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. In recent years, efforts and investments have been put in strengthening health management information systems (HMIS) in Sub-Saharan Africa to improve data accessibility to decision-makers. This study assessed the quality of routine HMIS data at primary healthcare facility (HF) and district levels in Tanzania. Methods: The assessment involved Outpatient, Inpatient, Antenatal care, Family Planning, Post-natal care, Labour and Delivery, and Provider-initiated Testing and Counselling service areas in primary health care facilities. Records were tracked and compared at different phases of the data journey i.e. registers, tally-sheets, HF monthly reports submitted to the district level, and electronic database. We assessed the availability and utilization of each HMIS tool and quantified completeness and accuracy levels of the collected data in each phase of the reporting system. Results: A total of 115 HFs (including hospitals, health centres, and dispensaries) 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 HMIS tools. There was a limited use of tally-sheets (77.8%; IQR:35.6%-100%). Tools availability at the dispensary was 91.1%, health-centre 82.2% and hospital 77.8%, and was low 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 the HF phase of the data journey and more profound in hospitals. Tool availability and data quality in the reproductive health service area outperformed the general services (outpatient and inpatient) areas. Indicators with large number of clients, multiple variables, disease categorization, or those linked with dispensing of medicine performed poorly. Conclusion: There are high variations in the tool utilisation and data accuracy at facility and district levels. A significant improvement on patient registration and use of tally-sheets was observed over the years. The routine HMIS is weak and data at district level inaccurately reflects what is available at the source (HFs). These results highlight the need to design tailored and inter-service strategies for improving data quality.

Feasibility, usability and acceptability of a novel digital hybrid-system for reporting of routine health information in Southern Tanzania: A mixed-methods study

“…Aggregated summary data from these reports is entered manually in the electronic DHIS2 at district level [6] where various users can view this summary data at different levels of aggregation (figure 1 b below). While the system is well established, incompleteness and inconsistency of data due to calculation and reporting errors have been described [7][8][9][10]. Also, digital aggregated data is often not available on time for decision making especially at sub-national level [11].…”

Factors affecting data quality of health management information system at township level, Bago region, Myanmar

“…22 These revelations were consistently conveyed in the outcomes of updated studies conducted by Mboera et al in Tanzania, and Kebede, Adeba et al in Ethiopia. 19,21,23 Besides, these findings cause PRISM's concepts more confirmable. Although in this study the HMIS data quality was not directly linked with other organizational factors (e.g.…”

“…This similar situation was also found in recent studies from different countries; Tanzania, Ethiopia, and Rwanda. [18][19][20] Besides, this study reported 100% timeliness coverage. This figure is consistent with the national report timeliness rate (97%) in Myanmar in 2017, but highly different from the report timeliness rates in a Myanmar study of Saw et al and an Ethiopian study of Ouedraogo et al at 50% and 70% respectively.…”

“…These HMIS technical issues were agreed with the most recent studies done on similar healthcare settings across the same designs in Tanzania in 2020, and in Ethiopia in 2021. 19,24 Also, another Ethiopian mixed-methods study reported similar technical issues that filling several registry forms, parallel reporting, and difficulties in integrating the HMIS data from disharmonized sources cause the HMIS data poor. 25 In this study, long reporting formats and meanings of the HMIS manual's contents do not affect the HMIS data quality.…”

Improvements in malaria surveillance through the electronic Integrated Disease Surveillance and Response (eIDSR) system in mainland Tanzania, 2013-2021

“…These differences may be partly attributed to the fact that the epidemiological week calendar year does not exactly align with the monthly calendar year. In addition, it is also likely that the larger differences observed in low, moderate and high epidemiological strata were due to data quality issues affecting reporting in the health facilities with high disease burden, [30] since data extraction entails an additional workload and time. Finally, there were periods of lower weekly reporting completeness between 2020 and 2021 that possibly suggest there was an issue with eIDSR data transmission as opposed to failure by the health facilities in sending the weekly reports.…”