Presented at the 2016 ASTMH conference

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