# Understanding Social Inequalities in Childhood Asthma: Quantifying the Mediating Role of Modifiable Early-Life Risk Factors in Seven European Birth Cohorts

Angela Pinot de Moira, Anne V Aurup, Demetris Avraam, Daniela Zugna, Aksel Karl Georg Jensen, Marieke Welten, Timothy Cadman, Blandine de Lauzon-Guillain, Liesbeth Duijts, Ahmed Elhakeem, et al.

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Angela Pinot de Moira, Anne V Aurup, Demetris Avraam, Daniela Zugna, Aksel Karl Georg Jensen, et al. Understanding Social Inequalities in Childhood Asthma: Quantifying the Mediating Role of Modifiable Early-Life Risk Factors in Seven European Birth Cohorts. Journal of Allergy and Clinical Immunology: In Practice, 2025, 13 (6), pp.1385 - 1396. 10.1016/j.jaip.2025.02.032. hal-05204376

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# Understanding Social Inequalities in Childhood Asthma: Quantifying the Mediating Role of Modifiable Early-Life Risk Factors in Seven European Birth Cohorts

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Angela Pinot de Moira, PhDª, Anne V. Aurup, MScᶜ, Demetris Avraam, PhDᵇ,ᵈ, Daniela Zugna, PhDᵉ,
Aksel Karl Georg Jensen, PhDᵇ, Marieke Welten, MSc<f,g>, Timothy Cadman, PhDʰ,ᵢ, Blandine de Lauzon-Guillain, PhDj,
Liesbeth Duijts, PhDf,g, Ahmed Elhakeem, PhDk,l, Ana Esplugues, PhDm,n,o, Judith Garcia-Aymerich, PhDm,p,q,
Gonzalo García-Baquero, PhDr,s, Llúcia González Safont, PhDm,o, Jennifer R. Harris, PhDt, Carmen Iñiguez, PhDm,u,
Vincent W.V. Jaddoe, PhDf,g, Rosemary R.C. McEachan, PhDv, Johanna L.T. Nader, PhDw,
Loreto Santa Marina, PhDm,u,x, Morris A. Swertz, PhDb,h,i, Muriel Tafflet, MScj, Martine Vrijheid, PhDm,p,q,
John Wright, PhDv, Tiffany C. Yang, PhDv, David Taylor-Robinson, PhDd, Lorenzo Richiardi, PhDᵉ, and
Anne-Marie Nybo Andersen, PhDᵇ London, Liverpool, Bristol, and Bradford, United Kingdom; Copenhagen, Denmark; Turin, Italy;
Rotterdam and Groningen, the Netherlands; Paris, France; Valencia, Barcelona, Salamanca, and San Sebastian, Spain; and Oslo, Norway

**What is already known about this topic?** Children from disadvantaged socioeconomic circumstances have an increased risk of asthma. In the few studies that have assessed pathways to these inequalities, bronchiolitis, home environment, and perinatal factors have been identified as potential mediators.

**What does this article add to our knowledge?** We uniquely examined multiple potential mediators of inequalities across several European birth cohorts, with findings suggesting that maternal smoking, adverse birth outcomes, and breastfeeding duration have a consistent role in mediating social inequalities in asthma.

**How does this study impact current management guidelines?** Our study indicates that public health actions in the perinatal period to help mothers cease smoking, improve birth outcomes, and support breastfeeding could provide some benefit in helping to address inequalities in childhood asthma.

## GRAPHICAL ABSTRACT

<figure><img src="image_3.png" /></figure>
Abbreviations used

ALSPAC-Avon Longitudinal Study of Parents and Children

BiB-Born in Bradford

CI-Confidence interval

DNBC-Danish National Birth Cohort

EDEN-Study on the pre- & early postnatal determinants of child
health & development

EHII-Equivalized Household Income Indicator

EU-SILC-EU Statistics on Income and Living Conditions

Gen R-Generation R

INMA-Environment and Childhood Project

IPD-Individual participant data

ISAAC-International Study of Asthma and Allergy in Childhood

ISCED-International Standard Classification of Education

MoBa-Norwegian Mother, Father and Child Cohort Study

NDE-Natural direct effect

NIE-Natural indirect effect

PM-Proportion mediated

RR-Risk ratio

SEC-Socioeconomic circumstance

TE-Total effect

BACKGROUND: Children growing up in disadvantaged socioeconomic circumstances (SECs) have an increased risk of asthma.

OBJECTIVE: To increase our understanding of the pathways to inequalities in asthma and potential targets for intervention by (1) examining how the social patterning of asthma and its early-

life risk factors varies across countries and (2) quantifying the
mediation of observed inequalities by early-life risk factors.

METHODS: We used data for 107,884 mother-child dyads from 7 European birth cohorts across 6 countries. Maternal education was the primary exposure measure of early-life SECs. The outcome was current asthma in childhood (3-12 years). Inequalities were examined using multivariable regression and random effects meta-analysis. The mediating effects of early-life risk factors (maternal smoking during pregnancy, adverse birth outcomes, and breastfeeding duration) were examined using counterfactual mediation analysis.

RESULTS: In meta-analysis, children of mothers with low/ medium versus high education had a 17% (95% confidence interval: 8%-27%, I² = 21.6%) increased risk of asthma. Cohort- specific risk ratios ranged between 1.07 (0.97-1.18, Danish National Birth Cohort, Denmark) and 1.61 (1.08-2.40, study on the pre- & early postnatal determinants of child health & development, France). The early-life risk factors were similarly socially patterned, but with greater heterogeneity across cohorts (I² range = 66.2%-95.3%). The mediation analysis suggested that these factors play a relevant role in mediating observed inequalities (proportion mediated range: 0.08-0.72).

CONCLUSIONS: There was a consistent tendency for children from disadvantaged SECs to be at greater risk of asthma in the European cohorts examined. Our results suggest that early-life risk factors partially mediate these disparities and, therefore, that public health interventions in the perinatal period may help to address inequalities in asthma. © 2025 The Authors. Published

<footnote>ªSchool of Public Health, Imperial College London, London, United Kingdom</footnote>

<footnote>ᵇDepartment of Public Health, University of Copenhagen, Copenhagen, Denmark</footnote>

<footnote>Department of Nutrition, Exercise and Sports, University of Copenhagen, Copenhagen, Denmark</footnote>

<footnote>dDepartment of Public Health, Policy and Systems, University of Liverpool, Liverpool, United Kingdom</footnote>

<footnote n="5">⁵Cancer Epidemiology Unit, Department of Medical Sciences, University of Turin, Turin, Italy</footnote>

<footnote>Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands</footnote>

<footnote n="6">⁶Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands</footnote>

<footnote>ᵇGenomics Coordination Center, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands</footnote>

<footnote n="1">¹Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands</footnote>

<footnote>Université Paris Cité and Université Sorbonne Paris Nord, INSERM, INRAE, Centre for Research in Epidemiology and Statistics, Paris, France</footnote>

<footnote>kPopulation Health Science, Bristol Medical School, Bristol, United Kingdom</footnote>

<footnote n="1">¹MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom</footnote>

<footnote>mSpanish Consortium for Research on Epidemiology and Public Health (CIBER-ESP), Madrid, Spain</footnote>

<footnote>Department of Nursing, University of Valencia, Valencia, Spain</footnote>

<footnote n="5">⁵Epidemiology and Environmental Health Joint Research Unit, the Foundation for the Promotion of Health and Biomedical Research of Valencia Region, Universitat Jaume I-Universitat de València, Valencia, Spain</footnote>

<footnote>Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain</footnote>

<footnote n="9">⁹Universitat Pompeu Fabra (UPF), Barcelona, Spain</footnote>

<footnote>CEADIR, Faculty of Biology, University of Salamanca, Campus Miguel de Unamuno, Avda Licenciado Méndez Nieto s/n, Salamanca, Spain</footnote>

<footnote>Biodonostia, Environmental Epidemiology and Child Development Group, San Sebastian, Spain</footnote>

<footnote n="1">¹Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway</footnote>

<footnote>Department of Statistics and Operational Research, Universitat de València, València, Spain</footnote>

<footnote n="5">⁵Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, United Kingdom</footnote>

<footnote>Department of Genetics and Bioinformatics, Division of Health Data and Digitalisation, Norwegian Institute of Public Health, Oslo, Norway</footnote>

<footnote n="6">⁶Health Department of Basque Government, Sub-directorate of Public Health of Gipuzkoa, San Sebastian, Spain</footnote>

<footnote>This study has received funding from the European Union's Horizon 2020 research and innovation program (grant agreement no. 733206 LifeCycle, Grant Agreement no. 824989 EUCAN-Connect). A. Pinot de Moira was supported by a Lundbeck Foundation Postdoctoral Fellowship (R264-2017-3099) and a National Heart and Lung Institute Research Fellowship during the study. T. Cadman, L. Duijts, A. Elhakeem, V. W. V. Jaddoe, R. R. C. McEachan, L. Richiardi, M. A. Swertz, M. Welten, and A.-M. N. Andersen received funding from the European Union's Horizon 2020 research and innovation program. L. Duijts received additional funding from ZonMW (CoKIDS study no. 10150062010006), Stichting Vrienden van Sophia, and COVID-19 transmission in households. D. Taylor-Robinson is funded by a National Institute for Health and Care Research (NIHR) professorship (NIHR302438) and by the NIHR School for Public Health Research (PD-SPH-2015). J. R. Harris was supported, in part, by funding from the Research Council of Norway through its centers of excellence funding scheme (project number 262700). Study-specific funding is provided in the section "Cohort study specific funding" in this article's Online Repository at www.jaci-inpractice.org.</footnote>

<footnote>Conflicts of interest: The authors declare that they have no relevant conflicts of interest.</footnote>

<footnote>Received for publication August 19, 2024; revised February 22, 2025; accepted for publication February 25, 2025.</footnote>

<footnote>Available online May 1, 2025.</footnote>

<footnote>Corresponding author: Angela Pinot de Moira, PhD, Small Area Health Statistics Unit, School of Public Health, Imperial College London, White City Campus, Room 709, 90 Wood Lane, London W12 0BZ, United Kingdom. E-mail: angela.roy19@imperial.ac.uk.</footnote>

<footnote>2213-2198</footnote>

<footnote>© 2025 The Authors. Published by Elsevier Inc. on behalf of the American Academy of Allergy, Asthma & Immunology. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).</footnote>

<footnote>https://doi.org/10.1016/j.jaip.2025.02.032</footnote>
by Elsevier Inc. on behalf of the American Academy of Allergy, Asthma & Immunology. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). (J Allergy Clin Immunol Pract 2025;13:1385-96)

**Key words:** Childhood asthma; Socioeconomic; Inequalities; Birth cohort; Meta-analysis; Cross-cohort

Asthma is the most common noncommunicable disease of childhood and ranks among the top 10 causes of disability-adjusted life years in mid-childhood.¹ It comes with considerable direct and indirect costs,¹ and substantially impacts the quality of life of both the patient and their family.² Although the etiology of asthma is not fully understood, evidence suggests that genetic, social, environmental, and lifestyle factors play a role, and that early-life events are particularly important in its development.³,⁴

Within high-income countries, evidence suggests that the highest burden of childhood asthma falls on children growing up in disadvantaged socioeconomic circumstances (SECs).⁵⁻⁸ However, the extent of this social patterning appears to vary by country. For example, in an analysis of birth cohorts across 10 European countries, France, the United Kingdom, and the Netherlands had the highest prevalence and greatest inequality of childhood asthma. In these countries, children born to mothers in the lowest tertile of education were 107%, 73%, and 65% more likely to develop asthma than children born to mothers in the highest tertile.⁹

Although a handful of studies have examined factors that might be driving these inequalities,⁵⁻⁷,¹⁰,¹¹ relatively few have applied modern counterfactual mediation methods in their analyses. Findings from 2 UK studies that have applied these methods suggest that home environment, bronchiolitis, and perinatal factors, such as maternal smoking, breastfeeding duration, birth weight, and preterm delivery, may explain some of the inequalities observed in the United Kingdom.⁵,¹¹ However, no studies, to our knowledge, have examined whether the pathways to inequalities in asthma differ across countries. Examining differences across different social and cultural country contexts may provide a greater depth of understanding regarding the drivers of inequalities, thus facilitating the identification and development of suitably targeted interventions aimed at preventing the development of asthma among underserved groups.

The aim of this study was to examine, across different European countries, the extent of social inequalities in asthma, the social patterning of modifiable early-life risk factors, and the degree to which these risk factors mediate observed inequalities in asthma. We hypothesized that inequalities in asthma may vary across countries and that any differences may be explained by differences in the social patterning and mediation by early-life risk factors.

# METHODS

## Study design and participants

We used longitudinal data from birth cohorts in the EU Child Cohort Network,¹² a network of harmonized birth cohort data established for the Horizon2020 LifeCycle project. Details of available variables and their harmonization are provided in https://data-catalogue.molgeniscloud.org/catalogue/ssr-catalogue/EUChild Network and elsewhere.¹³

Cohorts were eligible for this study if they had data on maternal education level, childhood asthma (3-12 years), and a number of predefined potential mediators (detailed in the section “Description of the participating cohorts” in this article’s Online Repository at www.jaci-inpractice.org). The participating cohorts were Avon Longitudinal Study of Parents and Children (ALSPAC-UK), Born in Bradford (BiB-UK), Danish National Birth Cohort (DNBC-Denmark), Study on the pre- & early postnatal determinants of child health & development (EDEN-France), the Generation R study (Gen R-the Netherlands), Environment and Childhood Project (INMA-Spain), and Norwegian Mother, Father and Child Cohort Study (MoBa-Norway). Descriptions of the participating cohorts are provided in the section “Description of the participating cohorts” and Tables E1 and E2 in this article’s Online Repository at www.jaci-inpractice.org.

Analyses were restricted to children from singleton pregnancies.

## Variables

**Outcome.** Measures of current asthma in childhood (3-12 years) were harmonized using data obtained from parental questionnaires (detailed in Table E3 in this article’s Online Repository at www.jaci-inpractice.org) according to the MeDALL (Mechanisms of the Development of ALLergy) definition,¹⁴ whereby a child was classified as having current asthma when at least 2 of 3 criteria were reported: (1) doctor diagnosis of asthma ever, (2) asthma medication use in the past year, (3) wheezing in the past year according to the ISAAC (International Study of Asthma and Allergy in Childhood) parental core questionnaire.¹⁴,¹⁵ Records with missing data on 2 of the 3 criteria were coded as missing. Where current asthma was available at multiple ages for an individual, we analyzed their asthma status at the age closest to 7 years, the mean age of measurement for the pooled study population.

**Exposure.** Our primary measure of SECs was the reported maternal education level, based on the highest ongoing or completed education at the time of the index pregnancy (Table E4, available in this article’s Online Repository at www.jaci-inpractice.org). Education closely relates to future income and employment, family resources, and health literacy.¹⁶ For the main analysis, level of education was stratified into 2 categories according to the International Standard Classification of Education 97/2011 (ISCED-97/2011): low/medium education level (no education to postsecondary nontertiary; ISCED-2011: 0-4, ISCED-97: 0-4) and high education level (short cycle tertiary to doctoral or equivalent; ISCED-2011: 5-8, ISCED-97: 5-6). This stratification was chosen because of the small number of mothers with lower levels of education in some cohorts and to simplify interpretation of results.

For sensitivity analysis, maternal education was stratified into 3 categories (low, medium, and high). In addition, an “Equivalized Household Income Indicator” (EHII) derived using EU Statistics on Income and Living Conditions (EU-SILC) and cohort-specific data (described in Table E4 in this article’s Online Repository at www.jaci-inpractice.org and elsewhere¹⁷) was used as an alternative measure of SECs. Similarly for maternal education, EHII was categorized into low/medium (<66th percentile) and high (≥66th percentile) based on country-specific distributions of equivalized total disposable household income in EU-SILC.

**Potential mediators.** We selected early-life risk factors that have been associated with childhood asthma, are often socially patterned, and (in theory) are modifiable. These were maternal
<figure><img src="image_4.png" /><figcaption>FIGURE 1. Simplified directed acyclic graph (DAG) representing the causal model. The DAG depicts the assumed relationships between the exposure (E: SECs in pregnancy [maternal education or household income]), the investigated potential mediators (M1: maternal smoking in pregnancy, M2: adverse birth outcomes, and M3: duration of breastfeeding), and the outcome (Y: childhood asthma). C<sub>E-Y</sub>, C<sub>E-M</sub>: confounders of E and Y, and C<sub>M-Y</sub>: confounders of M and Y. An explanation for the assumed causal relationships is provided in the section "Assumed relationships in the causal model" in this article's Online Repository at www.jaci-inpractice.org. SEC, Socioeconomic circumstance.</figcaption></figure>

smoking during pregnancy (yes/no), preterm birth (<37 weeks' gestation, yes/no), caesarean section delivery (yes/no), low birth weight (<2500 g, yes/no), and breastfeeding duration (never, <5 months, ≥5 months [based on available data]), as detailed in Table E5 in this article's Online Repository at www.jaci-inpractice.org. Because of the close association between preterm birth, caesarean delivery, and low birth weight, which would be difficult to disentangle in a mediation analysis, we consolidated these exposures into a single "adverse birth outcome" variable (yes/no) based on the child having experienced any one of these exposures.

**Covariates.** Potential exposure-outcome confounders were identified based on the literature and a directed acyclic graph constructed to represent the causal model (Figure 1). Confounders included maternal age, maternal asthma, mother's ethnic background, and parity. For mediation models, paternal asthma was also included to account for potential mediator-outcome confounding. Information on covariates was obtained from questionnaires, interviews, and medical records (Table E6, available in this article's Online Repository at www.jaci-inpractice.org).

**Statistical analysis**

All analyses were conducted in R (version 3.5.2, R Foundation for Statistical Computing, Vienna, Austria) using the federated analysis platform DataSHIELD,¹⁸ via a central analysis server using RStudio (https://rstudio.com/products/rstudio/#rstudio-server) using MOL-GENIS Armadillo (https://github.com/molgenis/molgenis-service-armadillo) or Obiba Opal (https://www.obiba.org/pages/products/opal/) for data access at each cohort, and DataSHIELD-specific libraries (dsBaseClient, version 6.3.0).

Our analysis involved 2 stages. First, we examined the distribution of childhood asthma and potential mediators across each cohort, and also their association with maternal education. We used descriptive plots (with maximal sample size) to visualize associations, plotting the prevalence of childhood asthma and potential mediators

against scaled maternal education within each cohort. From these plots, we estimated the absolute difference in prevalence between highest and lowest education levels (slope index of inequality) by calculating the slope of the line. We also quantified relative associations between maternal education, mediators, and childhood asthma using multivariable Poisson (for asthma) or logistic (for mediators) regression, adjusting for the covariates detailed above and subcohort in INMA (using the complete case sample; Figure E1, available in this article's Online Repository at www.jaci-inpractice.org). Models were fitted separately for each cohort and regression coefficients (βs) and standard errors combined by random effects meta-analysis using the restricted maximum likelihood estimator method. Between-cohort heterogeneity was evaluated using the I² and χ² Q-statistic.

Second, we used counterfactual mediation analysis to decompose the total effect (TE) of maternal education on childhood asthma into natural indirect effects (NIEs, ie, acting through the proposed mediators) and natural direct effects (NDEs, ie, acting through other mechanisms). Because we hypothesized that the extent of mediation by early-life risk factors would differ across cohorts, supported by observed heterogeneity in the social patterning of mediators across cohorts, we conducted separate mediation analyses in each cohort study and did not meta-analyze results. In addition, because mediation can exist when the effect of an exposure on an outcome is close to zero or nonsignificant due to inconsistent mediation (counteracting direct and indirect effects) and/or insufficient power,¹⁹ we conducted mediation analyses regardless of the strength or significance of the association between maternal education and childhood asthma.

We also hypothesized that the mediators would be temporally ordered and interdependent and therefore examined the 3 mediators using a sequential approach, whereby each set of mediators was added to the model according to their assumed causal order (Figure 1). This resulted in 3 different models: model 1 estimated the NIE through maternal smoking during pregnancy, model 2
<figure><img src="image_5.png" /><figcaption>**FIGURE 2.** Distribution of maternal education, childhood asthma, and potential mediators in the participating cohorts. (A) Maternal education by level. (B) Predicted equivalized total disposable household income. (C) Prevalence of childhood asthma. (D) Prevalence of maternal smoking in pregnancy. (E) Prevalence of an adverse birth outcome (child born preterm (<37 weeks), with a low birth weight (<2500 g), or by caesarean section). (F) Prevalence of breastfeeding by duration. Plots are restricted to live-born singleton children for whom there were data on maternal education. *ALSPAC*, Avon Longitudinal Study of Parents and Children; *BiB*, Born in Bradford; *DNBC*, Danish National Birth Cohort; *EDEN*, study on the pre- & early postnatal determinants of child health & development; *Gen R*, generation R; *INMA*, Environment and Childhood Project; *MoBa*, Norwegian Mother, Father and Child Cohort Study.</figcaption></figure>
<figure><img src="image_6.png" /></figure>

**FIGURE 3.** Prevalence of childhood asthma and potential mediators over maternal education. The prevalence of childhood asthma and investigated mediators are plotted over scaled maternal education, separately for each birth cohort. Maternal education was scaled within each cohort by ranking the 2 maternal education groups from low to high and allocating a score between 0 and 1, representing the midpoint of the category's range in the cumulative distribution. (A) Childhood asthma. (B) Smoking in pregnancy. (C) Adverse birth outcomes. (D) Breastfeeding ≥5 months. Plots use maximal sample size. ALSPAC, Avon Longitudinal Study of Parents and Children; BiB, Born in Bradford; DNBC, Danish National Birth Cohort; EDEN, study on the pre- & early postnatal determinants of child health & development; Gen R, generation R; INMA, Environment and Childhood Project; MoBa, Norwegian Mother, Father and Child Cohort Study; SII, slope index of inequality.

estimated the joint NIE through both maternal smoking and adverse birth outcomes, and model 3 estimated the joint NIE through maternal smoking, adverse birth outcomes, and breastfeeding duration.

NDEs, NIEs, and TEs were estimated by an imputation method using the DataSHIELD *dsMediationClient* package (version 0.0.3), adapted from the *Medflex* package in R. This allows for multiple, potentially interacting mediators.²⁰ From the NDEs and NIEs, we also estimated the proportion mediated (PM), namely

the proportion of the TEs that is due to the effect of the exposure on the mediator(s), calculated as $(RR_{NDE}(RR_{NIE} - 1))/(RR_{NDE} \times RR_{NIE} - 1)$

To test the robustness of our findings, we conducted 4 sensitivity analyses. First, we repeated the mediation analysis excluding children less than 6 years of age. Second, the mediation analysis was repeated using maternal education categorized into 3 levels (low, medium, and high). Third, we repeated all analyses using EHII as an alternative measure of SECs. Fourth, potential exposure-mediator
<figure><img src="image_7.png" /></figure>

**FIGURE 4.** Forest plot displaying associations of maternal education with childhood asthma. Results compare children born to mothers with a low/medium education level with children born to mothers with a high education level. Analyses were restricted to children for whom there were complete data (Figure E1, available in this article's Online Repository at www.jaci-inpractice.org). *Adjusted for history of maternal asthma, maternal age, and maternal parity, plus maternal ethnic background in BiB and Gen R. Only limited data on maternal asthma were available in BiB, and therefore maternal education is not adjusted for in this cohort. See Figure 1 for the assumed causal structure. ALSPAC, Avon Longitudinal Study of Parents and Children; BiB, Born in Bradford; CI, confidence interval; DNBC, Danish National Birth Cohort; EDEN, study on the pre- & early postnatal determinants of child health & development; Gen R, generation R; INMA, Environment and Childhood Project; MoBa, Norwegian Mother, Father and Child Cohort Study; RE, random effect.

interactions were investigated by allowing for all possible 2-way interactions between SECs and mediators. We also examined possible interactions with sex assigned at birth.

## RESULTS

In total, data on maternal education were available for 194,248 children, with the numbers in each cohort ranging from 1885 (EDEN) to 94,771 (MoBa). Among these, 107,884 also had data on childhood asthma, and 76,098 had complete data on SECs, childhood asthma, all mediators, and covariates (complete case study population; Figure E1, available in this article's Online Repository at www.jaci-inpractice.org). A comparison of the mother-child dyads from the original study populations with those with complete data is provided in Table E7 in this article's Online Repository at www.jaci-inpractice.org.

The distribution of maternal education and childhood asthma varied across cohorts (Figure 2). The proportion of mothers with a low/medium education level ranged from 35.9% (MoBa) to 87.1% (ALSPAC), whereas childhood asthma prevalence ranged from 5.6% (MoBa) to 17.1% (ALSPAC), which is comparable to estimates reported elsewhere using similar criteria.²¹

The distribution of potential mediators also varied: 8.8% (MoBa) to 32.4% (INMA) of mothers smoked during pregnancy, 13.6% (EDEN) to 27.5% (BiB) of children experienced an adverse birth outcome, and 28.7% (EDEN) to 80.6% (MoBa) of children were breastfed for ≥5 months.

There was a consistent tendency toward greater risk of childhood asthma among children born to mothers with a low/medium versus high education level, with greatest absolute inequalities observed in EDEN and lowest observed in DNBC (Figure 3). Relative inequalities followed a similar pattern (Figure 4; Figure E2, available in this article's Online Repository at www.jaci-inpractice.org, provides unadjusted associations). In adjusted analyses, the increased risk associated with a low/medium maternal education level was greatest in EDEN (relative risk [RR] = 1.61, 95% confidence interval [CI]: 1.08-2.40) and ALSPAC (RR = 1.26, 95% CI: 1.00-1.58), and lowest in DNBC (RR = 1.07, 95% CI: 0.97-1.18). The combined RR was 1.17 (95% CI: 1.08-1.27), with low heterogeneity across cohorts ($I^2$ = 21.6%).

With respect to the early-life risk factors, smoking during pregnancy and breastfeeding for ≥5 months showed clear social patterning in all cohorts (Figures 3 and 5), but with considerable
<figure><img src="image_8.png" /><figcaption>**FIGURE 5.** Forest plot displaying associations of maternal education with potential mediators. Results compare children born to mothers with a low/medium education level with children born to mothers with a high education level. Analyses were restricted to children for whom there were complete data (Figure E1, available in this article's Online Repository at www.jaci-inpractice.org). *Adjusted for history of maternal asthma, maternal age, and maternal parity, plus maternal ethnic background in BiB and Gen R. Only limited data on maternal asthma were available in BiB, and therefore maternal asthma is not adjusted for in this cohort. See Figure 1 for the assumed causal structure. ALSPAC, Avon Longitudinal Study of Parents and Children; BiB, Born in Bradford; CI, confidence interval; DNBC, Danish National Birth Cohort; EDEN, study on the pre- & early postnatal determinants of child health & development; Gen R, generation R; INMA, Environment and Childhood Project; MoBa, Norwegian Mother, Father and Child Cohort Study; RE, random effect.</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>FIGURE 6. Decomposition of the total effect of maternal education on childhood asthma into natural direct (NDE) and natural indirect effects (NIE) in the 7 cohorts. Each potential mediator is added stepwise. Results compare children born to mothers with a low/medium education level with children born to mothers with a high education level in 3 counterfactual mediation models examining the cumulative effects of smoking during pregnancy, adverse birth outcomes, and breastfeeding duration. Results are adjusted for maternal age, maternal history of asthma, paternal history of asthma, and maternal parity, plus maternal ethnic background in Gen R and BiB. Data on paternal asthma were unavailable in BiB, and only limited data were available for maternal asthma; therefore, maternal and paternal asthma are not adjusted for in this cohort. ALSPAC, Avon Longitudinal Study of Parents and Children; BiB, Born in Bradford; CI, confidence interval; DNBC, Danish National Birth Cohort; EDEN, study on the pre- & early postnatal determinants of child health & development; Gen R, generation R; INMA, Environment and Childhood Project; MoBa, Norwegian Mother, Father and Child Cohort Study; NA, NDE and NIE are in opposite directions of the null, therefore PM is outside of the range [0, 1] and not interpretable; PM, proportion mediated; RR, risk ratio; TE, total effect.</figcaption></figure>

heterogeneity ($I^2$ = 94.6% and $I^2$ = 93.7%, respectively). Weaker social gradients were observed for breastfeeding <5 months and adverse birth outcomes, with medium-high heterogeneity ($I^2$ = 95.3% and $I^2$ = 66.2%, respectively). Although associations between maternal education and adverse birth outcomes in BiB differed from those in other cohorts in unadjusted analysis (Figure 3), the direction of the association aligned with that of other cohorts in adjusted analysis (Figure 5). Overall, there was evidence of an association between each of these factors and childhood asthma, with low-medium heterogeneity across cohorts ($I^2$ range: 7.1%-60.5%, Figure E3, available in this article's Online Repository at www.jaci-inpractice.org).

Consistent with their strong social patterning and association with childhood asthma, the mediation analysis indicated that these early-life risk factors partially mediate the TE of maternal education on childhood asthma. In the joint model including all risk factors (bottom row of each panel in Figure 6), the NIE was similar across cohorts, ranging from RR = 1.03 (95% CI: 0.91-1.16; EDEN) to RR = 1.09 (95% CI: 1.02-1.17; BiB), although

the CIs for EDEN, Gen R, and INMA crossed the null. The PM by the 3 risk factors ranged from 0.08 (EDEN) to 0.72 (DNBC).

In the sequential analysis (Figure 6), the NIE through smoking was RR = 0.98 (95% CI: 0.93-1.04) in Gen R, RR = 1.03 in ALSPAC (95% CI: 1.01-1.05) and EDEN (95% CI: 0.97-1.09), and RR = 1.01 in the remaining cohorts. The PM by maternal smoking ranged from 0.06 (BiB and MoBa) to 0.15 (DNBC). Adding adverse birth outcomes to the mediation pathway had little/no effect on the NIE in ALSPAC, BiB, EDEN, Gen R, and INMA, and marginally increased the NIE to RR = 1.02 (95% CI: 1.00-1.04; PM 0.30) in DNBC and to RR = 1.05 (95% CI: 1.02-1.07; PM 0.28) in MoBa. Including breastfeeding duration in the mediation pathway increased the indirect effect to 1.05-1.09 and the PM to 0.35-0.72 in ALSPAC, BiB, DNBC, Gen R, and INMA, but did not change the mediated effect in EDEN and MoBa.

In sensitivity analyses, the mediation analysis was repeated after excluding children younger than 6 years in EDEN (n = 354). Because all children in BiB were younger than 6 years,
they were also excluded from this sensitivity analysis, whereas all children in the other cohorts were 6 years or older and therefore included. The joint model including all risk factors could not be run for EDEN after excluding children younger than 6 years due to the risk of disclosure; otherwise, findings were largely consistent with the main analysis (Figure E4, available in this article's Online Repository at www.jaci-inpractice.org).

Mediation was also re-examined using 3 levels of maternal education (low, medium, and high; Figure E5, available in this article's Online Repository at www.jaci-inpractice.org). The risk of childhood asthma was lower in EDEN and Gen R among children born to mothers with low versus high education, but CIs were wide and crossed the null due to few mothers with low education. Where inequalities were observed, mediation patterns were generally consistent with the main analysis, except in MoBa, where adding breastfeeding duration to the mediation pathway increased the NIE for low maternal education from RR = 1.10 to 1.14 (95% CI: 1.06-1.23).

Repeating analyses with household income showed a similar tendency of higher prevalence of childhood asthma among children from low/medium versus high income families (Figure E6, available in this article's Online Repository at www.jaci-inpractice.org). Overall, low/medium household income was associated with a 17% (9%-25%; $I^2 < 0.01$%) increased risk of childhood asthma compared with high household income (Figure E7, available in this article's Online Repository at www.jaci-inpractice.org).

In the joint mediation model for household income including all risk factors, the early-life risk factors did not mediate the TE of household income on asthma in EDEN (NIE RR: 0.99 [95% CI: 0.89-1.10]) but partially mediated the TE in all other cohorts (NIE RR range: 1.03-1.09; PM: 0.17-0.63; Figure E8, available in this article's Online Repository at www.jaci-inpractice.org); however, the CIs for BiB, Gen R, and INMA crossed the null. As for maternal education, adding breastfeeding duration to the mediation pathway had the greatest effect on the NIE in all cohorts except EDEN and MoBa.

In further sensitivity analysis, we examined exposure-sex and exposure-mediator interactions. There was no evidence of exposure-sex interactions ($P > .05$). Evidence of maternal education-mediator interactions was observed in DNBC, suggesting differential susceptibility to mediators across education levels (Table E8, available in this article's Online Repository at www.jaci-inpractice.org). There was no evidence of income-mediator interaction in DNBC ($P > .08$) but evidence of income-mediator interaction in MoBa ($P = .004$; Table E9, available in this article's Online Repository at www.jaci-inpractice.org). No evidence of exposure-mediator interaction was observed in the other cohorts ($P > .09$).

## DISCUSSION

Using data from 7 European birth cohorts, we showed greater risk of childhood asthma among children born to mothers with lower versus higher education, with greatest prevalence of asthma and levels of inequality in the French and UK cohorts. We examined to what extent these observed inequalities could be explained by 3 modifiable early-life risk factors: maternal smoking during pregnancy, adverse birth outcomes, and breastfeeding duration. Each of these showed clear social patterning,

particularly maternal smoking and breastfeeding duration, albeit with considerable heterogeneity across cohorts. Results from mediation analysis suggested that the risk factors jointly play a role in driving inequalities but do not completely explain observed inequalities in asthma. Our results were consistent in sensitivity analyses using household income as an alternative measure of SECs. These findings suggest that public health interventions that provide greater perinatal support to help mothers cease smoking during pregnancy, improve birth outcomes, and support the initiation and continuation of breastfeeding may be important in addressing social inequalities in asthma, but will not be sufficient to fully tackle them.

Our finding of an association between disadvantaged SECs and increased risk of childhood asthma in European cohorts is consistent with a number of previous studies.⁵,⁶,⁸,⁹,²² A previous meta-analysis of 10 European birth cohorts, different from those included here apart from EDEN and INMA, similarly observed highest prevalence of childhood asthma and levels of inequality in the UK and the French cohort EDEN.⁹ However, that study also observed high levels of inequality in the Netherlands, which was less apparent here after adjustment for confounders. We observed comparatively large relative inequality in MoBa despite high levels of maternal education and the strong welfare state in Norway. A number of other studies have observed similar persistently large inequalities in countries with universal, generous welfare provision.²³ Several theories seek to explain this paradox, such as persistent inequalities in access to material resources, changes in the composition of social groups, and the increasing importance of behavior and nonmaterial resources in shaping population health.²³ Nonetheless, absolute inequalities were comparatively smaller in this cohort due to the low prevalence of asthma.

This study adds to the literature by also examining how early-life risk factors for asthma differ in their social patterning and mediation of inequalities across European countries. The early-life factors examined (maternal smoking during pregnancy, adverse birth outcomes, and breastfeeding duration) are each known to be socially patterned²⁴⁻²⁶ and associated with childhood asthma.²⁷⁻³¹ as corroborated by our findings. In line with this, our study suggests that these factors do play a role in explaining social inequalities in asthma. Contrary to our hypothesis, the joint mediated effect of all factors combined was fairly consistent across cohorts. However, the PM by the risk factors varied across cohorts. This suggests that although the pathways to inequalities may be similar across the cohorts, the relative importance of these pathways in explaining inequalities varies.

Because of the dependence of the mediators examined and the imputation mediation approach used not allowing the decomposition of the NIE into the effect mediated through each sequential mediator,³² further studies are required to determine the independent effects of each mediator. However, our results seem to highlight an important role for breastfeeding in the mediation pathway between SECs and childhood asthma in all cohorts other than EDEN, which should be investigated further.

Few other studies have applied modern mediation methods to examine pathways to social inequalities in asthma. In the UK Millennium Cohort Study, Creese et al⁵ found perinatal factors (maternal body mass index, smoking and alcohol consumption, breastfeeding duration, birth weight, preterm birth, and wheeze) to mediate 37.5% of observed inequalities in persistent
wheeze. This is comparable to the PM by early-life risk factors observed here in the UK birth cohorts ALSPAC and BiB (0.36 and 0.48). Other studies applying traditional mediation methods have similarly identified maternal smoking and breastfeeding duration as potential mediators of social inequalities in wheeze,¹⁰ nonatopic asthma,⁷ and asthma symptoms.⁶

Because maternal smoking, adverse birth outcomes, and breastfeeding duration did not completely mediate observed inequalities in asthma, our findings suggest that other factors will also be important in driving inequalities. In the same study by Creese et al,⁵ home environment was also identified as an important mediator, with perinatal and home environment jointly mediating 61.2% of observed inequalities. Findings from other studies suggest that bronchiolitis may also partially mediate inequalities in chronic asthma.¹¹ Other factors that may be important in driving social inequalities in asthma include outdoor environment (eg, ambient air pollution and access to green space), lifestyle (diet and exercise), and maternal and child mental health, which are similarly known to be socially patterned and associated with asthma. In the current study, there was a slight tendency for the PM by early-life risk factors to be smaller in cohorts with greater inequalities, which could suggest that the relative importance of other risk factors in mediating inequalities is greater in these cohorts.

A key strength of this study is the use of counterfactual mediation methods to explore mediating pathways; it is unique in its application of these methods to investigate pathways to inequalities in asthma across several European birth cohorts. It is also the first study to apply these methods using the federated analysis platform DataSHIELD. Counterfactual methods offer greater scope for addressing the sources of bias that can lead to flawed results in traditional mediation analysis,³³ including investigating exposure-mediator interactions and multiple sequential mediators.

Another key strength is the use of harmonized data, facilitating comparison across cohorts. In particular, the majority of participating cohorts ascertained asthma symptoms using questions from ISAAC, or adaptations thereof. To define childhood asthma, we applied a criterion developed by a panel of experts that considers asthma symptoms, medication use, and doctor diagnosis.¹⁴ Because of the EHII developed for LifeCycle,¹⁷ which allows comparison of income across countries, we were also able to examine whether findings were consistent across different dimensions of SECs.

However, this study also has several limitations. First, although analyzing data via the federated analysis platform DataSHIELD has a number of key benefits such as the ability to analyze individual participant data (IPD) while avoiding the governance and security issues surrounding data transfers, this comes with some analytical constraints. For example, methods that are able to decompose the NIE into the effects through each of the sequential mediators, such as the extended imputation approach,³² are not yet implemented in DataSHIELD. Similarly, methods to meta-analyze mediation analyses with IPD³⁴ are also unavailable in DataSHIELD, and further methodological work is needed to develop approaches for meta-analyzing NDEs and NIEs in the absence of IPD when the mediator or outcome follows nonlinear models;³²,³⁵ this precluded us from formally

testing the equality of direct and indirect effects across cohorts. In addition, multiple imputation functionality to deal with missing data was not available in all cohorts at the time of analysis: our complete case analysis may have reduced precision and introduced bias.

Although harmonizing data enables comparisons across cohorts, this often comes at the cost of less granular measures. We also adapted some aspects of the analysis to accommodate all cohorts. For example, we compared low/medium with high education rather than low education with high/medium education because of the small number of mothers with lower levels of education in some cohorts.

Finally, it is increasingly recognized that asthma has multiple subtypes with different risk factors and pathophysiological mechanisms.³⁶ There is evidence that certain subtypes of asthma may be more strongly socially patterned; for example, asthma associated with lower SECs appears to be more severe, persistent, and more likely to be nonallergic.⁵,⁷,¹¹ Therefore, by examining asthma as a single entity, we may have underestimated the true extent of social inequalities in asthma and the complexity of the mediating pathways.

In summary, our findings suggest that children from disadvantaged SECs are at increased risk of developing asthma, but that the extent of this social patterning varies across countries. Our findings also suggest that maternal smoking during pregnancy, adverse birth outcomes, and duration of breastfeeding play a relevant role in mediating observed inequalities, but do not completely account for the TE of SECs on asthma.

## Acknowledgments

We thank all cohort data managers and IT teams and the technical teams that implemented the DataSHIELD network (MOLGENIS Armadillo, Obiba Opal, and the DataSHIELD community). Study-specific acknowledgements are provided in the section “Study specific acknowledgements” in this article’s Online Repository at www.jaci-inpractice.org.

## Author contributions

A. Pinot de Moira conceived the study. A. Pinot de Moira and A. V. Aurup designed the study and carried out the analysis with input from D. Avraam, D. Zugna, A. K. G. Jensen, M. Welten, D. Taylor-Robinson, L. Richiardi, and A.-M. Nybo Andersen. A. Pinot de Moira drafted the manuscript with further contributions from A. V. Aurup. All authors interpreted the data and critically reviewed the manuscript. All analyses were performed using the federated data analysis platform DataSHIELD, which allows the remote analysis of individual participant data from different study centers without the need to physically transfer data. The following authors have direct access to and have verified the underlying data for their cohort: T. Cadman and A. Elhakeem (Avon Longitudinal Study of Parents and Children); T. C. Yang (Born in Bradford); A. Pinot de Moira (Danish National Birth Cohort); B. de Lauzon-Guillain and M. Tafflet (study on the pre- & early postnatal determinants of child health & development); M. Welten (generation R); A. Esplugues, J. Garcia-Aymerich, G. García-Baquero, L. G. Safont, C. Iñiguez, L. S. Marina, and M. Vrijheid (Environment and Childhood
Project); J. Nader (Norwegian Mother, Father and Child Cohort Study). All authors accept responsibility to submit for publication.

## Ethics approval

Study-specific ethics approval statements are available in the section “Ethics approval” in this article’s Online Repository at www.jaci-inpractice.org.

## Consent to participate

Study-specific informed consent statements are available in the section “Consent to participate” in this article’s Online Repository at www.jaci-inpractice.org.

## Data sharing

This study analyzed data from the EU Child Cohort Network (ECCN), a FAIR (findable, accessible, interoperable and reusable) network of birth cohort data, established as part of the Horizon2020 LifeCycle project. For the analysis we received data-cataloguing and federated analysis support from the Horizon2020 EUCAN-connect project. Details of the variables included in the network and their harmonization are provided in the network's data catalogue (https://data-catalogue.molgenis cloud.org/catalogue/catalogue/#/variable-explorer). Please contact individual cohorts for further details of their data sharing policy. For further enquiries about the EU Child Cohort Network, please contact the coordinating center (EU Child. Cohort@erasmusmc.nl) and EUCAN-connect MOLGENIS support (molgenis-support@umcg.nl). The analytical code is available on GitHub.

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