ArticlePreventive oncology & epidemiology2026
Spatial Analysis of Lung Cancer Mortality and Its Potential Risk Factors: A Regional Study in Kansas.
Article in Preventive oncology & epidemiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
Introduction: Lung cancer is a leading cause of cancer deaths in the U.S., with significant geographic disparities in incidence and mortality. Understanding the relationship between spatial variations and other risk factors to lung cancer mortality counts (LCMC) is critical for guiding targeted public health interventions. Objective: This study examines how spatial variations, demographic, socioeconomic, behavioral, health, and environmental risk factors are associated with LCMC in Kansas. Methods: LCMC data from 105 Kansas counties were analyzed using Poisson and Negative Binomial models incorporating Conditional Autoregressive (CAR) and Besag-York-Mollié 2 (BYM2) spatial effects models. Predictors included elderly population (%), rurality, poverty, housing, smoking, obesity, pollution, and proximity to coal power plants. Model performance was assessed using Deviance Information Criterion and the Mean Absolute Percentage Error. Results: The Poisson BYM2 model with correlated heterogeneity provided the best overall performance. In this model, the elderly population (%) and rurality were significantly and positively associated while PM Conclusion: Spatial models, particularly BYM2, provide valuable insights into LCMC hotspots and risk factors. Public health strategies should focus on equity in high-risk clusters through targeted interventions and improved access to healthcare.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.