Malaria is an acute febrile illness caused by a parasitic
infection transmitted by Anopheles mosquitoes. Human malaria is caused by five
different Plasmodium parasites, with P. falciparum being the predominant
species in sub-Saharan Africa (SSA) [1]. In the past 15–20 years, the combined
efforts of Ministries of Health (MOHs) and National Malaria Control Programs
(NMCPs), and their partners, including PMI, have made tremendous progress
against malaria. This progress resulted from the massive scale-up of various
malaria prevention and control interventions, including facility and
community-based confirmatory testing and treatment of malaria cases,
intermittent preventive treatment in pregnancy (IPTp), and seasonal malaria
chemoprevention (SMC), along with indoor residual spraying (IRS) and
insecticide-treated nets (ITNs).
MOH / NMCPs use programmatic intervention coverage and
effectiveness data to regularly monitor impact of interventions; modify
intervention implementation approaches (e.g., if coverage estimates are
sub-par) or switch interventions altogether (e.g., if effectiveness is observed
to be lower than expected). Intervention coverage and effectiveness has
traditionally been assessed by post-intervention campaign surveys or periodic
nationally-representative surveys (e.g., Demographic and Health Surveys [DHS],
Malaria Indicator Surveys [MIS], Multiple Indicator Cluster Surveys [MICS]), or
estimated by complex mathematical modelling. The limitations of surveys are
that—while generally robust—they only occur every 2–5 years; take time; require
significant human, logistical and financial resources and capabilities; and may
not be powered sufficiently enough to provide sub-national intervention
estimates. Similarly, mathematical modelling may be limited by the available
data and the significant technical expertise needed to develop and run the
models, let alone run them continuously. Additionally, neither surveys or
modeling may avail necessary estimates at key strategic moments in the malaria
programming planning, implementation and monitoring cycle, such as the
development of national strategies or design of necessary donor documents
(e.g., Global Fund Concept Notes or PMI Malaria Operational Plans).
Countries health
management information systems (HMIS) have been dramatically strengthened in
the past few years, with countries being able to consistently and fully report
on outpatient, inpatient and other programmatic data—much of this progress has
been made by adopting, piloting and rolling out the district health management
information system 2 (DHIS2), an open-source data-system software specifically
developed to capture health data in lower-and-middle income countries. Because
of their sheer volume across space and time, data collected and reported through
HMIS like DHIS2 complement and even offer an alternative to nationally
representative and other ad hoc surveys to assess health intervention coverage
and effectiveness, and ultimately impact on health outcomes. [2–4]