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 tofacilitate 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 MethodologyIn 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
2005Reflections 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 DistrictThe 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
2006Monitoring 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-basedsurveillance 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.References
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