2014.ISDS.Abstracts.Final.pdf


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ISDS 2014 Conference Abstracts

Integrated Disease Surveillance to Reduce Data 
Fragmentation – An Application to Malaria Control
Kate Zinszer1, 2, Arash Shaban-Nejad1, Sonia Menon1, Anya Okhmatovskaia1, Lauren 
Carroll2, Ian Painter2, Neil Abernethy2 and David Buckeridge*1

1Clinical and Health Informatics Research Group, McGill University, Montreal, QC, Canada; 2Department of Biomedical Informatics 
and Medical Education University of Washington, Seattle, WA, USA

Objective
Driven by the need to bring malaria surveillance data from different 

sources together to support evidence-based decision making, we are 
conducting the “Scalable Data Integration for Disease Surveillance” 
(SDIDS) project. This project aims to foster the integration of 
existing surveillance data to support evidence-based decision-making 
in malaria control and demonstrate a model applicable to other 
diseases. Central to this initiative is collaboration between academia, 
governmental and NGO sectors.

Introduction
There is growing recognition that an inability to access timely 

health indicators can hamper both the design and the effective 
implementation of infectious diseases control interventions. In 
malaria control, the global use of standard interventions has driven 
down the burden of disease in many regions. Further gains in high-
transmission areas and elimination in lower transmission settings, 
however, will require an enhanced understanding of malaria 
epidemiology, population characteristics, and efficacy of clinical and 
public health programs at the local level. Currently, there is a dearth 
of information available to fine-tune malaria control interventions at 
the local level. A key obstacle is the fragmentation of data into silos, 
as existing data cannot be brought together to estimate accurate and 
timely health metrics.

Methods
Under this initiative, our overarching aims are to demonstrate an 

approach to integrating existing data collected by organizations for 
malaria surveillance in Uganda and The Gambia and to make the 
resulting information available to guide malaria control activities. In 
the first phase, we developed a catalogue of data sources for malaria 
control, describing how malaria control programs and sponsors 
analyze data, and use information to make programmatic decisions. 
In the second phase, we aligned these data with indicators and 
descriptive and analytical epidemiological methods required to guide 
the delivery and evaluation of disease control interventions. In the 
third and final phase, we are developing and evaluating software tools 
to interact with the aligned data sources and calculate and analyze 
indicators that are of value to malaria control programs and funding 
agencies.

Results
At the completion of our project, we will have developed an open-

access prototype system that will support sharing of comparable 
surveillance data within and across countries.

Conclusions
A cornerstone of our approach is its ability to employ a common 

knowledge platform to scale-up and extend structural and semantic 
mapping across existing data sources to other geographical regions 
and global health priority diseases in resource-constrained settings. 
Our multi-sectorial partnership also aims at facilitating the up-stream 

flow of information from the national to the international level, to 
effectively contribute to designing evidence-based disease control 
policies.

Keywords
Integrated disease surveillance; Malaria control; data fragmentation; 
open-access prototype system

Acknowledgments

The Bill and Melinda Gates Foundation

*David Buckeridge
E-mail: david.buckeridge@mcgill.ca

Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * (1):e181, 201