A Review of Applications of GIS in Pneumoconiosis Research: Implications for Nursing Practice and Occupational Health Nursing

Background: Pneumoconiosis (including silicosis, coal workers’ pneumoconiosis, and asbestosis) remains a major occupational health concern, particularly in settings with sustained exposure to respirable mineral dust. Conventional epidemiological approaches often under-represent spatial heterogeneity in exposure contexts, potentially masking localized disease clusters and delaying targeted prevention. Geographic Information Systems (GIS) and spatial statistics provide a means to integrate georeferenced occupational, environmental, and health data, enabling spatially explicit surveillance and risk assessment. This review provides an overview of GIS applications in pneumoconiosis research and discusses implications for nursing practice and occupational health nursing. Methods: A structured literature search was conducted in Web of Science, Scopus, PubMed, and Google Scholar using combinations of keywords related to pneumoconiosis (e.g., “silicosis”, “coal workers’ pneumoconiosis”, “asbestosis”), GIS (e.g., “geographic information system”, “spatial analysis”), and spatial statistical methods (e.g., “Moran’s I”, “Getis-Ord Gi*”, “kernel density”, “SaTScan”, “spatial regression”, “GWR/MGWR”). Eligible publications included peer-reviewed studies applying GIS/spatial analytics to pneumoconiosis outcomes, exposure assessment, clustering/hotspot detection, or spatial risk modeling. Consistent with the template structure, findings are synthesized into three sections: (i) methods to characterize spatial patterns; (ii) hotspot/cluster analysis approaches; and (iii) spatial regression models to identify risk factors. Results: GIS-enabled mapping and spatial statistics consistently revealed non-random spatial patterns of pneumoconiosis, with clustering frequently aligned with mining, stone processing, construction corridors, and industrial zones. Hotspot methods (e.g., KDE, LISA, Getis-Ord Gi*, scan statistics) were useful in detecting localized high-risk areas for intensified screening and prevention. Spatial regression approaches (e.g., spatial lag/error models, Bayesian CAR, and GWR/MGWR) improved inference by accounting for spatial dependence and local non-stationarity, identifying associations between pneumoconiosis indicators and contextual factors such as industrial density, dust exposure proxies, sociodemographic vulnerability, and access to occupational health services. Nursing-relevant applications include improved surveillance sensitivity, geographically targeted health education, risk communication, and prioritization of screening/referral resources. Conclusions: GIS and spatial statistics strengthen pneumoconiosis research by uncovering spatial inequities in exposure and disease burden and by improving the explanatory power of risk models. For occupational health nursing, spatially informed evidence supports proactive surveillance, targeted interventions, and resource allocation. Future work should prioritize interoperable data pipelines, privacy-preserving geocoding, and capacity building to integrate GIS into routine occupational health nursing practice.