Research

Spatial and multidimensional visualization of Indonesia's village health statistics

Bambang Parmanto¹ , Maria V Paramita^{* 1} , Wayan Sugiantara^{* 1} , Gede Pramana^{* 1} , Matthew Scotch^{* 2} and Donald S Burke^{* 3}

¹Health Information Management, University of Pittsburgh, 6051 Forbes Tower, Pittsburgh, Pennsylvania, USA

²Center for Medical Informatics, Yale University, 300 George St, Suite 501, New Haven, Connecticut, USA

³Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, USA

author email corresponding author email* Contributed equally

International Journal of Health Geographics 2008, 7:30doi:10.1186/1476-072X-7-30

Published:	11 June 2008

Abstract

Background

A community health assessment (CHA) is used to identify and address health issues in a given population. Effective CHA requires timely and comprehensive information from a wide variety of sources, such as: socio-economic data, disease surveillance, healthcare utilization, environmental data, and health resource allocation.

Indonesia is a developing country with 235 million inhabitants over 13,000 islands. There are significant barriers to conducting CHA in developing countries like Indonesia, such as the high cost of computing resources and the lack of computing skills necessary to support such an assessment.

At the University of Pittsburgh, we have developed the Spatial OLAP (On-Line Analytical Processing) Visualization and Analysis Tool (SOVAT) for performing CHA. SOVAT combines Geographic Information System (GIS) technology along with an advanced multidimensional data warehouse structure to facilitate analysis of large, disparate health, environmental, population, and spatial data.

The objective of this paper is to demonstrate the potential of SOVAT for facilitating CHA among developing countries by using health, population, healthcare resources, and spatial data from Indonesia for use in two CHA cases studies.

Results

Bureau of Statistics administered data sets from the Indonesian Census, and the Indonesian village statistics, were used in the case studies. The data consisted of: healthcare resources (number of healthcare professionals and facilities), population (census), morbidity and mortality, and spatial (GIS-formatted) information.

The data was formatted, combined, and populated into SOVAT for CHA use. Case study 1 involves the distribution of healthcare professionals in Indonesia, while case study 2 involves malaria mortality. Screen shots are shown for both cases. The results for the CHA were retrieved in seconds and presented through the geospatial and numerical SOVAT interface.

Conclusion

The case studies show the potential of spatial and multidimensional analysis using SOVAT for community health assessment in developing countries. Since SOVAT is based primarily on open-source components and can be deployed using small personal computers, it is cost-effective for developing countries. Also, combining the strength in analysis and the ease of use makes tools like SOVAT ideal for healthcare professionals without extensive computer skills.