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Montazeri et al. BMC Cancer (2020) 20:1170 https://doi.org/10.1186/s12885-020-07674-8 RESEARCH ARTICLE Open Access Spatio-temporal mapping of breast and prostate cancers in South Iran from 2014 to 2017 Mahdieh Montazeri1,2, Benyamin Hoseini3,4, Neda Firouraghi5, Fatemeh Kiani5, Hosein Raouf-Mobini6, Adele Biabangard6, Ali Dadashi7, Vahideh Zolfaghari8, Leila Ahmadian1, Saeid Eslami5, Robert Bergquist9, Nasser Bagheri10 and Behzad Kiani5* Abstract Background: The most common gender-specific malignancies are cancers of the breast and the prostate. In developing countries, cancer screening of all at risk is impractical because of healthcare resource limitations. Thus, determining high-risk areas might be an important first screening step. This study explores incidence patterns of potential high-risk clusters of breast and prostate cancers in southern Iran. Methods: This cross-sectional study was conducted in the province of Kerman, South Iran. Patient data were aggregated at the county and district levels calculating the incidence rate per 100,000 people both for cancers of the breast and the prostate. We used the natural-break classification with five classes to produce descriptive maps. A spatial clustering analysis (Anselin Local Moran’s I) was used to identify potential clusters and outliers in the pattern of these cancers from 2014 to 2017. Results: There were 1350 breast cancer patients (including, 42 male cases) and 478 prostate cancer patients in the province of Kerman, Iran during the study period. After 45 years of age, the number of men with diagnosed prostate cancer increased similarly to that of breast cancer for women after 25 years of age. The age-standardised incidence rate of breast cancer for women showed an increase from 29.93 to 32.27 cases per 100,000 people and that of prostate cancer from 13.93 to 15.47 cases per 100,000 during 2014–2017. Cluster analysis at the county level identified high-high clusters of breast cancer in the north-western part of the province for all years studied, but the analysis at the district level showed high-high clusters for only two of the years. With regard to prostate cancer, cluster analysis at the county and district levels identified high-high clusters in this area of the province for two of the study years. Conclusions: North-western Kerman had a significantly higher incidence rate of both breast and prostate cancer than the average, which should help in designing tailored screening and surveillance systems. Furthermore, this study generates new hypotheses regarding the potential relationship between increased incidence of cancers in certain geographical areas and environmental risk factors. Keywords: Spatial analyses, Cluster analyses, Breast Cancer, Prostate Cancer, Spatio-temporal, Geographical information systems, Iran * Correspondence: [email protected] 5Department of Medical Informatics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran Full list of author information is available at the end of the article © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Montazeri et al. BMC Cancer (2020) 20:1170 Page 2 of 13 Background analyses using this data structure [22, 23]. For example, Cancers are the second leading cause of death worldwide spatial autocorrelation is a method of exploratory data [1], which can partly be explained by the fact that the analysis which allows detecting spatial data dependence world’spopulationisageing[2]. Furthermore, human ex- [24]. There are two kinds of spatial autocorrelation posure to multiple risk factors has increased the cancer bur- methods: global and local statistics. Global methods are den worldwide [3]. Despite advances in timely diagnosis and more sensitive to departures from the null hypothesis, medical treatment of neoplasms in recent years, malignan- which examine whether data, here patients, are randomly cies in middle to low-income countries are expected to al- distributed or if there is a spatial pattern. They can identify most double by 2030 compared to high-income nations [4]. spatial structures in the pattern of cancer incidence but do If cancer is diagnosed promptly, cures can sometimes be not determine where the clusters are. Local cluster statis- found and life prolonged leading to considerably lower dis- tics, on the other hand, can quantify spatial autocorrelation ease burdens [5, 6]. However, health systems, particularly in and clustering, but only in limited areas. These methods developing countries, are not capable of screening all people may find restricted areas characterized as high-high (HH), to identify patients in the early stages of the disease. Identi- high-low (HL), low-low (LL) or low-high (LH) risk of inci- fying high-risk geographical areas could help decreasing the dence within a region. HH and LL are defined as target cost of screening, finding the people at risk and implement- areas surrounded by areas with similar incidence rates, ing more efficient diagnostic strategies [7]. Investigating while for HL and LH, the target areas are surrounded by high-risk areas should also provide valuable knowledge to areas with dissimilar cancer incidence rates. In other scientists about the aetiology of some malignancies [3]. words, HH and LL indicate clusters, while HL and LH Cancer of the breast and the prostate are the two most point to outliers [25]. This study aimed to identify the common, gender-specific malignancies worldwide [8]. Fur- spatial patterns of cancer of the breast and the prostate thermore, these neoplasms cause high numbers of and to investigate the potential clustering in gender- disability-adjusted life years (DALYs) [9]. Risk factors for specific patterns of these cancers in southern Iran between these two diseases are diverse and interrelated, as they in- 2014 and 2017. clude genetic [10], social-economic [11] as well as lifestyle and environmental factors [12]. Further, there are interac- Method tions between these risk factors, particularly with those in- Study area and time volving the environment [13, 14], whose spatial variation This study was conducted in the province of Kerman, lo- may lead to heterogeneity in the pattern of cancers in a cated in southern Iran (Fig. 1). The first administrative given geographic catchment area. Studies by Wang et al. level of Iran subdivisions is the province, each of which is [15, 16] found a significant spatial variation of prostate can- further divided into counties that are in turn divided into cer incidence and prostate cancer-specific mortality in districts. Our study area contained the 22 counties and 58 Pennsylvania, USA. They evaluated potential effects of indi- districts of Kerman Province, which covers an area of 183, vidual and county-level risk factors and found that spatial 285 km2 and has, according to the National Census of variations in prostate cancer-specific mortality rates existed 2015, a population of 3,164,718 people [26]. The study in Pennsylvania with a particularly high risk in the Pen covered the time span of March 2014–March 2017. State catchment area. County-level health and environmen- tal factors might contribute to spatial heterogeneity in pros- Data sources tate cancer-specific mortality as shown by Olfatifar et al. Data were obtained from two different sources with three [17], who examined spatial clustering of breast cancer at different spatial scales (individual, county and district). The the provincial level in Iran between 2004 and 2010. Their individual patient data (supplementary files 1–2) were ob- results highlight that the breast tumour incidence varied tained through the population-based cancer registry of Ker- across the provinces [17]. At the same time, Rohani-Rasaf man. They were geocoded and aggregated to both county et al. [18] detected some high-risk regions in Tehran, the and district. The digital maps (county and district) were ob- capital of Iran, both for cancers of the breast and the pros- tained through the mapping organisation of the country. tate. Most studies in Iran have applied spatial analyses at a We used a crude incidence per 100,000 people and the very coarse level (province scale) and the results are there- age-standardised rate (ASR) per 100,000 people for the fore not as useful as a finer scale quite naturally. descriptive statistics. Geographical information systems (GIS) constitute a set of useful tools for the identification of high-risk areas of Inclusion and exclusion criteria cancer occurrence as well as investigation of the environ- We included all residents of Kerman diagnosed with ei- mental effects on cancer incidence [19–21]. GIS ap- ther cancer of the breast or the prostate. Individuals proaches combine spatial and non-spatial data producing who had come to the province for cancer treatment but geodatabases that make it possible to perform spatial lived outside of the province were excluded. Montazeri et al. BMC Cancer (2020) 20:1170 Page 3 of 13 Fig. 1 Map of Kerman counties and districts including the distribution of cancer (prostate and breast) patients during 2014–2017.

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