IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i3p1052-d486590.html
   My bibliography  Save this article

Spatial Variation of Survival for Colorectal Cancer in Malaysia

Author

Listed:
  • Anis Kausar Ghazali

    (Biostatistics and Research Methodology Unit, School of Medical Sciences, Health Campus, Universiti Sains Malaysia, Kubang Kerian 16150, Kelantan, Malaysia)

  • Thomas Keegan

    (Lancaster Medical School, Faculty of Health and Medicine, Lancaster University, Lancaster LA1 4YW, UK)

  • Benjamin M. Taylor

    (Blackpool Teaching Hospitals NHS Foundation Trust, Blackpool FY3 8NR, UK)

Abstract

A patient’s survival may depend on several known and unknown factors and it may also vary spatially across a region. Socioeconomic status, accessibility to healthcare and other environmental factors are likely to contribute to survival rates. The aim of the study was to model the spatial variation in survival for colorectal cancer patients in Malaysia, accounting for individual and socioeconomic risk factors. We conducted a retrospective study of 4412 colorectal cancer (ICD-10, C18-C20) patients diagnosed from 2008 to 2013 to model survival in CRC patients. We used the data recorded in the database of the Malaysian National Cancer Patient Registry-Colorectal Cancer (NCPR-CRC). Spatial location was assigned based on the patients’ central district location, which involves 144 administrative districts of Malaysia. We fitted a parametric proportional hazards model in which the spatially correlated frailties were modelled by a log-Gaussian stochastic process to analyse the spatially referenced survival data, which is also known as a spatial survival model. After controlling for individual and area level characteristics, our findings indicate wide spatial variation in colorectal cancer survival across Malaysia. Better healthcare provision and higher socioeconomic index in the districts where patients live decreased the risk of death from colorectal cancer, but these associations were not statistically significant. Reliable measurement of environmental factors is needed to provide good insight into the effects of potential risk factors for the disease. For example, a better metric is needed to measure socioeconomic status and accessibility to healthcare in the country. The findings provide new information that might be of use to the Ministry of Health in identifying populations with an increased risk of poor survival, and for planning and providing cancer control services.

Suggested Citation

  • Anis Kausar Ghazali & Thomas Keegan & Benjamin M. Taylor, 2021. "Spatial Variation of Survival for Colorectal Cancer in Malaysia," IJERPH, MDPI, vol. 18(3), pages 1-12, January.
  • Handle: RePEc:gam:jijerp:v:18:y:2021:i:3:p:1052-:d:486590
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/3/1052/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/18/3/1052/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Taylor, Benjamin M. & Rowlingson, Barry S., 2017. "spatsurv: An R Package for Bayesian Inference with Spatial Survival Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 77(i04).
    2. Hines, R. & Markossian, T. & Johnson, A. & Dong, F. & Bayakly, R., 2014. "Geographic residency status and census tract socioeconomic status as determinants of colorectal cancer outcomes," American Journal of Public Health, American Public Health Association, vol. 104(3), pages 63-71.
    3. Henderson R. & Shimakura S. & Gorst D., 2002. "Modeling Spatial Variation in Leukemia Survival Data," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 965-972, December.
    4. Carroll, Rachel & Lawson, Andrew B. & Jackson, Chandra L. & Zhao, Shanshan, 2017. "Assessment of spatial variation in breast cancer-specific mortality using Louisiana SEER data," Social Science & Medicine, Elsevier, vol. 193(C), pages 1-7.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Paik, Jane & Ying, Zhiliang, 2012. "A composite likelihood approach for spatially correlated survival data," Computational Statistics & Data Analysis, Elsevier, vol. 56(1), pages 209-216, January.
    2. Luping Zhao & Timothy E. Hanson, 2011. "Spatially Dependent Polya Tree Modeling for Survival Data," Biometrics, The International Biometric Society, vol. 67(2), pages 391-403, June.
    3. Haiming Zhou & Timothy Hanson & Jiajia Zhang, 2017. "Generalized accelerated failure time spatial frailty model for arbitrarily censored data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(3), pages 495-515, July.
    4. Soubeyrand, Samuel & Chadoeuf, Joel, 2007. "Residual-based specification of a hidden random field included in a hierarchical spatial model," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 6404-6422, August.
    5. Jiajia Zhang & Andrew B. Lawson, 2011. "Bayesian parametric accelerated failure time spatial model and its application to prostate cancer," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(3), pages 591-603, November.
    6. Guanyu Hu & Yishu Xue & Fred Huffer, 2021. "A Comparison of Bayesian Accelerated Failure Time Models with Spatially Varying Coefficients," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(2), pages 541-557, November.
    7. Akim Adekpedjou & Sophie Dabo‐Niang, 2021. "Semiparametric estimation with spatially correlated recurrent events," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(4), pages 1097-1126, December.
    8. Moreva, Olga & Schlather, Martin, 2023. "Bivariate covariance functions of Pólya type," Journal of Multivariate Analysis, Elsevier, vol. 194(C).
    9. James Wolter, 2015. "Kernel Estimation Of Hazard Functions When Observations Have Dependent and Common Covariates," Economics Series Working Papers 761, University of Oxford, Department of Economics.
    10. Minnie M. Joo & Brandon Bolte & Nguyen Huynh & Bumba Mukherjee, 2023. "Bayesian Spatial Split-Population Survival Model with Applications to Democratic Regime Failure and Civil War Recurrence," Mathematics, MDPI, vol. 11(8), pages 1-23, April.
    11. Wolter, James Lewis, 2016. "Kernel estimation of hazard functions when observations have dependent and common covariates," Journal of Econometrics, Elsevier, vol. 193(1), pages 1-16.
    12. Wei-Yin Kuo & Han-Sheng Hsu & Pei-Tseng Kung & Wen-Chen Tsai, 2021. "Impact of Socioeconomic Status on Cancer Incidence Risk, Cancer Staging, and Survival of Patients with Colorectal Cancer under Universal Health Insurance Coverage in Taiwan," IJERPH, MDPI, vol. 18(22), pages 1-17, November.
    13. Whitney E. Zahnd & Cathryn Murphy & Marie Knoll & Gabriel A. Benavidez & Kelsey R. Day & Radhika Ranganathan & Parthenia Luke & Anja Zgodic & Kewei Shi & Melinda A. Merrell & Elizabeth L. Crouch & Hea, 2021. "The Intersection of Rural Residence and Minority Race/Ethnicity in Cancer Disparities in the United States," IJERPH, MDPI, vol. 18(4), pages 1-26, February.
    14. Lijiang Geng & Guanyu Hu, 2022. "Bayesian spatial homogeneity pursuit for survival data with an application to the SEER respiratory cancer data," Biometrics, The International Biometric Society, vol. 78(2), pages 536-547, June.
    15. Carroll, Rachel & Lawson, Andrew B. & Jackson, Chandra L. & Zhao, Shanshan, 2017. "Assessment of spatial variation in breast cancer-specific mortality using Louisiana SEER data," Social Science & Medicine, Elsevier, vol. 193(C), pages 1-7.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:18:y:2021:i:3:p:1052-:d:486590. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.