Quality of routine facility data for monitoring priority maternal and newborn indicators in DHIS2: A case study from Gombe State, Nigeria

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Study Justification:
– Routine health information systems are crucial for monitoring service delivery in healthcare.
– DHIS2 is a widely used open source software platform for monitoring health indicators.
– This study aims to assess the quality of routine facility data in DHIS2 for monitoring maternal and neonatal health indicators in Gombe State, Nigeria.
Study Highlights:
– Data from 497 facilities offering antenatal and postnatal care services and 486 facilities offering labor and delivery services were assessed.
– Data quality was evaluated based on completeness and timeliness, internal consistency, and external consistency.
– Facility-reported data in DHIS2 were found to be incomplete, under-reported events documented in facility registers, and showed inconsistencies over time and with external data sources.
– The best quality data elements were those aligned with Gombe’s health program priorities and contact indicators.
Study Recommendations:
– Coordinated action at multiple levels of the health system is needed to improve data quality in DHIS2.
– Recommendations include maximizing reporting of existing data, rationalizing data flow, routinizing data quality review and supervision, and ensuring ongoing maintenance of DHIS2.
Key Role Players:
– Gombe State Ministry of Health
– London School of Hygiene & Tropical Medicine
– Health facility staff
– District health office
– Data collection team
Cost Items for Planning Recommendations:
– Training and capacity building for health facility staff
– Equipment and technology for data collection and reporting
– Supervision and monitoring activities
– Maintenance and support for DHIS2 software
Note: The actual cost will depend on the specific context and requirements of Gombe State.

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is rated 7 because it provides specific details about the study methodology, data sources, and findings. However, it does not mention the sample size or the statistical analysis conducted. To improve the evidence, the abstract could include information about the sample size and the statistical tests used to analyze the data.

Introduction Routine 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. Methods For 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. Results Of 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. Conclusion This 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.

Gombe State approval for the study was obtained from Gombe State Ministry of Health. Ethical approval was obtained from the London School of Hygiene & Tropical Medicine (reference 14091). Gombe State has a projected population of 2.9 million (2006 census: 2.4 million) and is located within north-eastern Nigeria, where maternal and newborn mortality are estimated to be higher than the rest of the country (1,549 maternal deaths per 100,000 live births in 2015 and 35 neonatal deaths per 1,000 live births in 2017). [22, 24–26] In 2017, Gombe State had a total of 615 health facilities across 11 Local Government Areas (LGA, equivalent to a district); each LGA has 10–11 political wards (114 wards, total). As in other states in Nigeria, Gombe facility staff generally complete 13 paper-based registers to document the services they provide. Every month, a subset of data in these registers are tallied and summarized in a paper-based report and sent to the LGA (district) health office to be entered into DHIS2. We accessed three data sources for this study: facility-reported data in DHIS2, an external facility survey, and an external household survey as described below. In 2017, DHIS2 contained monthly reports for 615 Gombe public and private health facilities across 11 districts: 587 primary facilities offering basic preventative and curative services and 28 referral facilities offering specialized care. Of these, 471 of the 587 primary facilities had been appointed to provide antenatal care and postnatal care services, 460 of the 587 primary facilities provided labor and delivery services, and 26 of the 28 referral facilities were equipped to provide both types of services, in addition to specialized care. Therefore, in total, 497 facilities provided antenatal and postnatal care services and 486 facilities provided labor and delivery services. For these 497 and 486 facilities, respectively, monthly aggregated DHIS2 data for the reference year July 2016-June 2017 were downloaded at one time and included 15 maternal and newborn health-related data elements. Additionally, we downloaded data for July 2013-June 2016 as comparison years for assessing the consistency of data over time. In July 2017, a facility-level survey was conducted in 97 primary and 18 referral facilities across Gombe to assess their readiness to provide maternal and newborn health services. Detailed methods are reported elsewhere.[27] Briefly, these primary and referral facilities were a state-wide random sample drawn from all government-owned primary health facilities and a census of all 18 government-owned referral health facilities. The facility survey protocol was similar to a Service Availability and Readiness Assessment, which included an inventory of equipment and supplies that were available and functioning on the day of survey; an inventory of staff employed at the facility, their cadre, training and whether they were present on the day of survey; and an interview with the in-charge of the facility about the services available at that facility and about recent supervision visits they had received. Additionally, this survey included data extraction from the facility’s paper-based antenatal and postnatal care register and the labor and delivery register (Nigeria health management information system, version 2013).[28] A trained third party data collection team tallied and recorded the register data for each month of the six-month period immediately prior to the survey: January-June 2017. We compared the facilities’ paper-based register data with the facilities’ data downloaded from DHIS2. These extracted data are shown in Table 1. Notes: Indicators in italic type cannot be calculated only from routine facility data. *Gombe facility registers and DHIS2 track early postpartum-postnatal care within 1 and 3 days of birth. To ensure exclusion of care provided to mothers and newborns during labor and delivery, we used early postpartum-postnatal care within 3 days of birth. Also in July 2017, a household-level survey was conducted in catchment areas of the 97 primary facilities from the July 2017 facility survey to assess access to and quality of maternal and newborn services. [27] These catchment areas represented 79 enumeration areas: some facilities serving more than one enumeration area. All households in each enumeration area were surveyed (or in a segment of between 75 households from the enumeration area if more than 75 households were present). The household survey comprised of three modules. (1) A household module asked all household heads about characteristics of the household, ownership of commodities and registered all normally resident people in the household. (2) A women’s module asked all women aged 13–49 years and normally resident in the household about the health care available to them, their recent contact with frontline workers and their birth history in the two years preceding the survey. (3) A mother’s module asked all women who reported a birth in the last two years (identified in the women’s module) a detailed set of questions about their contact with health services across the continuum of care from pregnancy to postnatal care. Informed consent was obtained at the community leadership-level and at the individual-level for each respondent; all invited participants agreed be interviewed. Among 965 surveyed women who reported a live birth in the 12 months prior to the survey, 588 women had visited the facility at least once during their pregnancy and 377 women gave birth at a facility. For DHIS2 reported indicators that were also estimated in the household survey, we compared estimates from this household survey to those from the 79 matching facilities in DHIS2. Calculations of point estimates and their 95% confidence intervals were done using the svyset Stata command (StataCorp, College Station, USA) to adjust for clustering at the enumeration area-level. To determine globally-defined priority maternal and newborn health data in DHIS2, we referred to the Ending Preventable Maternal Mortality and Every Newborn Action Plan strategy documents which described priority indicators to monitor progress towards targets during the Sustainable Development Goals era. [7, 8] For content of care indicators that were referenced by these strategy documents, but not yet fully defined, we referred to indicators defined in Carvajal-Aguirre et al. [29] We focused our data quality review on health services that should be received by all pregnant women and newborns accessing either primary or referral health facilities. Therefore, rare events and outcomes such as deaths, adolescent births, pre-term births, deliveries by caesarean section, and kangaroo mother care were excluded from our analyses. For Gombe State, we identified 14 priority maternal and newborn health indicators that were captured at the facility-level by health care workers. (Table 1) These 14 indicators are made up of 17 distinct data elements contained within the paper-based facility registers, including three denominators to determine how many women and newborns have accessed these facilities for services: women who visited the facility at least once during their pregnancy; women who gave birth in a facility; and live births among the facility births. For Gombe State, 15 of these 17 distinct data elements were reported in DHIS2; the data for women receiving oxytocin for the prevention of postpartum hemorrhage and newborns receiving essential care were captured in facility registers, but not reported in DHIS2. Therefore, the final set of data assessed included 15 data elements used to calculate 12 priority indicators. We reviewed the quality of the DHIS2 data according to metrics of three routine data quality dimensions outlined by the World Health Organization data quality review toolkit: completeness and timeliness; internal consistency; and external consistency. [23] Table 2 outlines the data quality metrics assessed, the criterion for each metric, and the data sources used. A stratified analysis was performed by facility type for primary and referral facilities. Notes: ANC = antenatal care, PNC = postnatal care, SD = standard deviation. * WHO threshold for good data quality should be adapted for each health program and/or country. **For the period under review, downloaded data from Gombe State’s DHIS2 did not distinguish between missing values and true zero values; both are presented as missing values.

Based on the provided information, here are some potential innovations that can be used to improve access to maternal health:

1. Digitalization of facility registers: Instead of relying on paper-based registers, implementing a digital system for recording and reporting maternal health data can improve data accuracy, completeness, and timeliness.

2. Real-time data entry and reporting: Introducing a system that allows health facility staff to enter data directly into the system in real-time can ensure that data is up-to-date and readily available for monitoring and decision-making.

3. Automated data quality checks: Implementing automated data quality checks within the digital system can help identify and correct errors or inconsistencies in the data, ensuring higher data quality.

4. Mobile health (mHealth) applications: Developing mobile applications that allow pregnant women and new mothers to access information, receive reminders for antenatal and postnatal care appointments, and communicate with healthcare providers can improve access to maternal health services.

5. Telemedicine services: Introducing telemedicine services, such as remote consultations and monitoring, can help overcome geographical barriers and improve access to healthcare for pregnant women, especially in remote or underserved areas.

6. Community health worker programs: Training and deploying community health workers who can provide basic maternal health services, education, and referrals in communities can improve access to care, particularly in areas with limited healthcare infrastructure.

7. Public-private partnerships: Collaborating with private healthcare providers to expand access to maternal health services can help alleviate the burden on public healthcare facilities and increase service availability.

8. Mobile clinics or outreach programs: Establishing mobile clinics or organizing regular outreach programs in underserved areas can bring maternal health services closer to the communities, reducing barriers to access.

9. Health education and awareness campaigns: Conducting targeted health education and awareness campaigns to increase knowledge about maternal health, promote early antenatal care, and encourage facility-based deliveries can improve utilization of maternal health services.

10. Strengthening referral systems: Enhancing the coordination and communication between different levels of healthcare facilities through improved referral systems can ensure timely access to appropriate care for pregnant women with complications.

These innovations can help address the challenges identified in the case study and contribute to improving access to maternal health services in Gombe State, Nigeria.
AI Innovations Description
The recommendation to improve access to maternal health based on the case study from Gombe State, Nigeria is to take coordinated action at multiple levels of the health system. This includes:

1. Maximizing reporting of existing data: Efforts should be made to ensure that health facilities consistently report data into the District Health Information System, version 2 (DHIS2). This can be achieved through training and capacity building for facility staff on data collection, entry, and reporting.

2. Rationalizing data flow: Streamlining the process of data flow from facility registers to DHIS2 is crucial. This may involve simplifying data collection tools, improving data entry processes, and establishing clear communication channels between health facilities and the district health office.

3. Routinizing data quality review, feedback, and supervision: Regular data quality reviews should be conducted to identify and address issues such as completeness, timeliness, internal consistency, and external consistency of the data. Feedback and supervision mechanisms should be established to provide support and guidance to facility staff in improving data quality.

4. Ensuring ongoing maintenance of DHIS2: Continuous technical support and maintenance of the DHIS2 platform are essential to ensure its functionality and reliability. This includes regular updates, troubleshooting, and addressing any technical issues that may arise.

By implementing these recommendations, the quality of routine facility data in DHIS2 can be improved, leading to better monitoring of priority maternal and newborn health indicators. This, in turn, can contribute to more effective decision-making and resource allocation for maternal health services, ultimately improving access to maternal health care.
AI Innovations Methodology
Based on the provided description, here are some potential recommendations to improve access to maternal health:

1. Strengthen data collection and reporting systems: Implement measures to improve the completeness and timeliness of data reported in the District Health Information System, version 2 (DHIS2). This could include training healthcare workers on accurate data entry, ensuring regular data collection and reporting, and providing necessary resources for data management.

2. Enhance data quality review processes: Establish a systematic and routine data quality review process to identify and address inconsistencies and errors in the reported data. This could involve regular audits, feedback mechanisms, and supervision to ensure data accuracy and reliability.

3. Improve coordination and communication: Enhance coordination between different levels of the health system, including health facilities, district health offices, and the DHIS2 management team. This could involve establishing clear communication channels, sharing best practices, and providing support and guidance to improve data quality and reporting.

4. Strengthen health facility readiness: Conduct facility-level assessments to identify gaps in the readiness of health facilities to provide maternal and newborn health services. This could include assessing the availability of equipment, supplies, and trained staff, as well as the quality of care provided. Addressing these gaps can help improve access to quality maternal health services.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could be developed as follows:

1. Define indicators: Identify specific indicators that reflect access to maternal health services, such as the number of antenatal care visits, facility-based deliveries, or postnatal care coverage. These indicators should align with the recommendations and reflect the desired outcomes.

2. Collect baseline data: Gather baseline data on the selected indicators from the DHIS2, facility surveys, and household surveys. This will provide a starting point for comparison and evaluation.

3. Implement recommendations: Implement the recommended interventions, such as strengthening data collection and reporting systems, enhancing data quality review processes, improving coordination and communication, and addressing facility readiness.

4. Monitor and evaluate: Continuously monitor the implementation of the recommendations and collect data on the selected indicators. This can be done through routine data collection, surveys, or other data sources.

5. Analyze the impact: Compare the post-intervention data with the baseline data to assess the impact of the recommendations on the selected indicators. This could involve statistical analysis, trend analysis, or other evaluation methods.

6. Draw conclusions and make adjustments: Based on the analysis, draw conclusions about the effectiveness of the recommendations in improving access to maternal health. Identify any challenges or areas for improvement and make necessary adjustments to the interventions.

7. Communicate findings: Share the findings of the impact assessment with relevant stakeholders, including policymakers, healthcare providers, and communities. This can help inform decision-making and further improve access to maternal health services.

By following this methodology, it will be possible to simulate the impact of the recommendations on improving access to maternal health and provide evidence-based insights for decision-making and policy development.

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