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Linking GIS with Health Information System and Lymphatic Filariasis Elimination campaign as a case study

Raju, KHK. and Sabesan, S.
Vector Control Research Centre,
Puducherry – 605 006, India.

Updating information is important in all fields, but it is vital for health in case of beneficiaries as well as providers / planners. The health issues are always related to space and time, and therefore it is ideal to link Geographical Information Systems (GIS) with Health Information Systems (HIS). Since a global programme for elimination of lymphatic filariasis has been launched recently, the present article highlights this as a case study.

Like any other field, Health Sector also started using Information Technology to disseminate data and health knowledge. Usage of new tools and techniques with Geographical Information System (GIS) in health sector has become an important part of health management, policy making and the development of Health Information Systems (HIS).

In the past, information systems for disease control were designed for use centrally with little or no feedback to the source of information and with no impact on local activities. The information systems are a vital element for strengthening the national and local capacities for assessing the situations, for selecting appropriate control measures, and for adapting activities to changes in the situation. It was seen that information systems should therefore be reoriented to deal with specific disease and decentralized in such a way that information available is to be used by those who need it [1].

As every disease problem or health event requires a different response and policy decision, information should reflect a realistic assessment of the situations at local, national and global levels. This can be done with best available data and taking into consideration disease transmission dynamics, demographics, availability of and accessibility to existing health and social services as well as other geographic and environmental features. And this up to date information should be accessible to decision-makers at all levels of the public health system to properly plan, manage and monitor any public health programme.

GIS in Health Systems
GIS contains two closely integrated databases: one spatial (locational) and the other attribute (statistical) [2]. Blend of these two databases turns GIS into a more powerful analytical tool. GIS provide ideal platforms for the convergence of disease-specific information and their analyses in relation to population settlements, surrounding social and health services and the natural environment. They are highly suitable for analysing epidemiological data, revealing trends and interrelationships that would be more difficult to discover in tabular format. Moreover GIS allows policy makers to easily visualize problems in relation to existing health and social services and the natural environment and so more effectively target resources.

It is evident that many questions concerning the provision of health care are related to space. Disease status is not alike in any place. Health problems vary in space and time so do the needs of the people. Where should control activities start and when should start and what services should they offer to answer efficiently the needs of populations varying in numbers, densities and health problems? These are problems that the health information system will able to solve with help of GIS and its spatial analysis tools. Health officials can also use maps produced by GIS, as a monitoring and evaluation tool, showing the spatial distribution and differential evolution of diseases.

The system should essentially facilitate

  • Database development from different sources.
  • Automation of thematic maps.
  • Helping and monitoring disease control programmes.
  • Dealing with the spatial aspects of their information.
  • Routine epidemiological evaluations.
  • Stratification of areas based on a set of rules.
  • Feedback to the peripheral level / general public.

Applications of GIS in Disease Surveillance
Epidemiologists have traditionally used maps when analyzing associations between location, environment, and disease. GIS is particularly well suited for studying these associations because of its spatial analysis and display capabilities [3]. Recently GIS has been used in the surveillance and monitoring of vector – borne diseases, water borne diseases, in environmental health, and the analysis of disease policy and planning.

GIS allows analysis of data generated by global positioning systems (GPS). Combined with data from surveillance and management activities, GIS and GPS provide a powerful tool for the analysis and display of areas of high disease prevalence and the monitoring of ongoing control efforts. The combination of GIS and GPS enhances the quality of spatial and non-spatial data for analysis and decision-making by providing an integrated approach to disease control and surveillance at the local, regional, and / or national level.

Elimination of Lymphatic Filariasis (LFE) Campaign:
India accounts for about 40% of the 120 million estimated cases globally with either disease or infection (microfilaria cases). India is the single largest country implementing ELF programme. The National Health Policy (2002) envisages Elimination of LF by the year 2015. WHO’s strategy to eliminate lymphatic filariasis targeted for global elimination by 2020 The LF strategy has two components: (i) to stop the spread of infection (i.e. interrupting transmission), and (ii) to alleviate the suffering of affected individuals (i.e. morbidity management).

To interrupt transmission, districts in which lymphatic filariasis is endemic must be identified, and then community-wide (“mass treatment”) programmes implemented to treat the entire at-risk population. Mass Drug Administration (MDA) with an annual, single dose of diethylcarbamazine citrate (6mg / kg body weight), repeated four to six times, can interrupt transmission and thereby eliminate filariasis infection in all endemic areas. Initially, MDA was introduced in India as a pilot scheme during 1997 in 13 districts from seven states. In 2001, the programme was scaled up to cover 31 districts from the same states. A massive expansion had been done in 2004 and 2005 to cover more than 200 districts from 20 states and union territories [4].

LF Risk Mapping:
The present data on spatial distribution of filariasis in India represent the information available as on 1995. Also, there is little doubt that much variation may exist between the surveys, because they were carried out at different points of time in different locations, and with small sample size. Further, there is a need for better understanding of the epidemiology in terms of spatial heterogeneity, and risk factors influencing the occurrence of the disease towards fine tuning the control / elimination strategy in India. Our recent study has shown that the areas with risk of filariasis transmission can be identified at macro level [5] using the geo-environmental risk model (GERM) particularly like the situation in India, where some geographical areas are not surveyed even one time, and their level of risk status not known.

Data on geo-environmental variables (altitude, temperature, rainfall, relative humidity, soil type and vegetation indices) associated with the occurrence of filariasis has initially been customized on GIS platform. The range of values of these variables that are conducive or otherwise for the transmission of filariasis are used for creating filariasis transmission risk index (FTRI). Filariasis risk map has been created for Tamil Nadu region of India as a case study [4], and exhibited the delimitation of areas in terms of Filariasis Transmission Risk Index, as being at ‘risk’ or at ‘no-risk’ derived from geo-environmental variables. This will be useful immediately for operational purpose.

Decision Making Tools (DMTs):
A decision support tool has been built-up with user interface facilities for browsing, spatial structured querying, thematic mapping, data editing and drawing information (demographic features, infection /disease prevalence, filariasis environmental risk status and the ongoing control operations) of each district / region (Fig.1). Besides the visualization of site characterization, this system allows integration of data from any desktop data base software or worksheet. It has facilities to update the data from different nodes or centres (Fig.2). The tool designed here is only a model, and this could be put into function country wide, with all the desired data and queries. Further, the critical level of different components is required to be defined in the system, before it is taken to the real time situation.

The GIS linked health information system is very useful in health sector for decision making policies by showing spatial distribution of disease in space and time, facilitate the monitoring and appraisal of the effectiveness of health programme, and it has got an immediate relevance in the ongoing LF elimination campaign.

Authors are thankful to Dr. P.K.Das, the Director of Vector Control Research Centre, Puducherry for providing facilities.


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