Evidence map›Paper›PMID 42168936›Full record

ArticleBMC public health2026

Applying a complex systems-informed approach to population health interventions: a methodological case-study example of traffic restriction schemes outside schools.

Olivia Alliott, David Ogilvie, Lucy Anderson, Richard Patterson, Kate Garrott, Emma Carey, Cornelia Guell, Jenna Panter, project team

Abstract read
In one paragraph

Article in BMC public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Olivia AlliottUniversity of Cambridge, Institute of Metabolic Science Epidemiology, School of Clinical Medicine, Box 285, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK.
David OgilvieUniversity of Cambridge, Institute of Metabolic Science Epidemiology, School of Clinical Medicine, Box 285, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK.
Lucy AndersonBristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK.
Richard PattersonUniversity of Cambridge, Institute of Metabolic Science Epidemiology, School of Clinical Medicine, Box 285, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK.
Kate GarrottUniversity of Cambridge, Institute of Metabolic Science Epidemiology, School of Clinical Medicine, Box 285, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK.
Emma CareyUniversity of Cambridge, Institute of Metabolic Science Epidemiology, School of Clinical Medicine, Box 285, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK.
Cornelia GuellEuropean Centre for Environment and Human Health, University of Exeter Medical School, Penryn, UK.
Jenna PanterUniversity of Cambridge, Institute of Metabolic Science Epidemiology, School of Clinical Medicine, Box 285, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK. jenna.panter@ims.cam.ac.uk.
project team

Funding

Medical Research Council Unit Programmes MC_UU_00006/7, MC_UU_12015/6 and MC_UU_12015/7National Institute for Health and Care Research (NIHR), School for Public Health Research (SPHR) HPHP-WP2.3- TRS
6 · The paper itself

Abstract

backgroundPopulation health interventions may change physical, social or fiscal environments to shift health behaviours and often operate within a complex system of interacting factors. Systems methods are frequently suggested as a way to provide an overview of the interactions that determine behaviours or actions and may help to understand how or why interventions might (or might not) change health. However, there are few examples of how these methods have been used to guide empirical evaluations of public health interventions.

methodsWe describe our application of a qualitative complex systems approach within an ongoing evaluation of a population health intervention: traffic restriction schemes, often referred to as School Streets, where motor vehicle access outside schools is restricted at pick-up and drop-off times. This paper presents a methodological case study illustrating how these methods can be applied in practice. We intended to use systems methods across our evaluation; here we describe how we use these methods to build theory. Following the five-step approach outlined by Alvarado et al., (2023), we produced research propositions, systems archetypes and a causal loop diagram to understand the underlying system in which traffic restriction schemes are implemented and their potential impact on school-based active travel.

resultsIn bringing together diverse and multi-disciplinary evidence from different stakeholders we identified unanticipated systems interactions such as increased initial tensions and conflicts between users of different active travel modes (e.g. cyclists vs pedestrians). Using causal loop diagrams and systems archetypes, we developed research propositions focused on funding and implementation, safety, habit formation and the potential for unintended consequences.

conclusionsTaking a complex systems approach has deepened our understanding of how traffic restriction schemes interact with their broader context and helped us develop a guiding theoretical framework for our ongoing evaluation. We intend this work to stimulate discussion, offer insights for future evaluations of population-level health interventions, and encourage public health researchers to adopt systems methods in their own evaluations.

Indexed as

Health PromotionPopulation HealthSchoolsSystems AnalysisTransportationHumansQualitative ResearchActive travelCausal loop diagramsComplex systems approachPopulation health interventionPublic health evaluationQualitative systems methodsSchool StreetsSystems archetypesTraffic restriction schemes

Identifiers

PMID42168936
PMCPMC13383520

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.