https://orcid.org/0009-0000-7240-0757
-
Views
-
Cite
Cite
Sudirham, Beyond the map: a multifaceted approach to understanding the life-course impacts of spatial exposures on health, American Journal of Epidemiology, Volume 194, Issue 3, March 2025, Pages 871–872, https://doi.org/10.1093/aje/kwae415
Close - Share Icon Share
Extract
This article is part of a Special Collection on Methods in Social Epidemiology.
The study “Improving spatial exposure data for everyone – lifecourse social context and ascertaining residential history,” by Sims et al.,1 presents a notable contribution to the field of environmental epidemiology. However, there are several avenues to enhance the study design for greater rigor and generalizability.
The life-course social context concept needs to be operationalized more deeply. The life-course perspective and fundamental cause theory are 2 social determinants of health theories that can be integrated to create a more comprehensive understanding of how social factors affect health trajectories over time.2 Furthermore, the integration of ideas related to spatial inequality, such as the environmental justice framework and the spatial mismatch hypothesis, might clarify the ways in which environmental exposures and residential segregation interact to generate health inequalities.3
To enrich data quality, the study should leverage multiple data sources, including administrative records, remote sensing data, and social media. By integrating geographic information systems and using finer-grained geographic units, exposure data can have a higher spatial resolution.4 Machine-learning tools, such as latent class analysis, can be used to identify unique groups of individuals with comparable exposure profiles and health outcomes. Additionally, machine-learning algorithms can be used to combine and analyze diverse data sources, enhancing the robustness and depth of the analysis.5,6 Spatial patterns in exposure and health outcomes can be found using spatial analytic approaches like spatial autocorrelation analysis and kernel density estimation. Time-series analysis makes it possible to document how spatial exposures are dynamic and how they affect health.7
