Data Quality
Ensuring the quality of MNCH data
Monitoring a city’s maternal, newborn, and child health (MNCH) program involves regularly collecting data and tracking progress towards the intended outcomes (CDC, 2014). One key data source available in most countries is the Health Management Information System (HMIS), which includes a wealth of information on outputs and outcomes of the MNCH program at the facility, city, region, and national levels. However, multiple challenges currently exist in achieving hgh-quality HMIS data including incomplete data due to poor reporting practices, inconsistent data quality across facilities, lack of standardized data collection methods, difficulty in accessing and analyzing complex data, limited human resources to manage data, and potential biases in reporting, making it difficult to accurately assess MNCH service utilization and health outcomes (Samal & Dehury, 2016).Â
This intervention focuses on steps to improve the quality of MNCH data by periodically conducting data quality assessments (DQA) of the HMIS recording and reporting at the facility level. This will focus on three main aspects of data quality:Â
- Data Availability: This is a check to identify if the data tools are available, if they are completed, and correctly filled out.
- Data Consistency: Data consistency is a check to ensure that the data tools are complete the same way from month to month.
- Data Validity: This is a check to see if the data is accurately collated and transferred from tool to tool, for instance, from the daily register to the monthly summary form.
What Are the Benefits of Stronger Data Quality?
- Strengthens HMIS data on MNCH, which is beneficial not only for TCI but also for the country and its stakeholders.
- Empowers local government staff to conduct this intervention, facilitating sustainability.
- Supports data for decision-making using high-quality data, aligning plans and activities with the real situation that needs to be addressed.
How to Implement
1. Preparatory Phase: Prior to Assessment
- Prepare the DQA checklist. The team should use the local tools for DQA if available. If not, the tool should be developed based on global/national standards for HMIS data processes and quality.
- Pull relevant facilities’ MNCH data for the period of assessment from the HMIS database (often the DHIS2).
- Conduct a DQA training and planning meeting with the assessors. The assessment team should comprise regional and city-level data officers with support from the TCI team. If a country already has a DQA process, TCI should ensure that the expected assessors are included in the training. The training will entail presentations, document reviews, role plays, and pre-post tests to ensure adequate capacity strengthening of the assessment team.
- Divide the assessors into small teams of 2-3 people and assign the facilities to be visited.
- Notify the facilities of the visitation dates.
- Make logistics provisions for the assessment team. Plan to visit not more than 2 facilities in a day and prepare to spend an average of about 4 hours in each facility.
2. Assessment Phase: In the Facility
- Visit the facilities and administer the checklists. The team will review relevant tools based on country context, such as daily intake forms, daily MNCH registers, and monthly HMIS reporting tools. They will then input appropriate scores on the DQA checklist.
- Compute the scores and identify gaps. At the end of the assessment, the scores for each facility are computed. Aggregate scores for the city are also computed. Data entry and computation should be done by the regional & city data officers, with support from the TCI team. If feasible, initial feedback should be provided to the facility for immediate course corrections.
3. Dissemination Phase: After the Assessments
- Disseminate the results of the assessment. The outcome of the assessment should be disseminated to stakeholders who are most suitable in addressing the identified gaps/issues, based on the country context. The stakeholders could include: facility officers-in-charge (OICs), city data officers, and city MNCH program officers. Where appropriate, the facilities with the highest scores in each city should be commended so that other facilities can learn from their good practices.
- Develop and execute a plan on addressing the identified gaps. The weak aspects, where most facilities score low, should be noted and addressed. A detailed plan to address the identified gaps should be developed, and its execution should be monitored.
What's the Evidence?
Improving the quality of MNCH data in HMIS helps stakeholders make appropriate assessments and decisions towards strengthening their local MNCH programs.Â
For example, incomplete HMIS reporting, as shown in this WHO resource, affects the accuracy of trends of MNCH indicators. Program managers might think there are improvements in facility births, when the trend was driven by improved reporting rates over time.Â
The resource offers other examples of how data completeness, consistency, and validity could influence the interpretation of program outcomes, emphasizing the importance of using high-quality data for decision-making.
Exploration of incomplete data, from Data quality considerations for MNCAH managers.
Key Indicators
- Percentage of facilities where DQA was conducted in the expected regularity (e.g., quarterly).
- Percentage of facilities demonstrating high-quality HMIS data on MNCH.
- Percentage of facilities invited to participate in dissemination activities to identify gaps and solutions.
- Percentage of cities with data quality improvement plans based on DQA results.
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Tips
- If there are already country/regional guidelines for the DQAs to be conducted, work with the local government to ensure these are implemented regularly. Do not begin new DQA processes if TCI can help strengthen existing processes. This fosters local ownership and sustainability.
- Ensure the use of local DQA tools where available. This allows TCI to support the regular utilization of the tools that cities are already expected to implement.
- Preparation is key to a fruitful DQA. Review tools and facility HMIS data, and train the assessors adequately before conducting the assessments.
Challenges
- Lack of local funding to conduct the DQA as expected (e.g., quarterly).
- Competing priorities of city and regional data officers.
- Difficulty scheduling with facilities, without affecting their delivery of services.
Key Resources
- Site assessment of data quality: data verification and system assessment. WHO 2022
- Analysis and use of health facility data: guidance for maternal, newborn, child and adolescent health programme managers. WHO 2023
- Assessment of health facility data quality. WHO & Republic of Uganda 2011
- Digital health. WHO
- Digital Health Resources. Global Digital HealthÂ
