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Maximizing Use of Existing Data to Strengthen Program Design, Evaluation and Impact Lab

What are the data problems that this Lab is working to solve?

Global health researchers and their resources are stretched thin around the world. Health outcomes could be greatly improved through more data and better data, but we are not currently utilizing the capacity of researchers, non-governmental organizations (NGOs) and in-country leaders to its full extent. We could strengthen capacity through maximizing the use of already available data to meet baseline data needs.

Baseline data collection is resource and time intensive, especially for the NGOs that invest in conducting baseline surveys. Most data collected in LMICs may already exist in publicly available data sets, but the data is underutilized. This is coupled with quality issues, such as small sample size and a lack of analytical capacity in staff human resources.

Key data issues:

  • Responsive to context: Adequate assessment of baseline conditions is especially important to ensure interventions respond to the varying health needs within populations and promote equitable policies.
  • Increased data use: Utilizing publicly available data to meet baseline data needs can, in turn, increase the use and quality of publically available data (collected regularly) to better inform program design, implementation, evaluation and learning.
  • Data-informed decision-making: Maximizing the use of available data strengthens the capacity of researchers, NGOs and in-country leaders to collect, analyze, and inform programmatic decisions.

How are partners navigating this innovation?

  • Developing a hypothesis: With extensive background research completed, the Lab partners will build their dataset of NGO baseline reports, and select publicly available data from the Demographic and Health Surveys Program (DHS) and Multiple Indicator Cluster Surveys (MICS). This can provide valid estimates of baseline conditions for certain health and social indicators, saving time and resources, and reducing the burden on data collectors and respondents.
  • Testing a hypothesis: In conducting modelling and analyses, partners investigate the validity of using publicly available data to complement or replace baseline data collection of NGOs related to maternal, newborn and child health in several LMICs, by comparing indicators obtained from both sources.
  • Developing tools and sharing learnings: Depending on the result of hypothesis, learnings will be shared and if applicable, recommendations will be developed on the use of publicly available data.
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