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Supporting Data-Informed Decision-making Among Public Health Officials

HIGHLIGHTS

  • Supporting data-driven decision-making is critical to reduce maternal and neonatal mortality and ensure that public health spending is targeted to the greatest needs and most impactful interventions.
  • Partnering with Clinton Health Access Initiative (CHAI) Nigeria, we’re exploring how public sector health officials interact with and act on maternal and newborn health data.
  • Our research uncovered five key insights and suggests opportunities to support effective utilization of high-quality data to strengthen policies and programs.

The Challenge

Nigeria’s maternal and neonatal mortality rates are among the highest in the world, and have persisted despite substantial attention and investment from the country’s public health system and donors. Supporting data-driven decision-making is critical to ensure that public health spending is targeted to the greatest needs and most impactful interventions to reduce mortality.

Health information systems—through which quantitative data are collected, processed, reported, and used to influence policymaking, program action, and research—are considered a foundation of public health. Yet while most births still occur at the community level in Nigeria, decision-makers rarely access and use data that captures these births and related mortality for program monitoring and quality improvement. 

 

Our Approach

To address a need for comprehensive and high-quality community data on maternal and neonatal mortalities, state governments in Nigeria have been working with the Clinton Health Access Initiative (CHAI) to implement Community Based Health Management Information Systems (CBHMIS) and gather valuable insights for government decision-making and action.

We partnered with CHAI Nigeria to explore how public sector health officials in Nigeria perceive and interact with maternal and newborn health data and the behavioral factors that influence their demand for and utilization of data for action.

To understand the behavioral barriers that hinder these upstream actors from utilizing community data to inform decision making, we conducted a qualitative diagnosis in three states in Nigeria with particularly high maternal mortality burdens: Lagos, Kaduna and Kano. The research included interviews with state-level public health officials ranging from powerful executives who approve resource allocation decisions to mid-level officials who both influence and carry out decisions.

 

Results

The research generated insight into how these high-level stakeholders perceive and interact with data to make decisions, and how different factors may impede demand for and use of community-based MNH data for decision making, including: 

  1. Missed opportunities to make data-informed choices by health officials despite a strong belief in the importance of data-informed decision-making at all levels, due to limited mental models about what constitutes a “decision” and what it means for one to be “data-informed”.
  2. High agency among health officials to make program and policy recommendations, but limitations in their effectiveness due technical and communication skills and knowledge gaps. 
  3. A challenging implementation context—budget shortages, human resource shortages, political interference, insecurity, cultural objections, and competing programs, which reinforces the importance of strong leadership that ensures high-level decisions are prioritized and that resources are made available to address on-the-ground challenges.
  4. Performance monitoring and evaluation norms and structures that focus attention and accountability on implementation and execution of annual operational plans rather than impact on priority outcomes. 
  5. Strong appetite and understanding on the importance of community data among stakeholders, yet differing perceptions of its purpose and lingering concerns about reliability that may limit its use.

Each of these insights has implications which suggest approaches that can be taken to address important features of the context that influence high-level decision makers’ behavior in Nigeria. A full project brief detailing these results will be available in Spring 2025.

As part of our ongoing engagement with CHAI, we’re working to engineer and analyze a set of practical and actionable solutions that respond to the insights emerging from the diagnosis.

 

Takeaway

Engaging directly with key stakeholders in a public health system to understand their perspectives and experiences through the behavioral design process can lead to practical and actionable solutions to support data-informed decisions and action that can save women’s and children’s lives.

Interested in our work applying behavioral science to global health? Email health@ideas42.org or reach out to us on LinkedIn.