Understanding performance data: health management information system data accuracy in Southern Nations Nationalities and People’s Region, Ethiopia

Abstract

BackgroundHealth management information system (HMIS) is a system whereby health data are recorded, stored, retrieved and processed to improve decision-making. HMIS data quality should be monitored routinely as production of high quality statistics depends on assessment of data quality and actions taken to improve it. Thus, this study assessed accuracy of the routine HMIS data.MethodsFacility based cross-sectional study was conducted in Southern Nations Nationalities and People’s region in 2017. Document review was done in 163 facilities of different levels. Statistical Package for the Social Sciences (SPSS) for windows version 20 was used to perform data analysis. Data accuracy was presented in terms of mean and standard deviation of data verification factor.ResultsThough inaccuracy was noted for all data elements, 96.9 and 84.7% of facilities reported institutional maternal death and skilled birth attendance within acceptable range respectively while confirmed malaria (45.4%), antenatal care fourth visit (46.6%), postnatal care (55.2%), fully immunized (55.8%), severe acute malnutrition (54.6%) and total malaria (50.3%) were reported accurately only by about half of facilities. Antenatal care fourth visit was over reported by 24% while total malaria was under reported by 28%. Reasons for variations included technical, behavioral and organizational factors.ConclusionsMajority of facilities over reported services while under reporting diseases. Data quality should be monitored routinely against data quality parameters quantitatively and/or qualitatively to catch-up country’s information revolution agenda.

Eight-year Retrospective Analysis of Malaria Trends in Gedeo Zone, South Ethiopia (2012-2019)

“…Yirgacheffe rural district had the highest deviation in terms of con rmed malaria records between HC and HO data followed by Dilla town and Dilla zuria district, whereas Wonago district was the least. Similar to our nding, a three-month facility-based study comprising of various settings conducted in southern Ethiopia showed that majority of facilities under-reported total malaria (both con rmed and clinical malaria) cases [10]. This in malaria data between the two systems, HC and HO, could be due to errors during entering the data from the sources (HCs) into recording formats of HMIS, lack of cross-checking and proo ng habits, training gaps on HMIS data use and unintentional/intentional false reports.…”

“…In Ethiopia, malaria data is captured through the health management information system (HMIS) at different tiers of the healthcare delivery systems. The hierarchy of data ow is from Health Posts (at kebele level) and Health Centers (HCs) to district health o ces (HOs) which in turn channels to Zonal Health Departments, then to Regional Health Bureaus and nally to the Federal Ministry of Health [10].…”

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.…”

Using Routine Health Information Data for Research in Low- and Middle-Income Countries: A Systematic Review

“…Despite the increasing use of RHIS data for research purposes, the quality of these data remains imperfect and such issues should be identi ed and addressed in order to limit estimation error and bias. RHIS data quality issues remain a particular concern in some settings [113][114][115] , however, other studies have shown that strategies that have been implemented to improve RHIS data across different international contexts can be successful 5,116 . Multiple strategies were discussed in the articles we reviewed in our paper, including strategies to address common data quality issues such as missingness and data validity, for example the simple exclusion of missing data and various imputation and interpolation methods.…”