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Abstract
The purpose of this paper is to provide a set of guidelines by which health information system can be refocused using GIS as a monitoring tool to improve the timeliness, quality, access and use of Health Management information. This exercise also shows that an alternative way of improving the flow of Health management information is to dedicate resources specifically to co-ordinate access, use and ongoing development of relevant information.
Introduction
Spatial analysis and mapping in epidemiology have a long history but until recently, their use in public health has been limited. However, recent advances in GIS and mapping technologies and increased awareness have created new opportunities for public health administrators to enhance their planning, analysis and monitoring capabilities.
In the context, the reliable and timely information can be used to:
Doers are GIS specialists involved in GIS creation and maintenance.
Users are decision makers, planners who are interested in analysing the GIS data that have been created. For instance, Executives will need summary information on the achievement of aims and objectives, the costs and the efficiency of services. Mid-level Managers will want information relating to performance indicators, activity levels, resource used and relative effectiveness of care on treatment. Clinicians will need information to assist with the management of individual patients and compare treatment outcomes. Public Health staff will need information to assist with the management of individual patients and the delivery of public health services.
Viewers are interested in the final viewing of the GIS analysis results. For instance, epidemiologists need information on disease patterns while top level programme managers require specific details on health care on administrative programmes.
A GIS can help to focus the Health Management Information for the above groups and perform the following key functions:
For any health management information in the Public Health, the data is collected at the PHC level and later consolidated at block level, Health Unit District level (HUD), District level and finally at state level. In the above example, distribution of Leprosy Cases by typewise in Rural and Urban areas of Ramnad district detected is simulated. Cases have been classified as Pauci Bacillary (PB) and Multi Bacillary (MB). In fig 1, the district map of Ramnad with the geographic data is shown. The distribution of cases at the HUD level (Fig 2), at the block level (Fig 3) and at PHC level (Fig 4) are depicted.
Next example is a ranged pattern map. (Fig. 5) It presents the prevalence rates of leprosy in the 29 districts at the start of the Multi Drug Therapy (MDT) during mid 80’s, at the time of integration of leprosy services with the Primary Health Care and during March 2001. The prevalence rate has reached to 3.7 leprosy affected individuals per 10,000 population in Mar 2001 against 117 per 10,000 as recorded in mid 80’s. While satisfactory progress continues to be made towards the elimination of leprosy as public health problem, a “Final push” is being given to reduce the prevalence below 1 per 10,000 population by the year 2004.
Summary and conclusion
GIS and maps are valuable in strengthening the whole process of HMIS and analysis. It serves as a common platform for convergence of multi-disease surveillance activities standardised georeferencing of epidemiological data facilitates standardised approaches to data management. The process provides an excellent means of analysing epidemiological data, revealing trends, dependencies and inter-relationships that would otherwise remain hidden in data shown only in tabular format. A GIS can serve as an entry point for integrating disease surveillance activities where appropriate.
Aknowledgement
I should not fail to acknowledge the encouragement provided by Ms.Nanda Paithankar, Monitoring Adviser, DANLEP, New Delhi. I gratefully thank the Deputy Director (Lep), Ramnad dist., Tamil Nadu for his assistance in marking the health facilities.
GIS as a tool for monitoring Health Management Information System
B. Suresh
MIS Coordinator, DANIDA/DANLEP, Chennai
b.suresh@mail.com
B. Suresh
MIS Coordinator, DANIDA/DANLEP, Chennai
b.suresh@mail.com
Abstract
The purpose of this paper is to provide a set of guidelines by which health information system can be refocused using GIS as a monitoring tool to improve the timeliness, quality, access and use of Health Management information. This exercise also shows that an alternative way of improving the flow of Health management information is to dedicate resources specifically to co-ordinate access, use and ongoing development of relevant information.
Introduction
Spatial analysis and mapping in epidemiology have a long history but until recently, their use in public health has been limited. However, recent advances in GIS and mapping technologies and increased awareness have created new opportunities for public health administrators to enhance their planning, analysis and monitoring capabilities.
In the context, the reliable and timely information can be used to:
- design the functions of health care services and administrative services.
- monitor health status and service need.
- set priorities for the allocation of health care resources.
- evaluate health programmes & health care outcomes e.g. changes in health status as a result of intervention on health care programme.
- identify environmental, socio-economic and other risk factors, which influence health, under serviced, poor, inaccessible areas and other geographic and demographic factors.
- project perceived health problems with incidence rate.
- focus population sub groups with specific health problems, needs & demands.
Doers are GIS specialists involved in GIS creation and maintenance.
Users are decision makers, planners who are interested in analysing the GIS data that have been created. For instance, Executives will need summary information on the achievement of aims and objectives, the costs and the efficiency of services. Mid-level Managers will want information relating to performance indicators, activity levels, resource used and relative effectiveness of care on treatment. Clinicians will need information to assist with the management of individual patients and compare treatment outcomes. Public Health staff will need information to assist with the management of individual patients and the delivery of public health services.
  Distribution of Leprosy Cases by typewise in Ramnad district at various levels |
Viewers are interested in the final viewing of the GIS analysis results. For instance, epidemiologists need information on disease patterns while top level programme managers require specific details on health care on administrative programmes.
A GIS can help to focus the Health Management Information for the above groups and perform the following key functions:
- Generate “thematic maps” (ranged colour maps on proportional symbal maps to denote the intensity of a mapped variable).
- Allow for overlaying of different pieces of information.
- Create buffer areas around selected features (eg. a radius of 10 Km around a health centre to denote a catchment area)
- Calculate distances between two points
- Permit dynamic link between databases and maps so that data updates are automatically reflected on maps.
- Permit interactive queries of information contained within the map, table or graph.
 Fig. 5: Districtwise Prevalence Rates of Leprosy - An example for ranged pattern map |
For any health management information in the Public Health, the data is collected at the PHC level and later consolidated at block level, Health Unit District level (HUD), District level and finally at state level. In the above example, distribution of Leprosy Cases by typewise in Rural and Urban areas of Ramnad district detected is simulated. Cases have been classified as Pauci Bacillary (PB) and Multi Bacillary (MB). In fig 1, the district map of Ramnad with the geographic data is shown. The distribution of cases at the HUD level (Fig 2), at the block level (Fig 3) and at PHC level (Fig 4) are depicted.
Next example is a ranged pattern map. (Fig. 5) It presents the prevalence rates of leprosy in the 29 districts at the start of the Multi Drug Therapy (MDT) during mid 80’s, at the time of integration of leprosy services with the Primary Health Care and during March 2001. The prevalence rate has reached to 3.7 leprosy affected individuals per 10,000 population in Mar 2001 against 117 per 10,000 as recorded in mid 80’s. While satisfactory progress continues to be made towards the elimination of leprosy as public health problem, a “Final push” is being given to reduce the prevalence below 1 per 10,000 population by the year 2004.
Summary and conclusion
GIS and maps are valuable in strengthening the whole process of HMIS and analysis. It serves as a common platform for convergence of multi-disease surveillance activities standardised georeferencing of epidemiological data facilitates standardised approaches to data management. The process provides an excellent means of analysing epidemiological data, revealing trends, dependencies and inter-relationships that would otherwise remain hidden in data shown only in tabular format. A GIS can serve as an entry point for integrating disease surveillance activities where appropriate.
Aknowledgement
I should not fail to acknowledge the encouragement provided by Ms.Nanda Paithankar, Monitoring Adviser, DANLEP, New Delhi. I gratefully thank the Deputy Director (Lep), Ramnad dist., Tamil Nadu for his assistance in marking the health facilities.