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Background <p>Prostate cancer incidence varies markedly across the United States (U.S.), yet the broad contextual predictors associated with this variation and the spatial scales at which these associations operate remain insufficiently characterized within a unified spatial framework.</p> Objective <p>This study aimed to examine annual state-level associations between prostate cancer incidence and selected contextual predictors across the U.S. from 2018 to 2022 using Multiscale Geographically Weighted Regression (MGWR).</p> Methods <p>Five annual state-level datasets covering the 50 U.S. states were assembled for 2018–2022. After exploratory spatial analysis, correlation screening, collinearity diagnostics, theory-informed re-evaluation, and representative-year sensitivity analyses, six predictors were retained for the final annual models: elevation, rainfall, atmospheric pressure, obesity prevalence, the percentage of males aged 65 years and older among the male population, and the percentage of the Black or African American alone population. Ordinary Least Squares (OLS), Geographically Weighted Regression (GWR), and MGWR were fitted separately for each annual dataset. Model performance was compared using R², adjusted R², residual sum of squares, corrected Akaike information criterion (AICc), bandwidth diagnostics, and residual spatial autocorrelation.</p> Results <p>Across all five annual analyses, MGWR showed the most favorable overall performance, with adjusted R² values ranging from 0.555 to 0.618 and AICc values ranging from 124.934 to 132.779. MGWR also identified variable-specific adaptive bandwidths ranging from 24 to 40 nearest neighbors, indicating spatial scale heterogeneity among predictors. Local coefficient surfaces showed clear spatial non-stationarity in both coefficient magnitude and, for some predictors, coefficient direction. Residual Moran’s I tests were non-significant for all five annual models, suggesting that substantial residual spatial autocorrelation had been reduced after model fitting. Multivariate clustering of standardized local coefficients further revealed recurring regional groupings in coefficient-pattern similarity.</p> Conclusion <p>At the state level in the U.S., prostate cancer incidence was associated with geographically differentiated contextual patterns rather than a single spatially uniform relationship structure. In this setting, the main value of MGWR lay in its ability to characterize multi-scale and regionally varying association patterns across repeated annual spatial analyses. These findings may support geographically differentiated surveillance and future hypothesis generation while underscoring the importance of considering spatial heterogeneity in ecological studies of prostate cancer incidence.</p>

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Spatial analysis of predictors of prostate cancer incidence in the united states using multiscale geographically weighted regression (MGWR)

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月26日

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  • Yang Liu,
  • Peihai Zhang,
  • Ziyang Ma,
  • Jintao Wei

摘要

Background

Prostate cancer incidence varies markedly across the United States (U.S.), yet the broad contextual predictors associated with this variation and the spatial scales at which these associations operate remain insufficiently characterized within a unified spatial framework.

Objective

This study aimed to examine annual state-level associations between prostate cancer incidence and selected contextual predictors across the U.S. from 2018 to 2022 using Multiscale Geographically Weighted Regression (MGWR).

Methods

Five annual state-level datasets covering the 50 U.S. states were assembled for 2018–2022. After exploratory spatial analysis, correlation screening, collinearity diagnostics, theory-informed re-evaluation, and representative-year sensitivity analyses, six predictors were retained for the final annual models: elevation, rainfall, atmospheric pressure, obesity prevalence, the percentage of males aged 65 years and older among the male population, and the percentage of the Black or African American alone population. Ordinary Least Squares (OLS), Geographically Weighted Regression (GWR), and MGWR were fitted separately for each annual dataset. Model performance was compared using R², adjusted R², residual sum of squares, corrected Akaike information criterion (AICc), bandwidth diagnostics, and residual spatial autocorrelation.

Results

Across all five annual analyses, MGWR showed the most favorable overall performance, with adjusted R² values ranging from 0.555 to 0.618 and AICc values ranging from 124.934 to 132.779. MGWR also identified variable-specific adaptive bandwidths ranging from 24 to 40 nearest neighbors, indicating spatial scale heterogeneity among predictors. Local coefficient surfaces showed clear spatial non-stationarity in both coefficient magnitude and, for some predictors, coefficient direction. Residual Moran’s I tests were non-significant for all five annual models, suggesting that substantial residual spatial autocorrelation had been reduced after model fitting. Multivariate clustering of standardized local coefficients further revealed recurring regional groupings in coefficient-pattern similarity.

Conclusion

At the state level in the U.S., prostate cancer incidence was associated with geographically differentiated contextual patterns rather than a single spatially uniform relationship structure. In this setting, the main value of MGWR lay in its ability to characterize multi-scale and regionally varying association patterns across repeated annual spatial analyses. These findings may support geographically differentiated surveillance and future hypothesis generation while underscoring the importance of considering spatial heterogeneity in ecological studies of prostate cancer incidence.