Date Published: September 27, 2017
Publisher: The American Society of Tropical Medicine and Hygiene
Author(s): Ruth A. Ashton, Adam Bennett, Joshua Yukich, Achuyt Bhattarai, Joseph Keating, Thomas P. Eisele.
Coverage of malaria control interventions is increasing dramatically across endemic countries. Evaluating the impact of malaria control programs and specific interventions on health indicators is essential to enable countries to select the most effective and appropriate combination of tools to accelerate progress or proceed toward malaria elimination. When key malaria interventions have been proven effective under controlled settings, further evaluations of the impact of the intervention using randomized approaches may not be appropriate or ethical. Alternatives to randomized controlled trials are therefore required for rigorous evaluation under conditions of routine program delivery. Routine health management information system (HMIS) data are a potentially rich source of data for impact evaluation, but have been underused in impact evaluation due to concerns over internal validity, completeness, and potential bias in estimates of program or intervention impact. A range of methodologies were identified that have been used for impact evaluations with malaria outcome indicators generated from HMIS data. Methods used to maximize internal validity of HMIS data are presented, together with recommendations on reducing bias in impact estimates. Interrupted time series and dose-response analyses are proposed as the strongest quasi-experimental impact evaluation designs for analysis of malaria outcome indicators from routine HMIS data. Interrupted time series analysis compares the outcome trend and level before and after the introduction of an intervention, set of interventions or program. The dose-response national platform approach explores associations between intervention coverage or program intensity and the outcome at a subnational (district or health facility catchment) level.
With a renewed interest in achieving malaria elimination and funding available from a variety of sources for malaria control, many malaria endemic countries have successfully increased coverage of malaria prevention and control interventions as part of their national strategic plans.1 As countries consider approaches to sustain these gains or progress toward elimination, there is a continued need for rigorous evaluation to demonstrate the impact of interventions delivered by national control or elimination programs, and to advocate for continued investment in malaria control and elimination.
The core function of an HMIS is to collect, transmit, and analyze indicators required for health system management.21 The current review focusses on health facility-based HMIS data, but is also relevant to any parallel malaria-specific reporting systems operating in countries. In general, clinical data from individual patients are aggregated by age category (< 5 or ≥ 5 years) at facility-level each month, then reported to the supervising administrative unit (e.g., district) using a standardized format. Districts review and analyze data received from facilities, provide feedback, and may aggregate data into district totals to submit to the next level (e.g., region). HMIS data are common across all malaria-endemic countries, yet have been underused in impact evaluation due to quality and bias concerns. Increasing investments in malaria surveillance, broad availability of RDTs, improved systems to record, transmit, and process these results, and the reduced power of population malariometric surveys in areas of low transmission prompt a necessary reconsideration of the utility of HMIS data in impact evaluation. Source: http://doi.org/10.4269/ajtmh.16-0734