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

Abstract

Background Effective planning for disease prevention and control requires accurate, adequately-analysed, interpreted and communicated data. This study assessed the quality of routine Health Management Information System (HMIS) data at healthcare facility (HF) and district levels in Tanzania. Methods HMIS tools used at primary health care facilities (dispensary, health centre, hospital) and district office were reviewed to assess their availability, completeness, and accuracy of collected data. The assessment involved seven health service areas namely, Outpatient department, Inpatient department, Antenatal care, Family Planning, Post-natal care, Labour and Delivery and Provider-initiated Testing and Counselling. Results A total of 115 HFs in 11 districts were assessed. Registers (availability rate = 91.1%; interquartile range (IQR):66.7%-100%) and report forms (86.9%; IQR:62.2%-100%) were the most utilized tools. There was a limited use of tally-sheets (77.8%; IQR:35.6%-100%). Tools availability at dispensary was 91.1%, health-centre 82.2% and hospital 77.8%, and was poor in urban districts. The availability rate at the district level was 65% (IQR:48%-75%). Reports were highly over-represented in comparison to registers’ records, with large differences observed at HF phase of the data journey and more profound in hospitals. Tool availability and data quality varied by service-areas, indicators, facility level, and districts, however, with a remarkable improvement over the years. Conclusion There are high variations and improvements in the tool utilisation and data accuracy at facility and district levels. The routine HMIS is weak and data at district level inaccurately reflects what is available at the HFs. These results highlight the need to design tailored and inter-service strategies for improving data quality.

Twenty Years of Integrated Disease Surveillance and Response in Sub-Saharan Africa: Challenges and Opportunities for Effective Management of Infectious Disease Epidemics

“…Challenges of IDSR in SSA This review indicates that in most countries, data generated through the routine HMIS, which is the key source of IDSR, are rarely assessed for their quality, analysed and used to support decision-making [29]. Several studies in SSA have revealed weaknesses in case identi cation and recording at the primary healthcare facilities [14,24,37,57,72,82]. The quality of data management system remains a challenge, with incomplete and inconsistent data constantly being reported at different levels of the surveillance system.…”

“…The quality of data management system remains a challenge, with incomplete and inconsistent data constantly being reported at different levels of the surveillance system. In Ethiopia, Liberia and Tanzania, assessments of the information systems have identi ed some data quality issues and lack of use of the generated data [45,56,72,83,84]. In a study in Ethiopia, though the surveillance system was found to be simple, useful, exible, acceptable and representative, it lacked regular data analysis and feedback dissemination [35].…”

“…Incomplete data ling and inadequate organization have been reported as an inbuilt shortcoming at all levels of IDSR in SSA countries [38,39,58,87,88]. Routine data analysis is still inadequate at facility and district levels in the majority of the countries mainly due to lack of clear guidelines on how and when to analyse data [57,58,63,72]. Reasons identi ed for limited data analysis included lack of skilled personnel, poor understanding of the use of surveillance data in planning together as well as inadequate infrastructure [72].…”

“…The routine health information system in several SSA countries is characterised by persistent incompleteness and other data quality issues [70,71,72]. A high level of mismatch between the entries in the HMIS registers, tally sheets and the electronic District Health Information System (DHIS2) database have also been reported [72,73]. Moreover, since most primary level health care facilities lack diagnostic capabilities, the data they generate rely on syndromic, with low speci city [74].…”

“…Moreover, since most primary level health care facilities lack diagnostic capabilities, the data they generate rely on syndromic, with low speci city [74]. Thus, despite some progress in recent years, the core IDSR data source is still weak and inaccurately re ects what exists is generated from the primary healthcare facility levels [72].…”