Treffer: Lessons from piloting and scaling a real-time DHIS2 based treatment reporting tool for mass drug administration in Nigeria.
Weitere Informationen
Background: Mass drug administration (MDA) is the main intervention strategy for the elimination of several neglected tropical diseases (NTDs). In many endemic settings, monitoring and collation of MDA treatment data are conducted through paper-based forms and Excel-based spreadsheets. These methods are often slow, prone to errors and do not facilitate timely evidence-based decision making during and after MDA campaigns. The Nigerian National NTD programme and Sightsavers, developed a DHIS2 based platform for real-time collection, monitoring, and reporting of MDA treatment data. We piloted and scaled this DHIS2-based platform, monitoring the data quality, access, government ownership and utility of data for programmatic action at all levels. Methods: Three study areas (Jigawa, Enugu and Kwara States), with upcoming MDA campaigns were selected based on geographic spread and model of non-governmental development organisation (NGDO) implementing partner support. Following a pilot in Jigawa State, the DHIS2 platform was scaled-up across all three study areas, alongside the existing Excel-based systems. Programmatic data routinely collected via the two platforms were compared. Instances of data entry and access were monitored via the platform's metadata and a monitored helpline. Data was collected from participants through a self-administered questionnaire, field diaries and focus group discussions/key informant interviews. Quantitative data was analysed using Stata analytical software, while qualitative data was thematically analysed. Results: There was increased access and use of data at all levels within the DHIS2 system along with improved perceptions of government ownership of the data. Participants reported the ability to address errors and improve decision-making during campaigns as significant benefits of the tool. Scaling up DHIS2 was feasible, and similar benefits were observed in all the models of NGDO partner assistance. Conclusion: The DHIS2 tool enhanced all components of ownership, as well as demonstrated ability to be replicated in different settings. However, operating models, cultural contexts, and technical capacities across the diverse locations need to be considered when scaling up the tool. Author summary: Mass drug administration (MDA) campaigns for neglected tropical diseases (NTDs) often rely on paper-based forms and Excel spreadsheets for data collation, monitoring, and reporting. These traditional methods are slow, error prone, and do not support timely, evidence-based decision-making to enhance MDA campaigns. The need for accurate, real-time reporting of MDA data during implementation remains critical for many national NTD programs. We piloted a new DHIS2-based platform co-developed by the national NTD programme and Sightsavers for reporting MDA data in Nigeria. This tool was subsequently scaled up in three different programmatic settings to assess its feasibility and explore the challenges of implementing the platform. We also assessed the potential of the tool to improve government ownership among program implementers. Findings indicates that the platform enhanced data access and ownership across the different levels of the health system. Program implementers also found the platform highly beneficial, and there was notable enthusiasm for its use, as it facilitated quicker decision-making during and after the MDA campaign. Key considerations regarding partner support, technological capabilities, internet penetration especially in rural areas, among others, are suggested as issues that must be addressed when scaling the tool more widely within a national program. [ABSTRACT FROM AUTHOR]
Copyright of PLoS Neglected Tropical Diseases is the property of Public Library of Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)