Spatial analysis of colorectal cancer cases in Kuala Lumpur

• Published in: Asian Pacific Journal of Cancer Prevention
• Main Author: Shah S.A.; Neoh H.-M.; Rahim S.S.S.A.; Azhar Z.I.; Hassan M.R.; Safian N.; Jamal R.
• Format: Article
• Language: English
• Published: Asian Pacific Organization for Cancer Prevention 2014
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84896810239&doi=10.7314%2fAPJCP.2014.15.3.1149&partnerID=40&md5=5b22bfa48a6bb633c752a0c77b105af1

Background: In Malaysia, data from the Malaysian Health Ministry showed colorectal cancer (CRC) to be the second most common type of cancer in 2007-2009, after breast cancer. The same was apparent after looking at males and females cases separately. In the present study, the Geographic Information System (GIS) was employed to describe the distribution of CRC cases in Kuala Lumpur (KL), Malaysia, according to socio-demographic factors (age, gender, ethnicity and district). Materials and Methods: This retrospective review concerned data for patients diagnosed with colorectal cancer in the years 1995 to 2011 collected from the Wilayah Persekutuan Health Office, taken from the cancer notification form (NCR-2), and patient medical records from the Surgical Department, Universiti Kebangsaan Malaysia Medical Centre (UKMMC). A total of 146 cases were analyzed. All the data collected were analysed using ArcGIS version 10.0 and SPSS version 19.0. Results: Patients aged 60 to 69 years accounted for the highest proportion of cases (34.2%) and males slightly predominated 76 (52.1%), Chinese had the highest number of registered cases at 108 (74.0%) and staging revealed most cases in the 3rd and 4th stages. Kernel density analysis showed more cases are concentrated up in the northern area of Petaling and Kuala Lumpur subdistricts. Spatial global pattern analysis by average nearest neighbour resulted in nearest neighbour ratio of 0.75, with Z-score of -5.59, p value of <0.01 and the z-score of -5.59. Spatial autocorrelation (Moran's I) showed clustering significant with p<0.01, Z score 3.14 and Moran's Index of 0.007. When mapping clusters with hotspot analysis (Getis-Ord Gi), hot and cold spots were identified. Hot spot areas fell on the northeast side of KL. Conclusions: This study demonstrated significant spatial patterns of cancer incidence in KL. Knowledge about these spatial patterns can provide useful information to policymakers in the planning of screening of CRC in the targeted population and improvement of healthcare facilities to provide better treatment for CRC patients.