Penggunaan data surveilans gabungan dan meteorologi untuk memprediksi demam berdarah dengue di Yogyakarta
Dedik Sulistiawan(1), Lutfan Lazuardi(2*)
(1) Departemen Kebijakan dan Manajemen Kesehatan Fakultas Kedokteran Universitas Gadjah Mada
(2) Departemen Kebijakan dan Manajemen Kesehatan Fakultas Kedokteran Universitas Gadjah Mada
(*) Corresponding Author
Abstract
Use of a combined surveillance and meteorological data for predicting dengue hemorrhagic fever in Yogyakarta
Purpose
This study aimed to predict the incidence of dengue hemorrhagic fever using meteorological data such as rainfall, rainy days, air temperature, humidity, and dengue hemorrhagic fever surveillance data month by month in Northern Yogyakarta Municipality (Climatic Zone 138) through 2010-2016.
Method
This research was a descriptive study with a predictive design with temporal approach. This research processed secondary data of DHF incidence from Yogyakarta Municipality Health Office and climate variables from Meteorology Climatology and Geophysics Agency (BMKG) Yogyakarta from 2010 to 2016. Data were analyzed with univariate tests and presented in frequency distribution, bivariate analysis was performed using Pearson/ Spearman correlation tests, and multivariate analysis used Poisson regression, negative binomial regression, and generalized poisson regression tests.
Results
DHF incidence in Northern Yogyakarta Municipality (Climatic Zone 138) was associated with meteorological factors in the same month up to 3 months earlier. Predictors of DHF case were dengue incidence of previous month, rainfall 2 months earlier, current temperature, and relative humidity of the previous month.Conclusion
The best prediction model of DHF incidence in Northern Yogyakarta Municipality (Climatic Zone 138) was a combination of surveillance and meteorological data. It is necessary to develop an awareness system of DHF incidence with meteorological database and surveillance in order to control the incidence of DHF in Yogyakarta Municipality.
Keywords
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- Adde A, Roucou P, Mangeas M, Ardillon V, Desenclos JC, Rousset D, Girod R, Briolant S, Quenel P, Flamand C. Predicting dengue fever outbreaks in French Guiana using climate Indicators. PLoS neglected tropical diseases. 2016 Apr 29;10(4):e0004681.
- World Health Organization. Dengue and Severe Dengue. 2016.
- Karyanti MR, Uiterwaal CS, Kusriastuti R, Hadinegoro SR, Rovers MM, Heesterbeek H, Hoes AW, Bruijning-Verhagen P. The changing incidence of dengue haemorrhagic fever in Indonesia: a 45-year registry-based analysis. BMC infectious diseases. 2014 Jul 26;14(1):412.
- Kementerian Kesehatan. Profil Kesehatan Indonesia 2015. Jakarta: 2016..
- Dinas Kesehatan Provinsi Daerah Istimewa Yogyakarta. Profil Kesehatan Daerah Istimewa Yogyakarta 2015. Yogyakarta: 2016.
- E Setiati T, FP Wagenaar J, D de Kruif M, TA Mairuhu A, CM van Grop E, Soemantri A. Changing epidemiology of dengue haemorrhagic fever in Indonesia.
- Fullerton LM, Dickin SK, Schuster-Wallace CJ. Mapping Global Vulnerability to Dengue using the Water Associated Disease Index. United Nations University. 2014.
- Racloz V, Ramsey R, Tong S, Hu W. Surveillance of dengue fever virus: a review of epidemiological models and early warning systems. PLoS neglected tropical diseases. 2012 May 22;6(5):e1648.
- Ariati J, Anwar A. Model Prediksi Kejadian Demam Berdarah Dengue (DBD) Berdasarkan Faktor Iklim di Kota Bogor, Jawa Barat. Buletin Penelitian Kesehatan. 2014;42(4 Des):249-56.
- Bhatt S, Gething PW, Brady OJ, Messina JP, Farlow AW, Moyes CL, Drake JM, Brownstein JS, Hoen AG, Sankoh O, Myers MF. The global distribution and burden of dengue. Nature. 2013 Apr 25;496(7446):504-7.
- Hii YL, Zhu H, Ng N, Ng LC, Rocklöv J. Forecast of dengue incidence using temperature and rainfall. PLoS neglected tropical diseases. 2012 Nov 29;6(11):e1908.
- Sang S, Gu S, Bi P, Yang W, Yang Z, Xu L, Yang J, Liu X, Jiang T, Wu H, Chu C. Predicting unprecedented dengue outbreak using imported cases and climatic factors in Guangzhou, 2014. PLoS neglected tropical diseases. 2015 May 28;9(5):e0003808.
- World Health Organization, Special Programme for Research, Training in Tropical Diseases, World Health Organization. Department of Control of Neglected Tropical Diseases, World Health Organization. Epidemic, Pandemic Alert. Dengue: guidelines for diagnosis, treatment, prevention and control. World Health Organization; 2009.
- Naish S, Dale P, Mackenzie JS, McBride J, Mengersen K, Tong S. Climate change and dengue: a critical and systematic review of quantitative modelling approaches. BMC infectious diseases. 2014 Mar 26;14(1):167.
- Badan Meteorologi Klimatologi dan Geofisika. Prakiraan Musim Kemarau 2016 DI Yogyakarta. Yogyakarta: Stasiun Geofisika Kelas I Yogyakarta; 2016.
- Achmadi UF. Manajemen penyakit berbasis wilayah. Kesmas: National Public Health Journal. 2009 Feb 1;3(4):147-53.
- Ramadona AL, Lazuardi L, Hii YL, Holmner Å, Kusnanto H, Rocklöv J. Prediction of Dengue Outbreaks Based on Disease Surveillance and Meteorological Data. PloS one. 2016 Mar 31;11(3):e0152688.
- Ismail N, Jemain AA. Handling overdispersion with negative binomial and generalized Poisson regression models. InCasualty Actuarial Society Forum 2007 (pp. 103-158). Citeseer.
- Yusuf OB, Ugalahi LO. On the Performance of the Poisson, Negative Binomial and Generalized Poisson Regression Models in the Prediction of Antenatal Care Visits in Nigeria. American Journal of Mathematics and Statistics. 2015;5(3):128-36.
- Indriani C, Fuad A, Kusnanto H. Pola Spasial-Temporal Epidemi Demam Chikungunya dan Demam Berdarah Dengue di Kota Yogyakarta Tahun 2008. Berita Kedokteran Masyarakat (BKM). 2011;27(1):41.
DOI: https://doi.org/10.22146/bkm.26250
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