HUBUNGAN ANTARA NILAI NORMALIZED DIFFERENCE VEGETATION INDEX (NDVI) DENGAN INSIDENSI MALARIA: TINJAUAN SISTEMATIK DAN META-ANALISIS
DOI:
https://doi.org/10.31004/jkt.v4i2.15031Keywords:
Deforestasi, Malaria, Normalized Difference Vegetation Index, Plasmodium, Perubahan iklimAbstract
Malaria merupakan penyakit akut yang disebabkan oleh plasmodium. Pada tahun 2020 terdapat 59,5 kasus dari 1000 populasi berisiko. Mortalitas secara global menunjukan sebanyak 15,3 kematian akibat malaria dari 100.000 populasi berisiko. Kerusakan lingkungan dapat mengakitbatkan beberapa efek, salah satunya adalah perubahan dinamika dari vektor penyakit infeksi, salah satunya adalah nyamuk vektor dari malaria. Normalized Difference Vegetation Index (NDVI) merupakan nilai untuk menghitung rapatan vegetasi pada suatu daerah, yang digunakan untuk memprediksi kejadian malaria. Tujuan dari tinjauan sistematis dan meta-analisis ini adalah untuk mengetahui korelasi antara NDVI dengan insiden dari malaria di dunia. Pencarian studi dilakukan di tiga basis data daring, yaitu Pubmed, Scopus, dan Embase hingga Agustus 2022. Luaran yang dicari adalah koefisien korelasi antara nilai NDVI dengan jumlah kasus malaria di suatu daerah. Dari pencarian tersebut, didapatkan 8 studi yang diinklusi untuk dianalisis lebih lanjut, dimana 4 studi bisa dilakukan meta-analisis. Hasil meta-analisis menunjukkan bahwa nilai NDVI berkorelasi kuat terhadap insidensi malaria (r = 0.823, 95%CI = 0.253 to 0.969). Hal ini menunjukkan nilai NDVI berbanding terbalik dengan kasus malaria, yang mungkin terjadi akibat konversi lahan sehingga menimbulkan banyaknya genangan air yang ideal bagi siklus perkembangbiakan nyamuk.References
A, M. (2009). Nyamuk vektor malaria dan hubungannya dengan aktivitas kehidupan manusia di Indonesia. Jurnal Aspirator, 1(2), 94–102.
Adeola, A. M., Botai, J. O., Olwoch, J. M., Rautenbach, H. C. J. d. W., Adisa, O. M., de Jager, C., Botai, C. M., & Aaron, M. (2019). Predicting malaria cases using remotely sensed environmental variables in Nkomazi, South Africa. Geospatial Health, 14(1). https://doi.org/10.4081/gh.2019.676
Amadi, J. A., Olago, D. O., Ong’amo, G. O., Oriaso, S. O., Nanyingi, M., Nyamongo, I. K., & Estambale, B. B. A. (2018). Sensitivity of vegetation to climate variability and its implications for malaria risk in Baringo, Kenya. PLoS ONE, 13(7). https://doi.org/10.1371/journal.pone.0199357
Aragão, L. E. O. C., Malhi, Y., Barbier, N., Lima, A., Shimabukuro, Y., Anderson, L., & Saatchi, S. (2008). Interactions between rainfall, deforestation and fires during recent years in the Brazilian Amazonia. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 363(1498), 1779–1785. https://doi.org/10.1098/RSTB.2007.0026
Chirombo, J., Ceccato, P., Lowe, R., Terlouw, D. J., Thomson, M. C., Gumbo, A., Diggle, P. J., & Read, J. M. (2020). Childhood malaria case incidence in Malawi between 2004 and 2017: Spatio-temporal modelling of climate and non-climate factors. Malaria Journal, 19(1), 1–13. https://doi.org/10.1186/s12936-019-3097-z
Gaudart, J., Touré, O., Dessay, N., Dicko, A. L., Ranque, S., Forest, L., Demongeot, J., & Doumbo, O. K. (2009). Modelling malaria incidence with environmental dependency in a locality of Sudanese savannah area, Mali. Malaria Journal, 8(1), 61. https://doi.org/10.1186/1475-2875-8-61
Gomez-Elipe, A., Otero, A., Van Herp, M., & Aguirre-Jaime, A. (2007). Forecasting malaria incidence based on monthly case reports and environmental factors in Karuzi, Burundi, 1997-2003. Malaria Journal, 6, 1–10. https://doi.org/10.1186/1475-2875-6-129
Graves, P. M., Osgood, D. E., Thomson, M. C., Sereke, K., Araia, A., Zerom, M., Ceccato, P., Bell, M., Corral, J. Del, Ghebreselassie, S., Brantly, E. P., & Ghebremeskel, T. (2008). Effectiveness of malaria control during changing climate conditions in Eritrea, 1998-2003. Tropical Medicine and International Health, 13(2), 218–228. https://doi.org/10.1111/J.1365-3156.2007.01993.X
Hay, S. I., Snow, R. W., & Rogers, D. J. (1998). Predicting malaria seasons in Kenya using multitemporal meteorological satellite sensor data. Transactions of the Royal Society of Tropical Medicine and Hygiene, 92(1), 12–20. https://doi.org/10.1016/S0035-9203(98)90936-1
Huang, F., Zhou, S., Zhang, S., Wang, H., & Tang, L. (2011). Temporal correlation analysis between malaria and meteorological factors in Motuo County, Tibet. Malaria Journal, 10. https://doi.org/10.1186/1475-2875-10-54
Hundessa, S., Li, S., Liu, D. L., Guo, J., Guo, Y., Zhang, W., & Williams, G. (2018). Projecting environmental suitable areas for malaria transmission in China under climate change scenarios. Environmental Research, 162(November 2017), 203–210. https://doi.org/10.1016/j.envres.2017.12.021
Hurtado, L. A., Calzada, J. E., Rigg, C. A., Castillo, M., & Chaves, L. F. (2018). Climatic fluctuations and malaria transmission dynamics, prior to elimination, in Guna Yala, República de Panamá. Malaria Journal, 17(1), 1–12. https://doi.org/10.1186/s12936-018-2235-3
Jason, W. (2007). MODELING MALARIA TRANSMISSION RISK USING SATELLITE-BASED REMOTE SENSING IMAGERY: A FIVE-YEAR DATA ANALYSIS IN DEMOCRATIC PEOPLE’S REPUBLIC OF KOREA. Northwest Missouri state University.
Kibret, S., Glenn Wilson, G., Ryder, D., Tekie, H., & Petros, B. (2019). Environmental and meteorological factors linked to malaria transmission around large dams at three ecological settings in Ethiopia. Malaria Journal, 18(1), 1–16. https://doi.org/10.1186/s12936-019-2689-y
Kurniati, E. (n.d.). Analisis Sebaran Habitat Anopheles spp. Vektor Penyakit Malaria di Kabupaten Jombang, Jawa Timur. https://www.academia.edu/6508331/Aplikasi_Penginderaan_Jauh_dan_Sistem_Informasi_Geografis_untuk_Analisis_Sebaran_Habitat_Anopheles_spp_Vektor_Penyakit_Malaria_di
McMahon, A., Mihretie, A., Ahmed, A. A., Lake, M., Awoke, W., & Wimberly, M. C. (2021). Remote sensing of environmental risk factors for malaria in different geographic contexts. International Journal of Health Geographics, 20(1), 1–15. https://doi.org/10.1186/s12942-021-00282-0
Mohammadkhani, M., Khanjani, N., Bakhtiari, B., & Sheikhzadeh, K. (2016). The relation between climatic factors and malaria incidence in Kerman, South East of Iran. Parasite Epidemiology and Control, 1(3), 205. https://doi.org/10.1016/J.PAREPI.2016.06.001
Mukaka, M. M. (2012). Statistics corner: A guide to appropriate use of correlation coefficient in medical research. Malawi Medical Journal?: The Journal of Medical Association of Malawi, 24(3), 69–71.
Okiring, J., Routledge, I., Epstein, A., Namuganga, J. F., Kamya, E. V., Obeng-Amoako, G. O., Sebuguzi, C. M., Rutazaana, D., Kalyango, J. N., Kamya, M. R., Dorsey, G., Wesonga, R., Kiwuwa, S. M., & Nankabirwa, J. I. (2021). Associations between environmental covariates and temporal changes in malaria incidence in high transmission settings of Uganda: a distributed lag nonlinear analysis. BMC Public Health, 21(1), 1–11. https://doi.org/10.1186/s12889-021-11949-5
Organization, W. H. (2022). Malaria.
Patz, J. A., Strzepek, K., Lele, S., Hedden, M., Greene, S., Noden, B., Hay, S. I., Kalkstein, L., & Beier, J. C. (1998). Predicting key malaria transmission factors, biting and entomological inoculation rates, using modelled soil moisture in Kenya. Tropical Medicine and International Health, 3(10), 818–827. https://doi.org/10.1046/j.1365-3156.1998.00309.x
Piedrahita, S., Altamiranda-Saavedra, M., & Correa, M. M. (2020). Spatial fine-resolution model of malaria risk for the Colombian Pacific region. Tropical Medicine and International Health, 25(8), 1024–1031. https://doi.org/10.1111/tmi.13443
Rigg, C. A., Hurtado, L. A., Calzada, J. E., & Chaves, L. F. (2019). Malaria infection rates in Anopheles albimanus (Diptera: Culicidae) at Ipetí-Guna, a village within a region targeted for malaria elimination in Panamá. Infection, Genetics and Evolution, 69(December 2018), 216–223. https://doi.org/10.1016/j.meegid.2019.02.003
Siya, A., Kalule, B. J., Ssentongo, B., Lukwa, A. T., & Egeru, A. (2020). Malaria patterns across altitudinal zones of Mount Elgon following intensified control and prevention programs in Uganda. BMC Infectious Diseases, 20(1), 1–16. https://doi.org/10.1186/s12879-020-05158-5
Smith, R. C., & Choudhury, B. J. (2007). On the correlation of indices of vegetation and surface temperature over south-eastern Australia. Http://Dx.Doi.Org/10.1080/01431169008955164, 11(11), 2113–2120. https://doi.org/10.1080/01431169008955164
Solano-Villarreal, E., Valdivia, W., Pearcy, M., Linard, C., Pasapera-Gonzales, J., Moreno-Gutierrez, D., Lejeune, P., Llanos-Cuentas, A., Speybroeck, N., Hayette, M.-P., & Rosas-Aguirre, A. (2019). Malaria risk assessment and mapping using satellite imagery and boosted regression trees in the Peruvian Amazon. Scientific Reports, 9(1), 15173. https://doi.org/10.1038/s41598-019-51564-4
Willa, R. W., & Kazwaini, M. (2016). Penyebaran Kasus Dan Habitat Perkembangbiakan Vektor Malaria Di Kabupaten Sumba Timur Provinsi Nusa Tenggara Timur. Jurnal Ekologi Kesehatan, 14(3), 218–228. https://doi.org/10.22435/jek.v14i3.4692.218-228
World Health Organization, G. H. O. D. R. H. S. (2020). Incidence of malaria (per 1,000 population at risk).
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