IntroductionRoutine health information systems are critical for monitoring service delivery. District Heath Information System, version 2 (DHIS2) is an open source software platform used in more than 60 countries, on which global initiatives increasingly rely for such monitoring. We used facility-reported data in DHIS2 for Gombe State, north-eastern Nigeria, to present a case study of data quality to monitor priority maternal and neonatal health indicators.MethodsFor all health facilities in DHIS2 offering antenatal and postnatal care services (n = 497) and labor and delivery services (n = 486), we assessed the quality of data for July 2016-June 2017 according to the World Health Organization data quality review guidance. Using data from DHIS2 as well as external facility-level and population-level household surveys, we reviewed three data quality dimensions—completeness and timeliness, internal consistency, and external consistency—and considered the opportunities for improvement.ResultsOf 14 priority maternal and neonatal health indicators that could be tracked through facility-based data, 12 were included in Gombe’s DHIS2. During July 2016-June 2017, facility-reported data in DHIS2 were incomplete at least 40% of the time, under-reported 10%-60% of the events documented in facility registers, and showed inconsistencies over time, between related indicators, and with an external data source. The best quality data elements were those that aligned with Gombe’s health program priorities, particularly older health programs, and those that reflected contact indicators rather than indicators related to the provision of commodities or content of care.ConclusionThis case study from Gombe State, Nigeria, demonstrates the high potential for effective monitoring of maternal and neonatal health using DHIS2. However, coordinated action at multiple levels of the health system is needed to maximize reporting of existing data; rationalize data flow; routinize data quality review, feedback, and supervision; and ensure ongoing maintenance of DHIS2.

Background Improving the quality of facility-based births is a critical strategy for reducing the high burden of maternal and neonatal mortality and morbidity across all settings. Accurate data on childbirth care is essential for monitoring progress. In northeastern Nigeria, we assessed the validity of childbirth care indicators in a rural primary health care context, as documented by health workers and reported by women at different recall periods. Methods We compared birth observations (gold standard) to: (i) facility exit interviews with observed women; (ii) household follow-up interviews 9-22 months after childbirth; and (iii) health worker documentation in the maternity register. We calculated sensitivity, specificity, and area under the receiver operating curve (AUC) to determine individual-level reporting accuracy. We calculated the inflation factor (IF) to determine population-level validity. Results Twenty-five childbirth care indicators were assessed to validate health worker documentation and women’s self-reports. During exit interviews, women’s recall had high validity (AUC≥0.70 and 0.75

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.