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Bivariate spatiotemporal disease mapping of cancer of the breast and cervix uteri among Iranian women

Mehdi Raei, Volker Johann Schmid, Behzad Mahaki
  • Mehdi Raei
    Student Research Committee, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran, Islamic Republic of
  • Volker Johann Schmid
    Bioimaging Group, Department of Statistics, Institue of Statistics, Ludwig Maximilans University, Munich, Germany
  • Behzad Mahaki
    Department of Epidemiology and Biostatistics, School of Health, Isfahan University of Medical Sciences, Isfahan; Research Center for Environmental Determinants of Health, Kermanshah University of Medical Sciences, Kermanshah, Iran, Islamic Republic of | behzad.mahaki@gmail.com

Abstract

Cervical cancer in women is one of the most common cancers and breast cancer has grown dramatically in recent years. The purpose of this study was to map the incidence of breast and cervix uteri cancer among Iranian women over a 6-year period (2004-2009) searching for trend changes and risk factors. Cancer incidence data were extracted from the annual reports of the National Cancer Registry in Iran. Hierarchical Bayesian models, including random spatial and temporal effects was utilized together with bivariate, spatio-temporal shared component modelling. The provinces Tehran, Isfahan, Mazandaran and Gilan were found to have the highest relative risk (RR) of breast cancer, while the highest RR of cervix uteri cancer was observed in Tehran, Golestan, Khuzestan and Khorasan Razavi. Shared risk factors (smoking component) between the two cancers were seen to have the highest influence in Tehran, Khorasan Razavi, Yazd, Isfahan, Golestan, Khuzestan, Fars and Mazandaran, while the least were observed in Kohgiluyeh Boyerahmad. Apparent differences and distinctions between high-risk and low-risk provinces reveal a pattern of obvious dispersion for these cancers in Iran that should be considered when allocating healthcare resources and services in different areas.

Keywords

Breast cancer; Cervix cancer; Bivariate disease mapping; Shared component model; Spatio-temporal analysis; Iran.

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Submitted: 2017-11-09 01:27:19
Published: 2018-05-08 10:52:40
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Copyright (c) 2018 Mehdi Raei, Volker Schmid, Behzad Mahaki

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