Web-based Geographic Information System to Support Dengue Hemorrhagic Fever Surveillance

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Web-based Geographic Information System to SupportDengue Hemorrhagic Fever Surveillance in SlemanDistrict, Yogyakarta, Indonesia

Hari KusnantoDepartment 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

IntroductionDengue hemorrhagic fever (DHF) is an infectious disease, caused by four antigenicallyrelated serotypes of dengue virus. Aedes aegyptimosquito 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 500,000cases 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 Indonesiahas 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)
http://dhf.simkes.org/http://w3.whosea.org/en/Section10/Section332_1101.htmhttp://w3.whosea.org/en/Section10/Section332_1101.htmhttp://dhf.simkes.org/


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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 diseaseof children and now is also common in adults

3, 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 theexception 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 structuredparamilitary approach of Ae. aegypti larval control.

5However, 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, Aeaegypti 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 usedMesocyclops spp as biological control agent. One of the key success

factors of dengue control program in Vietnam was community involvement for clean-upcampaigns, distribution of Mesocyclops, and reporting of suspected dengue cases to the

communal health centre.7Case 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.orgmayhelp 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 leastthree 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 theCommunity 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 proactivelyvisited 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 DHFepidemics, 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
http://dhf.simkes.org/http://dhf.simkes.org/


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facilitate the dissemination of surveillance information. The Global Public Health

Intelligence Network is an example of a secure, internet-based restricted access systemfor 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 thewebsite 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, usinginternet as a means for effective data integration, visualization, and dissemination.

MethodsThis 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 thansequential.

The Real World

Systems Thinking about

The Real World

1. Descr i beSi t uat i on

2. Dr aw Ri chPi ct ur e of

3. For mul at e

RootDef i ni t i ons of

4. Bui l d

Concept ualModel s of The

5. Compar eModel s wi t h

6.

7. Take Act i on t oI mpr ove t he

Def i ne

Possi bl eChanges whi ch

Figure 2. The seven steps of Soft Systems Methodology


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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 theresearchers 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 mnemonicCATWOE 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, andrepresent 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, theconceptual 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 ofhospitals 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 theprogress of the study. Participation of these various stakeholders in DHF control are

needed to compare the conceptual models and the real world practices of relevantpurposeful 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.

ResultsResearch participants, who identified and expressed problematic situations, showed thatdengue 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 tomake 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 proportionwas often undetected.


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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 thehighest 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. SardjitoHospital were in accordance with those obtained from other hospitals to which patients

from Sleman District were admitted with DHF.

November02

August02

June02

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cases

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 increasefrom 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 villagesof 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 responseto significant increase of DHF cases was fogging, to eliminate adult mosquitoes, usually

with limited success and not sustainable due to its cost.


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2003

2005

2004

Figure 4.

Distribution of DHFcases in villages of

Sleman District in2003, 2004 and 2005


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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 canbe 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

cases by hospitals

Timely reports of vector

densities by communityhealth centers

Field epidemiologicalinvestigation and mapping of

dengue cases and vectordensities with GPS by

Community Health Centersand District Health Office

Data analyses andreports with graphs andmaps (GIS) by District

Health Office

Web publishing by web

administrators

Lower morbidity(complications) and lower

mortality?

Decrease incidenceof DF/DHF

Input

PerformanceMonitoring

Figure 5. Conceptual model of dengue surveillance system in Sleman District


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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 DistrictHealth 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). Lessonslearned 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) .

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Figure 6. Weekly-report of the number of DHF cases from January 2005 to early April

2006


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Monitoring of larvae in households was routinely carried out by technicians of

several Community Health Centers. The data on vector density was not analyzed andused 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)

DiscussionThe soft system methodology adopted in this study has provided learning opportunities

18,

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


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surveillance was suggested to improve sensitivity of detecting an imminent DHF

epidemics.20

Many public health surveillance systems in developing countries faceshortage of budget, so that they cant 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 byhospital 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 themexamine 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 aegyptiin 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 inthe 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 toenhance community participation in vector surveillance and control.

ConclusionThe 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 toapproaching zero level in the community, although this vision needs to be sustained

through continuous communication and learning. This study also suggests that althoughthe 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 spatialmapping 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.

AcknowledgementThis research is funded by Asian Media Information and Communication Centre, GrantNo. 0402A5_L48.


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References

1World Health Organization, Dengue/DHF, Hhttp://w3.whosea.org/en/Section10/Section332_1102.htmH

(accessed June 11, 2006)

2Mairuhu ATA, Wagenaar J, Brandjes DPM & van Gorp ECM (2004) Dengue: an arthropod-borne

disease of global importance.European Journal of Clinical Microbiology and Infectious Diseases23, 425-433.

3Porter KR, Becket CG, Kosasih H, Alisjahbana B, Rudiman PIR, Widjaja S, Listiyaningsih E, Maroef

CN, McArdle JL, Parwati I, Sudjana P, Jusuf H, Yuwono D & Wuryadi S (2005) Epidemiology of dengue

and dengue hemorrhagic fever in a cohort of adults living in Bandung, West Java, Indonesia.American

Journal of Tropical Medicine and Hygiene72, 60-66.

4Ai-leen GT & Song RJ (2000) The use of GIS in ovitrap monitoring for dengue control in Singapore.Dengue Bulletin24, 110-116.

5Guzman MG & Kouri G (2003) Dengue and dengue hemorrhagic fever in the Americas: lessons and

challenges.Journal of Clinical Virology27, 1-13.

6Gubler DJ (2005) The emergence of eidemic dengue fever/dengue hemorrhagic fever in the Americas: a

case of failed public health policy. Pan American Journal of Public Health17, 221-224.

7Kay B & Nam VS (2005) New strategy against Aedes aegypti in Vietnam.Lancet365:613-617.

8Spiegel J, Bennet S, Hattersley L, Hayden MH, Kittayapong P, Nalim S, Wang DNC, Zielinski-Gutierrez

E & Gubler D (2005) Barriers and bridges to prevention and control of dengue: the need for a social-ecological approach.EcoHeatlh2, 273-290.

9Kolivras KN (2006) Mosquito habitat and dengue risk potential in Hawaii: a conceptual framework andGIS application. The Professional Geographer58, 139-154.

10Carbajo AE, Schweigmann N, Curto SI, de Garin A & Bejaran R (2001) Dengue transmission risk maps

of Argentina. Tropical Medicine and International Health6, 170-183.

11Thomson MC & Connor SJ (2000) Environmental information systems for the control of arthropod

vectors of diseases.Medical and Veterinary Entomology14, 227-244.

12Center for Disease Control and Prevention (2001) Updated guidelines for evaluating public health

surveillance systems: recommendations from the guidelines working group.MMWR50. RR-13.

13Mykhalovskiy E & Weir L (2006) The global public health intelligence network and early warningoubreak detection. A Canadian Contribution to Global Public Health. Canadian Journal of Public Health

97, 42-44.

14Elden M & Chisholm RF (1993) Emerging varieties of action research: introduction to the special issue.

Human Relations46, 121-142.

15McKay J & Marshall P (2001) The dual imperatives of action resesarch. Information Technology &

People14, 4659.

16Checkland PB (1981) Systems thinking, systems practice, Wiley, Chichester.

17Checkland PB & Scholes J (1990) Soft systems methodology in action, Wiley, Chichester.


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18Pala O, Veni JAM & van Mullekom T (2003) Validity in SSM: neglected areas.Journal of the

Operational Research Society54, 706-712.

19Teixe MG, Barreto ML, Costa MC, Ferreira LDA, Vasconcelos PFC & Cairncross S (2002) Dynamics

of dengue virus circulation: a silent epidemic in a complex urban area. Tropical Medicine and International

Health7, 757-762.20Cummings DA, Irizarry RA, Huang NE, Endy TP, Nisalak A, Ungchusak K & Burke DS (2004)Travelling waves in the occurrence of dengue haemorrhagic fever in Thailand.Nature427, 344-347.

21Chadee DD, Williams FLR & Kitron UD (2005) Impact of vector control on a dengue fever outbreak in

Trinidad, West Indies, in 1998. Tropical Medicine and International Health 10, 747-754.22Spiegel J, Yassi A, Tate R (2002) Dengue in Cuba: mobilisation against Aedes aegypti.Lancet InfectiousDiseases2, 207-208.

23Ai-leen GT & Song RJ (2000) ibid.

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Web-based Geographic Information System to Support Dengue Hemorrhagic Fever Surveillance