Ten common pitfalls in spatial epidemiology and how to avoid them
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Spatial epidemiology provides powerful tools for understanding geographic patterns in health and disease, but methodological and interpretative pitfalls can undermine the validity of spatial analyses. This editorial highlights ten common pitfalls spanning spatial dependence, scale, ecological inference, small-area estimation, spatial confounding, hotspot interpretation, model validation, measurement and statistical uncertainty, and causal interpretation. For each, we provide practical guidance to support more rigorous and reliable spatial epidemiological research. As geospatial data, artificial intelligence, and analytical methods continue to advance, careful spatial reasoning remains essential to ensure that methodological sophistication translates into valid, interpretable, and meaningful public health evidence.
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Not applicableCRediT authorship contribution
Behzad Kiani conceptualised the article and wrote the original draft. Nima Kianfar contributed to the conceptualisation and drafting of the manuscript. Munazza Fatima and Robert Bergquist reviewed and edited the manuscript. All authors reviewed and approved the final version.
Data Availability Statement
All data generated or analyzed during this study are included in this published article.
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