In: N K Tripathi, P K Joshi, and H Mehmood (eds). Managing Health Geospatially, New Delhi: TERI. pp. 240. [Proceedings of Fourth International Conference on Health GIS, New Delhi, India, 5-6 August 2011]
New Approaches to Disease Surveillance and Early Outbreak Detection in India Eva Pilot (1), Biranchi Jena (3), Thomas Krafft (1, 2), Alexandra Ziemann (1, 2), Ramana Rao (3), Boris Kauhl (1), R.R. Pardeshi (4), Shrutika Salvi (5) 1) Geomed Research Forschungsgesellschaft, Bad Honnef, Germany, 2) Department of International Health, Faculty of Health, Medicine and Life Sciences, Maastricht University, The Netherlands, 3) Emergency & Health System Research, GVK Emergency Management Research Institute (GVK EMRI), Hyderabad, India, 4) Pune Municipal Corporation (PMC), Pune, India, 5) Bharati Vidyapeeth Institute of Environment Education and Research (BVIEER), Pune, India Lead author and contact person: Eva Pilot /
[email protected] KEY WORDS: disease surveillance, early outbreak detection, syndromic surveillance, health geography ABSTRACT: The administrative responsibility for disease surveillance in India is shared between the central government and the respective state governments. While the central government is responsible for the central health programs the state government is in charge of the municipality based disease surveillance. This leads to a high variability in the comprehensiveness and efficacy of the surveillance systems. Though the Integrated Disease Surveillance Project (IDSP) has tried to overcome some of these problems very much still depends on regional initiatives. Two examples of new approaches to disease surveillance that both include GIS components will be presented and discussed. The leading emergency medical service provider in India, GVK Emergency Management and Research Institute, routinely collects electronic information on each medical emergency case in its dispatch centres. A pilot study assessed the potential of this data source for syndromic surveillance of infectious diseases in the state of Andhra Pradesh. Based on a context and stakeholder analysis the framework conditions for syndromic surveillance in India were explored and a pilot system was developed. Pune was one of the cities in India most affected during the H1N1 pandemic 2009. Though early outbreak detection is still weak at municipal level in India, the Pune Municipal Corporation established a systematic health screening and hospital based disease surveillance system for H1N1. Using also concepts of health geography spatial and statistical analyses were conducted. The experiences from Pune and Andhra Pradesh proved that surveillance systems can be established and maintained even in countries where in general the health data quality and monitoring is still in a state of development. 1.
GENERAL INTRODUCTION
emerging disease outbreaks like SARS 2003, Avian Influenza 2005 and most recently the H1N1
All countries need effective disease surveillance
pandemic 2009 called for the necessity for all
systems for early detection of outbreaks. Recent
176
In: N K Tripathi, P K Joshi, and H Mehmood (eds). Managing Health Geospatially, New Delhi: TERI. pp. 240. [Proceedings of Fourth International Conference on Health GIS, New Delhi, India, 5-6 August 2011] countries to have effective disease surveillance
Institute (GVK EMRI) currently operating in 11
systems for early detection of outbreaks and rapid
states of the country provides a chance for setting
reporting. Therefore the World Health Organization
up an emergency data based syndromic surveillance
revised their International Health Regulations to
and early warning system for disease outbreaks that
extend traditional infectious disease notification to
covers a large part of the population. With the
include surveillance for public health events even
further growth of GVK EMRI the service coverage
when the causative agent is not known (WHO
of the population will expand fostering the
2008). With globalization and economic growth
possibility of a nationwide syndromic surveillance
more travelers increase the risk of transmission of
approach.
diseases. Infectious diseases or any other kind of public health threats do not respect borders. An early detection of cases and a harmonized and rapid response is needed to effectively control the spread of diseases (Kant et al 2010). Vector and disease surveillance
are
costly
manpower
to
be
and
need
maintained.
extensive Syndromic
The
System
for
Early-warning
based
on
Emergency Data (SEED) was set up as a pilot project to systematically test and evaluate the applicability of EMS data in India for the use of syndromic surveillance. 2.2 Methodology
surveillance as pre-diagnostic surveillance approach based on existing data sources has proven useful for
The
project
group
comprises
the
principal
earlier detection of emerging and re-emerging
investigators GVK EMRI (India) and GEOMED
infectious disease surveillance and earlier outbreak
Research (Germany) and further experts from India
detection.
and Europe. The SEED project is closely linked to the European Commission co-funded project
The Indian Integrated Disease Surveillance Project
European Emergency Data-based System for
(IDSP) was launched by the Government of India
Information on, Detection and Analysis of Risks
(GoI) with assistance from the World Health
and
Organization in 2004. It is a state based passive
www.sidartha.eu), applying temporal modelling
surveillance project which is connected to the
(e.g., ARIMA) and spatial analysis. The project was
already
programs
divided into two phases, the conceptualization and
implemented and steered from the GoI. Under the
the test phase. During the conceptualization phase
IDSP in Andhra Pradesh (AP), village and sub
the focus was on the analysis of the specific context
centre level syndromic information are submitted
and user needs at the national, regional and local
on a weekly basis as part of the state surveillance
level in India and the identification of the best
unit. Still the reporting quality needs to be
suitable methodology for data collection and
enhanced with focus on frequency, completeness
analysis.
existing
disease
control
Threats
to
Health
(SIDARTHa;
and timeliness (Gaikwad et al, 2010). The data used for the syndromic surveillance 2.
EXAMPLE: SEED
2.1 Introduction
approach is generated automatically and captured at the dispatch center in Hyderabad. Using historical data, local spatial-temporal baselines and thresholds
The emergency medical services (EMS) system
for syndromes are calculated and periodically
from GVK Emergency Management and Research
updated within SEED. Routinely collected data are
177
In: N K Tripathi, P K Joshi, and H Mehmood (eds). Managing Health Geospatially, New Delhi: TERI. pp. 240. [Proceedings of Fourth International Conference on Health GIS, New Delhi, India, 5-6 August 2011] automatically transferred to the SEED application
Unspecific fever together with some other simple
in near real time and analyzed for temporal and
symptoms proved to be a very good indicator for
spatial aberrations. The system compares the actual
the detection of Dengue. The processing of data can
demand with the expected demand (forecasting) for
be done automatically and the surveillance results can be used on a near real time basis. The SEED project is the first attempt in India to systematically develop a real time data basis for disease surveillance. The SEED concept does not replace but supplement and enhance existing surveillance systems in India. The concept can be applied to other disease syndromes reflected by GVK EMRI data and can be transferred to other AP districts and Indian states served by GVK EMRI.
a certain time and region (baselines) and issues an
3
EXAMPLE:
H1N1
alert if a predefined threshold is exceeded. (Figure
SURVEILLANCE, PUNE
DISEASE
3.1 Introduction
1). Figure 1. SEED concept
The need for disease surveillance is increasing, especially in a globalizing world where boundaries
The test phase comprises the setup of the pilot syndromic surveillance system covering three districts in AP: Srikakulam, Anantapur and Guntur (identified and selected based on socio-economic characteristics like the levels of female literacy, urbanization, proportion of scheduled caste/tribes and infant mortality rate), the test run and evaluation of the system and the definition of a concept for the follow-up implementation and roll-
don’t exist anymore and the spread of infectious diseases is faster than ever before. One example is the H1N1 pandemic which spread in a short period of time in 2009 all around the world. Pune in the Indian state of Maharashtra was one of the cities in India most affected by the H1N1 pandemic. The Pune Municipal Corporation (PMC) established a systematic health screening and hospital based disease surveillance system for H1N1.
out to other regions. The SEED pilot syndromic surveillance system will continuously monitor the
The case of H1N1 highlighted the enormous
demand for emergency medical service focusing on
difficulties the Indian public health system is
fever cases (Jena et al, 2010). The system was
confronted with during large disease outbreaks
tested during a Dengue fever outbreak in 2010.
(Jacob John et al 2011). And it underlines the need for near real time disease surveillance for the
2.3
Results / Discussion
earliest detection of new emerging infectious
The pilot study showed the capability of emergency
diseases: The GoI has set up two institutions to help
data to be used for early detection and monitoring
during
of infectious disease outbreaks in India. The sharp
Communicable Disease (NICD) and the National
increase in the number of cases and the long lasting
Institute of Virology (NIV). But these two main
aberration from the predicted demand allowed for
laboratories work under different institutions – the
early detection of the Dengue outbreak in 2010.
Ministry of Health controls NICD whereas the NIV
outbreaks,
the
National
Institute
of
178
In: N K Tripathi, P K Joshi, and H Mehmood (eds). Managing Health Geospatially, New Delhi: TERI. pp. 240. [Proceedings of Fourth International Conference on Health GIS, New Delhi, India, 5-6 August 2011] is part of the Indian Council of Medical Research.
the city to get treatment for H1N1 in Pune. No
Delays in reporting are common and coordination is
spatial clustering of cases was observed (Figure 2).
wanted. Local governments tend to only inform state governments when the problems aggravate. There is no institutional mechanism for sharing samples
and
information
among
various
government and institutional laboratories (Sharma, 2003). 3.2 Methodology Figure 2. Critical H1N1 patients in Pune, India A comprehensive assessment was arranged to evaluate the administrative set up for health data
The PMC hospitals did not report a single death
collection used in Pune city during the pandemic of
case during the H1N1 pandemic. This is mainly the
H1N1. This assessment was conducted through
result of the severe cases bypassing the PMC
several expert interviews with key stakeholders
hospitals because of the absent of Intensive Care
such as the NIV, the Center for Disaster
Unit (ICU) units within the hospitals. The data
Management
Health
analysis also revealed the treatment seeking
Department of the PMC. Site visits where
behavior of the patients: most are getting screened
conducted during the assessment at the Health
at the PMC hospitals but receive treatment at the
Department
private hospitals.
and
which
Studies
is
and
responsible
the
for
data
collection, the hospitals as data provider and laboratories in Pune and the PMC quarantine ward.
For the first time the PMC established a comprehensive disease surveillance and reporting
Data collected daily from 15 private /trust hospitals,
system on a daily basis. The experience gained
44 PMC and the Sassoon (state) hospital was
during the H1N1 crisis proved the usefulness of
collated on a daily basis by the statistical officer at
establishing
the PMC. This data set contained information on
disease reporting and surveillance system.
a
permanent
municipality
based
suspected and confirmed H1N1 cases in Pune city (e.g., death toll, number of patients screened, number of patient given Tamiflu, number of patient tested positive for H1N1, number of patients admitted to the hospital and the severely ill patients residence address). The severely ill patient data from PMC were spatially analyzed by mapping all hospitals used for surveillance and the patients address from Pune and the surrounding areas by GIS.
A functioning nationally acknowledged laboratory network at the local level is needed to reduce the burden on the NIV which was the only WHO acknowledged laboratory for India during the H1N1 outbreak. NIV was processing a high amount of tests during the outbreak. One interviewed expert reported that the comprehensive surveillance set up by PMC led to a higher detection rate of H1N1 cases than in other regions of India. Further, the expert highlighted that the higher number of case
3.3 Results / Discussion
reports from Pune was connected also to the location of the NIV in Pune itself. Ironically, better
It was observed that a large amount of patients come from outside the administrative boundaries of
179
In: N K Tripathi, P K Joshi, and H Mehmood (eds). Managing Health Geospatially, New Delhi: TERI. pp. 240. [Proceedings of Fourth International Conference on Health GIS, New Delhi, India, 5-6 August 2011] surveillance resulted in the (wrong?) impression
Continuing challenge of infectious diseases in
that Pune was hit worse than other places.
India, the Lancet, 377, 252 – 269.
During the crisis the PMC gained a lot of
Jena B, Prassad M, Murthy S, Rao R, 2010,
experience on the use of an enhanced disease
Demand pattern of Medical Emergency Services
surveillance network based on hospital data. PMC
for Infectious Diseases in Andhra Pradesh – A Geo-
also learned about deficiencies in the health sector:
spatial Temporal Analysis of Fever cases, Indian
the lack of any ICU units at any PMC hospital led
Emergency Journal, 5, 9 – 12.
to a dependence on private or state hospitals. There was an increased risk of infection by transporting the patient from the quarantine ward to the next ICU also which could have been avoided or reduced by an integrated isolation hospital concept. 4
Kant L, Krishnan SK, 2010, The current status and communication technology in disease surveillance, India: a case study.BMC Public Health, 10, 11-15. May L, Chretien J, Pavlin J, 2009, Beyond traditional
CONCLUSION
Syndromic surveillance allows for early warning of outbreaks and therefore an earlier public health
surveillance:
applying
syndromic
surveillance to developing settings-opportunities and challenges. BMC Public Health, 9, 242 – 253.
response. It should be built on existing public
Sharma
health surveillance infrastructure and existing data.
investigation in India. The Lancet Infectious
“While syndromic surveillance is augmenting
Diseases, 3, 607.
D,
2003,
Delays
hinder
outbreak
traditional surveillance in the developed world, it also has the potential to improve timely detection of
World Health Organization, 2008, International
infectious
Health Regulation 2005, Geneva, 2nd edition.
disease
outbreaks
in
developing
countries, most of which lack access to strong public
health
infrastructure
and
specialized
laboratories” (May et al, 2009). The experiences from Pune and Andhra Pradesh support this finding: surveillance systems can be established and maintained even in countries where in general the health data quality and monitoring is still in a
ACKNOWLEDGEMENTS: The SEED Project is co-funded by the German Federal Ministry of Education and Research (BMBF) and the Indian Council for Medical Research (ICMR) in the Field of Public Health 2009-2011 (Grant Agreement No.: BMBF:IND08/005;ICMR:INDO/TRC/612/09IHD)
state of development. REFERENCES: Gaikwad A et al, 2010, Exploratory Study of Syndromic Surveillance data for stratification of symptoms and diseases in Andhra Pradesh, Indian Emergency Journal, 5, 9 – 12. Jacob John T, Dandona L, Sharma P, Kakkar M, 2011, India:Towards Universal Health Coverage 1
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