of epidemiological and clinical studies.

Assessing causality in epidemiology: revisiting Bradford Hill to incorporate developments in causal thinking

The nine Bradford Hill (BH) viewpoints (sometimes referred to as criteria) are commonly used to assess causality within epidemiology. However, causal thinking has since developed, with three of the most prominent approaches implicitly or explicitly building on the potential outcomes framework: directed acyclic graphs (DAGs), sufficient-component cause models (SCC models, also referred to as 'causal pies') and the grading of recommendations, assessment, development and evaluation (GRADE) methodology. This paper explores how these approaches relate to BH's viewpoints and considers implications for improving causal assessment. We mapped the three approaches above against each BH viewpoint. We found overlap across the approaches and BH viewpoints, underscoring BH viewpoints' enduring importance. Mapping the approaches helped elucidate the theoretical underpinning of each viewpoint and articulate the conditions when the viewpoint would be relevant. Our comparisons identified commonality on four viewpoints: strength of association (including analysis of plausible confounding); temporality; plausibility (encoded by DAGs or SCC models to articulate mediation and interaction, respectively); and experiments (including implications of study design on exchangeability). Consistency may be more usefully operationalised by considering an effect size's transportability to a different population or unexplained inconsistency in effect sizes (statistical heterogeneity). Because specificity rarely occurs, falsification exposures or outcomes (i.e., negative controls) may be more useful. The presence of a dose-response relationship may be less than widely perceived as it can easily arise from confounding. We found limited utility for coherence and analogy. This study highlights a need for greater clarity on BH viewpoints to improve causal assessment.

Situating agent-based modelling in population health research

Today's most troublesome population health challenges are often driven by social and environmental determinants, which are difficult to model using traditional epidemiological methods. We agree with those who have argued for the wider adoption of agent-based modelling (ABM) in taking on these challenges. However, while ABM has been used occasionally in population health, we argue that for ABM to be most effective in the field it should be used as a means for answering questions normally inaccessible to the traditional epidemiological toolkit. In an effort to clearly illustrate the utility of ABM for population health research, and to clear up persistent misunderstandings regarding the method's conceptual underpinnings, we offer a detailed presentation of the core concepts of complex systems theory, and summarise why simulations are essential to the study of complex systems. We then examine the current state of the art in ABM for population health, and propose they are well-suited for the study of the 'wicked' problems in population health, and could make significant contributions to theory and intervention development in these areas.

Use of directed acyclic graphs (DAGs) to identify confounders in applied health research: review and recommendations

Background

Directed acyclic graphs (DAGs) are an increasingly popular approach for identifying confounding variables that require conditioning when estimating causal effects. This review examined the use of DAGs in applied health research to inform recommendations for improving their transparency and utility in future research.

Methods

Original health research articles published during 1999–2017 mentioning ‘directed acyclic graphs’ (or similar) or citing DAGitty were identified from Scopus, Web of Science, Medline and Embase. Data were extracted on the reporting of: estimands, DAGs and adjustment sets, alongside the characteristics of each article’s largest DAG.

Results

A total of 234 articles were identified that reported using DAGs. A fifth (n = 48, 21%) reported their target estimand(s) and half (n = 115, 48%) reported the adjustment set(s) implied by their DAG(s). Two-thirds of the articles (n = 144, 62%) made at least one DAG available. DAGs varied in size but averaged 12 nodes [interquartile range (IQR): 9–16, range: 3–28] and 29 arcs (IQR: 19–42, range: 3–99). The median saturation (i.e. percentage of total possible arcs) was 46% (IQR: 31–67, range: 12–100). 37% (n = 53) of the DAGs included unobserved variables, 17% (n = 25) included ‘super-nodes’ (i.e. nodes containing more than one variable) and 34% (n = 49) were visually arranged so that the constituent arcs flowed in the same direction (e.g. top-to-bottom).

Conclusion

There is substantial variation in the use and reporting of DAGs in applied health research. Although this partly reflects their flexibility, it also highlights some potential areas for improvement. This review hence offers several recommendations to improve the reporting and use of DAGs in future research.

Sustaining planetary health through systems thinking: Public health's critical role

Understanding and responding to adverse human health impacts of global environmental change will be a major priority of 21st century public health professionals. The emerging field of planetary health aims to face this challenge by studying and promoting policies that protect the health of humans and of the Earth's natural systems that support them. Public health, drawing on its experience of guiding policies to improve population health, has contributed to planetary health's development. Yet, few public health practitioners are familiar with planetary health's systems-oriented approaches for understanding relationships between economic development, environmental degradation, and human health. In this narrative review, we present key planetary health concepts and show how systems thinking has guided its development. We discuss historical approaches to studying impacts of economic development on human health and the environment. We then review novel conceptual frameworks adopted by planetary health scientists to study and forecast impacts of policies that influence human health and Earth's natural systems at varying spatiotemporal scales. We conclude by presenting examples of how applying the "Doughnut" model (an economic framework where the needs of people are met without overshooting the world's ecological limits) could guide policies for promoting health co-benefits to humans and natural systems.

DNA methylation differences at birth after conception through ART

STUDY QUESTION

Is there a relation between ART and DNA methylation (DNAm) patterns in cord blood, including any differences between IVF and ICSI?

SUMMARY ANSWER

DNAm at 19 CpGs was associated with conception via ART, with no difference found between IVF and ICSI.

WHAT IS KNOWN ALREADY

Prior studies on either IVF or ICSI show conflicting outcomes, as both widespread effects on DNAm and highly localized associations have been reported. No study on both IVF and ICSI and genome-wide neonatal DNAm has been performed.

STUDY DESIGN, SIZE, DURATION

This was a cross-sectional study comprising 87 infants conceived with IVF or ICSI and 70 conceived following medically unassisted conception. The requirement for inclusion in the study was an understanding of the Swedish language and exclusion was the use of donor gametes.

PARTICIPANTS/MATERIALS, SETTING, METHODS

Participants were from the UppstART study, which was recruited from fertility and reproductive health clinics, and the Born into Life cohort, which is recruited from the larger LifeGene study. We measured DNAm from DNA extracted from cord blood collected at birth using a micro-array (450k array). Group differences in DNAm at individual CpG dinucleotides (CpGs) were determined using robust linear models and post-hoc Tukey’s tests.

MAIN RESULTS AND THE ROLE OF CHANCE

We found no association of ART conception with global methylation levels, imprinted loci and meta-stable epialleles. In contrast, we identify 19 CpGs at which DNAm was associated with being conceived via ART (effect estimates: 0.5–4.9%, P_FDR < 0.05), but no difference was found between IVF and ICSI. The associated CpGs map to genes related to brain function/development or genes connected to the plethora of conditions linked to subfertility, but functional annotation did not point to any likely functional consequences.

LIMITATIONS, REASONS FOR CAUTION

We measured DNAm in cord blood and not at later ages or in other tissues. Given the number of tests performed, our study power is limited and the findings need to be replicated in an independent study.

WIDER IMPLICATIONS OF THE FINDINGS

We find that ART is associated with DNAm differences in cord blood when compared to non-ART samples, but these differences are limited in number and effect size and have unknown functional consequences in adult blood. We did not find indications of differences between IVF and ICSI.

STUDY FUNDING/COMPETING INTEREST(S)

E.W.T. was supported by a VENI grant from the Netherlands Organization for Scientific Research (91617128) and JPI-H2020 Joint Programming Initiative a Healthy Diet for a Healthy Life (JPI HDHL) under proposal number 655 (PREcisE Project) through ZonMw (529051023). Financial support was provided from the European Union’s Seventh Framework Program IDEAL (259679), the Swedish Research Council (K2011-69X-21871-01-6, 2011-3060, 2015-02434 and 2018-02640) and the Strategic Research Program in Epidemiology Young Scholar Awards, Karolinska Institute (to A.N.I.) and through the Swedish Initiative for Research on Microdata in the Social And Medical Sciences (SIMSAM) framework grant no 340-2013-5867, grants provided by the Stockholm County Council (ALF-projects), the Strategic Research Program in Epidemiology at Karolinska Institutet and the Swedish Heart-Lung Foundation and Danderyd University Hospital (Stockholm, Sweden). The funders had no role in study design, data collection, analysis, decision to publish or preparation of the manuscript. The authors declare no competing interests.

TRIAL REGISTRATION NUMBER

N/A.

Proxy gene-by-environment Mendelian randomization study confirms a causal effect of maternal smoking on offspring birthweight, but little evidence of long-term influences on offspring health

Background

A lack of genetic data across generations makes transgenerational Mendelian randomization (MR) difficult. We used UK Biobank and a novel proxy gene-by-environment MR to investigate effects of maternal smoking heaviness in pregnancy on offspring health, using participants’ (generation one: G1) genotype (rs16969968 in CHRNA5) as a proxy for their mothers’ (G0) genotype.

Methods

We validated this approach by replicating an established effect of maternal smoking heaviness on offspring birthweight. Then we applied this approach to explore effects of maternal (G0) smoking heaviness on offspring (G1) later life outcomes and on birthweight of G1 women’s children (G2).

Results

Each additional smoking-increasing allele in offspring (G1) was associated with a 0.018 [95% confidence interval (CI): -0.026, -0.009] kg lower G1 birthweight in maternal (G0) smoking stratum, but no meaningful effect (-0.002 kg; 95% CI: -0.008, 0.003) in maternal non-smoking stratum (interaction P-value = 0.004). The differences in associations of rs16969968 with grandchild’s (G2) birthweight between grandmothers (G0) who did, versus did not, smoke were heterogeneous (interaction P-value = 0.042) among mothers (G1) who did (-0.020 kg/allele; 95% CI: -0.044, 0.003), versus did not (0.007 kg/allele; 95% CI: -0.005, 0.020), smoke in pregnancy.

Conclusions

Our study demonstrated how offspring genotype can be used to proxy for the mother’s genotype in gene-by-environment MR. We confirmed the causal effect of maternal (G0) smoking on offspring (G1) birthweight, but found little evidence of an effect on G1 longer-term health outcomes. For grandchild’s (G2) birthweight, the effect of grandmother’s (G0) smoking heaviness in pregnancy may be modulated by maternal (G1) smoking status in pregnancy.

Marginal measures and causal effects using the relative survival framework

BACKGROUND

In population-based cancer survival studies, the event of interest is usually death due to cancer. However, other competing events may be present. Relative survival is a commonly used measure in cancer studies that circumvents problems caused by the inaccuracy of the cause of death information. A summary of the prognosis of the cancer population and potential differences between subgroups can be obtained using marginal estimates of relative survival.

METHODS

We utilize regression standardization to obtain marginal estimates of interest in a relative survival framework. Such measures include the standardized relative survival, standardized all-cause survival and standardized crude probabilities of death. Contrasts of these can be formed to explore differences between exposure groups and under certain assumptions are interpreted as causal effects. The difference in standardized all-cause survival can also provide an estimate for the impact of eliminating cancer-related differences between exposure groups. The potential avoidable deaths after such hypothetical scenarios can also be estimated. To illustrate the methods we use the example of survival differences across socio-economic groups for colon cancer.

RESULTS

Using relative survival, a range of marginal measures and contrasts were estimated. For these measures we either focused on cancer-related differences only or chose to incorporate both cancer and other cause differences. The impact of eliminating differences between groups was also estimated. Another useful way for quantifying that impact is the avoidable deaths under hypothetical scenarios.

CONCLUSIONS

Marginal estimates within the relative survival framework provide useful summary measures and can be applied to better understand differences across exposure groups.

Understanding disparities in cancer prognosis: An extension of mediation analysis to the relative survival framework

Mediation analysis can be applied to investigate the effect of a third variable on the pathway between an exposure and the outcome. Such applications include investigating the determinants that drive differences in cancer survival across subgroups. However, cancer disparities may be the result of complex mechanisms that involve both cancer-related and other-cause mortality differences making it difficult to identify the causing factors. Relative survival, a commonly used measure in cancer epidemiology, can be used to focus on cancer-related differences. We extended mediation analysis to the relative survival framework for exploring cancer inequalities. The marginal effects were obtained using regression standardization, after fitting a relative survival model. Contrasts of interests included both marginal relative survival and marginal all-cause survival differences between exposure groups. Such contrasts include the indirect effect due to a mediator that is identifiable under certain assumptions. A separate model was fitted for the mediator and uncertainty was estimated using parametric bootstrapping. The avoidable deaths under interventions can also be estimated to quantify the impact of eliminating differences. The methods are illustrated using data for individuals diagnosed with colon cancer. Mediation analysis within relative survival allows focus on factors that account for cancer-related differences instead of all-cause differences and helps improve our understanding on cancer inequalities.

A self-consistent probabilistic formulation for inference of interactions

Large molecular interaction networks are nowadays assembled in biomedical researches along with important technological advances. Diverse interaction measures, for which input solely consisting of the incidence of causal-factors, with the corresponding outcome of an inquired effect, are formulated without an obvious mathematical unity. Consequently, conceptual and practical ambivalences arise. We identify here a probabilistic requirement consistent with that input, and find, by the rules of probability theory, that it leads to a model multiplicative in the complement of the effect. Important practical properties are revealed along these theoretical derivations, that has not been noticed before.

Vaccination assessments using the Demographic and Health Survey, 2005-2018: a scoping review

OBJECTIVE

To characterise studies which have used Demographic and Health Survey (DHS) datasets to evaluate vaccination status.

DESIGN

Scoping review.

DATA SOURCES

Electronic databases including PubMed, EBSCOhost and POPLINE, from 2005 to 2018.

STUDY SELECTION

All English studies with vaccination status as the outcome and the use of DHS data.

DATA EXTRACTION

Studies were selected using a predetermined list of eligibility criteria and data were extracted independently by two authors. Data related to the study population, the outcome of interest (vaccination) and commonly seen predictors were extracted.

RESULTS

A total of 125 articles were identified for inclusion in the review. The number of countries covered by individual studies varied widely (1-86), with the most published papers using data from India, Nigeria, Pakistan and Ethiopia. Many different definitions of full vaccination were used although the majority used a traditional schedule recommended in the WHO's Expanded Programme on Immunisation. We found studies analysed a wide variety of predictors, but the most common were maternal education, wealth, urbanicity and child's sex. Most commonly reported predictors had consistent relationships with the vaccination outcome, outside of sibling composition.

CONCLUSIONS

Researchers make frequent use of the DHS dataset to describe vaccination patterns within one or more countries. A clearer idea of past use of DHS can inform the development of more rigorous studies in the future. Researchers should carefully consider whether a variable needs to be included in the multivariable model, or if there are mediating relationships across predictor variables.

Win-Win: Reconciling Social Epidemiology and Causal Inference

Social epidemiology is concerned with the health effects of forces that are "above the skin." Although causal inference should be a key goal for social epidemiology, social epidemiology and quantitative causal inference have been seemingly at odds over the years. This does not have to be the case and, in fact, both fields stand to gain through a closer engagement of social epidemiology with formal causal inference approaches. We discuss the misconceptions that have led to an uneasy relationship between these 2 fields, propose a way forward that illustrates how the 2 areas can come together to inform causal questions, and discuss the implications of this approach. We argue that quantitative causal inference in social epidemiology is an opportunity to do better science that matters, a win-win for both fields.

Evidence synthesis for constructing directed acyclic graphs (ESC-DAGs): a novel and systematic method for building directed acyclic graphs

Background

Directed acyclic graphs (DAGs) are popular tools for identifying appropriate adjustment strategies for epidemiological analysis. However, a lack of direction on how to build them is problematic. As a solution, we propose using a combination of evidence synthesis strategies and causal inference principles to integrate the DAG-building exercise within the review stages of research projects. We demonstrate this idea by introducing a novel protocol: ‘Evidence Synthesis for Constructing Directed Acyclic Graphs’ (ESC-DAGs)’.

Methods

ESC-DAGs operates on empirical studies identified by a literature search, ideally a novel systematic review or review of systematic reviews. It involves three key stages: (i) the conclusions of each study are ‘mapped’ into a DAG; (ii) the causal structures in these DAGs are systematically assessed using several causal inference principles and are corrected accordingly; (iii) the resulting DAGs are then synthesised into one or more ‘integrated DAGs’. This demonstration article didactically applies ESC-DAGs to the literature on parental influences on offspring alcohol use during adolescence.

Conclusions

ESC-DAGs is a practical, systematic and transparent approach for developing DAGs from background knowledge. These DAGs can then direct primary data analysis and DAG-based sensitivity analysis. ESC-DAGs has a modular design to allow researchers who are experienced DAG users to both use and improve upon the approach. It is also accessible to researchers with limited experience of DAGs or evidence synthesis.

The Epidemiologic Toolbox: Identifying, Honing, and Using the Right Tools for the Job

There has been much debate about the relative emphasis of the field of epidemiology on causal inference. We believe this debate does short shrift to the breadth of the field. Epidemiologists answer myriad questions that are not causal and hypothesize about and investigate causal relationships without estimating causal effects. Descriptive studies face significant and often overlooked inferential and interpretational challenges; we briefly articulate some of them and argue that a more detailed treatment of biases that affect single-sample estimation problems would benefit all types of epidemiologic studies. Lumping all questions about causality creates ambiguity about the utility of different conceptual models and causal frameworks; 2 distinct types of causal questions include 1) hypothesis generation and theorization about causal structures and 2) hypothesis-driven causal effect estimation. The potential outcomes framework and causal graph theory help efficiently and reliably guide epidemiologic studies designed to estimate a causal effect to best leverage prior data, avoid cognitive fallacies, minimize biases, and understand heterogeneity in treatment effects. Appropriate matching of theoretical frameworks to research questions can increase the rigor of epidemiologic research and increase the utility of such research to improve public health.

Obesity-associated methylation in breast tumors: a possible link to disparate outcomes?

PURPOSE

As a primary risk factor and modifier of breast cancer incidence and prognosis, obesity may contribute to race disparities in breast cancer outcomes. This study examined association between obesity and DNA methylation in non-Hispanic Black and White women diagnosed with breast cancer.

METHODS

Genome-wide DNA methylation was measured in the breast cancer tumor tissue of 96 women using the EPIC array. To examine the association between obesity and tumor methylation, linear regression models were used-regressing methylation β value for each cytosine and guanine (CpG) site on body mass index adjusting for covariates. Significance was set at false discovery rate (FDR) < 0.05. In the top 20 CpG sites, we explored the interactions with race and estrogen receptor (ER) status. We used multivariable Cox-proportional hazard models to examine whether methylation in the top 20 sites was associated with all-cause mortality.

RESULTS

While none of the CpG sites passed the FDR threshold for significance, among the top 20 CpG sites, we observed interactions with race (TOMM20) and ER status (PSMB1, QSOX1 and PHF1). The same CpG sites in TOMM20, PSMB1, and QSOX1 were associated with all-cause mortality.

CONCLUSIONS

We identified novel interactions between obesity-associated methylation and both race and ER status in genes that have been associated with tumor regulation. Our data suggest that dysregulation in two sites may associate with all-cause mortality.

Educational note: types of causes

We explore the different types of causes that are commonly investigated by epidemiologists. We first distinguish between causes which are events (including actions) and causes which are states. Second, we distinguish between modifiable and non-modifiable states. This yields three types of causes: fixed states (non-modifiable), dynamic states (modifiable) and events (including actions). Different causes may have different characteristics: the methods available to study them, the types of possible biases, and therefore the types of evidence needed to infer causality, may differ according to the specific cause-effect relationship under study. Nevertheless, there are also substantial commonalities. This paper is intended to improve understanding of the different types of causes, and the different types of causality, that underpin epidemiological practice.

Invited Commentary: Counterfactuals in Social Epidemiology-Thinking Outside of "the Box"

There are tensions inherent between many of the social exposures examined within social epidemiology and the assumptions embedded in quantitative potential-outcomes-based causal inference framework. The potential-outcomes framework characteristically requires a well-defined hypothetical intervention. As noted by Galea and Hernán (Am J Epidemiol. 2020;189(3):167-170), for many social exposures, such well-defined hypothetical exposures do not exist or there is no consensus on what they might be. Nevertheless, the quantitative potential-outcomes framework can still be useful for the study of some of these social exposures by creative adaptations that 1) redefine the exposure, 2) separate the exposure from the hypothetical intervention, or 3) allow for a distribution of hypothetical interventions. These various approaches and adaptations are reviewed and discussed. However, even these approaches have their limits. For certain important historical and social determinants of health such as social movements or wars, the quantitative potential-outcomes framework with well-defined hypothetical interventions is the wrong tool. Other modes of inquiry are needed.

Mendel's laws, Mendelian randomization and causal inference in observational data: substantive and nomenclatural issues

We respond to criticisms of Mendelian randomization (MR) by Mukamal, Stampfer and Rimm (MSR). MSR consider that MR is receiving too much attention and should be renamed. We explain how MR links to Mendel's laws, the origin of the name and our lack of concern regarding nomenclature. We address MSR's substantive points regarding MR of alcohol and cardiovascular disease, an issue on which they dispute the MR findings. We demonstrate that their strictures with respect to population stratification, confounding, weak instrument bias, pleiotropy and confounding have been addressed, and summarise how the field has advanced in relation to the issues they raise. We agree with MSR that "the hard problem of conducting high-quality, reproducible epidemiology" should be addressed by epidemiologists. However we see more evidence of confrontation of this issue within MR, as opposed to conventional observational epidemiology, within which the same methods that have demonstrably failed in the past are simply rolled out into new areas, leaving their previous failures unexamined.

The causal web of foetal alcohol spectrum disorders: a review and causal diagram

Foetal alcohol spectrum disorders (FASDs) are a leading cause of developmental disability. Prenatal alcohol use is the sole necessary cause of FASD, but it is not always sufficient. Multiple factors influence a child's susceptibility to FASD following prenatal alcohol exposure. Much of the FASD risk factor literature has been limited to discussions of association, rather than causation. While knowledge of predictor variables is important for identifying who is most at risk of FASD and for targeting interventions, causal knowledge is important for identifying effective mechanisms for prevention and intervention programmes. We conducted a systematic search and narrative synthesis of the evidence and used this to create a causal diagram (directed acyclic graph; DAG) to describe the causal pathways to FASD. Our results show that the aetiology of FASD is multifaceted and complex. FASD risk is determined by a range of lifestyle, sociodemographic, maternal, social, gestational, and genetic factors. The causal diagram that we present in this review provides a comprehensive summary of causal risk factors for FASD and can be used as a tool to inform data collection and statistical modelling strategies to minimise bias in future studies of FASD.

Optimal Pubertal Induction in Girls with Turner Syndrome Using Either Oral or Transdermal Estradiol: A Proposed Modern Strategy

BACKGROUND

Most girls with Turner syndrome (TS) require pubertal induction with estrogen, followed by long term replacement. However, no adequately powered prospective studies comparing transdermal with oral 17β-estradiol administration exist. This reflects the difficulty of securing funding to study a rare condition with relatively low morbidity/mortality when competing against conditions such as cancer and vascular disease. Protocol Consensus: The TS Working Group of the European Society for Paediatric Endocrinology (ESPE) has agreed to both a 3-year oral and a 3-year transdermal regimen for pubertal induction. Prerequisites include suitable 17β-estradiol tablets and matrix patches to allow the delivery of incremental doses based on body weight. Study Proposal: An international prospective cohort study with single centre analysis is proposed in which clinicians and families are invited to choose either of the agreed regimens, usually starting at 11 years. We hypothesise that pubertal induction with transdermal estradiol will result in better outcomes for some key parameters. The primary outcome measure chosen is height gain during the induction period.

ANALYSIS

Assessment of the demographics and drop-out rates of patients choosing either oral or transdermal preparations; and appropriate analysis of outcomes including pubertal height gain, final height, liver enzyme and lipid profile, adherence/acceptability, cardiovascular health, including systolic and diastolic blood pressure and aortic root diameter and bone health.

CONCLUSION

The proposed model of prospective data collection according to internationally agreed protocols aims to break the current impasse in obtaining evidence-based management for TS and could be applied to other rare paediatric endocrine conditions.

Racial Disparities in Breast Cancer Outcomes in the Metropolitan Atlanta Area: New Insights and Approaches for Health Equity

BACKGROUND

Racial disparities in breast cancer (BC) outcomes persist where non-Hispanic black (NHB) women are more likely to die from BC than non-Hispanic white (NHW) women, and the extent of this disparity varies geographically. We evaluated tumor, treatment, and patient characteristics that contribute to racial differences in BC mortality in Atlanta, Georgia, where the disparity was previously characterized as especially large.

METHODS

We identified 4943 NHW and 3580 NHB women in the Georgia Cancer Registry with stage I-IV BC diagnoses in Atlanta (2010-2014). We used Cox proportional hazard regression to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) comparing NHB vs NHW BC mortality by tumor, treatment, and patient characteristics on the additive and multiplicative scales. We additionally estimated the mediating effects of these characteristics on the association between race and BC mortality.

RESULTS

At diagnosis, NHB women were younger-with higher stage, node-positive, and triple-negative tumors relative to NHW women. In age-adjusted models, NHB women with luminal A disease had a 2.43 times higher rate of BC mortality compared to their NHW counterparts (95% CI = 1.99 to 2.97). High socioeconomic status (SES) NHB women had more than twice the mortality rates than their white counterparts (HR = 2.67, 95% CI = 1.65 to 4.33). Racial disparities among women without insurance, in the lowest SES index, or diagnosed with triple-negative BC were less pronounced.

CONCLUSIONS

In Atlanta, the largest racial disparities are observed in luminal tumors and most pronounced among women of high SES. More research is needed to understand drivers of disparities within these treatable features.

Post-Modern Epidemiology: When Methods Meet Matter

In the last third of the 20th century, etiological epidemiology within academia in high-income countries shifted its primary concern from attempting to tackle the apparent epidemic of noncommunicable diseases to an increasing focus on developing statistical and causal inference methodologies. This move was mutually constitutive with the failure of applied epidemiology to make major progress, with many of the advances in understanding the causes of noncommunicable diseases coming from outside the discipline, while ironically revealing the infectious origins of several major conditions. Conversely, there were many examples of epidemiologic studies promoting ineffective interventions and little evident attempt to account for such failure. Major advances in concrete understanding of disease etiology have been driven by a willingness to learn about and incorporate into epidemiology developments in biology and cognate data science disciplines. If fundamental epidemiologic principles regarding the rooting of disease risk within populations are retained, recent methodological developments combined with increased biological understanding and data sciences capability should herald a fruitful post-Modern Epidemiology world.

Investigating causal pathways in severe falciparum malaria: A pooled retrospective analysis of clinical studies

BACKGROUND

Severe falciparum malaria is a medical emergency characterised by potentially lethal vital organ dysfunction. Patient fatality rates even with parenteral artesunate treatment remain high. Despite considerable research into adjuvant therapies targeting organ and tissue dysfunction, none have shown efficacy apart from renal replacement therapy. Understanding the causal contributions of clinical and laboratory abnormalities to mortality is essential for the design and evaluation of novel therapeutic interventions.

METHODS AND FINDINGS

We used a structural model causal inference approach to investigate causal relationships between epidemiological, laboratory, and clinical variables in patients with severe falciparum malaria enrolled in clinical trials and their in-hospital mortality. Under this causal model, we analysed records from 9,040 hospitalised children (0-12 years, n = 5,635) and adults (n = 3,405, 12-87 years) with severe falciparum malaria from 15 countries in Africa and Asia who were studied prospectively over the past 35 years. On admission, patient covariates associated with increased in-hospital mortality were severity of acidosis (odds ratio [OR] 2.10 for a 7-mEq/L increase in base deficit [95% CI 1.93-2.28]), renal impairment (OR 1.71 for a 2-fold increase in blood urea nitrogen [95% CI 1.58, 1.86]), coma (OR 3.59 [95% CI 3.07-4.21]), seizures (OR 1.40 [95% CI 1.16-1.68]), shock (OR 1.51 [95% CI 1.14-1.99]), and presumed pulmonary oedema (OR 1.58 [95% CI 1.04-2.39]). Lower in-hospital mortality was associated with moderate anaemia (OR 0.87 for a decrease of 10 percentage points in haematocrit [95% CI 0.80-0.95]). Circulating parasite density was not associated with mortality (OR 1.02 for a 6-fold increase [95% CI 0.94-1.11]), so the pathological effects of parasitaemia appear to be mediated entirely by the downstream effects of sequestration. Treatment with an artemisinin derivative decreased mortality compared with quinine (OR 0.64 [95% CI 0.56-0.74]). These estimates were consistent across children and adults (mainly representing African and Asian patients, respectively). Using inverse probability weighting, transfusion was not estimated to be beneficial in children with admission haematocrit values between 15% and 25% (OR 0.99 [95% CI 0.97-1.02]). Except for the effects of artemisinin treatment and transfusion, causal interpretations of these estimates could be biased by unmeasured confounding from severe bacterial sepsis, immunity, and duration of illness.

CONCLUSION

These data suggest that moderate anaemia is associated with a reduced risk of death in severe falciparum malaria. This is possibly a direct causal association. The severe anaemia threshold criteria for a definition of severe falciparum malaria should be reconsidered.

Do natural experiments have an important future in the study of mental disorders?

There is an enormous interest in identifying the causes of psychiatric disorders but there are considerable challenges in identifying which risks are genuinely causal. Traditionally risk factors have been inferred from observational designs. However, association with psychiatric outcome does not equate to causation. There are a number of threats that clinicians and researchers face in making causal inferences from traditional observational designs because adversities or exposures are not randomly allocated to individuals. Natural experiments provide an alternative strategy to randomized controlled trials as they take advantage of situations whereby links between exposure and other variables are separated by naturally occurring events or situations. In this review, we describe a growing range of different types of natural experiment and highlight that there is a greater confidence about findings where there is a convergence of findings across different designs. For example, exposure to hostile parenting is consistently found to be associated with conduct problems using different natural experiment designs providing support for this being a causal risk factor. Different genetically informative designs have repeatedly found that exposure to negative life events and being bullied are linked to later depression. However, for exposure to prenatal cigarette smoking, while findings from natural experiment designs are consistent with a causal effect on offspring lower birth weight, they do not support the hypothesis that intra-uterine cigarette smoking has a causal effect on attention-deficit/hyperactivity disorder and conduct problems and emerging findings highlight caution about inferring causal effects on bipolar disorder and schizophrenia.

Children's mobility and environmental exposures in urban landscapes: A cross-sectional study of 10-11 year old Scottish children

Research into how the environment affects health and related behaviour is typically limited in at least two ways: it represents the environment to which people are exposed using fixed areal units, and, it focuses on one or two environmental characteristics only. This study developed a methodology for describing children's mobility and the complexity of their environmental exposure across a 1934 km2 study area, including urban, suburban and rural zones. It conceptualised and modelled this area as a landscape, comprised of spatially discrete amenities, infrastructure features, differing land covers/use and broader environmental contexts. The model used a 25 m2 grid system (∼3 million cells). For each cell, there was detailed built-environment information. We joined data for 100 10/11-year-old children who had worn GPS trackers to provide individual-level mobility information for one week during 2015/16 to our model. Using negative binomial regression, we explored which landscape features were associated with a child visiting that space and time spent there. We examined whether relationships between the features across our study area and children's use of the space differed by their sociodemographic characteristics. We found that children often used specific amenities outside their home neighbourhood, even if they were also available close to home. They spent more time in cells containing roads/transportation stops, food/drink retail (Incidence rate ratio (IRR):4.02, 95%CI 2.33 to 6.94), places of worship (IRR:5.98, 95%CI 3.33 to 10.72) and libraries (IRR:7.40, 95%CI 2.13 to 25.68), independently of proximity to home. This has importance for the optimal location of place-based health interventions. If we want to target children, we need to understand that using fixed neighbourhood boundaries may not be the best way to do it. The variations we found in time spent in certain areas by sex and socio-economic position also raise the possibility that interventions which ignore these differences may exacerbate inequalities.