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Background
Adapting and enhancing malaria information systems in countries entering pre-elimination
Methods
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Adapting and enhancing malaria information systems in countries entering pre-elimination

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Presented at the 2016 ASTMH conference

Published in: Health & Medicine
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Adapting and enhancing malaria information systems in countries entering pre-elimination

  1. 1. Background Adapting and enhancing malaria information systems in countries entering pre-elimination Methods There has been substantial progress in addressing sub-Saharan Africa’s malaria burden. As countries reduce transmission, strong health management and information systems (HMIS) become critical to provide data to monitor progress, identify rebounds, and tailor new approaches for residual foci transmission. Although investments have been made in strengthening HMIS and measuring results, there is little guidance for countries needing to adapt HMIS to changing malaria epidemiology and changing HMIS technology. A systematic literature review was conducted to answer the following questions: (1) What assessments of HMIS performance exist, and which have been conducted, both specific to malaria, and with respect to broader information systems? and (2) Have any lessons been learned by assessing one system that may help strengthen others at various stages of malaria control? This work is part of a larger objective to systematically measure the functionality of HMIS and to identify supporting factors for countries with the potential to reduce malaria transmission substantially. Key findings • Systematic literature review and synthesis • Preferred Reporting Items for Systematic Reviews and Meta Analysis (PRISMA) Conclusions Assessments of HMIS are key for our understanding of target areas in need of additional efforts or resources. The lack of a comprehensive functionality metric makes it difficult to compare HMIS across countries and time. However, the analysis of various elements of HMIS functionality may provide insight into the way in which country HMIS compare. The results of this desk review will be supplemented with in-country work to develop country case studies and a toolbox to help countries at various stages of malaria control learn from one another to adapt HMIS and strengthen routine data capture. Acknowledgments This research has been supported by the President’s Malaria Initiative (PMI) through the United States Agency for International Development (USAID) under the terms of MEASURE Evaluation cooperative agreement AIDOAA-L-14-00004. The opinions expressed are those of the authors and do not necessarily reflect the views of USAID, or the United States Government. Tanzania, Uganda, Botswana, Ghana, Mozambique, Rwanda, Zambia, Kenya, Malawi, Nigeria, South Africa, Sudan, and Sierra Leone • While the articles discussed elements of HMIS, there was no comprehensive measurement that allowed functionality to be compared and used to rank country performance across HMIS and time. Jui A. Shah1, Michael Paula1, Yazoume Ye1 1MEASURE Evaluation/ICF Figure 1: PRISMA flow diagram Table 1: Number of articles that reviewed functionality aspects of HMIS Photo credit: Fadhili Akida HMIS Aspect Number of studies Data quality 12 System structure and collection tools 13 Processes 5 Personnel 12 Context 3 Data use 8 Although defining functionality of an entire routine health information system is difficult because of the unique contextual setting of each HMIS, we identified six common aspects of performance that affect system functionality (Table 1). Three were considered most influential to HMIS functionality: personnel, data quality, and system structure and collection tools. • Personnel: Personnel attributes, such as motivation, coordination, and capacity to collect data, affect HMIS effectiveness, and personnel well prepared to collect HMIS data will have a positive effect on the entire HMIS. Supportive supervision and training in data collection can create environments in which workers appreciate the importance of routine data. • Data quality: Quality, most commonly described as the timeliness, completeness, and accuracy of HMIS data, has been discussed extensively in the literature. Common issues related to poor data quality include error-prone manual input, reporting duplication, and missing data. Poor data quality has been shown to limit the use of HMIS data by policymakers, inhibiting a primary function of HMIS. Data quality can be improved through strong monitoring and evaluation of HMIS structures, appropriate supervision, and the use of data assessment tools. • System structure and collection tools: The architecture and methods of data collection comprises forms, registers, manuals, and dashboards. In resource-limited countries, HMIS data flow may emphasize national use, leading to confusion about the way in which information should be captured and stored at local levels. • 5 electronic bibliographic databases: PubMed, Cochrane Library, SCOPUS, Global Health, and Popline • Combination of key word searches and Medical Subject Headings terms: (1) Africa and (2) HMIS • Inclusion criteria: peer-reviewed articles or institutional reports on HMIS performance in African countries between 2005 and 2015 • 3 studies were dropped for lack of focus on HMIS • 2 articles were excluded because full text inaccessible 1,581 peer-reviewed articles 1,569 after duplicates removed 1,545 after titles screened for relevant scope 25 articles after assessed for eligibility 20 articles in final review • 2 reviewers synthesized and analyzed publications using a data extraction framework • Synthesis focused on specific elements of the HMIS and malaria information system, including data quality, system structure collection and tools, processes, personnel, context, and interventions. Model Data Sources Screening process Synthesis Countries covered • 3 Performance of Routine Information System Management (PRISM) tool • 1 Routine Data Quality Assessment tool • 1 WHO Data Quality Report Card tool Methods used in the studies • 7 qualitative reviews • 5 literature reviews • 3 mixed-methods studies • 2 cross-sectional analyses • 2 surveys • 1 quantitative assessment HMIS assessment tools employed by the studies Areas of performance

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