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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.
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