Aheto et al. BMC Cancer          (2021) 21:508 
https://doi.org/10.1186/s12885-021-08254-0
RESEARCH ARTICLE Open Access
Geospatial analysis, web-based mapping
and determinants of prostate cancer
incidence in Georgia counties: evidence
from the 2012–2016 SEER data
Justice Moses K. Aheto1,2* , Ovie A. Utuama2 and Getachew A. Dagne2
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.
Keywords: Prostate cancer, Geospatial modelling, Mapping prostate cancer, Disease mapping, R-INLA, SEER
program, Georgia, USA
* Correspondence: justiceaheto@yahoo.com; jmkaheto@ug.edu.gh
1Department of Biostatistics, School of Public Health, College of Health
Sciences, University of Ghana, P. O. Box LG13, Legon, Accra, Ghana
2College of Public Health, University of South Florida, Tampa, USA
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Aheto et al. BMC Cancer          (2021) 21:508 Page 2 of 13
Background and physician offices [10, 11]. Patients must be Georgia
Prostate cancer is the leading diagnosis of malignancy residents at the time of diagnosis, even though the ad-
and the second cause of mortality among American dress of residence is not reported in the registry. Only
men, with an estimated national annual health care cost patients with an International Classification of Diseases
of $9.8 billion [1, 2]. The United States Cancer Statistics for Oncology, third edition, (ICD-O-3) with topography
reported 192,443 new cases of prostate cancer in 2016, code C61 and behaviour code 3 were included for ana-
with an incidence rate of 101 per 100,000 men, and 30, lysis. SEER, being one of the oldest registries in the
370 prostate cancer deaths or 19 deaths per 100,000 dur- country, represents the gold standard in reporting stan-
ing the same year [3]. Despite an overall decline in inci- dards and data quality, with completeness rates of more
dence across the United States since the early 1990s [4], than 97% [12–14].
there remain pockets of high prostate cancer burden. SEER data are publicly available deidentified records of
In the United States, the state of Georgia has the sec- cancer cases. Permission was sought from and granted
ond largest annual incidence rate of prostate cancer [3]. by SEER Program to access and use the data for this
In 2016, there were 7160 reported new cases and 889 study. We did not attempt to identify, contact patients
deaths in the state, with associated incidence and mor- or link records to identifiable health information.
tality rates of 133 and 23 per 100,000 men, respectively
[3]. African American (AA) men not only have higher Outcome variable
incidence of prostate cancer but also demonstrate 60% The outcome variable is the number of incident prostate
more mortality than white men, after controlling for in- cancer cases per county. Detailed information is pro-
cidence [5]. As 32% of Georgia consists of AA [6], it rep- vided under the statistical analysis section.
resents an unusual opportunity to investigate
community factors associated with a high-risk popula- Covariates
tion. Although a few studies have identified high pros- The covariates used in this study were county-level vari-
tate cancer incidence in the southwest of the state [7, 8], ables for the period 2012–2016 identified in the litera-
the sociodemographic characteristics of these regions ture to be associated with the prostate cancer incidence
are not well described. [2, 15–17]. These included percentage of blacks in the
For the purpose of planning for prostate cancer inter- counties, number of persons above 65 years of age in the
ventions with limited health resources, it is important to counties, number of persons having at least a bachelor’s
characterize and identify predictors of high prostate can- degree in the counties, mean age at diagnosis, number of
cer burden at the community level. The present study, persons living below poverty in the counties, number of
therefore, aims to 1) model and map Georgia county in- foreign born persons in the counties, percentage of the
cidence of prostate cancer, 2) evaluate county sociode- rural population in the counties, monthly median family
mographic factors associated with high incidence of income in the counties, and number of unemployed.
prostate cancer.
Statistical analysis
Methods We employed a Bayesian geospatial model to investigate
Data source and study population both measured and unmeasured spatial risk factors for
We used the Surveillance, Epidemiology, End Results prostate cancer among men residing in 159 counties in
(SEER) population-based cancer registry, which is pub- Georgia State.
licly available data to investigate county-level distribu-
tion of prostate cancer cases in the state of Georgia. For Model formulation
this ecological study, only newly diagnosed cases 40 We set Yi to be the observed counts of prostate cancer
years and older from January 1, 2012 through December cases in county i and Ei as the expected number of pros-
31, 2016 were used for this study, because case reporting tate cancer cases in county i. We implemented Besag-
to SEER from the greater Georgia area started in 2010 York-Mollié (BYM) model [18] to analyse the data. We
and at the time of analysis SEER’s most current county assumed that Yi are conditionally independently Poisson
attributes data spanned the 2012 to 2016 period. The distributed, and modelled as:
greater Georgia area includes all counties in the state,
except the 15 represented by the older Atlanta and Rural
Y 
 PoissonðE θ Þ; i ¼ 1; 2;…; n
Georgia areas previously reported to SEER [9]. There- i i i
fore, since 2010 SEER captures cancer data from all 159
counties in Georgia. The SEER Georgia registry reports where n is the number of counties (i.e n = 159) and θi is
clinical, or preferentially pathologic diagnosis of cancer the relative risk in county i. We expressed the logarithm
from eligible patient records in hospitals, laboratories of θi as:
Aheto et al. BMC Cancer          (2021) 21:508 Page 3 of 13
logðθiÞ ¼ β þ dð Þ
0
x βþ u þ v ; Mapping predicted risk of prostate cancer incidence from0 i i i
the Bayesian spatial model
where β0 is the intercept parameter that represents the Substantial geographical variations in prostate cancer in-
overall risk, d(.) is a vector of observed covariates, β is a cidence were found in the study (Fig. 3). In addition, we
vector of regression coefficients for the covariates, ui is a presented the web-based interactive map of Fig. 3 in the
spatial structured effect component. We modelled the ui supplementary material online. The predicted mean rela-
using conditional autoregressive (CAR) distribution tive risk (RR) was 1.20 with a range of 0.53 (95% CI:
given as: ui j u−i
Nð
2
u σ; uδi nδ Þ; and vi is an unstructured 0.34, 0.78) to 2.92 (95% CI: 2.13, 3.86). Individuals resid-i
spatial effect defined as vi ¼ Nð0; σ2vÞ. ing in Towns, Clay, Union, Putnam, Quitman, and
The relative risk θi quantifies whether county i has Greene counties were at increased risk of prostate can-
higher (θi > 1) or lower (θi < 1) risk than the average risk cer incidence while those residing in Chattahoochee
in the reference population. We produced the probabil- were at the lowest risk of prostate cancer incidence.
ities of predicted relative risk being greater than a given Presented in Figs. 4 and 5 are the predictive maps of
threshold c (exceedance probabilities, i.e. P(θi > c)). the probability that the relative risk will exceed 1.5 and 2
Finally, we visualised the risk of prostate cancer by respectively at a given county in the Georgia State. We
mapping the predicted relative risk and exceedance also presented the web-based interactive map of Figs. 4
probabilities. We developed interactive web-based maps and 5 in the supplementary material online. The deep
to guide optimal policy formulation and intervention red regions represent counties where the probability of
strategies targeted at improving the survival of prostate the relative risk exceeding 1.5 (Fig. 4) and 2 (Fig. 5) are
cancer patients and the overall health of men in Georgia. high.
Using the Bayesian framework, we implemented our The probability that the relative risk will exceed 1.5 is
Poisson model through recommended strategies (i.e. In- highest in Union, Towns, Putnam, Greene and Quitman
tegrated Nested Laplace Approximation (INLA) with counties (Fig. 4). Also, the probability that the relative
Stochastic Partial Differential Equation (SPDE)) [19, 20]. risk will exceed 2 is highest in Towns county with a
We followed non-informative approach in choosing our probability of 0.99 (Fig. 5).
priors due to lack of reliable prior information about all
parameters, and thus used the default priors available in Discussion
the R-INLA package. All the analyses were implemented The study sets out to use Bayesian geospatial methods
in R-INLA package [21, 22]. We used 95% Bayesian to model and map prostate cancer incidence in Georgia
Credible intervals to declare statistical significance. counties, and to evaluate county sociodemographic fac-
tors associated with high incidence of prostate cancer
Results for the purpose of optimal planning for prostate cancer
Sample characteristics interventions amidst limited public health resources.
On average, 31.6% Georgia county residents were Afri- Critical risk factors for prostate cancer identified in the
can American or black while the percentage of persons present study included number of persons above 65
aged ≥65 years was 15.6%. The mean percentage of per- years of age and below poverty, median family income
sons having at least bachelor’s degree in the counties and number of foreign born and the unemployed in
was 17.5% while the overall percentages of persons counties. In contrast to previous studies [5, 7], our study
below poverty and foreign born were 21.6 and 4.6% re- did not find an association between prostate cancer inci-
spectively, and with an average of 60.% rural population dence and proportions of blacks and rural population.
among all counties. Overall, the median annual family One of the important aims of this study is identifi-
income was $51,116 and the mean percentage of un- cation of high-risk counties for public health interven-
employed was 9.1% (Table 1). tions amidst limited public health resources. This is
critical because residential location of people could
Risk factors from non-spatial and spatial models act as a marker for the socioeconomic, personal, and
Number of persons above age 65 years and below pov- climatic/environmental factors that influence access to
erty, higher median family income, number of foreign healthcare services and the general health of the
born and unemployed were risk factors independently people. Thus, spatial modelling and mapping provides
associated with prostate cancer risk in the non-spatial the required tools to obtain an improved understand-
model (Fig. 1). ing of health outcomes of people by place for tar-
Except for number of foreign born, all these significant geted public health interventions [7, 23–27]. The
risk factors in the non-spatial model were also signifi- predicted relative risk ranges from 0.53 (95% CI: 0.34,
cant in the spatial model with the same direction of ef- 0.78) in Chattahoochee to 2.92 (95% CI: 2.13, 3.86) in
fects (Fig. 2). Towns with a mean of 1.20. The study identified
Aheto et al. BMC Cancer          (2021) 21:508 Page 4 of 13
Table 1 Georgia county characteristics and crude prostate cancer incident rates, 2012–2016
% % % % % % rural % annual mean male incidence
65+ black bachelor’s poverty foreign population unemployed family annual population rate
yrs degree born income cases
median per mean per 100,
county 000 men
All 15.65 31.69 17.53 21.66 4.61 60.48 9.14 $51,116 40 29,743 158.83
Appling 15.52 0.44 11.92 20.64 4.38 71.44 8.46 $46,350 22 9159 240.20
Atkinson 11.47 42.42 6.68 27.56 13.06 100.00 5.91 $35,000 1 4240 23.58
Bacon 14.35 58.80 13.20 18.30 4.68 69.29 5.29 $46,060 5 5491 91.06
Baker 19.54 21.48 11.00 15.66 5.05 100.00 3.20 $52,280 3 1673 179.32
Baldwin 13.78 51.12 18.42 29.70 2.55 35.14 8.20 $50,230 26 22,683 114.62
Banks 16.24 32.81 10.99 15.52 4.85 93.83 7.66 $50,010 8 9298 86.04
Barrow 11.19 48.78 16.79 14.47 6.91 30.06 8.60 $58,020 42 34,208 122.78
Bartow 12.76 11.24 18.51 14.76 4.68 35.23 7.65 $57,670 59 49,433 119.35
Ben Hill . 0.21 6.06 . . 34.00 . . 12 8439 142.20
Berrien 16.11 55.09 12.27 25.58 3.13 76.14 10.43 $43,070 15 9501 157.88
Bibb 13.94 30.36 24.59 27.79 3.61 14.41 11.32 $50,130 115 73,286 156.92
Bleckley 16.92 59.22 17.72 23.02 1.35 51.59 6.58 $49,520 8 6217 128.68
Brantley 14.42 49.38 9.51 21.18 0.85 99.45 9.15 $43,880 8 9189 87.06
Brooks 18.34 8.11 12.44 24.71 3.45 71.04 17.67 $44,000 12 7901 151.88
Bryan 9.99 5.33 33.34 13.27 4.54 52.34 9.26 $76,470 16 14,852 107.73
Bulloch 10.19 46.74 28.25 31.55 3.47 48.28 9.72 $50,350 28 35,030 79.93
Burke 14.02 29.67 10.40 30.50 2.05 75.00 7.30 $39,800 9 11,186 80.46
Butts 14.45 37.61 10.20 20.53 2.99 77.94 8.35 $53,170 19 12,522 151.73
Calhoun 12.53 32.86 10.29 32.71 4.21 100.00 14.41 $33,330 8 3953 202.38
Camden 11.18 33.55 22.62 13.98 3.74 31.44 9.27 $60,560 35 25,569 136.88
Candler 16.47 55.10 14.23 29.72 4.94 66.97 7.09 $37,140 8 5437 147.14
Carroll 12.41 38.48 18.19 19.26 3.71 41.83 10.80 $55,020 53 53,793 98.53
Catoosa 15.77 24.11 19.65 11.85 2.41 28.10 7.25 $62,270 39 31,028 125.69
Charlton 12.79 60.83 8.99 20.58 8.98 51.02 12.02 $53,970 7 6847 102.23
Chatham 13.59 0.28 32.89 17.99 6.18 4.50 9.25 $61,810 153 127,704 119.81
Chattahoochee 3.85 46.88 30.12 14.30 7.45 29.52 15.96 $47,800 3 7039 42.62
Chattooga 16.16 49.64 8.86 22.40 3.08 57.56 10.06 $41,890 23 13,513 170.21
Cherokee 11.88 44.13 35.50 10.02 8.88 17.10 5.52 $84,420 89 105,874 84.06
Clarke 9.60 25.51 40.75 35.20 10.06 5.86 8.62 $51,160 54 55,388 97.49
Clay 23.89 25.22 7.44 39.81 2.64 100.00 18.94 $35,430 7 1462 478.80
Clayton 8.18 10.80 19.03 24.28 14.19 0.89 12.18 $47,260 130 124,232 104.64
Clinch 15.13 42.17 14.38 35.30 2.88 60.43 11.27 $37,070 10 3315 301.66
Cobb 10.58 31.16 44.99 11.64 15.68 0.25 6.78 $82,200 447 334,369 133.68
Coffee 12.57 25.63 13.08 24.50 5.87 66.58 7.27 $43,440 36 21,455 167.79
Colquitt 13.92 36.89 12.92 24.99 10.60 58.95 7.74 $39,510 22 22,576 97.45
Columbia 11.66 17.14 35.10 9.49 6.97 16.23 6.87 $79,820 66 60,328 109.40
Cook 14.71 33.46 13.84 26.23 2.61 59.41 5.37 $39,560 14 8372 167.22
Coweta 12.47 70.71 28.09 11.98 5.70 32.93 6.57 $74,710 81 62,242 130.14
Crawford 16.62 35.85 13.11 19.08 1.47 100.00 9.64 $48,160 8 6381 125.37
Crisp 15.45 36.08 15.08 32.93 2.65 47.03 13.93 $37,730 15 11,221 133.68
Aheto et al. BMC Cancer          (2021) 21:508 Page 5 of 13
Table 1 Georgia county characteristics and crude prostate cancer incident rates, 2012–2016 (Continued)
% % % % % % rural % annual mean male incidence
65+ black bachelor’s poverty foreign population unemployed family annual population rate
yrs degree born income cases
median per mean per 100,
county 000 men
Dade 16.95 42.47 13.80 16.61 2.27 72.13 5.85 $56,020 12 8192 146.48
Dawson 18.19 9.72 29.84 13.42 3.42 80.31 7.47 $69,480 21 11,164 188.10
Decatur 10.33 29.67 41.74 18.99 16.28 56.48 9.76 $62,010 17 13,605 124.95
DeKalb 15.50 22.28 16.89 26.48 2.83 0.26 6.65 $45,580 410 331,355 123.73
Dodge 15.01 37.79 13.54 22.21 1.89 72.23 10.53 $46,660 13 11,449 113.55
Dooly 15.61 34.48 11.26 24.26 3.99 53.67 9.42 $45,240 7 8053 86.92
Dougherty 13.48 35.42 19.77 30.51 2.36 13.96 17.03 $39,890 87 43,927 198.06
Douglas 10.35 29.89 26.15 15.21 8.38 15.76 9.04 $65,010 67 63,772 105.06
Early 18.34 23.10 14.07 31.22 1.50 65.95 7.88 $36,070 10 5191 192.64
Echols 11.13 31.76 7.87 30.21 14.81 100.00 7.04 $50,860 3 2040 147.06
Effingham 10.59 59.60 18.28 10.84 2.71 67.05 5.99 $70,710 22 26,017 84.56
Elbert 18.88 42.31 11.69 19.75 2.24 70.62 8.29 $43,470 17 9656 176.06
Emanuel 15.63 63.49 11.71 29.49 0.98 66.88 11.55 $37,840 25 11,038 226.49
Evans 15.72 27.03 15.10 26.15 4.15 61.28 7.85 $48,520 7 5387 129.94
Fannin 25.49 35.35 17.72 18.00 1.87 100.00 9.23 $50,730 25 11,547 216.51
Fayette 16.04 39.31 45.78 7.14 9.23 18.18 6.35 $96,220 65 51,505 126.20
Floyd 15.50 16.50 19.80 19.75 6.77 36.82 9.28 $53,410 102 46,640 218.70
Forsyth 11.09 42.40 48.27 6.42 14.74 9.92 4.85 $103,920 114 87,194 130.74
Franklin 19.15 5.29 12.64 25.24 3.18 88.93 7.92 $46,800 11 10,911 100.82
Fulton 10.39 17.53 49.81 16.95 12.52 1.08 8.90 $80,420 593 448,267 132.29
Gilmer 21.64 16.52 17.84 19.45 6.91 87.64 7.75 $51,700 25 14,146 176.73
Glascock 16.90 23.58 8.22 15.19 0.66 100.00 6.85 $51,990 1 1487 67.25
Glynn 17.48 45.16 28.16 18.71 5.49 20.57 7.79 $56,320 58 37,855 153.22
Gordon 13.40 36.00 12.92 20.60 9.85 51.56 7.73 $45,890 30 27,283 109.96
Grady 16.28 32.73 12.77 29.62 5.69 62.36 9.18 $40,870 16 12,115 132.07
Greene 25.96 0.74 24.75 24.31 4.95 82.75 6.61 $54,440 22 7809 281.73
Gwinnett 8.59 36.48 34.93 13.02 24.72 0.49 6.85 $69,230 415 397,153 104.49
Habersham 17.70 62.20 17.51 18.33 8.80 58.76 7.22 $50,790 42 20,301 206.89
Hall 13.55 24.21 22.47 17.72 16.54 20.56 5.72 $60,460 134 89,601 149.55
Hancock 19.39 63.74 10.89 31.36 2.66 61.59 10.31 $30,910 11 5170 212.77
Haralson 15.82 32.82 13.76 20.28 1.39 77.36 10.29 $51,340 10 14,072 71.06
Harris 16.13 62.82 26.60 8.38 2.12 96.68 8.08 $81,000 29 15,975 181.53
Hart 20.38 46.10 13.65 21.22 2.60 74.47 5.59 $47,930 24 12,455 192.69
Heard 15.32 12.82 10.50 17.04 0.65 100.00 9.78 $54,820 8 5885 135.94
Henry 10.33 40.86 27.48 12.08 7.44 13.85 8.92 $69,640 103 97,859 105.25
Houston 11.80 58.90 24.03 17.95 5.53 9.96 8.80 $63,930 68 68,066 99.90
Irwin 17.70 20.66 11.15 24.89 0.56 64.71 6.92 $44,210 12 4804 249.79
Jackson 13.42 16.03 19.07 13.52 4.67 60.01 6.77 $62,980 45 30,002 149.99
Jasper 15.29 25.10 10.36 20.06 2.59 81.76 8.77 $45,140 6 6916 86.76
Jeff Davis . 38.94 8.35 . . 69.51 . . 19 7464 254.56
Jefferson 16.88 6.44 10.42 28.91 2.01 80.67 13.28 $41,100 12 8183 146.65
Aheto et al. BMC Cancer          (2021) 21:508 Page 6 of 13
Table 1 Georgia county characteristics and crude prostate cancer incident rates, 2012–2016 (Continued)
% % % % % % rural % annual mean male incidence
65+ black bachelor’s poverty foreign population unemployed family annual population rate
yrs degree born income cases
median per mean per 100,
county 000 men
Jenkins 17.74 19.93 13.03 28.32 2.13 66.10 6.22 $41,910 6 3959 151.55
Johnson 15.48 52.27 8.69 25.17 0.68 65.41 9.27 $43,700 7 5592 125.18
Jones 15.77 31.51 20.23 13.69 1.03 67.71 8.05 $64,010 21 13,870 151.41
Lamar 15.95 18.24 17.16 22.17 2.31 60.87 13.04 $51,290 20 8852 225.94
Lanier 12.37 35.79 15.40 28.22 1.56 71.13 13.89 $44,600 9 5084 177.03
Laurens 16.30 36.51 15.24 27.75 2.17 56.64 5.94 $42,940 25 23,066 108.38
Lee 10.64 38.35 24.16 11.91 3.93 36.23 6.93 $72,360 19 14,097 134.78
Liberty 7.49 52.55 18.90 16.93 6.00 23.16 13.00 $46,500 13 30,962 41.99
Lincoln 20.51 0.76 13.15 25.36 1.40 100.00 9.06 $47,840 7 3896 179.67
Long 8.60 1.82 15.24 16.19 6.41 81.34 16.32 $54,780 7 7162 97.74
Lowndes 10.82 47.04 23.86 24.98 4.22 27.20 10.97 $50,800 48 53,285 90.08
Lumpkin 15.72 31.18 26.99 21.64 3.49 83.94 6.79 $51,680 27 14,894 181.28
Macon 14.39 11.11 8.53 32.56 2.59 53.19 16.93 $38,120 9 7973 112.88
Madison 15.88 17.56 15.47 16.10 3.84 91.88 8.01 $53,000 31 13,898 223.05
Marion 17.41 32.91 11.08 25.23 2.38 100.00 14.45 $44,250 12 4305 278.75
McDuffie 15.78 5.49 14.18 26.06 2.74 60.96 8.59 $45,190 18 10,250 175.61
McIntosh 20.12 57.78 13.77 20.13 2.09 74.31 9.93 $54,360 10 6989 143.08
Meriwether 18.40 12.50 10.15 23.71 0.60 83.28 11.18 $46,610 20 10,492 190.62
Miller 19.86 35.23 11.40 25.14 0.10 100.00 7.85 $47,530 11 2929 375.55
Mitchell 14.84 58.54 11.99 29.86 2.63 54.51 16.63 $37,780 27 12,186 221.57
Monroe 16.70 55.71 22.18 13.25 2.19 80.23 9.04 $60,030 13 13,271 97.96
Montgomery 15.54 58.77 15.55 22.82 5.42 98.71 5.77 $47,480 7 4695 149.09
Morgan 18.01 19.52 20.77 13.27 1.79 75.37 6.95 $58,750 18 8636 208.43
Murray 12.97 20.51 10.86 18.83 7.60 70.13 8.89 $46,560 28 19,652 142.48
Muscogee 12.00 12.96 25.00 20.91 5.51 2.98 10.05 $53,730 167 90,870 183.78
Newton 11.78 35.61 19.81 17.04 6.05 31.24 10.57 $57,230 79 47,626 165.88
Oconee 13.49 55.26 46.56 7.14 6.32 50.32 4.22 $85,780 22 16,007 137.44
Oglethorpe 17.30 34.38 16.62 17.91 2.33 99.25 5.61 $52,680 11 7385 148.95
Paulding 9.37 32.32 24.63 10.74 5.17 20.05 6.76 $69,820 73 69,578 104.92
Peach 13.07 25.00 20.15 21.02 5.34 38.22 10.45 $53,280 24 13,416 178.89
Pickens 20.28 59.26 24.76 10.27 2.99 73.10 7.20 $65,680 24 14,440 166.20
Pierce 15.54 31.91 12.90 19.94 2.45 79.35 8.30 $50,720 13 9202 141.27
Pike 14.51 57.78 15.31 12.13 0.91 98.96 10.01 $62,520 14 8742 160.15
Polk 14.88 26.79 12.98 20.14 7.05 51.42 8.73 $48,100 41 20,518 199.82
Pulaski 18.25 37.87 11.81 23.75 1.48 66.70 5.92 $46,830 8 5191 154.11
Putnam 21.31 44.37 18.29 17.76 5.72 80.95 8.03 $56,540 43 10,331 416.22
Quitman 25.49 14.91 8.56 25.68 1.62 73.10 18.53 $34,690 3 1200 250.00
Rabun 25.47 12.50 26.34 21.77 5.75 79.28 6.75 $53,470 18 8025 224.30
Randolph 18.29 56.68 13.35 28.66 2.39 50.63 9.75 $35,570 6 3552 168.92
Richmond 12.56 10.48 21.04 25.19 3.50 9.22 11.48 $46,840 137 97,015 141.22
Rockdale 12.57 5.95 25.96 17.16 9.61 14.93 10.26 $57,620 59 40,533 145.56
Aheto et al. BMC Cancer          (2021) 21:508 Page 7 of 13
Table 1 Georgia county characteristics and crude prostate cancer incident rates, 2012–2016 (Continued)
% % % % % % rural % annual mean male incidence
65+ black bachelor’s poverty foreign population unemployed family annual population rate
yrs degree born income cases
median per mean per 100,
county 000 men
Schley 15.31 21.68 14.86 21.87 1.61 100.00 12.96 $47,760 2 2407 83.09
Screven 16.72 40.45 14.38 25.00 1.01 78.92 8.49 $42,460 10 7116 140.53
Seminole 21.30 45.34 14.92 19.08 1.38 68.55 8.74 $43,540 7 4139 169.12
Spalding 16.23 1.69 15.39 23.57 3.45 41.62 10.16 $50,060 63 31,046 202.92
Stephens 18.30 51.67 17.62 20.04 2.10 58.56 10.55 $50,870 19 12,528 151.66
Stewart 15.23 48.43 10.42 41.41 29.13 100.00 13.89 $22,500 3 3682 81.48
Sumter 14.88 22.67 19.95 33.62 3.13 41.78 12.70 $42,090 20 15,627 127.98
Talbot 19.61 20.36 12.65 20.69 0.97 93.88 9.31 $44,730 11 3245 338.98
Taliaferro 22.32 19.69 8.77 31.38 3.41 100.00 11.65 $41,630 2 841 237.81
Tattnall 12.16 0.00 11.37 27.68 3.58 68.24 5.07 $46,550 18 14,860 121.13
Taylor 18.02 61.97 11.32 28.39 1.22 100.00 17.91 $31,880 7 4301 162.75
Telfair 15.20 32.20 9.12 28.70 12.61 46.99 4.28 $30,470 13 9452 137.54
Terrell 17.56 32.26 12.08 34.72 0.59 51.00 12.13 $37,260 15 4479 334.90
Thomas 16.50 16.13 19.53 21.30 2.79 46.02 9.80 $46,330 32 21,179 151.09
Tift 13.67 41.55 17.52 27.48 6.42 40.78 5.09 $45,620 23 19,210 119.73
Toombs 14.99 26.02 17.02 26.56 6.16 51.06 10.83 $44,700 16 12,928 123.76
Towns 33.12 18.20 25.13 15.07 2.76 100.00 8.91 $48,720 19 4996 380.30
Treutlen 17.30 2.07 16.49 18.67 1.24 58.87 6.19 $55,410 3 3449 86.98
Troup 13.58 35.38 18.76 21.32 4.04 44.30 10.74 $52,120 52 32,215 161.42
Turner 18.35 42.67 12.28 27.65 4.31 49.73 9.13 $42,630 6 4358 137.68
Twiggs 19.53 69.23 11.64 30.32 1.08 100.00 7.74 $41,150 12 4398 272.85
Union 31.21 32.04 22.37 13.12 2.15 100.00 9.06 $53,700 26 10,397 250.07
Upson 17.56 38.54 13.35 22.94 1.34 46.91 12.47 $47,500 22 13,024 168.92
Walker 16.57 11.18 15.02 18.44 1.15 43.85 6.99 $51,320 43 33,781 127.29
Walton 14.17 2.97 18.58 13.20 3.86 42.66 7.81 $62,470 66 40,763 161.91
Ware 15.92 55.26 12.83 28.07 3.39 29.44 5.65 $42,150 32 18,069 177.10
Warren 20.48 2.69 12.09 26.38 1.98 100.00 12.54 $39,890 5 2694 185.60
Washington 15.88 46.51 12.30 26.40 1.67 65.60 10.70 $46,630 20 10,812 184.98
Wayne 14.51 32.56 13.32 20.58 2.82 57.94 12.83 $50,680 21 15,719 133.60
Webster 17.51 23.68 9.42 22.41 0.26 100.00 5.20 $51,370 4 1362 293.69
Wheeler 12.96 1.94 4.94 27.41 1.53 100.00 7.53 $36,210 5 4580 109.17
White 19.95 1.06 20.79 19.29 2.75 83.79 4.81 $50,120 18 13,269 135.65
Whitfield 12.71 28.43 13.55 19.63 18.31 29.08 9.63 $49,450 69 51,118 134.98
Wilcox 16.06 1.64 9.53 20.87 2.39 100.00 7.89 $45,350 7 5436 128.77
Wilkes 21.37 9.02 13.79 26.74 3.57 67.37 8.62 $47,480 10 5169 193.46
Wilkinson 17.77 34.09 8.64 20.79 0.77 100.00 7.38 $50,130 8 4582 174.60
Worth 16.90 47.81 10.21 18.44 1.65 69.16 8.23 $45,340 17 10,397 163.51
Towns (2.92) as the county with the highest prostate 2.39), Union (RR = 2.30), Greene (RR = 2.14) and Put-
cancer incidence. Other counties with relatively high nam (RR = 2.13) counties were at increased risk of
incidence include Clay (RR = 2.55), Quitman (RR = prostate cancer incidence.
Aheto et al. BMC Cancer          (2021) 21:508 Page 8 of 13
Fig. 1 Risk factors associated with prostate cancer incidence in the non-spatial model
Fig. 2 Risk factors associated with prostate cancer incidence in the spatial model
Aheto et al. BMC Cancer          (2021) 21:508 Page 9 of 13
Fig. 3 Spatial distribution of predicted prostate cancer relative risk in the Georgia State. Source: This map was produced by the authors
On closer examination of high risk prostate cancer large or predominantly black populations likely shared
counties, we observed that despite being predominantly these environmental conditions with Clay and Quitman,
white and better educated (25.1% with a Bachelor’s de- our modelling suggests that prostate cancer risk in both
gree) the main driver of risk in Towns County in the communities is multifactorial, resulting from a possible
north of Georgia was its older population, reporting the confluence of negative lifestyle, economic and environ-
largest proportion of persons at least 65 years of age mental factors experienced over long periods of time.
(33.1%). While advancing age is a well-known risk factor In comparing the high-risk counties with Chattahoo-
for prostate cancer, Clay and Quitman Counties in also chee and rural low-risk counties, we observed that popu-
suggest that low educational attainment (7.4 and 8.5% lation age was the single most obvious distinction. Low
with a Bachelor’s degree), high unemployment (18.9 and risk counties had a smaller proportion of elderly persons,
18.5%) and individual poverty (39.8 and 25.6%) may be irrespective of whether they were classified as rural, and
additional risk factors in black communities. Exactly in particular, Chattahoochee had the youngest popula-
how these socioeconomic indices may impact prostate tion (3.8% 65 years and older) with the highest educa-
cancer risk within older black populations is not well tional attainment (30% with a Bachelor’s degree).
known, but high cigarette use and alcohol consumption Our study supports the findings of others that re-
as well as poor diet have been hypothesized to mediate ported geographical differences in health outcomes such
or moderate this risk [28]. Furthermore, risk factors of as prostate and lung cancers, malaria, malnutrition, mor-
exposures to water, air and soil pollution from agricul- tality among others [5, 7, 23–25, 30]. Against the back-
tural farming of cash crops such as cotton, from the drop of a national reduction in incident prostate cancer,
southwest through to central Georgia, may also be in- there remain pockets of high risk in the north, southwest
volved [29]. As neighbouring lower risk counties with as well as central areas of Georgia. The present study
Aheto et al. BMC Cancer          (2021) 21:508 Page 10 of 13
Fig. 4 Predictive maps for exceedance probability of relative risk of 1.5 (i.e. P (RR > 1.5)). Source: This map was produced by the authors
suggests that there may be racial differences in prostate screening for all men [32, 33]. While current diagnostic
cancer risk within counties. The aging population may practices among prostate cancer patients may be of
be the main risk factor in overwhelmingly white counties interest and the scope of the present study may repre-
while limited education and poverty may play a larger sent a substantial post-recommendation period, our
role in black counties. It should be noted that although study design additionally prevents comparisons that are
several counties with large African American popula- better made over time among individual patients man-
tions were observed to have a high-risk of prostate can- aged by primary care physicians [32]. Furthermore, we
cer incidence, the present study found no association did not include individual-level diagnostic data in our
between race and prostate cancer risk, in part because analysis. With these constraints in mind, our results are
these counties tended to be considerably smaller than best suited for hypotheses generation.
predominantly white counties. Importantly, this is an
ecological study and the associations discussed herein Strengths and limitation
should not be regarded as causal or necessarily signifi- The use of Bayesian spatial analysis methods in this
cant at the level of individual prostate cancer patients. study provided an essential tool for the investigation of
Prostate Specific Antigen (PSA) screening has driven prostate cancer incidence in relation to risk factors to
prostate cancer diagnosis since the 1980s [31, 32]. How- help in the better understanding of spatial distribution
ever, this reliance on PSA has come at the cost of over- and potential etiologic mechanism of prostate cancer
treatment and its complications among many low risk disease using an internationally recognised gold standard
men, and in May 2012, the US Prevention Services Task SEER data. Our modelling approach also allowed coun-
Force (USPSTF) recommended against routine PSA ties with small counts to borrow information from their
Aheto et al. BMC Cancer          (2021) 21:508 Page 11 of 13
Fig. 5 Predictive maps for exceedance probability of relative risk of 2 (i.e. P (RR > 2)). Source: This map was produced by the authors
neighbouring counties thereby reducing the risk of in- that increased number of foreign born increases the risk
flated relative risk due to small expected counts. Fur- of prostate cancer disease supports previous studies that
thermore, unlike the frequentist spatial modelling reported prostate cancer inequality by race [7].
approach, our Bayesian spatial modelling approach
allowed graphical presentation of the posterior distribu-
tion of risk factor effects on the prostate cancer inci- Conclusion
dence as presented in Figs. 1 and 2. The present study Our modelling approach captured variation in prostate
might have left out some potential risk factors that cancer risk over the whole of the Georgia State. The risk
might explain some of the geographical differences in maps indicate substantial geographical variations in the
prostate cancer disease observed in the study so the risk of prostate cancer. This can be used as an effective
findings should be interpreted with caution. tool in the identification of counties that require tar-
Our findings broadly support previous studies [2, 15– geted interventions and further research by program
17, 34] that report that older ages (≥65 years), income managers and implementers as part of an overall strat-
(number below poverty and median family income), race egy in reducing the prostate cancer burden in the
(being a foreign born) and unemployed are critical risk Georgia State and the U.S. as a whole. For example, a
factors for prostate cancer disease. For example, the find- further research could aim at identifying as yet unidenti-
ing that the number of persons aged 65 years or older in- fied risk factors that might have accounted for the geo-
creased the risk of the disease supports previous studies graphical differences we observed in the prostate cancer
that reported that prostate cancer risk increases with age, disease among the counties in the Georgia State after we
and with incidence rate over 60% [34–36]. The finding have accounted for the present risk factors in our model.
Aheto et al. BMC Cancer          (2021) 21:508 Page 12 of 13
Furthermore, we advocate for implementation of fo- 4. Kelly SP, Rosenberg PS, Anderson WF, Andreotti G, Younes N, Cleary SD, et al.
cused strategies to decrease prostate cancer incidence Trends in the incidence of fatal prostate Cancer in the United States by race.
Eur Urol. 2017;71(2):195–201. https://doi.org/10.1016/j.eururo.2016.05.011.
and to improve survival in the presence of the identified 5. Wagner SE, Hurley DM, Hébert JR, McNamara C, Bayakly AR, Vena JE. Cancer
critical risk factors in this study. mortality-to-incidence ratios in Georgia: describing racial cancer disparities
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