Spatial Pattern Analysis of Malaria Cases in Muara Enim Regency using Moran Index and Local Indicator Spatial Autocorrelation

Elvi Sunarsih; Muhammad  Zulkarnain; Laila  Hanum; Rostika  Flora; Nurhayati Damiri

doi:10.3889/oamjms.2021.6456

Authors

Elvi Sunarsih Environmental Science Study Program, Sriwijaya University, Palembang, Indonesia https://orcid.org/0000-0001-9308-3199
Muhammad Zulkarnain Department of Public Health Science, Faculty of Medicine, Sriwijaya University, Palembang, Indonesia
Laila Hanum Department of Biology, Faculty of Mathematics and Natural Sciences, Sriwijaya University, Palembang, Indonesia
Rostika Flora Environmental Science Study Program, Sriwijaya University, Palembang, Indonesia https://orcid.org/0000-0002-2904-0407
Nurhayati Damiri Public Health Science Study Program, Faculty of Public Health, Sriwijaya University, Palembang, Indonesia

DOI:

https://doi.org/10.3889/oamjms.2021.6456

Keywords:

Spatial pattern, Malaria, Muara Enim regency, Moran index, Local indicator spatial autocorrelation

Abstract

BACKGROUND: Malaria is a disease which still becomes a global health issue, including in Indonesia, because of its potential vector which can infect and spread causing a wide impact. Until currently, malaria still becomes a serious threat to people living in tropical and subtropical areas. Muara Enim Regency is a malaria-endemic regency with the second highest positive case in South Sumatra Province, with an API value in 2019 was 0.18/1000 population.

AIM: The current research was performed to identify and change the spatial pattern of malaria cases, environmental variability (rainfall), population density using Moran index and local indicator spatial autocorrelation (LISA), and habitat in Muara Enim Regency.

METHODS: This research employed a quantitative research design with an analytical survey research method and a case–control approach. This research method was designed using a geographic information systems approach.

RESULTS: The results of the study showed that malaria cases in Muara Enim Regency in 2017 occurred in groups with a Moran index of 0.263, indicating a positive autocorrelation. Meanwhile, based on the LISA index, it was found that there were three districts categorized as high-high (HH) (quadrant 1), those are Lawang Kidul District, Muara Enim District, and Gunung Megang District, while in the low-high (LH) category (quadrant 2), there was Benakat District. In 2018, it also occurred in groups where the Moran index was 0.129, indicating a positive autocorrelation, while the LISA index found that there was one district categorized as HH (quadrant 1) which is Lawang Kidul District, and district categorized as LH (quadrant 2) was Gunung Megang District. In 2019, it happened randomly or spread with a Moran index of −0.022 indicating a negative autocorrelation, while based on the LISA index, it was found that there was one subdistrict categorized as HH category (quadrant 1) which is Lawang Kidul District and two districts categorized as LH (quadrant 2) which are Semende Darat Laut and Rambang Niru.

CONCLUSION: There was a change in the results of Moran index from a positive autocorrelation in 2017, 2018, to a negative autocorrelation in 2019 with the results of the LISA index for malaria cases in 2017–2019 in one subdistrict, namely, Lawang Kidul District categorized as a HH category (quadrant 1).

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References

Elyazar IR, Hay SI, Baird JK. Malaria distribution, prevalence, drug resistance and control in Indonesia. Adv Parasitol. 2011;74: 41-175. https://doi.org/10.1016/B978-0-12-385897-9.00002-1 PMid:21295677 DOI: https://doi.org/10.1016/B978-0-12-385897-9.00002-1

Hasyim H, Camelia A, Fajar NA. Determinan kejadian malaria di wilayah endemis. Kesmas Natl Public Health J. 2014;291:367. https://doi.org/10.21109/kesmas.v0i0.367. DOI: https://doi.org/10.21109/kesmas.v0i0.367

Maretasari G, Windusari Y, Lamin S, Hanum L, Septiawati D. Characteristics of habitat, distribution, and diversity of Anopheles Spp in kemelak bindung langit village, ogan komering ulu regency, South Sumatra.J Environ Sci Sustain Dev. 2019;2: 165-75. https://doi.org/10.7454/jessd.v2i2.1035 DOI: https://doi.org/10.7454/jessd.v2i2.1035

Coulibaly JT, N’Gbesso YK, Knopp S, N’Guessan NA, Silué KD, van Dam GJ, et al. Accuracy of urine circulating cathodic antigen test for the diagnosis of schistosoma mansoni in preschool-aged children before and after treatment. PLoS Negl Trop Dis. 2013;7(3):e2109. https://doi.org/10.1371/journal.pntd.0002109 PMid:23556011 DOI: https://doi.org/10.1371/journal.pntd.0002109

McGarigal K, Cushman SA, Neel MC, Ene E. FRAGSTATS: Spatial Pattern Analysis Program for Categorical Maps. Analysis. 2002. Retrieved from http://www.umass.edu/landeco/research/fragstats.html.

Cao J, Zhu J, Zhang Q, Wang K, Yang J, Wang Q. Modeling urban intersection form: Measurements, patterns, and distributions. Front Archit Res. 2021;10:33-49. https://doi.org/10.1016/j.foar.2020.11.003 DOI: https://doi.org/10.1016/j.foar.2020.11.003

Gemperli A, Vounatsou P, Kleinschmidt I, Bagayoko M, Lengeler C, Smith T. Spatial patterns of infant mortality in mali: The effect of malaria endemicity. Am J Epidemiol. 2004;159(1):64-72. https://doi.org/10.1093/aje/kwh001 PMid:14693661 DOI: https://doi.org/10.1093/aje/kwh001

Sunarsih E, Purba IG, Suheryanto S, Rosyada A, Razak R, Septiawati D. Spatial Modeling of Environmental Sanitation as the Distribution Determinant of Malaria Cases in Lahat Regency. United States: 2nd Sriwijaya International Conference on Public Health; 2020. https://doi.org/10.2991/ahsr.k.200612.023 DOI: https://doi.org/10.2991/ahsr.k.200612.023

Legendre P, Fortin MJ. Spatial Pattern and Ecological Analysis. Vegetatio. 1989;80:107-138. https://doi.org/10.1109/34.824819

Jain AK, Duin RP, Mao J. Statistical Pattern Recognition:A Review. United States: IEEE Trans Pattern Anal Mach Intell; 2000. https://doi.org/10.1109/34.824819. DOI: https://doi.org/10.1109/34.824819

Prahutama A, Hoyyi A. Spatial pattern penyebaran malaria di Jawa Tengah. Statistika 2016;4:25.

Barati M, Keshavarz-Valian H, Habibi-Nokhandan M, Raeisi A, Faraji L, Salahi-Moghaddam A. Spatial outline of malaria transmission in Iran. Asian Pac J Trop Med. 2012;5:789-95. https://doi.org/10.1016/s1995-7645(12)60145-x PMid:23043918 DOI: https://doi.org/10.1016/S1995-7645(12)60145-X

Suryaningtyas NH, Margarethy I, Salim M. Analisis data spasial malaria di kabupaten kulon progo Tahun 2017. Spirakel. 2019;11:63-71. DOI: https://doi.org/10.22435/blb.v14i1.302

Narulita I. Distribusi spasial dan temporal curah hujan di das cerucuk, Pulau Belitung.J Ris Geol Dan Pertamb. 2016;26:141. https://doi.org/10.14203/risetgeotam2016.v26.194 DOI: https://doi.org/10.14203/risetgeotam2016.v26.194

Pravitasari D, Hakim RB. Analisis Cluster K-means Dan Autokorelasi Spasial Untuk Identifikasi Pola Penyebaran Kasus Malaria (Studi kasus : Kasus malaria di kabupaten Purworejo Tahun 2015. Berlin, Germany: ResearchGate; 2016. p. 848-53. https://doi.org/10.32832/hearty.v5i1.1051 DOI: https://doi.org/10.32832/hearty.v5i1.1051

Laumalay HM, Satoto TB, Fuad A. Analisis spasial karakteristik habitat perkembangbiakan Anopheles Spp di desa lifuleo kecamatan kupang barat. Bul Penelit Kesehat. 2019;47(3): 207-16. https://doi.org/10.22435/bpk.v47i3.1451 DOI: https://doi.org/10.22435/bpk.v47i3.1451

Wigaty L, Bakri S, Santoso T, Wulan SR, Wardani D. Pengaruh perubahan penggunaan lahan terhadap angka kesakitan malaria: Studi Di Provinsi Lampung.J Sylva Lestari. 2016;4:1. https://doi.org/10.23960/jsl341-10 DOI: https://doi.org/10.23960/jsl341-10

Anasiru RH. Spatial analysis in the classification of critical land in the sub-basin of Langge Gorontalo. Inform Pertan. 2016;25:261-72. DOI: https://doi.org/10.21082/ip.v25n2.2016.p261-272