Causal mediation approaches for understanding pathways to inequalities and policy entry points: examples from early years health and development

The reduction of health inequalities has been a priority of researchers, decision-makers and practitioners for many years. Advances in causal mediation analysis offer great promise for identifying intervention targets and inferring how policy actions might alter health inequalities. However, these methods are sometimes presented in a manner that is not accessible to the wider community of health researchers. Causal mediation methods also have a range of limitations and assumptions that have implications for their application and the interpretation of results. In this paper, we consider three types of questions that can be used to guide policy actions to reduce health inequalities, addressed using causal mediation methods: (1) which mediating pathways offer most promise for the reduction of health inequalities and should be the focus of further, more indepth analysis? 2) In the face of two competing pathways, which one is most likely to lead to a narrowing of health inequalities? 3) What would be the impact of a hypothetical intervention on one specific mediating pathway when implemented under different scenarios? Focusing on early years' health, we use real life examples of the application of causal mediation methods to address these three types of question. In doing so, we discuss the relative strengths and limitations of these methods and introduce key mediation concepts relevant to health inequalities researchers.

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Publication Details

Journal
Journal of Epidemiology & Community Health
Published
2025-11-03
DOI
https://doi.org/10.1136/jech-2025-224260
Citations
3
Primary Topic
Advanced Causal Inference Techniques
Type
article
Field-Weighted Citation Impact
7.89

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article

Causal mediation approaches for understanding pathways to inequalities and policy entry points: examples from early years health and development

Steven Hope, Michael J. Green, John Lynch, Anna Pearce et al.
3 citations
Journal of Epidemiology & Community Health
Advanced Causal Inference Techniques
7.89
article

Causal mediation approaches for understanding pathways to inequalities and policy entry points: examples from early years health and development

Steven Hope, Michael J. Green, John Lynch, Anna Pearce, Daniela K Schlüter, Russell Viner, Joost Oude Groeniger, Bianca De Stavola, David Taylor-Robinson
article en
3 citations

Abstract

The reduction of health inequalities has been a priority of researchers, decision-makers and practitioners for many years. Advances in causal mediation analysis offer great promise for identifying intervention targets and inferring how policy actions might alter health inequalities. However, these methods are sometimes presented in a manner that is not accessible to the wider community of health researchers. Causal mediation methods also have a range of limitations and assumptions that have implications for their application and the interpretation of results. In this paper, we consider three types of questions that can be used to guide policy actions to reduce health inequalities, addressed using causal mediation methods: (1) which mediating pathways offer most promise for the reduction of health inequalities and should be the focus of further, more indepth analysis? 2) In the face of two competing pathways, which one is most likely to lead to a narrowing of health inequalities? 3) What would be the impact of a hypothetical intervention on one specific mediating pathway when implemented under different scenarios? Focusing on early years' health, we use real life examples of the application of causal mediation methods to address these three types of question. In doing so, we discuss the relative strengths and limitations of these methods and introduce key mediation concepts relevant to health inequalities researchers.

Journal of Epidemiology & Community HealthVol. 80(2)
University of Liverpool (GB), Duke University (US), Great Ormond Street Hospital (GB), Erasmus MC (NL), Public Health England (GB), MRC/CSO Social and Public Health Sciences Unit (GB), University College London (GB), The University of Adelaide (AU), University of Glasgow (GB)
National Institute for Health and Care Research, University College London, University of Glasgow, ZonMw, Medical Research Council, School for Public Health Research
Openalex Percentile: Top 2%
Advanced Causal Inference Techniques
7.89
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