Geospatial Health 2018; volume 13:732 Trend and spatial analysis of prostate cancer mortality in the state of Sergipe, Brazil José Augusto Passos Góes,1 Danilo de Gois Souza,1 Lucas Almeida Andrade,1 Jéssica Cunha,2 Simone Kameo,3 Marco Aurélio O. Góes,4 Andreia Freire de Menezes,1,2 Marco Antônio P. Nunes,4,5 Karina Conceição Gomes Machado Araújo,5,6 Allan D. Santos1,2 1Department of Nursing; 2Post-Graduate Program in Nursing; 3Department of Health Education; 4Department of Medicine; 5Post-Graduate Program in Health Sciences; 6Department of Morphology, Federal University of Sergipe, Sergipe, Brazil hotspot densities of the highest rates of prostate cancer mortality Abstract in the north-eastern and central regions of the state. High-risk This is an ecological study with exploratory analysis of spatial clusters were identified for prostate cancer mortality (I = 0.55, P<0.01). There was an increase in prostate cancer mortality rates and temporal data based on mortality data with respect to prostate and a heterogeneous geographic distribution of risk areas, with cancer obtained from the Mortality Information System concern- high-risk priority areas identified in certain regions of the state. ing residents of the state of Sergipe, Brazil between 2000 and These priority areas include the municipalities located in the 2015. The analysis of temporal trends was performed using the Northeast (Amparo do Sãoonly Francisco, Aquidabã, Canhoba, Cedro Joinpoint Regression Program through Poisson regression. Spatial de São João and Telha), the West (Frei Paulo and Pedra Mole) and analysis was performed using the empirical Bayesian model, the south-western region of the state (Poço Verde and Simão Kernel analysis, Global Moran and Local indices. There were Dias). 1,986 deaths due to prostate cancer, most of which occurring after use 60 years of age. An increasing, non-constant but significant trend in mortality rates was noted. The kernel density estimator showed Introduction Neoplasms are considered a worldwide public health problem Correspondence: Karina Conceição Gomes Machado Araújo, Post- and have been highlighted due to the increase in incidence, mor- Graduate Program in Health Sciences, Federal University of Sergipe, bidity/mortality rates and the high costs of prevention, diagnosis Av. Marechal Rondon, s/n, Jd. Rosa Elze, São Cristóvão, SE, 49100- and treatment of individuals with this pathology (Ministry of 000, Sergipe, Brazil. Health, 2006). Among the different types of cancer that affect the E-mail: [email protected] male population, prostate cancers characterized by its high inci- dence and mortality in a global context. It is the second most fre- Key words: Prostatic neoplasms; Mortality; Time series studies; Spatial quent type of cancer in the male population and represents the analysis; Brazil. third leading cause of cancer death in men worldwide (Ferlay et Contributions: the authors contributed equally. al., 2015). About 70% of the diagnosed cases occur in economi- cally more developed regions, which can be explained by the bet- Conflict of interest: the authors declare no potential conflict of interest. ter monitoring and diagnosis of the disease in these areas. Non-commercialHowever, in some middle-income areas, such as countries in Funding: research funding sources: SVS/MS (Ministry of Health - Africa and South America, the incidence is also high (National Brazil). Cancer Institute, 2015). According to estimates by the Brazilian National Cancer Institute, approximately 596,000new cases of Ethical statement: the research was approved by the Ethics Committee cancer were registered in the country between 2016 and 2017, of the Federal University of Sergipe, under the protocol number 1.408.888. 295,200 of whom males with 61,200 cases being prostate cancer, which represents approximately 6% of all deaths due to neoplasms Received for publication: 2 August 2018. (National Cancer Institute, 2015). Revision received: 1 September 2018. Prostate cancer occurs due to the hyperplasia of prostate gland Accepted for publication: 9 October 2018. cells. The prostrate is located below the bladder in front of the rec- tum and produces part of the seminal fluid (10-30%) ejaculated ©Copyright J. A. Passos Góes et al., 2018 Licensee PAGEPress, Italy during sexual intercourse. Prostate cancer can present both a slow Geospatial Health 2018; 13:732 or a rapid evolution; when slow, monitoring is recommended so doi:10.4081/gh.2018.732 that any worsening can be picked up. Rapid evolution increases the risk for metastasis eventually death (Haas et al., 2008; This article is distributed under the terms of the Creative Commons National Cancer Institute, 2016). The diagnosis and treatment of Attribution Noncommercial License (CC BY-NC 4.0) which permits any prostate cancer affect the daily life of men, due to physiological noncommercial use, distribution, and reproduction in any medium, pro- changes related to aging and/or health conditions, including male vided the original author(s) and source are credited. sexuality (Vieira, 2010). [Geospatial Health 2018; 13:732] [page 353] Article Health professionals and managers need accurate epidemio- indicating agglomeration in a spatial distribution and continuous logical information on this health problem to plan and implement surface from the data (Bailey and Gatrell, 1995). policies to reduce prostate cancer in the state of Sergipe. Given the Spatial autocorrelation between mortality rates was used to lack of local studies addressing this issue, we aimed to visualize investigate whether the spatial distribution of the disease occurs and analyze temporal trends and the spatial distributions of randomly or follows some pattern of occurrence in space. A spatial prostate cancer mortality in the state of Sergipe between the years proximity matrix obtained by the contiguity criterion was adopted, 2000 and 2015. adopting a significance level of 5% and calculating the Moran Global Index (I), varying between -1 and +1, representing the spa- tial autocorrelation expression of prostate cancer mortality in the geographic space analyzed to identify spatial clusters and risk Materials and Methods areas. Values close to zero indicate spatial randomness; values between 0 and +1 indicate positive spatial autocorrelation and This is an ecological, time-series study with spatial analysis between -1 and 0 negative spatial autocorrelation (Druck et al., techniques using secondary data from the Mortality Information 2004). System (SIM) of the Department of Informatics of the Unified Moran’s Mirroring Diagram was used to indicate the critical or Health System (Ministry of Health, 2017). The historical series transitional areas in order to compare the value of each municipal- (2000 to 2015) from the municipalities of the state of Sergipe of ity with neighbouring municipalities and to verify the spatial the specific mortality for prostate cancer, according to the defini- th dependence shown by the Local Index of Spatial Association to tion of the International Classification of Diseases, 10 revision detect regions with significant spatial autocorrelation (Druck et al., (WHO, 2016), codes C61, was used. 2004). Spatial quadrants were generated: Q1 (high/high) and Q2 (low/low) municipalities with values similar to those of their Study area neighbours, indicating positive spatial association areas character- Sergipe is located in the northeast of Brazil, comprises 75 izing spatial aggregates; Q3only (high/low) and Q4 (low/high) indicat- municipalities with Aracaju as state capital. It has a population of ing negative areas of spatial association where the municipalities 2,265,779 inhabitants and an area of 21,910,354 km² with a popu- have distinct values to their neighbours, characterizing discrepant lation density of 94.3inhab./km (Brazilian Institute of Geography observations, represented visibly by BoxMap (Bailey and Gatrell, and Statistics - IBGE, 2010). Mortality rates were calculated per 1995; Santos anduse Raia Junior, 2006). Areas having positive spatial 100,000 inhabitants, with the male population as the denominator. autocorrelation (identified by BoxMap) with statistically signifi- Data from the 2010 from the Brazilian Demographic Census and cant spaces above 95% were produced, generating a Moran map, the intercensorial projections produced by the Brazilian Institute of used for the visualization of clusters and the identification of pri- Geography and Statistics Foundation and made available by ority areas. Moran maps were constructed for spatial representa- Datasus (Ministry of Health, 2017) were used. tion when the municipalities presented statistically significant dif- ferences (P<0.05). Statistics The IBGE provided the cartographic base of the state of Bayesian estimation was used to minimize the instability Sergipe (IBGE, 2010). Thecartographic projection corresponded to caused by the random fluctuation of the cases, smoothing the stan- the Universal Transverse Mercator system, using the Terra Datum dardized rates by applying weighted averages and creating a sec- model SIRGAS 2000. The descriptive data were tabulated and ana- ond corrected rate. The Empirical Bayesian Rate illustrated a cor- lyzed by the programs GraphPad Prism version 5.01 and Microsoft rection of the multiplicative rate equal to 100,000, taking into Excel 2010. For the spatial analysis we used the program account the male population at risk by municipal area and the num- TerraView 4.2.2 (TerraView, 2010) and QGIS2.18.3 software ber