Risk Factor and Cluster Analysis to Identify Malaria Hot Spot for Control Strategy in Samigaluh Sub-District, Kulon Progo, Indonesia

  • Sulistyawati SULISTYAWATI Department of Public Health, Universitas Ahmad Dahlan, Yogyakarta, Indonesia
  • Isnah FITRIANI Department of Public Health, Universitas Ahmad Dahlan, Yogyakarta, Indonesia
Keywords: Malaria; Risk factor; Cluster analysis; Indonesia

Abstract

Abstract Background: In 2015, Indonesia government targeted to eliminate malaria in Java Island. Nevertheless, until now malaria still occurs, including in Samigaluh, Kulon Progo District although many malaria programs has been run. Complexity and dynamic of the population also limited budget may become the reason of malaria combat difficulties. Subsequently, a method to direct the policymaker on how to provide program effectively and efficiently was needed.  We examined malaria risk factor using statistical and cluster analysis. Methods: A quantitative study with case-control approach was conducted during Spring 2017 in Samigaluh II Public Health Centre, Indonesia. The structured questioner was used to collect the information from both of case and control which were people who had blood examination regarding malaria diagnosed during January-December 2016. Global Positioning System was used to record the geographical position of house participant which was used in cluster analysis. Results: Occupation was recognized as the significant risk factor to malaria. One most likely cluster was detected and translated as the source of transmission because of its fall in malaria hotspot. Conclusion: Satscan be able to detect a spatial cluster of malaria case and a promising method for supporting malaria control.   

References

1. WHO (2017). Malaria. [cited 2017 Sep 27]. http://www.who.int/mediacentre/factsheets/fs094/en/
2. Pusat Data dan Informasi Kementerian Kesehatan Republik Indonesia (2016). Info Datin Malaria. Indonesia Ministry of Health, Jakarta, pp.: 1–7.
3. Supargiyono S, Bretscher MT, Wijayanti MA et al (2013). Seasonal changes in the anti-body responses against Plasmodium fal-ciparum merozoite surface antigens in ar-eas of differing malaria endemicity in In-donesia. Malar J, 12:444.
4. Murhandarwati EEH, Fuad A, Sulistyawati S et al (2015). Change of strategy is required for malaria elimination: a case study in Purworejo District, Central Java Province, Indonesia. Malar J, 14:318.
5. Direktorat PPBB Ditjen PP dan PL Kemen-terian Kesehatan Republik Indonesia (2011). Buku Saku Menuju Eliminasi Malaria. Indonesia Ministry of Health, Jakarta, pp.: 1–30.
6. Soto-Calle V, Rosas-Aguirre A, Llanos-Cuentas A et al (2017). Spatio-temporal analysis of malaria incidence in the Peru-vian Amazon Region between 2002 and 2013. Sci Rep, 7(April 2016):40350.
7. Rulisa S, Kateera F, Bizimana JP et al (2013). Malaria Prevalence, Spatial Clustering and Risk Factors in a Low Endemic Area of Eastern Rwanda: A Cross Sectional Study. PLoS One, 8(7):e69443.
8. Rosas-Aguirre A, Ponce OJ, Carrasco-Escobar G et al (2015). Plasmodium vi-vax malaria at households: spatial cluster-ing and risk factors in a low endemicity urban area of the northwestern Peruvian coast. Malar J, 14(1):176.
9. Suwasono H, Sudini J, Priyadi, Kino, Tamat (1999). Review malaria di Wilayah Pusk-esmas Samigaluh II, Kabupaten Kulon-progo, Daerah Istimewa Yogyakarta Ta-hun 1998. Bull Penelit Kesehat, 27(3&4).
10. Nalim S, Hartono, Sugeng, Bogh C, Bos R (2002). Rapid Assessment of Correlation between Remotely Sensed Data and malaria Prevalence in the Menoreh Hills Area of Central Java, Indone-sia-Final Report. WHO, Geneva, pp.:1-13.
11. Herdiana H, Cotter C, Coutrier FN et al (2016). Malaria Risk Factor Assessment Using Active and Passive Surveillance Data from Aceh Besar, Indonesia, a Low Endemic, Malaria Elimination Setting with Plasmodium Knowlesi, Plasmodium Vivax, and Plasmodium Falciparum. Ma-lar J, 15(1):1–15.
12. Wiraharjanegara HA and Kesetyaningsih TW (2009). Prevalence Distribution of Malaria in Primary Health Care Kokap I and Girimulyo I Kulonprogo District Year and Its Correlation with The Risk Factors. Mutiara Med, 9(2): 108-114.
13. Homan T, Maire N, Hiscox A et al (2016). Spatially variable risk factors for malaria in a geographically heterogeneous land-scape, western Kenya: an explorative study. Malar J, 15:1.
14. Monroe A, Asamoah O, Lam Y et al (2015). Outdoor-sleeping and other night-time activities in northern Ghana: implications for residual transmission and malaria prevention Outdoor-sleeping and other night-time activities in northern Ghana: implications for residual transmission and malaria prev. Malar J, 14(1):35.
15. Chirebvu E, Chimbari MJ, Ngwenya BN (2014). Assessment of risk factors associ-ated with malaria transmission in Tubu Village, Northern Botswana. Malar Res Treat, 403069.
16. Barodji, Boewono DT, Boessri H, Sudini, Sumardi (2003). Bionomik Vektor dan Situasi malaria di kecamatan Kokap, Ka-bupaten Kulon Progo, Yogyakarta. J Ekol, 2:2.
17. Dunn CE, Le Mare A, Makungu C (2011). Malaria risk behaviours, socio-cultural practices and rural livelihoods in South-ern Tanzania: Implications for bednet us-age So Sci Med, 72(3):408–17
18. Murhandarwati EEH, Fuad A, Nugraheni MDF et al (2014). Early malaria resur-gence in pre-elimination areas in Kokap Subdistrict, Kulon Progo, Indonesia. Ma-lar J, 13:130.
19. Bousema T, Drakeley C, Gesase S et al (2010). Identification of hot spots of ma-laria transmission for targeted malaria control. J Infect Dis, 201(11):1764–74.
20. Kulon Progo District. Administration Map of Samigaluh: Peta Wilayah dan Citra Satelit. [cited 2017 Sep 26]. Available from: https://kulonprogokab.go.id/v3/portal/index.php/web/view_detil/12/peta-wilayah-dan-citra-satelit
Published
2019-09-03
How to Cite
1.
SULISTYAWATI S, FITRIANI I. Risk Factor and Cluster Analysis to Identify Malaria Hot Spot for Control Strategy in Samigaluh Sub-District, Kulon Progo, Indonesia. Iran J Public Health. 48(9):1647-1653.
Section
Original Article(s)