Exploring Spatial Patterns of Colorectal Cancer in Tehran City, Iran

Asian Pac J Cancer Prev. 2018 Apr 27;19(4):1099-1104. doi: 10.22034/APJCP.2018.19.4.1099.

Abstract

Objectives: Colorectal cancer (CRC) may now be the second most common cancer in the world. The aim of this study was to determine whether clusters of high and low risk of CRC might exist at the neighborhood level in Tehran city. Methods: In this study, new cases of CRC provided from Cancer Registry Data of the Management Center of Ministry of Health and Medical Education of Iran in the period from March 2008 to March 2011 were analyzed. Raw standardized incidence rates (SIRs) were calculated for CRC in each neighborhood, along with ratios of observed to expected cases. The York and Mollie (BYM) spatial model was used for smoothing of the estimated raw SIRs. To discover clusters of high and low CRC incidence a purely spatial scan statistic was applied. Results: A total of 2,815 new cases of CRC were identified and after removal of duplicate cases, 2,491 were geocoded to neighborhoods. The locations with higher than expected incidence of CRC were northern and central districts of Tehran city. An observed to expected ratio of 2.57 (p<0.001) was found for districts of 2, 6 and 11, whereas, the lowest ratio of 0.23 (p<0.001) was apparent for northeast and south areas of the city, including district 4. Conclusions: This study showed that there is a significant spatial variation in patterns of incidence of CRC at the neighborhood level in Tehran city. Identification of such spatial patterns and assessment of underlying risk factors can provide valuable information for policymakers responsible for equitable distribution of healthcare resources.

Keywords: Colorectal cancer; spatial analysis; neighborhood; York and Mollie (BYM) spatial model; Tehran.

MeSH terms

  • Colorectal Neoplasms / epidemiology*
  • Humans
  • Incidence
  • Iran / epidemiology
  • Middle Aged
  • Registries
  • Residence Characteristics
  • Retrospective Studies
  • Risk Factors
  • Spatial Analysis