Methodological guidance for selecting buffers in greenspace–health studies

作者
Mohammad Javad Zare Sakhvidi,Matthew H. E. M. Browning,Karl Samuelsson,S.M. Labib,Achilleas Psyllidis,Adeladza Kofi Amegah,Thomas Astell-Burt,Albert Bach,Michael Jerrett,Gregory N. Bratman,Matilda van den Bosch,Kees de Hoogh,Sjerp de Vries,Angel M. Dzhambov,Rohollah Fallah Madvari,Xiaoqi Feng,Amanda Fernandes,Elaine Fuertes,Vincenzo Giannico,Nelson Gouveia
出处
期刊:The Lancet Planetary Health [Elsevier BV]
卷期号:: 101370-101370
标识
DOI:10.1016/j.lanplh.2025.101370
摘要

Greenspace can promote health via diverse pathways. A common approach to assessing greenspace exposure is to estimate vegetation availability within buffers surrounding locations where people reside or spend time. However, no clear framework for informed buffer selection exists, and choices made show considerable heterogeneity, impeding evidence synthesis and causal inference. In this Personal View conducted by an interdisciplinary panel of experts, we aimed to establish a framework for informed buffer selection for epidemiological studies on greenspace. We began by reviewing available approaches for the selection of buffer types, which range from single fixed-location approaches to high-resolution mobility-based activity-space approaches, as well as different buffer sizes. We then summarised the determinants of buffer type and size selection including health outcomes and underlying mechanisms, study population, contextual factors, and data characteristics. Finally, based on these determinants, we developed recommendations for future research. Buffer type and size selection should be hypothesis driven, reflecting presumed greenspace-health mechanisms. Buffer selection should target activity-based approaches where feasible, and multiple buffer sizes should be tested. Overall, the assessment of greenspace exposure should shift from ad-hoc approaches to personalised, multiscale, and context-specific methods. We call for standardising and reporting the rationale for buffer selection to minimise bias and enhance comparability and evidence synthesis across studies.
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