Significant resources are committed to improving the data informing healthcare decisions. Data on disease patterns, current health care and health outcomes, and how health could be improved using different interventions, are essential to informing decisions about how to allocate healthcare resources. Given the expanding opportunities to collect data via routine data collection, surveys, surveillance systems, and clinical trials, and the significant costs associated with data collection, research funders and research users increasingly need to identify which data collection activities to prioritise.
Thanzi la Onse researchers have been exploring how we can quantify the benefits of specific investments in data collection to identify those that offer the biggest opportunity to improve health. Researchers have for a long time been aware of methods for estimating the health benefits of research. These methods, called value of information analysis, are founded on the understanding that research delivers value by supporting more informed resource allocation decisions. However, these methods have not proved practical enough to be used routinely.
In our new paper we provide a simplified method and tool for estimating the value of a research study in terms of net DALYs averted. The tool requires an assessment of the key data that will be collected in the proposed study and how uncertain we are currently about the questions being asked in the study. It also requires an understanding of how the data collected is likely to be used to support resource allocation decisions, and how different outcomes of the study would affect those decisions.
We hope that this tool will allow research funders, researchers, and health care decision makers to conduct assessments of the health benefits of different types of data collection. This in turn will allow the costs and benefits of data collection exercises to be weighed up, focusing research funding where it offers the most potential to improve health.
Read the full research publication here: A practical approach to identifying high priority areas for data collection
By: Beth Woods | September 2020