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Hierarchical Bayesian modeling of spatio-temporal patterns of lung cancer incidence risk in Georgia, USA: 2000–2007

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Abstract

Lung cancer is the second most commonly diagnosed cancer in both men and women in Georgia, USA. However, the spatio-temporal patterns of lung cancer risk in Georgia have not been fully studied. Hierarchical Bayesian models are used here to explore the spatio-temporal patterns of lung cancer incidence risk by race and gender in Georgia for the period of 2000–2007. With the census tract level as the spatial scale and the 2-year period aggregation as the temporal scale, we compare a total of seven Bayesian spatio-temporal models including two under a separate modeling framework and five under a joint modeling framework. One joint model outperforms others based on the deviance information criterion. Results show that the northwest region of Georgia has consistently high lung cancer incidence risk for all population groups during the study period. In addition, there are inverse relationships between the socioeconomic status and the lung cancer incidence risk among all Georgian population groups, and the relationships in males are stronger than those in females. By mapping more reliable variations in lung cancer incidence risk at a relatively fine spatio-temporal scale for different Georgian population groups, our study aims to better support healthcare performance assessment, etiological hypothesis generation, and health policy making.

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Abbreviations

CAR:

Conditional autoregression

CI:

Credible interval

DIC:

Deviance information criterion

GIS:

Geographical information system

MCMC:

Markov chain Monte Carlo

RR:

Relative risk

SES:

Socioeconomic status

SIR:

Standardized Incidence Ratio

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Acknowledgments

All of the authors want to thank Dr. Sara Wagner for her help on the Georgia cancer data acquisition. Dr. John E. Vena's effort was sponsored in part by a Distinguished Cancer Scientist award from the Georgia Cancer Coalition (GCC).

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Authors and Affiliations

  1. Department of Geography, University of North Carolina at Greensboro, Graham 230, 1009 Spring Garden St., Greensboro, NC, 27412, USA

    Ping Yin

  2. Department of Geography, University of Georgia, GG Building, 210 Field St., Room 204, Athens, GA, 30602, USA

    Lan Mu & Marguerite Madden

  3. Department of Public Health Sciences, Medical University of South Carolina, 135 Cannon St., Suite 303, MSC 835, Charleston, SC, 29425, USA

    John E. Vena

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  1. Ping Yin
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  2. Lan Mu
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  3. Marguerite Madden
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  4. John E. Vena
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Correspondence to Ping Yin.

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Yin, P., Mu, L., Madden, M. et al. Hierarchical Bayesian modeling of spatio-temporal patterns of lung cancer incidence risk in Georgia, USA: 2000–2007. J Geogr Syst 16, 387–407 (2014). https://doi.org/10.1007/s10109-014-0200-4

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  • DOI: https://doi.org/10.1007/s10109-014-0200-4

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