|Year : 2021 | Volume
| Issue : 1 | Page : 65
Joint disease mapping of breast, uterine, and ovarian cancers in cities of Isfahan Province from 2005 to 2010 using spatial shared component model
Marzieh Nasr1, Behzad Mahaki2, Mehdi Kargar3, Pejman Aghdak4
1 Department of Biostatistics, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran
2 Department of Biostatistics and Epidemiology, School of Health, Isfahan University of Medical Sciences, Isfahan; Department of Biostatistics, School of Public Health, Kermanshah University of Medical Sciences, Kermanshah, Iran
3 Health Education and Health Promotion, Health Promotion Department, Health School, Shiraz, Iran
4 Social Department of Health Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
|Date of Submission||17-Mar-2019|
|Date of Acceptance||23-Jan-2020|
|Date of Web Publication||25-Jun-2021|
Department of Biostatistics, Kermanshah University of Medical Sciences, Kermanshah
Source of Support: None, Conflict of Interest: None
Background: Breast, uterine, and ovarian cancers are the most prevalent types of cancers among women. The aim of this study was to estimate the relative risk of these cancers and recognizing spatial patterns of their shared and specific risk factors in cities of Isfahan province, one of the most populated provinces of Iran, using spatial shared component model. Methods: In this ecological study, the population consisted of all the registered patients having breast, ovarian, and uterine cancers in the cities of Isfahan from 2005 to 2010. In order to simultaneously analyze these diseases and clarify common and specific patterns of disease, spatial Shared component model was applied. Model fitting was done using Bayesian inference in OpenBUGS software. Results: The highest relative risk of breast cancer was seen in Isfahan (4.96), Shahreza (2.37), Dehaghan (5.01), Lenjan (2.33), and Najafabad (2.68), respectively. For ovarian cancer, Isfahan (4.29), Shahreza (2.51), Dehaghan (5.02), Lenjan (2.06), Najafabad (2.00), and Borkhar (2.39) had the highest relative risk, respectively. However, no significant difference was seen among the cities for uterine cancer (P > 0.05). Conclusions: Since ovarian and uterine cancers are the less prevalent disease in comparison with breast cancer, the preciseness of these estimates were improved remarkably over simple mapping models. Based on this model, the estimates were done according to the correlation between the diseases. After recognizing the spatial patterns of the shared and specific risk factors and reviewing of previous studies, regardless of risk factors data, environmental pollution arises as a potential risk factor.
Keywords: Breast neoplasms, mapping, ovarian neoplasms, uterine neoplasms
|How to cite this article:|
Nasr M, Mahaki B, Kargar M, Aghdak P. Joint disease mapping of breast, uterine, and ovarian cancers in cities of Isfahan Province from 2005 to 2010 using spatial shared component model. Int J Prev Med 2021;12:65
|How to cite this URL:|
Nasr M, Mahaki B, Kargar M, Aghdak P. Joint disease mapping of breast, uterine, and ovarian cancers in cities of Isfahan Province from 2005 to 2010 using spatial shared component model. Int J Prev Med [serial online] 2021 [cited 2021 Aug 3];12:65. Available from: https://www.ijpvmjournal.net/text.asp?2021/12/1/65/319447
| Introduction|| |
Malignant breast, uterine, and ovarian cancers are the prevalent diseases among women. Today, over one million women with female-related cancers are known around the world. The most prevalent malignant diseases among females are breast, uterine, and ovarian cancers. In addition, breast cancer is known as the second cause of female death.,, The incidence rate of this cancer is estimated 38 one per 100,000 ones as the commonest cause of female death between 45 and 55 year old ages. The standardized incidences of the mentioned cancer were reported 86.4, and 27.3% in developing countries and developed countries, respectively. Uterine cancer is the third prevalent cancer and one death causes among female around the world. The lead of disease is seen in most countries, so that 86% of reported cases in developing countries. In 2012, 239,000 females suffered from uterine cancer around the world, of which 152,000 of them passed away.
Iran has less incidence of breast cancer in comparison with other countries. However, it has increased in recent years in a way that breast cancer is known as the most prevalent malignancy among Iranian females. Uterine cancer was known as the 10th prevalent cancer (2.8%) among Iranian females in 2010. Ovarian cancer is the eight prevalent cancers and the 12th cause of death in Iran.
In order to also recognize high risk regions and environmental risk factor, different models of disease mapping are used. In fact, disease mapping consists of a series of statistical methods aiming to get precise estimates of incidence rates, disease prevalence and adapt them in the form of geographical maps. In recent years, different studies have been conducted about cancer mapping in Iran. However, few of them have focused on joint analysis with emphasis on risk factor roles.,,
Using shared component model (SC), the relative risks of breast, uterine, and ovarian cancers and the effects of shared and specific components of cancers were estimated in Isfahan province from 2005 to 2010 in the form of mapping. Then, the relative importance of the surrogate of the shared risk factor for the mentioned cancers was presented in order to compare the association of the factor with both diseases.
So it can be possible to recognize high and low risk regions for cancers. This information assists health planner in determining the priorities and focusing preventive policies in the known sensitive regions. Also, these findings make it possible to present some hypothesis about possible risk and protective factors specifically and in common for diseases.
| Methods|| |
In this ecological study, the studied population consisted of all the registered patients having breast cancer (C52 code in ICD10), ovarian cancer (C56), and uterine cancer (C55) in the cities of Isfahan province from 2005 to 2010. This study was approved with the ethical code IR.MUI.REC.1395.1.131 by Health system research (HSR) of Isfahan University of Medical Science. Spatial shared component model was used with Bayesian inference.
Shared component model
This model analyzes the general risk of three diseases into shared components and three disease-specific components. Shared components are the replacements for unobserved shared risk factors which are justifiable with spatial correlation. Each SC accompanies with scale parameters as the replacement of risk factors. Scale's parameters provide the difference of criterion related to common risk factors for every one of these diseases. Every specific disease represents the unique risk factor of every one of these diseases. They distribute differently rather than other specific disease in spatial level.
According to the following model, the map is divided into n non-overlapped adjacent regions (i = 1, 2, …, n). In addition, k denotes the kth disease (k = 1, 2, …K). Then, θik, the relative risk of diseases in i region, is modeled as:
Where Oik and Eik are the observed and the expected number of cases for k disease in i region. Eik is calculated by multiplying the overall crude incidence rate by the region population. Population of 2006 was used as the year of population statistics.
The disease risk of every one of the mentioned cancers is formulated as below:
θi1, θi2, θi3 represent, in turn, related risk of breast, uterine, and ovarian cancers in i number of cities. αk represents disease-specific intercept of kth cancer. λi1, λi2, λi3 represent, in turn, effect of spatial shared components as surrogate of shared risk factors for these three cancers. δi1, δi2, δi3 are the scale parameters of the shared risk factors. The estimate of these parameters determines the relative weight of the shared risk factors for the related diseases. εi1, εi2, εi3 represent surrogate of specific components as surrogates of specific risk factors for these studied cancers.,, To put it more simply, this model tries to estimate the relative risk of disease to recognize high risky regions, and decompose the risk of diseases to shared and specific effects. According to spatial distribution of shared and specific effects, we emphasize about possible risk factors. It can help politicians to conduct preventive planning in sensitive regions.
The fitting of the models with data was done by use of Bayesian process in OpenBUGS 3.2.1 (rev 781). Posterior distribution estimates for model's parameters was estimated by Monte Carlo Markov Chain methods (MCMC). Brooks-Gelman-Rubin diagnostic tool (BGR) was used in order to test the convergence of all parameters. All the maps were provided using ArcGIS (10.4.1).
| Results|| |
Observed and expected cases of breast, uterine, and ovarian cancers in all cities of Isfahan province were shown in [Table 1].
|Table 1: Observed and Expected cases of breast, uterine and ovarian cancers in cities of Isfahan province from 2005-2010|
Click here to view
Relative risk estimates of cancers
Using the spatial shared component (SC) model, the relative risk of every studied cancer were estimated in a 6-year period. The achieved geographical distribution is shown in [Figure 1]. All maps were numbered based on codes of [Table 1]. Also, the distribution of estimating shared and specific effect of the mentioned cancers was illustrated. The results show the following points, whereas relative risks and their Bayesian confidence intervals (BCI) are shown in parentheses:
|Figure 1: Maps of the estimated relative risk of (a) breast, (b) uterine, (c) ovarian cancers in Isfahan cities from 2005 to 2010, based on SC Model|
Click here to view
- Breast cancer: Isfahan [4.96; (3.19, 7.56)], Shahreza [2.37; (1.47, 3.72)], Dehaghan [5.01; (2.09, 9.85)], Lenjan [2.33; (1.47, 3.64)], Najafabad [2.68; (1.70, 4.13)], and Fereidan [1.67; (1.00, 2.74)] had the highest relative risk. In contrast, Aran & Bidgol [0.18; (0.06, 0.40)], Kashan [0.11; (0.05, 0.21)], Tiran & Karvan [0.38; (0.16, 0.72)], and Khansar [0.43; (0.14, 0.93)] had the least relative with respect to the other cities
- Uterine cancer: No significant difference was found among the cities (P > 0.05)
- Ovarian cancer: Isfahan [4.29; (2.68, 6.79)], Shahreza [2.51; (1.35, 4.37)], Dehaghan [5.02; (1.64, 12.32), Lenjan (2.06; (1.14, 3.48)], Najafabad [2.00; (1.12, 3.35)], Fereidan [1.89; (0.99, 3.51)], Borkhar [2.39; (1.29, 4.14)], and Fereidoonshahr [1.69; (0.71, 3.68)] had the highest relative risk. Aran & Bidgol [0.21; (0.05, 0.51)], Kashan [0.14; (0.04, 0.32)], Tiran & Karvan [0.41; (0.14, 0.86)], and also Natanz [0.32; (0.09, 0.76)] had the lowest levels of relative risk with respect to the other other province's cities.
Estimate of shared component factor of cancers
The regions that have higher risk of the shared risk factor of breast and ovarian cancers were the central regions of province including Isfahan, Borkhar, Shahreza, Dehaghan, Najafabad, Lenjan, and Khansar. No significant difference was found for two other shared components' distribution.
The relative importance of shared risk factor component for cancers
[Table 2] represents the posterior medians for scale parameters. These parameters determine the relative weight of the shared risk factors for the related diseases. According to their Bayesian confidence intervals, none of them were significant.
|Table 2: Posterior Median and 95% BCI*s for relative weight of shared risk factor (δ)|
Click here to view
Estimate of surrogate of specific risk factor components
- Breast cancer: Isfahan, Naein, Ardestan, Natanz, Borkhar, Shahinshahr, Meymeh, Golpayegan, Najafabad, Tiran & Karvan, Khomeinishahr, Lenjan, Dehaghan, and Shahreza exposed more to specific risk factor of breast cancer
- Uterine cancer: Naein, Isfahan, Borkhar, Shahinshahr and Meymeh, Golpayegan, Khansar, Fereidan, Fereidoonshahr, Chadegan, Khomeinishahr, Falavarjan, Lenjan, Mobarakeh, Dehaghan, Aran & Bidgol, and Shahreza were more exposed to specific risk factor of Uterine cancer
- Ovarian cancer: Isfahan, Felavarjan, Lenjan, Mobarakeh, Khansar, Semirom, Chadegan, Fereidan, Fereidoonshahr, Shahreza, and Dehaghan were more exposed to specific risk factor for ovarian cancer.
| Discussion|| |
Using SC, spatial distribution of relative risk related to breast, uterine, and ovarian among cities of Isfahan province was estimated and the shared and specific components of these cancers were recognized. This can lead to provide some hypothesis regarding risk and protective specifically and in common with the diseases.
The relative risk of breast cancer incidence was higher in Isfahan, Shahreza, Dehaghan, Lenjan, Najafabad, and Fereidan rather than other cities. The studies show that Isfahan province has the highest incidence of breast cancer in the country.,,, However, no studies were conducted yet in Isfahan cities to recognize the spatial pattern.
Relative risk distribution of ovarian cancer is the same as breast cancer. The investigation showed that Isfahan province has one of the highest ovarian cancer incidences in the country.,, Relative risk estimation of uterine cancer seems to be the same in all cities. However, no studies have been done to assess incidence rates or risk of this disease in this province. The map of [Figure 2]a, [Figure 2]b, [Figure 2]c shows spatial distribution of estimating cancer's shared risk factors. This model of breast and ovarian cancers are so similar to the relative risk model of every one of these cancers. The risk was reduced in cities far away from the center of province. Shared component patterns of breast and uterine cancers and also uterine and ovarian cancers show no important differences among the cities.
|Figure 2: Maps of the surrogate component of the shared risk factor of (a) breast and ovarian, (b) breast and uterine, (c) ovarian and uterine cancers; and the surrogate components of specific risk factors of (d) breast, (e) ovarian, (f) uterine cancers|
Click here to view
The map of [Figure 2]d, [Figure 2]e, [Figure 2]f shows cancer's specific component distribution. The severity of these factors in every map is approximately the same.
Some investigations indicate to Pb (lead) role in developing breast cancer tumors. Rashidi has found that the distribution of common malignancies such as breast cancer are so similar to the map of geographical pattern of soil Pb in Isfahan. Amini studied the pollution of soil to Pb in Isfahan province. He found that exposed soils were polluted by this element in Isfahan city. Using petrol contained lead, composite in city parks and mine extractions lead to increase the concentration of Pb (lead) remarkably in the soil.
Moradi investigated the incidence patterns of ovarian cancers in Iran. Isfahan province is one of regions in which this cancer has been developed increasingly. In this study, obesity, smoking, inactivity, inappropriate nutrition, early menarche, and late menopause were known as the risk factors of ovarian cancer.
Over 95% of uterine cancers' are caused by Human Papilloma Virus (HPV) infection., Mirzaei Kashani studied uterine cancer patients in Isfahan to estimate the incidence of HPV infection. He found that the rate of HPV infection in chronic cases of uterine cancer has increased.
Awareness of the spatial distribution of relative risk of cancers as well, their shared and specific risk factors can help politicians to recognize the high risky regions and proceed the prevention policies. Also, this study will serve as a base for future studies to assess association between the incidence of mentioned cancer and the suggested risk factors in sensitive regions.
However, our study has several limitations. The study is limited in having a province of Iran, and the findings clearly cannot be extrapolated to whole of Iran. The results are not used in personal level because of ecological bias, but they can be followed as an appropriate goal to find the causes and make. Seeing that, this model is disregarding surrogate of shared risk factor component in triple disease from in SC. This can be modified by using polytomous logit (PL) model. Since all cancer cases are not registered completely in Iranian nation-wide system, it is possible to lose some cases in some cities and in the first years of study process due to inefficient knowledge and facilities.
It is suggested to apply the spatio-temporal SC model to achieve accurate spatial models with respect to time dimension, and determine the temporal trend of relative risk of diseases. If recent information and different age groups become provided, valuable results of disease process and mark high risky population would be available for policy-makers of control, prevention, and treatment fields of moment's cancers. In order to overcome ecological bias, it is recommended to use multilevel models and regard individual level in the form of nest in geographical levels. This is done to study the association between risk factors and relative risk roles of cancers incidence.
| Conclusions|| |
The main conclusion to be drawn from this discussion is that the best way to make awareness of the spatial distribution of relative risk of cancers as well, their shared and specific risk factors, when we do not have access to original information of the risk factors is using SC model. On balance, considering environment health and controlling environmental pollution performing screening program and educational cancers in large scales can lead to increase women's knowledge toward early diagnosis methods of breast and ovarian cancers and transmitting ways of HPV. They are all known as control and preventive alternatives of cancers.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Akbari M, Abachizadeh K, Tabatabaei S, Ghanbari Motlagh A, Majd Jabari Z, Khayamzadeh M. Cancer in Iran. Qom: Darolfekr; 2008.
Fazeli Z, Najafian ZM, Eshrati B, Almasi HA. Five-year evaluation of epidemiological, geographical distribution and survival analysis of breast cancer in Markazi Province, 2007-11. J Arak Uni Med Sci 2014;16:73-80.
Parkin DM, Fernández LM. Use of statistics to assess the global burden of breast cancer. Breast J 2006;12:S70-80.
Ferlay J, Soerjomataram I, Dikshit R, Eser S, Mathers C, Rebelo M, et al
. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer 2015;136:E359-86.
Arbyn M, Castellsagué X, de Sanjosé S, Bruni L, Saraiya M, Bray F, et al
. Worldwide burden of cervical cancer in 2008. Ann Oncol 2011;22:2675-86.
Ministry of Health and Medical Education. Iranian Annual of national cancer registration report. Deputy for Health Center for Diseases Control & Management Tehran; 2012.
Lawson A, Browne WJ, Vidal Rodeiro CL. Disease Mapping with WinBUGS and MLwiN; Statistics in Practice; Wiley: Hoboken, NJ, USA; 2003.
Haddad-Khoshkar A, Jafari-Koshki T, Mahaki B. Investigating the incidence of prostate cancer in Iran 2005-2008 using Bayesian spatial ecological regression models. Asian Pac J Cancer Prev 2015;16:5917-21.
Jafari-Koshki T, Arsang-Jang S, Mahaki B. Bladder cancer in Iran: Geographical distribution and risk factors. Iran J Cancer Prev 2017;10:e5610.
Rastaghi S, Jafari-Koshki T, Mahaki B, Bashiri Y, Mehrabani K, Soleimani A. Trends and risk factors of gastric cancer in Iran (2005–2010). Int J Prev Med 2019;10:79.
] [Full text]
Knorr-Held L, Best NG. A shared component model for detecting joint and selective clustering of two diseases. J Royal Statistical Society: Series A (Statistics in Society) 2001;164:73-85.
Ahmadipanahmehrabadi V, Hassanzadeh A, Mahaki B. Bivariate spatio-temporal shared component modeling: Mapping of relative death risk due to colorectal and stomach cancers in Iran provinces. Int J Prev Med 2019;10:88.
] [Full text]
Downing A, Forman D, Gilthorpe MS, Edwards KL, Manda SO. Joint disease mapping using six cancers in the Yorkshire region of England. Int J Health Geogr 2008;7:41.
Raei M, Schmid VJ, Mahaki B. Bivariate spatiotemporal disease mapping of cancer of the breast and cervix uteri among Iranian women. Geospat Health 2018;13:645.
Esmaeimzadeh N, Salahi-Moghaddam A, Khoshdel A. Geographic distribution of important cancers in Iran. Hormozgan Med J 2015;19:66-76.
Khoshkar AH, Koshki TJ, Mahaki B. Comparison of Bayesian spatial ecological regression models for investigating the incidence of breast cancer in Iran, 2005. Asian Pac J Cancer Prev 2015;16:5669-73.
Mokarian F, Ramezani MA, Heydari K, Tabatabaeian M, Tavazohi H. Epidemiology and trend of cancer in Isfahan 2005-2010. J Res Med Sci 2011;16:1228-33.
Moradi Y, Jafari M, Chaichian S, Khateri S, Akbarian A, Moazzami B, et al
. Trends in ovarian cancer incidence in Iran. Iran J Cancer Prev 2016;9:e5452.
Cohen AJ, Brauer M, Burnett R, Anderson HR, Frostad J, Estep K, et al
. Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: An analysis of data from the Global Burden of Diseases Study 2015. Lancet 2017;389:1907-18.
Rashidi M, Rameshat MH, Gharib H, Rouzbahani R, Ghias M, Poursafa P. The association between spatial distribution of common malignancies and soil lead concentration in Isfahan, Iran. J Res Med Sci 2012;17:348-54.
Amini M, Afyuni M, Fathianpour N, Khademi H, Flühler H. Continuous soil pollution mapping using fuzzy logic and spatial interpolation. Geoderma 2005;124:223-33.
Burd EM. Human papillomavirus and cervical cancer. Clin Microbiol Rev 2003;16:1-17.
Miller DL, Puricelli MD, Stack MS. Virology and molecular pathogenesis of HPV (human papillomavirus) associated oropharyngeal squamous cell carcinoma. Biochem J 2012;443:339-53.
Mirzaie-Kashani E, Bouzari M, Talebi A, Arbabzadeh-Zavareh F. Detection of human papillomavirus in chronic cervicitis, cervical adenocarcinoma, intraepithelial neoplasia and squamus cell carcinoma. Jundishapur J Microbiol 2014;7:e9930.
Dreassi E. Polytomous disease mapping to detect uncommon risk factors for related diseases. Biom J 2007;49:520-9.
Oleson JJ, Smith BJ, Kim H. Joint spatio-temporal modeling of low incidence cancers sharing common risk factors. J Data Sci 2008;6:105-23.
[Figure 1], [Figure 2]
[Table 1], [Table 2]