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

Abstract

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.

Data quality of the routine health management information system at the primary healthcare facility and district levels in Tanzania

“…Over-reported routine HMIS data is a challenge in many countries in SSA [24,28]. Our results indicate a clear reduction in the variation of the mismatch, especially for OPD and IPD service areas from 2014 to 2017.…”

“…Since the error originates from the primary source it is di cult to correct it at later stages. A number of studies have reported similar ndings in other countries [11,12,24,26]. Our methodology detects that HF reports were manipulated before entering DHIS2.…”

“…Variations and inadequate utilization of HMIS tools by facility characteristics have been reported previously in Tanzania and elsewhere [11,21,24]. Private-owned facilities, hospitals and healthcare facilities with high client volume are known to signi cantly affect the quality of HMIS data due to poor adherence in recording procedures, incompleteness and late reporting [3,5,6,9,13,[25][26][27].…”

“…This phenomenon could explain a signi cant variation on quality observed between indicators when register counts were compared to subsequent sources [11,12]. Indicators dealing with a large amount of data and direct client contact, and those associated with dispensing medication/vaccination performed worse [24]. Complex indicators which include a subset of the population served (ANC-5c), time or day speci cation (PNC-1a and 2), categorization of disease severity had substantial quality issues.…”

“…For accuracy, we calculated a difference ratio (DR), an index measure that quanti es the difference between data sources [8,15,24]. Six DR indices, Diff1-6, were calculated and assessed, and grouped considering the three phases of the data journey, i.e.…”