The association between malaria incidences and air temperature at Kulon Progo District, Yogyakarta Special Province

https://doi.org/10.19106/JMedSci005202202007

. Nilasari(1*), Lutfan Lazuardi(2)

(1) Departement of Environmental Health, Faculty of Medicine, Universitas Gadjah Mada, Yogyakarta, Indonesia
(2) Departement of Health Policy and Management, Faculty of Medicine, Universitas Gadjah Mada, Yogyakarta, Indonesia.
(*) Corresponding Author

Abstract


Malaria is still a public health problem in Indonesia including in Kulon Progo District, Yogyakarta Special Province. Kulon Progo District remains become malaria endemic area, with significant number of malaria cases for more than the last ten years. Previous studies proved that malaria transmission is associated with climatic conditions. However, these conditions have never been investigated in Kulon Progo District. The aim of this ecological study was to investigate the association between the distribution of malaria cases and climatic condition in the Kulon Progo District using spatial-temporal approach. A total of 1439 malaria cases were collected during the period of 2005-2015. Time-trend, bivariate analysis, and spatial analysis were performed. The results showed that air temperature lag 0 (p = 0.0000; r = 0.5225), air temperature lag 1 (p = 0.0009; r = 0.2850), air temperature lag 2 (p = 0.0329; r = 0.1858) related to the incidence of malaria. Spatial analysis and time-trend analysis also showed direct relationship pattern between malaria and air temperature. In conclusion, there is a relationship between malaria cases and air temperature in Kulon Progo District. Spatial analysis approach is important for early alert system, to decrease morbidity and mortality due to malaria.


Keywords


malaria; air temperature; spatial analysis; Kulon ProgoDistrict; Yogyakarta;

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References

  1. Sucipto CD. Malaria complete manual. Yogyakarta: Gosyen Publishing. 2015.
  2. Harijanto PN, Nugroho A, Gunawan CA. Malaria from molecular to clinical: Ed.2. Jakarta: EGC. 2009.
  3. WHO. World malaria report 2015. Geneva, Switzerland: World Health Organization. 2015.
  4. Ministry of Health RI. A pocket book towards malaria elimination. Jakarta: Ministry of Health RI. 2011.
  5. Ministry of Health RI. Handbook of malaria case management.Jakarta: Ministry of Health RI. 2012.
  6. Ministry of Health RI. Malaria situation in Indonesia. Jakarta: Ministry of Health RI. 2014.
  7. District Health Office of Kulon Progo. Profile of the District Health Office in Kulon Progo 2016. Yogyakarta: District Health Office of Kulon Progo. 2016.
  8. Santjaka A. Malaria approach to the causality model. Yogyakarta: Nuha Medika. 2013.
  9. Gunawan NA. Spatial analysis of malaria in Banjarnegara District, Central Java Province 1996 - 2000. Jakarta: Faculty of Medicine, University of Indonesia. 2000.
  10. Riyanto D. Development of geographic information system applications. Yogyakarta: Gava Media. 2009.
  11. Arsin A, Karim SA. Spatial pattern of malaria cases with the application of Geographic Information Systems (GIS) in Central Halmahera Regency 2008. Journal of the Indonesian Epidemiology Society 2008; 1(2).33-7. https://doi.org/10.1145/3239438.3239492
  12. Chandra B. Health research methods. Jakarta: EGC. 2005.
  13. Dahlan MS. Diagnosing and managing 13 statistical diseases. Jakarta: Sagung Seto. 2010.
  14. Mock FJ. Land capability appraisal Indonesia water availability appraisal. Food and Agriculture Organitation of United Nations. 1973.
  15. Tjasyono. Climatology. Bandung: Bandung Institute of Technology. 2004.
  16. Raharjo M. Climograph study of climate change and malaria generation in Banjarnegara District. Indonesian Journal of Environmental Health 2003; 2(2):46-51.
  17. Huang F, Zhou S, Zhang S, Wang H, Tang L. Temporal correlation analysis between malaria and meteorological factors in Motuo County, Tibet. Malar J 2011; 10:54. https://dx.doi.org/10.1186/1475-2875-10-54
  18. Rahman A, Kogan F, Roytman L, Goldberg M, Guo W. Modelling and prediction of malaria vector distribution in Bangladesh From Remote-sensing data. International Journal Remot Sensing 2011; 32(5):1233-51. https://doi.org/10.1080/01431160903527447
  19. Hakim L, Ipa M. Early warning system for malaria outbreaks based on rainfall, vector density and malaria illness in Sukabumi. 2007; 34-40.
  20. Achmadi UF.Regional-based disease management. Jakarta: UI Press. 2010.



DOI: https://doi.org/10.19106/JMedSci005202202007

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