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 amo...
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| Format: | Article |
| Language: | English |
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BMC
2021-05-01
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| Series: | BMC Cancer |
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| Online Access: | https://doi.org/10.1186/s12885-021-08254-0 |
| _version_ | 1818738250610638848 |
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| author | Justice Moses K. Aheto Ovie A. Utuama Getachew A. Dagne |
| author_facet | Justice Moses K. Aheto Ovie A. Utuama Getachew A. Dagne |
| author_sort | Justice Moses K. Aheto |
| collection | DOAJ |
| description | 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. Methods 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 for the 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. |
| first_indexed | 2024-12-18T01:05:57Z |
| format | Article |
| id | doaj.art-0ace54736b844062b31b2069e9c8411a |
| institution | Directory Open Access Journal |
| issn | 1471-2407 |
| language | English |
| last_indexed | 2024-12-18T01:05:57Z |
| publishDate | 2021-05-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Cancer |
| spelling | doaj.art-0ace54736b844062b31b2069e9c8411a2022-12-21T21:26:13ZengBMCBMC Cancer1471-24072021-05-0121111310.1186/s12885-021-08254-0Geospatial analysis, web-based mapping and determinants of prostate cancer incidence in Georgia counties: evidence from the 2012–2016 SEER dataJustice Moses K. Aheto0Ovie A. Utuama1Getachew A. Dagne2Department of Biostatistics, School of Public Health, College of Health Sciences, University of GhanaCollege of Public Health, University of South FloridaCollege of Public Health, University of South FloridaAbstract 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. Methods 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 for the 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.https://doi.org/10.1186/s12885-021-08254-0Prostate cancerGeospatial modellingMapping prostate cancerDisease mappingR-INLASEER program |
| spellingShingle | Justice Moses K. Aheto Ovie A. Utuama Getachew A. Dagne Geospatial analysis, web-based mapping and determinants of prostate cancer incidence in Georgia counties: evidence from the 2012–2016 SEER data BMC Cancer Prostate cancer Geospatial modelling Mapping prostate cancer Disease mapping R-INLA SEER program |
| title | Geospatial analysis, web-based mapping and determinants of prostate cancer incidence in Georgia counties: evidence from the 2012–2016 SEER data |
| title_full | Geospatial analysis, web-based mapping and determinants of prostate cancer incidence in Georgia counties: evidence from the 2012–2016 SEER data |
| title_fullStr | Geospatial analysis, web-based mapping and determinants of prostate cancer incidence in Georgia counties: evidence from the 2012–2016 SEER data |
| title_full_unstemmed | Geospatial analysis, web-based mapping and determinants of prostate cancer incidence in Georgia counties: evidence from the 2012–2016 SEER data |
| title_short | Geospatial analysis, web-based mapping and determinants of prostate cancer incidence in Georgia counties: evidence from the 2012–2016 SEER data |
| title_sort | geospatial analysis web based mapping and determinants of prostate cancer incidence in georgia counties evidence from the 2012 2016 seer data |
| topic | Prostate cancer Geospatial modelling Mapping prostate cancer Disease mapping R-INLA SEER program |
| url | https://doi.org/10.1186/s12885-021-08254-0 |
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