Dengue Hemorrhagic Fever (DHF): Vulnerability Model Based on Population and Climate Factors in Bengkulu City
Abstract
Objective: The causes for the increasing number of dengue cases are complex and multifactorial. The approach taken must combine influencing factors, and comprehensive prevention strategy is needed that includes all the components of factors that influence dengue disease to predict the incidence of the disease. This research aimed to analyze the relationship between population and climate components including population density, population density <15 years old, sanitation, temperature, humidity and rainfall, on the incidence rate of Dengue Hemorrhagic Fever (DHF).
Material and Methods: This study used a cross-sectional design, with the research sample being all sub-districts in Bengkulu City, Indonesia (67 sub-districts). Data analysis was conducted using structural equation modeling to create a dengue modeling based on population and climate factors, through the SmartPLS application.
Results: Population and climate factors had a significant relationship with the incidence rate of dengue, with p-values of 0.018 and 0.000, respectively. Population and climate factors had a percentage effect on the incidence rate of dengue (36.9%).
Conclusion: Population and climate factors had an influence of 36.9% on the incidence of dengue. There were many factors affecting the incidence of dengue, so a more comprehensive modeling of the various influencing factors is needed. Dengue modeling is crucial as an early warning system for the early prevention of dengue outbreaks, so that the control strategies implemented can be more effective.
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Halstead SB. Dengue. Lancet 2007;370:1644–52.
CDC. Surveillance and control of aedes aegypti and aedes albopictus in the united states. Surveill Control 2017;1–16.
WHO. Dengue and severe dengue [homepage on the Internet]. Geneva: WHO; 2020 [cited 2020 Dec 19]. Available from: https://www.who.int/news-room/fact-sheets/detail/dengue-and-severe-dengue
European Centre for Disease Prevention and Control. Dengue worldwide overview [homepage on the Internet]. Solna: Surveilance and disease data; 2022 [cited 2022 Jul 7]. Available from: https://www.ecdc.europa.eu/en/dengue-monthly
Ministry of Health of the Republic of Indonesia. National Strategy for Dengue Control 2021-2025. Jakarta: Ministry of Health of the Republic of Indonesia; 2021;p.1–94.
Ministry of Health of the Republic of Indonesia. DHF outbreak areas in 11 City Regencies. Jakarta: Ministry of Health of the Republic of Indonesia; 2016;p.1-2
Bengkulu Provincial Health Office. Bengkulu Provincial Health Office Profile 2016. Bengkulu: Bengkulu Provincial Health Office; 2017;p.234-40.
Ministry of Health of the Republic of Indonesia. Indonesia Health Profile 2018. Jakarta: Ministry of Health of the Republic of Indonesia; 2019;p.107–8.
Bengkulu Provincial Health Office. Bengkulu Provincial Health Office Profile 2018. Bengkulu: Bengkulu Provincial Health Office; 2019;p.250-58.
Mardihusodo SJ, Satoto TBT, Garcia A, Fock DA. Pupal/demographic and adult aspiration survey of residental and public sites in yogyakarta, indonesia to inform development of targeted sources control strategy for dengue. Dengue Bull 2011;35:141–51.
WHO. Review of entomological sampling methods and indicators for esearch and training in tropical diseases. Ganeva: WHO Library Cataloguing-in-Publication Data; 2003;1-5.
Trapsilowati W, Mardihusodo SJ, Prabandari YS, Mardikanto T. Community participation in dengue hemorrhagic fever vector control in semarang city, central java province. Vektora J Vektor dan Reserv Penyakit 2015;7:15–22.
Guha-Sapir D, Schimmer B. Dengue fever: New paradigms for a changing epidemiology. Emerg Themes Epidemiol 2005;2:1–10.
Ministry of health of the republic of indonesia. Implementation of 3M plus mosquito nest eradication with the jumantik one house one movement. Jakarta: Ministry of Health of the Republic of Indonesia; 2016.
Kovats S, Ebi KL, Menne B. Methods of assessing human health vulnerability and public health adaptation to climate change. Ganeva; WHO Regional Office for South-East Asia; 2003;p.16-28.
Dari S, Nuddin A, Rusman ADP. Profile of residential density and population mobility on the prevalence of dengue hemorrhagic fever in the work area of the cempae health center, parepare city. J Ilm Mns Dan Kesehat 2020;3:155–62.
Dickin SK, Schuster-Wallace CJ, Elliott SJ. Developing a vulnerability mapping methodology: applying the water-associated disease index to dengue in malaysia. PLoS One 2013;8:e63584.
WHO. Global Strategy for Dengue Prevention and Control 2012–2020. Geneva: WHO; 2011;p.1–34.
Fullerton L, Dickin S, Schuster-Wallace CJ. Mapping global vulnerability to dengue using the water associated disease index waste to wealth view project. Hamilton: United Nations University; 2012;p.1–40.
WHO. Global Strategy for dengue prevention and control 2012–2020. Ganeva: WHO; 2012;p.1–34.
Hamid RS, Anwar SM. Variant based structural equation modeling (sem). basic concepts and applications of the smart pls 3.2.8 program in business research. 1st ed. abiratno, Nurdiyanti S, Raksanagara AD, editors. Jakarta: PT. Inkubator Penulis Indonesia; 2019;p.1–175.
Ghozali I. Structural equation modeling alternative method with partial least squares (PLS). 4th ed. Semarang: Badan Penerbit Universitas Diponegoro; 2014;p.24-39.
Haryono S. SEM methods for management research, AMOS, LISREL, PLS. 1st ed. Jakarta Timur: Penerbit Luxima Metro Media; 2017;p.366–434.
Dinata A, Wibawa Dhewantara P. Characteristics of physics, biology, and social environment in dhf endemic of banjar city in 2011. J Ekol Kesehat 2012;11:315–26.
Triana D, Rosana E, Anggraini R. Knowledge and attitudes towards behavior in malaria management in sukarami village, bengkulu city. Unnes J Public Heal 2017;6:107.
Denis R. T Vulnerability Level of Dengue Hemorrhagic Fever Based on Disease Vulnerability Index in Kepahiang District, Bengkulu Province. J Vokasi Kesehat 2023;2:23–32.
Priesley F, Reza M, Rusdji SR. Correlation between Mosquito Nest Eradication Behavior by Closing, Draining and Recycling Plus (PSN M Plus) to Dengue Hemorrhagic Fever (DHF) Incidence in Andalas Village. J Kesehat Andalas 2018;7:124–30.
Kurniawati RD, Ekawati E. 3M Plus Analysis as an Effort to Prevent Dengue Hemorrhagic Fever Transmission in the Margaasih Community Health Center, Bandung Regency. Vektora J Vektor dan Reserv Penyakit 2020;12:1–10.
Dompas BE, Sumampouw OJ, Umboh JML. Are the physical environmental factors of the house associated with the incidence of dengue hemorrhagic fever. Indones J Public Heal Community Med 2020;1:11–5.
Hartati E, Anas M, Djalilah GN, Paramita AL. Characteristics of patients with dengue hemorrhagic fever and its relationship with the prevalence of dengue shock syndrome in children. Gac Med Caracas 2021;129:S350–6.
Khairunnisa U, Wahyuningsih NE, Hapsari H. Density of aedes sp. Mosquito larvae (house index) as an indicator of dengue hemorrhagic fever vector surveillance in semarang city. J Kesehat Masy 2017;5:906–10.
Triana D, Gunasari LFV, Helmiyetti H, Martini M, Suwondo A, Sofro MAU, et al. Endemicity of dengue with density figure and maya index in bengkulu city, indonesia. Open Access Maced J Med Sci 2021;9:1504–11.
Cong NT, Nga PTT, Duoc V. Mapping vulnerability to dengue in mekong delta region, vietnam from 2002 to 2014 using a water-associated disease index approach. [monograph on the Internet] Kobe: Asia-Pacific Network for Global Change Research; 2017 [cited 2022 Jul 7]. Available from: https://www.apn-gcr.org/publication/mapping-vulnerability-to-dengue-in-mekong-delta-region-vietnam-from-2002-to-2014-using-geospatial-data-by-water-associated-disease-index-approach/
Sekarrini CE. Mapping of dengue hemorrhagic fever vulnerability based on geographic information. Sumatra J Disaster, Geogr Geogr Educ 2020;4:63–7.
Hikmawati I, Sholikhah U, Wahjono H, Martini M. Community vulnerability map in endemic areas of dengue hemorrhagic fever (DHF), Banyumas, Indonesia. Iran J Public Health 2020;49:472–8.
Pham NTT, Nguyen CT, Vu DT, Nakamura K. Mapping of dengue vulnerability in the mekong delta region of vietnam using a water-associated disease index and remote sensing approach. APN Sci Bull 2018;8:9–15.
Bengkulu City Population And Civil Registry Office (DUKCAPIL). Bengkulu city population data for 202. Bengkulu: DUKCAPIL. 2022; p.1-8.
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