Web-based Geographic Information System to Support Dengue Hemorrhagic Fever Surveillance in Sleman District, Yogyakarta, Indonesia
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Web-based Geographic Information System to Support Dengue Hemorrhagic Fever Surveillance in Sleman District, Yogyakarta, Indonesia Hari Kusnanto Department of Public Health, Faculty of Medicine and Center for Health Informatics and Learning, Gadjah Mada University, Yogyakarta, Indonesia Website for this project: http://dhf.simkes.org Introduction Dengue hemorrhagic fever (DHF) is an infectious disease, caused by four antigenically related serotypes of dengue virus. Aedes aegypti mosquito is the main vector in dengue epidemics. Aedes albopictus and Aedes polynesienses may also be involved in virus transmission. Dengue is considered as the most important arthropod-borne viral disease in humans, with an estimated 50 to 100 million dengue infections and 200,000 to 500,000 cases of potentially fatal DHF annually as of 2000. The disease is endemic in major urban and periurban areas of Indonesia. Concerns related to DHF have been raised due to the increasing trend of disease incidence (Figure 1), with the case-fatality rate in Indonesia has been the highest (1.21%) among Southeast Asian countries.1 Figure 1. Number of reported cases of Dengue Fever and Dengue Hemorrhagic Fever in WHO South East Asia Region by countries, from 1985 to 2004 Source:WHO, http://w3.whosea.org/en/Section10/Section332_1101.htm (accessed June 11, 2006)
The expansion of geographic areas, now endemic for dengue infections2, and the extension of age-range among people suffering from DHF, previously known as a disease of children and now is also common in adults3, have been noted during the past years. The control of DHF epidemics remains a formidable challenge to governments, public health practitioners and communities. Dengue infection has never been under control in Southeast Asia, with the exception of Singapore, which has been implementing a three-pronged approach of source reduction, public health education and law enforcement. 4 In the Americas, epidemic dengue was prevented for several decades due to a vertically structured paramilitary approach of Ae. aegypti larval control.5 However, the mosquito reinfested most countries of the Americas in the 1970s, producing epidemic dengue fever, followed by the emergence of DHF as an important public health problem. During the 1980s, Ae aegypti control shifted from top-down to bottom-up approach, which emphasized ownership of mosquito control in the hands of households and neighborhoods.6 Dengue vector control strategy in Vietnam focused on the most productive containers, and used Mesocyclops spp as biological control agent. One of the key success factors of dengue control program in Vietnam was community involvement for clean-up campaigns, distribution of Mesocyclops, and reporting of suspected dengue cases to the communal health centre.7 Case studies in different dengue endemic areas suggested that policy-makers, scientists, and citizens need to exchange knowledge, develop shared vision about dengue-vector control, and build transdisciplinary cooperation for sustainable dengue control efforts.8 The objective of this study is to develop and evaluate the use of web-based information system, mainly intended to support dengue surveillance activities. Case definition, diagnosis and treatment, available on the web site, http://dhf.simkes.org may help clinicians and epidemiologists to identify cases, provide treatment, prevent dengue transmission and control DHF epidemics. In addition, spatial distribution of DHF cases, reported by participating hospitals, and temporal trend of DHF incidence, are presented on the web-site, so that public health practitioners, non-governmental organization and the community may participate in DHF prevention and control initiatives. Geographic information system has been applied in the estimation of dengue risk potential in Hawaii9 and Argentina.10 Combined with remote sensing technologies, GPS (global positioning system) and mapping technology is now commonly used by vector control specialists11. DHF surveillance system in Sleman District, has been in existence for at least three decades. Cases diagnosed in hospitals with DHF are reported to District Health Office through the Community Health Center (Puskesmas). The confirmation of the reported cases and field epidemiological investigation are carried out by the staff of the Community Health Center. The weekly report from community health centers to the District Health Office was useless for taking action, because it was usually more than one month late. Since the past five years, staff from the District Health Office has proactively visited hospitals, at least once a week, to obtain the most recent data on hospital admission of DHF patients. The aggregated data summaries are reported. However, the dissemination of the surveillance report has been limited, compared to the sheer number of those who need to make decisions concerning immediate action for controlling DHF epidemics, monitor trends in the burden of dengue illnesses, prioritize resource allocation, and other uses of information obtained from surveillance data.12 Internet has been used to
facilitate the dissemination of surveillance information. The Global Public Health Intelligence Network is an example of a secure, internet-based restricted access system for outbreak alert, dealing with news information about public health events of potential international significance.13 The development of data analyses to describe spatial pattern and trend of DHF incidence, routine reporting on the website, and the use of information available in the website to support DHF prevention and control are the focus of this study. Soft systems approach became the analytical tool to obtain better understanding about the development, implementation, maintenance and continuous improvement of DHF surveillance, using internet as a means for effective data integration, visualization, and dissemination. Methods This study is an action research, commonly understood as research practices for the production of new knowledge through the seeking of solutions or improvements to real, practical problem situation. 14 Action research is more than just a problem solving approach, because the researcher works in a conceptual framework to develop, test and refine theories about aspects of certain problem context.15 Soft systems methodology16,17, a special form of action research implemented in this study, consists of 7 stages (Figure 2). These stages are iterative, rather than sequential. 7. Take Action to 1. Describe Improve the Situation Problem Situation 6. Define Possible 2. Draw Rich Changes which Picture of 5. Compare Models with The Real World Systems Thinking about 3. Formulate The Real World 4. Build Root Conceptual Definitions of Models of The Relevant Figure 2. The seven steps of Soft Systems Methodology
In the first and second stages, the problem situation is expressed as “rich picture”, to represent pictorially all the relevant information and relationships, so that the researchers gain a better understanding of the situation. Stage three is a systems thinking exercise to formulate root definitions, constructed for the relevant human activity systems, defined in the previous stages. Root definitions should encompass emergent properties of the systems of purposeful human activities in question, considering the mnemonic CATWOE to define the emergent properties. CATWOE stands for: 1. Customer: people affected by the system, either beneficiaries or victims; 2. Actor: people participating in the system; 3. Transformation: what the system changes; 4. Worldview: different views from different individuals about the purposeful activities should be taken into account wherever possible; 5. Ownership: persons with authority to make decisions with regards to the future of the system; 6. Environment: every system can be seen as a part of a wider system. Following root definitions of the relevant systems, conceptual models are constructed to identify minimum required activities for the purposeful human activity systems, and represent the relationships among these activities. The conceptual models built in stage four are theoretical and derived only from the root definitions. In stages five and six, the conceptual models are compared with the real world to highlight possible changes which can be implemented (in stage seven) to improve the problem situation. Public health staff in Sleman District Health Office (practitioners), managers of hospitals participating in DHF surveillance, clinicians, and lecturers of public health and tropical medicine (scientists) and community groups, involved in vector control activities, participated in the seminars, workshops and discussions, organized to monitor the progress of the study. Participation of these various stakeholders in DHF control are needed to compare the conceptual models and the real world practices of relevant purposeful human activities, to identify desirable and feasible changes to the existing surveillance system, and to build commitment to sustainable DHF prevention and control program in the community. All software used in this study are open source, such as Nvu version 1 (Linspire Inc.) for web design, Epiinfo and Epimap developed by CDC, Atlanta, USA, for data analyses, and GeoDa 0.9 (Beta) developed by Luc Anselin, University of Illinois for spatial data exploration and analysis. Results Research participants, who identified and expressed problematic situations, showed that dengue surveillance system in Sleman District had been fragmented and ineffective. Appropriate action to control the transmission of dengue virus could not be made due to the lack of relevant and timely data. Community health centers were not well-equipped to make diagnosis of DHF, however, they had to do field investigation of DHF cases, provided counseling and health education to the community, and led vector control initiatives in their catchment areas. Meanwhile, the hospitals which admitted cases with DHF did not send reports in time, so the increase of DHF cases at an epidemic proportion was often undetected.
Dengue transmission in the community does not occur randomly. The time-series plot based on discharge data from Dr. Sardjito Hospital (1995-2002) suggests that the highest incidence of DHF commonly occurred during the periods of April-June and November-January (Figure 3). The spatial distribution of cases was mainly concentrated in 7 subdistricts (number of cases greater than 25 persons from 1995 to 2002). Data from Dr. Sardjito Hospital were in accordance with those obtained from other hospitals to which patients from Sleman District were admitted with DHF. 25.00 20.00 15.00 cases 10.00 5.00 0.00 J M M J Se N J M M J Se N J M M J Se N J M M A O D F A J A O D F A J A O D F A J A O D F A J A N a ar ay ul pt ov a ar ay ul pt ov a ar ay ul pt ov a ar ay u ct ec e pr u u ct ec e pr u u ct ec e pr u u ct ec e pr u u ov n ch 95 y e e n ch 96 y e e n ch 97 y e e n ch 98 g ob e br il n g ob e br il n g ob e br il n g ob e br il n g e u 95 9 m m u 96 9 m m u 97 9 m m u 98 u er m u 9 e u er m u 0 e u er m u 0 e u er m u 0 e u m a 5 be be a 6 be be a 7 be be a st 98 be ar 9 9 st 99 be ar 0 0 st 00 be ar 1 0 st 01 be ar 3 0 st be r r9 r9 r r9 r9 r r9 r9 r 9 r9 y 9 9 r9 y 0 0 r0 y 1 0 r0 y 2 0 r0 y 5 5 y 6 6 y 7 7 y 8 8 9 9 9 0 0 0 0 1 1 0 2 2 9 9 9 9 9 0 1 2 5 6 7 8 month Figure 3. The number of DHF cases admitted to Dr. Sardjito Hospital from 1995 to 2002 The incidence of DHF in Sleman District reported to Sleman District Health Office prior to the beginning of the study (January 2005) showed significant increase from 552 cases in 2003 to 732 cases in 2004. It was noted that in 2004, not only did the number of DHF cases increase 32.6%, but the cases were also spread over a wider area in the district. In 2003, five or more DHF cases were reported only in 26.7% of all villages of Sleman District, while in 2004, they were reported in 48.8 %, and then decreased to 18.6% in 2005 (Figure 4). Although the general patterns of DHF spatial and temporal distribution in Sleman District were known, the public health practitioners and the community failed to make effective action to prevent DHF epidemics. The usual response to significant increase of DHF cases was fogging, to eliminate adult mosquitoes, usually with limited success and not sustainable due to its cost.
2003 Figure 4. Distribution of DHF cases in villages of Sleman District in 2003, 2004 and 2005 2004 2005
Reflections on the relevant human purposeful activities in dengue control indicated an important root definition of DHF surveillance system in Sleman District: “hospitals provide timely data of DF/DHF cases to Sleman District Health Office, and primary health centers provide timely data of vector density, so that the risk of dengue transmission can be mitigated, dengue infection can be prevented, and cases of DHF can be appropriately managed, involving health sector leadership and community participation” A simple conceptual model derived from the root definition is described in Figure 5. The model is than compared to the feasible and preferred activities in the real world. Timely reports of DF/DHF Timely reports of vector cases by hospitals densities by community health centers Field epidemiological investigation and mapping of dengue cases and vector densities with GPS by Community Health Centers and District Health Office Data analyses and reports with graphs and Lower morbidity maps (GIS) by District (complications) and lower Health Office mortality? Web publishing by web administrators Decrease incidence of DF/DHF Input Performance Monitoring Figure 5. Conceptual model of dengue surveillance system in Sleman District
The ideal activities specified in the conceptual model were only partially achieved in the real world. Hospitals could not send report timely, so that the staff from District Health Office proactively collected data, which had been aggregated by each hospital, every week. The weekly incidence of DHF cases showed that after 10 months of relatively low incidence of DHF in 2005, public health interventions failed to curb the dramatic rise of DHF cases in November 2005 until March 2006 (Figure 6). Lessons learned from this failure is that when the number of reported DHF reaches 10 cases (“rule of ten”) in a week, it is a danger sign for an imminent epidemics. The spatial distribution of DHF cases at the beginning of the increased number of cases in November 2005 (weeks 45, 46 and 47 of 2005) and the peak of the epidemics (week 1 of 2006) suggests that it was not the clustering of cases which may predict an epidemics, but the wider the spatial distribution of DHF cases the higher the chance for a forthcoming epidemics (Figure 7) . 60 50 40 cases 30 20 10 0 1234 56 789 11111111122222222 3333333333444444444 555555555566666666667777 0 134 5 6 7 8 9 0 12 34 5 6 70 12 34 5 6 7 8 9 0 12 4 5 6 7 8 9 0 12 34 5 6 7 8 9 0 12 34 5 6 7 8 9 0 12 3 week Figure 6. Weekly-report of the number of DHF cases from January 2005 to early April 2006
Monitoring of larvae in households was routinely carried out by technicians of several Community Health Centers. The data on vector density was not analyzed and used to support decision making, such as for public health education and clean-up campaign. Figure 7. The spatial distribution of DHF at the beginning (week 45, 46 and 47 of November 2005) and at the peak of the epidemics (first week of January 2006) Discussion The soft system methodology adopted in this study has provided learning opportunities18, how surveillance data can be applied to improve DHF prevention and control. The data used in the surveillance system was limited to the reports of DHF cases by hospitals, participating in the surveillance activities. This system is subject to serious limitations, because of dealing only with the tips of the iceberg. A prospective study in the city of Salvador found that a silent epidemic of dengue infections was undetected by the official surveillance system.19 Spatial and temporal analyses of data, which were presented also on the internet, had shown that DHF epidemics are looming, however, the actions undertaken were like fire-fighting, where efforts seemed too little and too late. Effective laboratory-based
surveillance was suggested to improve sensitivity of detecting an imminent DHF epidemics. 20 Many public health surveillance systems in developing countries face shortage of budget, so that they can’t afford laboratory infrastructure for surveillance purposes. In this study, the dissemination of weekly trend and spatial distribution of DHF cases through the internet has created an alert system, which could be easily accessed by hospital managers, clinicians, public health practitioners and the community. Additional information on vector population densities could improve the targeting of vector control,21 and therefore could prevent dengue transmission in the community. Monitoring dengue vector populations through larval surveillance has been carried out by entomological technicians in Community Health Centers. The difficulties confronted by these field workers were the reluctance of many households to let them examine the water storage inside the houses. The resistence to vector surveillance indicated the weakness of community ownership of dengue control. A successful campaign to combat Aedes aegypti in the city of Havana relied on vigorous activities to identify and manage suspected human cases, while simuntaneously identifying and eliminating actual and potential breeding sites.22 The use of ovitraps carefully placed in the areas where dengue transmissions likely occur may produce important data related to vector population densities23, and at the same time could serve as an educational tool to enhance community participation in vector surveillance and control. Conclusion The web-based DHF surveillance system in Sleman District has generated shared vision among key stakeholders (clinicians, hospital managers, public health practitioners, and some community leaders) about the importance of holding dengue transmission down to approaching zero level in the community, although this vision needs to be sustained through continuous communication and learning. This study also suggests that although the beginning of dengue epidemics could be detected, public health interventions failed to curb the outbreak, because silent intensive transmissions of dengue virus in the community were undetected by the surveillance system. It is therefore suggested that the web-based surveillance system should involve vector population densities spatial mapping and trend. The application of SMS (short message service) gateway by entomological technicians, using mobile phone may be a suitable technology to report vector density indexes in the community. Acknowledgement This research is funded by Asian Media Information and Communication Centre, Grant No. 0402A5_L48.
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