Sains Malaysiana 42(8)(2013): 1073–1080

Aplikasi Sistem Maklumat Geografi untuk Pemetaan Reruang-masa: Suatu Kajian Kes Denggi

di Daerah Seremban, Negeri Sembilan, Malaysia

(Application of Geographical Information System for Spatial-temporal Mapping:

A Case Study of Dengue Cases in Seremban, Negeri Sembilan, Malaysia)

Mohamad Naim Mohamad Rasidi

Unit Metodologi dan Statistik, Institut Kesihatan Umum, Kementerian Kesihatan Malaysia

Jalan Bangsar, 50590 Kuala Lumpur, Malaysia

Mazrura Sahani*

Program Kesihatan Persekitaran dan Keselamatan Industri, Pusat Pengajian Sains Diagnostik

& Kesihatan Gunaan, Fakulti Sains Kesihatan, Universiti Kebangsaan Malaysia

Jalan Raja Muda Abdul Aziz, 50300 Kuala Lumpur, Malaysia

Hidayatulfathi Othman

Pusat Pengajian Sains Diagnostik & Kesihatan Gunaan, Fakulti Sains Kesihatan

Universiti Kebangsaan Malaysia, Jalan Raja Muda Abdul Aziz

50300 Kuala Lumpur, Malaysia

Rozita Hod

Jabatan Kesihatan Masyarakat

Pusat Perubatan Universiti Kebangsaan Malaysia, Jalan Yaacob Latif, Bandar Tun Razak

56000 Cheras, Kuala Lumpur, Malaysia

Shaharudin Idrus

Institut Alam Sekitar & Pembangunan (LESTARI), Universiti Kebangsaan Malaysia

43600, UKM Bangi, Selangor D.E. Malaysia

Zainudin Mohd Ali

Jabatan Kesihatan Negeri Sembilan, Jalan Rasah, 70300 Seremban, Negeri Sembilan, Malaysia

Er Ah Choy

Pusat Pengajian Sosial, Pembangunan & Persekitaran, Fakulti Sains Sosial & Kemanusiaan

Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor D.E. Malaysia

Mohd Hafiz Rosli

Akademi Sukan, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor D.E. Malaysia

Diserahkan: 7 Mac 2013/Diterima: 27 Mac 2013

ABSTRAK

Penyakit denggi merupakan penyakit bawaan vektor yang menjadi salah satu ancaman utama kesihatan awam di Malaysia. Pemetaan taburan kes denggi daripada aspek reruang-masa boleh menjadi kaedah yang berguna dalam menilai risiko denggi kepada masyarakat. Kajian ini bertujuan untuk memetakan taburan reruang dan reruang-masa kes-kes denggi di dalam daerah Seremban. Metodologi dijalankan dengan Sistem Maklumat Geografi (GIS) khususnya analisis reruang dan reruang-masa. Analisis taburan reruang menggunakan Indeks Moran, purata kejiranan terdekat (ANN) dan anggaran kepadatan Kernel.  Analisis reruang-masa ditentukan dengan indeks kekerapan, jangka masa dan intensiti untuk mengenal pasti kawasan berisiko denggi mengikut masa. Sejumlah 6076 kes denggi dicatatkan di Pejabat Kesihatan Daerah Seremban dari tahun 2003 hingga 2009. Kadar insiden denggi adalah tinggi pada tahun 2003, 2008 dan 2009 dengan nisbah denggi : denggi berdarah adalah 21.6:1. Indeks Moran menunjukkan kes denggi berlaku dalam pengelompokan dengan skor Z adalah 16.384 (p=0.000). Analisis ANN dengan 0.264 (p= 0.000) dengan purata jarak insiden antara kes denggi di dalam kawasan kejiranan adalah 55 m. Anggaran kepadatan Kernel menunjukkan lokasi kawasan panas kes denggi tertumpu di Nilai dan Ampangan. Analisis reruang masa dengan purata nilai tertinggi indeks kekerapan, jangka masa dan intensiti masing-masing melebihi 0.023, 0.614 dan 0.657 di kawasan berisiko tinggi denggi di Nilai, Seremban dan Ampangan. Pengawalan denggi perlu diberi tumpuan kepada kawasan berisiko tinggi ini.

Kata kunci: Denggi; GIS; statistik reruang-masa

ABSTRACT

Dengue is a vector borne disease which is one of the major threats to public health in Malaysia. Mapping of dengue distribution in spatial and spatial-temporal aspects can be a useful method in assessing the risk of dengue to the community. This study aimed to map the spatial and spatial-temporal distribution of dengue cases in Seremban district. The Geographical Information System specifically the spatial and spatial-temporal analyses was applied. Spatial statistical analysis of dengue cases used the Moran’s Index, average nearest neighbourhood (ANN) and kernel density estimation. Spatial-temporal analysis was determined through frequency, duration and intensity indices to identify timely dengue risk area. A total of 6076 dengue cases were reported in Seremban Health District Office from 2003-2009. The result showed a high incidence rate in 2003, 2008 dan 2009 with ratio of dengue: dengue hemorrhagic fever of 21.6:1. Moran’s I showed dengue cases occurred in cluster with Z-score of 16.384(p=0.000). ANN analysis of 0.264 (p= 0.000) where the mean distance between every dengue case is 55 m. Kernel density estimation showed the dengue hotspots concentrated in Nilai and Ampangan. Spatial-temporal analysis with the highest mean of frequency, duration and intensity indices of above 0.023, 0.614 and 0.657 showed that the high risk dengue areas were Nilai, Seremban and Ampangan. The dengue control activities should be targeted at these high risk areas.

Keywords: Dengue; GIS; spatial-temporal analysis

RUJUKAN

Almeida, A.S., Andrade Mendronho, R. & Valencia, L.I.O. 2009. Spatial analysis of dengue and socioeconomic content of the city of Rio de Janeiro (southern Brazil). Rev Saúde Pública 43(4): 666-673.

Aziz, S. 2011. Evaluation of the spatial risk factors for high incidence of dengue fever and dengue hemorrhagic fever using GIS Application. Sains Malaysiana 40(8): 937-943.

Barreto, F.R., Teixeira M.C., Carvalho, M.S. & Barreto M.L. 2008. Spread pattern of the first dengue epidemic in the city of Salvador, Brazil. BMC Public Health 8:51. http://www. biomedcentral.com/1471-2458/8/51 (18 September 2011).

Bithell, J.F. 1990. An application of density estimation to geographical epidemiology. Statistics in Medicine 9: 691-701.

Er, A.C., Rosli, M.H., Asmahani, A., Mohamad Naim, M.R. & Harsuzilawati, M. 2010. Spatial mapping of dengue incidence: A case study in Hulu Langat District, Selangor, Malaysia. International Journal of Human and Social Sciences 5(6): 410-414.

Houghton, J.T., Ding, Y., Griggs, D.J., Noguer, M., van der Linden, P.J. & Xiaosu, D. 1996. Climate Change 2001: The Scientific Basis. Cambridge: Cambridge University Press.

IPCC. 2001. Synthesis Report. A Contribution of Working Groups I, II and III to the Third Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press.

Jabatan Perancangan Bandar dan Desa. 2007. Data Digital. Kuala Lumpur: Jabatan Perancangan Bandar Dan Desa.

Kementerian Kesihatan Malaysia. 2006. Fakta Kesihatan Malaysia, Sistem Informasi Dan Dokumentari.

Kementerian Kesihatan Malaysia. 2010. Current Situation of Dengue and Dengue Hemorrhagic Fever. http://www.moh. gov.my (20 Januari 2010).

King, C.C., Chao, D.Y., Wen, T.H., Kao, C.L., Ku, C.C., Wu, J.T.S., Huang, S.Y.J., Tipayamongkholgu, M., Shang, C.S., Li, Y.S., Wang, H.T., Liao, M.Y., Wang, W.K., Wu, H.C. & Chang, J.K.J. 2008. Epidemiologic characteristics of dengue in Taiwan and implications for global control: A review. Dlm Dengue. Kerala: Research Signpost. hlm. 1-44.

Levine, N. 2007. Crimestat: A Spatial Statistics Program for the Analysis of Crime Incident Locations (Version 3.0). Washington, D.C.: Ned Levine & Associates, Houston, TX and the National Institute of Justice.

Malavige, G.N., Fernando, S., Fernando, D.J. & Seneviratne, S.L. 2004. Review: Dengue viral infection. Postgraduate Medical Journal 80: 588-601.

Nazri, C.D., Abu Hassan, A., Rabiatul, A. & Rodziah, I. 2010. Spatial mapping of temporal risk characteristic of dengue cases in Subang Jaya. 2010 International Conference on Science and Social Research. December 5-7. Kuala Lumpur.

Nakhapakorn, K. & Jirakajohnkool, S. 2006. Temporal and spatial autocorrelation statistics of dengue fever. Dengue Bulletin 30: 171-183.

Seng, S.B., Chong, A.K. & Moore, A. 2005. Geostatistikal Modelling, Analysis and Mapping of Epidemiology of Dengue Fever in Johor State, Malaysia. The 17th Annual Colloquium of the Spatial Information Research Centre, University of Otago, Dunedin, New Zealand. November 24-25.

Shaharudin, I., Shamsul, A.S., Tahir, A., Mariam, M., Azah, D. & Nik Shamsidah, N.I. 2002. Sistem Maklumat Geografi (GIS) dan sektor kesihatan awam: Kajian demam denggi di Bandar Baru Bangi dan Kajang. Jurnal Kesihatan Masyarakat 8(S):34-42.

Siti Morni, U., Mazlin, M., Noraini, S. & Anizar, A. 2007. Generating a dengue risk map (DRM) based on environmental factors using remote sensing and GIS technologies. http:www.a-a-r-s.org/acrs/proceeding/ACRS2007/Papers/ TS27.4.pdf (2 Ogos 2011).

Wan Fairos, W.Y., Wan Azaki, W.H., Mohamad Alias, L. & Bee Wah, Y. 2010. Modelling dengue fever (DF) and dengue hemorrhagic fever (DHF) outbreak using Poisson and negative binomial model. World Academy of Science, Engineering and Technology 62: 903-908.

Wen, T.H., Lin, N.H., Chao, D.Y., Hwang, K.P., Kan, C.C., Lin, K.C.M., Wu, J.T.S., Huang, S.Y.J., Fan, I.C. & King, C.C. 2009. Spatial-temporal patterns of dengue in areas at risk of dengue hemorrhagic fever in Kaohsiung, Taiwan, 2002. International Journal of Infectious Diseases 14(4): 334-343.

Wen, T.H., Lin, H.N., Lin, C.H., King, C.C. & Su, M.D. 2006. Spatial mapping of temporal risk characteristics to improve environmental health risk identification: A case study of a dengue epidemic in Taiwan. Sci. Total Environ. 367: 631-640.

*Pengarang untuk surat-menyurat; email: mazrura@gmail.com