Sains Malaysiana 48(1)(2019): 217–225

http://dx.doi.org/10.17576/jsm-2019-4801-25

Mapping Lung Cancer Disease in Libya using Standardized Morbidity Ratio, BYM Model and Mixture Model, 2006 to 2011: Bayesian Epidemiological Study

(Pemetaan Penyakit Kanser Paru-Paru di Libya menggunakan Nisbah Morbiditi Piawai, Model BYM dan Model Campuran, 2006 hingga 2011: Kajian Bayesian Epidemiologi)

MARYAM AHMED ALRAMAH^1*, NOR AZAH SAMAT² & ZULKIFLEY MOHAMED²

¹Department of Statistics, Faculty of Science, University of Tripoli, Alfernag Tripoli, Libya

²Faculty of Science and Mathematics, University Pendidikan Sultan Idris, 35900 Tanjong Malim, Perak, Malaysia

Diserahkan: 17 Disember 2016/Diterima: 7 Februari 2018

ABSTRACT

Cancer represents a significant burden on both patients and their families and their societies, especially in developing countries, including Libya. Therefore, the aim of this study was to model the geographical distribution of lung cancer incidence in Libya. The correct choice of a statistical model is a very important step to producing a good map of disease in question. Therefore, in this study will use three models to estimate the relative risk for lung cancer disease, they are initially Standardized Morbidity Ratio, which is the most common statistic used in disease mapping, BYM model, and Mixture model. As an initial step, this study begins by providing a review of all models are proposed, which we then apply to lung cancer data in Libya. In this paper, we show some preliminary results, which are displayed and compared by using maps, tables, graphics and goodness-of-fit, the last measure of displaying the results is common in statistical modelling to compare fitted models. The main general results presented in this study show that the last two models, BYM and Mixture have been demonstrated to overcome the problem of the first model when there no observed lung cancer cases in certain districts. Also, other results show that Mixture model is most robust and gives a better relative risk estimate across compared it with a range of models.

Keywords: BYM model; disease mapping; Libya; lung cancer; mixture model; relative risk; Standardized Morbidity Ratio (SMR)

ABSTRAK

Kanser merupakan beban besar kepada pesakit dan keluarga mereka serta masyarakat, terutamanya di negara membangun, termasuk Libya. Oleh itu, tujuan kajian ini dijalankan adalah untuk merangka agihan geografi kejadian kanser paru-paru di Libya. Pemilihan model statistik yang betul merupakan satu langkah yang sangat penting untuk menghasilkan satu peta penyakit yang dikaji. Oleh itu, kajian ini akan menggunakan tiga model untuk menganggar risiko relatif bagi penyakit kanser paru-paru iaitu nisbah morbiditi piawai yang merupakan statistik biasa digunakan dalam pemetaan penyakit, model BYM dan model Campuran. Sebagai langkah pemula, kajian ini bermula dengan penyediaan ulasan untuk semua model yang dicadangkan, yang kemudiannya kami gunakan untuk data kanser paru-paru di Libya. Dalam kertas ini, kami tunjukkan beberapa keputusan awal yang dipapar dan dibandingkan dengan menggunakan peta, jadual, grafik dan kebagusan penyuaian yang merupakan langkah biasa terakhir untuk memaparkan keputusan dalam pemodelan statistik untuk membandingkan model suaian. Keputusan umum utama yang dikemukakan dalam kajian ini menunjukkan bahawa dua model terakhir, BYM dan Campuran telah menunjukkan kebolehan mengatasi masalah model pertama apabila tidak terdapat kes kanser paru-paru diperhatikan di daerah tertentu. Selain itu, keputusan lain juga menunjukkan bahawa model Campuran paling memberangsangkan dan memberikan penganggaran risiko relatif lebih baik berbanding model lain.

Kata kunci: Kanser paru-paru; Libya; model BYM; model Campuran; nisbah morbiditi dipiawaikan (SMR); pemetaan penyakit; risiko relatif

RUJUKAN

Abusaa, A. 2007. Second Annual Report Hospital-Based Cancer Registry Introduction and Outline. African Oncology Institute (AOI), Sabratha, African Oncology Institute (AOI). pp. 1-60.

Akhtar Shad, M.A., Abu Bakr, S.A., Dawi, & Huq, I.U. 1993. Cancer in Libya - A retrospective study (1981-1985). African Journal of Medicine and Medical Sciences 22(1): 17-24.

Alhdiri, M.A., Samat, N.A. & Zulkifley, M. 2016. Mapping Libya’s prostate cancer based on the SMR method: A geographical analysis. Geografia Malaysian Journal of Society and Space 12(9): 118-125.

Alsaker, M.H. 2013. Natural human factors plays in the incidence of malignant tumors in the north - west of Libya. PhD Thesis, Department of Geography, University of Tripoli (Unpublished).

Bell, B.S., Hoskins, R.E., Pickle, L.W. & Wartenberg, D. 2006. Current practices in spatial analysis of cancer data: Mapping health statistics to inform policymakers and the public. International Journal of Health Geographics 5(1): 49.

Besag, J., York, J. & Mollie, A. 1991. Bayesian image restoration with two applications in spatial statistics. Annals of the Institute of Statistical Mathematics 43: 1-59.

Bodalal, Z., Raouf, A. & Riyad, B. 2014. Cancers in Eastern Libya: First results from Benghazi Medical Center. World Journal of Gastroenterology 20(20): 6293-6301.

Clayton, D.G. & Kaldor, J. 1987. Empirical Bayes estimates of age-standardised relative risks for use in disease mapping. Biometrics 43: 671-691.

Ermiah, E., Fathi, A., Abdelbaset, B., Entesar, L., Seppo, P. & Yrjö, C. 2012. Diagnosis delay in Libyan female breast cancer. BMC Research Notes 5: 452.

Ferlay, J., Hai, R.S., Freddie, B., David, F., Colin, M. & Donald, M.P. 2010. Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. International Journal of Cancer 127(12): 2893-2917.

Information and Documentation Center. 2011. Libyan National Statistics Figures: Annual Statistical Report (in Arabic). Ministry of Health, Tripoli. (in Arabic). pp. 1-113.

John, R. & Hana, R. 2010. The Global Economic Cost of Cancer. Atlanta: American Cancer Society and Livestrong. pp. 1-14.

Lawson, A.B. 2013. Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology. 2nd ed. Boca Raton: Chapman and Hall/CRC.

Lawson, A.B. 2006. Statistical Methods in Spatial Epidemiology. England: Wiley.

Lawson, A.B. & Clark, A. 2002. Spatial mixture relative risk models applied to disease mapping. Statistics in Medicine 21: 359-370.

Lawson, A.B., Browne, A.B. & Vidal Rodeiro, C.L. 2003. Disease mapping with Win BUGS and MLwiN. England: John Wiley & Sons.

Lawson, A.B., Biggeri, A.B., Boehning, D., Lesare, E., Viel, J.F., Clark, A., Schlattmann, P. & Divino. 2000. Disease mapping models: An empirical evaluation. Statistics in Medicine 19: 2217-2241.

Meza, J.L. 2003. Empirical Bayes estimation smoothing of relative risks in disease mapping. Journal of Statistical Planning and Inference 112(1-2): 43-62.

Salim, E.I., Abdul Rahman, J. & Malcolm, A.M. 2011. Lung cancer incidence in the Arab league countries: Risk factors and control. Asian Pacific Journal of Cancer Prevention 12(1): 17-34.

Samat, N.A. & Percy, D.F. 2008. Standardized mortality and morbidity ratios and their application to dengue disease mapping in Malaysia. Proceedings of the Salford Postgraduate Annual Research Conference. Manchester: Salford University. pp. 200-210.

Siegel, R., Elizabeth, W., Otis, B. & Ahmed, J. 2011. Cancer statistics 2011: The impact of eliminating socioeconomic and racial disparities on premature cancer deaths. CA Cancer J. Clin. 61: 212-236.

Spiegelhalter, D.J., Best, N.G., Carlin, B.P. & Av Der. 2002. Bayesian measures of model complexity and fit (with discussion). Journal of the Royal Statistical Society: Series B 64: 583-640.

Washington, D.C. 2007. Food, Nutrition, Physical Activity, and the Prevention of Cancer: A Global Perspective/World Cancer Research Fund. American Institute for Cancer Research. American Institute for Cancer Research.

*Pengarang untuk surat-menyurat; email: m.alhdiri@yahoo.com