Improving the quality of routine maternal and newborn data captured in primary health facilities in Gombe State, Northeastern Nigeria: a before-and-after study

Abstract

ObjectivesPrimary objective: to assess nine data quality metrics for 14 maternal and newborn health data elements, following implementation of an integrated, district-focused data quality intervention. Secondary objective: to consider whether assessing the data quality metrics beyond completeness and accuracy of facility reporting offered new insight into reviewing routine data quality.DesignBefore-and-after study design.SettingPrimary health facilities in Gombe State, Northeastern Nigeria.ParticipantsMonitoring and evaluation officers and maternal, newborn and child health coordinators for state-level and all 11 local government areas (district-equivalent) overseeing 492 primary care facilities offering maternal and newborn care services.InterventionBetween April 2017 and December 2018, we implemented an integrated data quality intervention which included: introduction of job aids and regular self-assessment of data quality, peer-review and feedback, learning workshops, work planning for improvement, and ongoing support through social media.Outcome measures9 metrics for the data quality dimensions of completeness and timeliness, internal consistency of reported data, and external consistency.ResultsThe data quality intervention was associated with improvements in seven of nine data quality metrics assessed including availability and timeliness of reporting, completeness of data elements, accuracy of facility reporting, consistency between related data elements, and frequency of outliers reported. Improvement differed by data element type, with content of care and commodity-related data improving more than contact-related data. Increases in the consistency between related data elements demonstrated improved internal consistency within and across facility documentation.ConclusionsAn integrated district-focused data quality intervention—including regular self-assessment of data quality, peer-review and feedback, learning workshops, work planning for improvement, and ongoing support through social media—can increase the completeness, accuracy and internal consistency of facility-based routine data.

Data quality and associated factors in the health management information system at health centers in Shashogo district, Hadiya zone, southern Ethiopia, 2021

“…Supervised health workers reported a higher quality of data than those who were not supervised health workers. More than half (66.3%) of the respondents were supervised by their respective higher levels as per standards, and the finding was lower than that of a study done in Kenya where the figure was 79% [ 26 ]. This could be due to differences in location and sample size.…”

Quality of routine health facility data used for newborn indicators in low- and middle-income countries: A systematic review

“…The data sources evaluated in included studies were predominantly registers, with very few assessing individual patient case notes directly. Several studies also assessed aggregate reports such as those included in DHIS [ 30 , 33 , 50 , 51 , 53 , 61 , 63 , 64 , 59 ]. Only a few studies explored whether assessment of quality of specific data for newborn indicators was feasible given the design of registers or case notes [ 39 , 41 , 42 , 44 - 46 ].…”

Improving Birth Weight Measurement and Recording Practices in Kenya and Tanzania: a Prospective Intervention Study With Historical Controls

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