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A framework for the economic analysis of data collection methods for vital statistics
Eliana Jimenez-Soto1, Andrew Hodge1, Kim-Huong Nguyen2
1School of Population Health, The University of Queensland, Australia.
Plos One
|August 30, 2014
Summary
A new framework systematically assesses data collection methods for vital statistics, aiding evidence-based health policies. Rankings of methods are consistent across economic analyses but depend on how data quantities are measured.
Area of Science:
- Public Health
- Health Economics
- Health Information Systems
Background:
- Growing need for improved vital statistics and health data to inform evidence-based policies.
- Lack of a systematic framework for guiding investment decisions in data collection methods.
- Importance of cost-effective data collection for vital statistics and health information.
Purpose of the Study:
- To develop and present a systematic framework for assessing the comparative costs and outcomes/benefits of data collection methods for vital statistics.
- To provide a tool for guiding investment decisions in health data collection.
- To demonstrate the feasibility and necessity of economic evaluations for data collection methods.
Main Methods:
- Utilized a four-pronged framework incorporating cost-effectiveness and efficiency analysis.
- Developed a stylized example for a hypothetical low-income country to simulate framework application.
- Employed simulated data for analysis and illustration.
Main Results:
- The rankings of data collection methods were consistent regardless of whether cost-effectiveness or efficiency analysis was used.
- The measurement of quantities significantly influenced the ranking of data collection methods.
- Simulated data demonstrated the practical application of the framework.
Conclusions:
- This study presents the first systematic framework for economic evaluation of data collection methods (DCMs) for vital statistics.
- Systematic assessment of DCM costs and outputs is feasible and necessary for improving global health data.
- The proposed framework is adaptable for use in other health information domains.
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