Using data quality assurance to improve decision-making processes in Nigeria

Author: Dr Ibrahim Ahmed El-Imam, Center for International Health Education and Biosecurity at University of Maryland School of Medicine

Country of focus: Nigeria

Theme: Data for decision-making

In brief:

It is fundamental that public health decisions are based on high-quality and accurate data. Using responsive feedback, the Center for International Health Education and Biosecurity identified and resolved issues in data quality that informs the HIV Recent Infection public health policies and response in Nigeria.

Problem and context: 

The goal of HIV Recent Infection Surveillance, part of the CDC-funded SHIELD project, is to detect, analyze, characterize and provide real-time data for strategic decision making, to interrupt the active transmission of HIV at the community level. High data quality is essential for building effective and impactful surveillance system and decision -making.

To ensure availability of high-quality data for strategic decision-making, DQA strategies were deployed in facilities across 19 states in Nigeria. These included automated validation rules in the electronic medical records (EMR) and the National Data Repository (NDR), prompting healthcare workers at different levels to perform additional checks, weekly joint data quality review/feedback meetings with all stakeholders (facility level, implementing Partners, UMB and funders), on-site facility DQA and on-going mentoring.

These continuous engagements revealed different data errors, such as data incompleteness (missingness), inconsistency(sameness/matchiness) and inaccuracy (correctness).

In one scenario, data inconsistencies were due to implementers misunderstanding the term ‘ward’. This led to incorrect categorization of raw data in the EMR/data Repository.

Addressing the information gap

To address the issues, UMB team worked closely with facility healthcare workers and partners responsible for collecting and entering the data into the EMR. UMB built the capacities, and mentored the partners/healthcare workers to review and identify data-quality errors, providing a platform for bi-monthly peer review meetings and assigning identified errors to relevant partners. These recommendations were tracked by UMB to ensure accountability, ownership and continuous improvements.

UMB also supported the development and maintenance of electronic platforms for data entry (EMR), and for data repository (NDR), where data analytic dashboards were used to monitor data uploads and guide data utilization for public health responses. The dashboards monitor surveillance implementation progress to ensure data is meaningful and aligns with global guidance. Inconsistent data in the repository was flagged and investigated, alongside the partners and facility healthcare workers, by conducting an on-site data validation exercise.

How did things improve as a result? 

Throughout this process, all indicators on the central data repository showed remarkable improvement. The improved quality of data led to better decision-making for public health investigation and intervention. For example, the uptake of testing for recent infection and proportion of viral load has risen from 12% to17% and from 91.7% to 100% within 6 months, respectively. This success has led to additional surveillance/public health investigations. Now, fewer data errors are being encountered as the engagement and mentoring of facility healthcare continues, evident in the improvement of data ownership and accountability.

Lessons for others on using data for making better decisions:

  • Where possible, develop reliable data collecting tools. For example, automation and hard validation rules can verify algorithms.
  • Implement routine data quality assessments and reviews with stakeholders to improve ownership and accountability
  • Always compare data to global evidence or best practices in implementation literature to verify your findings and analysis.

Where can I find more information?

Dr Ibrahim Ahmed El-Imam

Director of Epidemiology, Surveillance and Evaluation

Center for International Health Education and Biosecurity,

Institute of Human Virology

University of Maryland School of Medicine

MGIC Nigeria

+2347088137881 Iahmed@mgic.umaryland.edu

Process

V: Seek evidence

In this step, we seek feedback that can provide evidence to help answer our learning questions.

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