Geospatial analysis, web-based mapping and determinants of prostate cancer incidence in Georgia counties: evidence from the 2012-2016 SEER data

Abstract

Background: Prostate cancer (CaP) cases are high in the United States. According to the American Cancer Society, there are an estimated number of 174,650 CaP new cases in 2019. The estimated number of deaths from CaP in 2019 is 31,620, making CaP the second leading cause of cancer deaths among American men with lung cancer been the first. Our goal is to estimate and map prostate cancer relative risk, with the ultimate goal of identifying counties at higher risk where interventions and further research can be targeted.Method: The 2012-2016 Surveillance, Epidemiology, and End Results (SEER) Program data was used in this study. Analyses were conducted on 159 Georgia counties. The outcome variable is incident prostate cancer. We employed a Bayesian geospatial model to investigate both measured and unmeasured spatial risk factors for prostate cancer. We visualised the risk of prostate cancer by mapping the predicted relative risk and exceedance probabilities. We finally developed interactive web-based maps to guide optimal policy formulation and intervention strategies.Results: Number of persons above age 65 years and below poverty, higher median family income, number of foreign born and unemployed were risk factors independently associated with prostate cancer risk in the non-spatial model. Except number of foreign born, all these risk factors were also significant in the spatial model with the same direction of effects. Substantial geographical variations in prostate cancer incidence were found in the study. The predicted mean relative risk was 1.20 with a range of 0.53 to 2.92. Individuals residing in Towns, Clay, Union, Putnam, Quitman, and Greene counties were at increased risk of prostate cancer incidence while those residing in Chattahoochee were at the lowest risk of prostate cancer incidence.Conclusion: Our results can be used as an effective tool in the identification of counties that require targeted interventions and further research by program managers and policy makers as part of an overall strategy in reducing the prostate cancer burden in Georgia State and the United States as a whole.

Bayesian spatiotemporal modelling and mapping of malaria risk among children aged below 5 years in Ghana

“…The uninformative priors on the log of the precision of the hyper-parameters included 𝜏 𝜇 ~logGamma (1, 0.00005) for structured spatial random effect and 𝜏 𝑣 ~𝑙𝑜𝑔𝐺𝑎𝑚𝑚𝑎 (1,0.00005) for unstructured spatial random effect [15,[17][18][19] . The prior on the precision of unstructured temporal effect included 𝜏 ∅ ~𝑙𝑜𝑔𝐺𝑎𝑚𝑚𝑎(1, 0005) and the prior specification of 𝜏 𝛾 ~𝑙𝑜𝑔𝐺𝑎𝑚𝑚𝑎(1, 001) on the precision of random walk of first order (RW1) was used [20].…”