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Space-Time Statistical Insights about Geographic Variation in Lung Cancer Incidence Rates: Florida, USA, 2000–2011

Author

Listed:
  • Lan Hu

    (School of Economic, Political and Policy Sciences, The University of Texas at Dallas, 800 West Campbell Road, Richardson, TX 75080, USA)

  • Daniel A. Griffith

    (School of Economic, Political and Policy Sciences, The University of Texas at Dallas, 800 West Campbell Road, Richardson, TX 75080, USA)

  • Yongwan Chun

    (School of Economic, Political and Policy Sciences, The University of Texas at Dallas, 800 West Campbell Road, Richardson, TX 75080, USA)

Abstract

The geographic distribution of lung cancer rates tends to vary across a geographic landscape, and covariates (e.g., smoking rates, demographic factors, socio-economic indicators) commonly are employed in spatial analysis to explain the spatial heterogeneity of these cancer rates. However, such cancer risk factors often are not available, and conventional statistical models are unable to fully capture hidden spatial effects in cancer rates. Introducing random effects in the model specifications can furnish an efficient approach to account for variations that are unexplained due to omitted variables. Especially, a random effects model can be effective for a phenomenon that is static over time. The goal of this paper is to investigate geographic variation in Florida lung cancer incidence data for the time period 2000–2011 using random effects models. In doing so, a Moran eigenvector spatial filtering technique is utilized, which can allow a decomposition of random effects into spatially structured (SSRE) and spatially unstructured (SURE) components. Analysis results confirm that random effects models capture a substantial amount of variation in the cancer data. Furthermore, the results suggest that spatial pattern in the cancer data displays a mixture of positive and negative spatial autocorrelation, although the global map pattern of the random effects term may appear random.

Suggested Citation

  • Lan Hu & Daniel A. Griffith & Yongwan Chun, 2018. "Space-Time Statistical Insights about Geographic Variation in Lung Cancer Incidence Rates: Florida, USA, 2000–2011," IJERPH, MDPI, vol. 15(11), pages 1-18, October.
    • Handle: RePEc:gam:jijerp:v:15:y:2018:i:11:p:2406-:d:179273
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    References listed on IDEAS

    as
    1. Daniel Griffith, 2006. "Hidden negative spatial autocorrelation," Journal of Geographical Systems, Springer, vol. 8(4), pages 335-355, October.
    2. Paul Clarke & Claire Crawford & Fiona Steele & Anna Vignoles, 2010. "The Choice between fixed and random effects models: some considerations for educational research," The Centre for Market and Public Organisation 10/240, The Centre for Market and Public Organisation, University of Bristol, UK.
    3. Frondel, Manuel & Vance, Colin, 2010. "Fixed, random, or something in between? A variant of Hausman's specification test for panel data estimators," Economics Letters, Elsevier, vol. 107(3), pages 327-329, June.
    4. Yongwan Chun & Daniel A. Griffith & Monghyeon Lee & Parmanand Sinha, 2016. "Eigenvector selection with stepwise regression techniques to construct eigenvector spatial filters," Journal of Geographical Systems, Springer, vol. 18(1), pages 67-85, January.
    5. Xiaoping Jin & Bradley P. Carlin & Sudipto Banerjee, 2005. "Generalized Hierarchical Multivariate CAR Models for Areal Data," Biometrics, The International Biometric Society, vol. 61(4), pages 950-961, December.
    6. repec:zbw:rwirep:0160 is not listed on IDEAS
    7. Julian Besag & Jeremy York & Annie Mollié, 1991. "Bayesian image restoration, with two applications in spatial statistics," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 43(1), pages 1-20, March.
    8. Boyle, Paul & Norman, Paul & Rees, Philip, 2002. "Does migration exaggerate the relationship between deprivation and limiting long-term illness? A Scottish analysis," Social Science & Medicine, Elsevier, vol. 55(1), pages 21-31, July.
    9. Bentham, Graham, 1988. "Migration and morbidity: Implications for geographical studies of disease," Social Science & Medicine, Elsevier, vol. 26(1), pages 49-54, January.
    10. Daniel A. Griffith, 2003. "Spatial Autocorrelation and Spatial Filtering," Advances in Spatial Science, Springer, number 978-3-540-24806-4, december.
    11. Yongwan Chun & Daniel Griffith & Monghyeon Lee & Parmanand Sinha, 2016. "Eigenvector selection with stepwise regression techniques to construct eigenvector spatial filters," Journal of Geographical Systems, Springer, vol. 18(1), pages 67-85, January.
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