How Exemplars used data and evidence for decision-making | Detailed strategies | Country examples and links to more detail |
Invest in data systems and foster a culture of data use | Foster a culture of data use through regular integration of M&E into program operations and at all levels of government | Senegal had quarterly data review meetings at health post/CHW level, including subsequent regional meetings () To address national goals for PMTCT, Rwanda's MOH created a new health-information management system called TRACnet, which consolidated mobile phone reports submitted by CHWs to provide timely data on HIV cases . () |
Invest in data systems and the human resource capacity necessary to operate them | Bangladesh established an HMIS unit at the Directorate General of Health Services and divisional training centers () | |
Use multiple data sources | Leverage the strengths and weaknesses of:
| To prepare for Maternal and Neonatal Tetanus Elimination, Senegal reviewed subnational tetanus case data and also supplemented this with field visits () Senegal used multiple data sources to inform Intermittent Preventive Treatment (IPT) delivery () |
Identify program areas of need | Use national and sub-national data to determine program areas with low coverage | Based on a study of treatment-seeking, Peru identified inconsistent progress in care-seeking () |
Use national and sub-national data to understand disease burden | Bangladesh designed Integrated Management of Childhood Illness (IMCI) to focus on the most common causes of death () Nepal used the Health Management Information System (HMIS) and DHS to monitor causes of under-five mortality, including measles and malaria () | |
Use existing research to identify effective solutions | Bangladesh introduced community-based treatment of neonatal sepsis based on a study in India () | |
Pilot at small scale when necessary | Select pilot sites based on goal of the pilot. To ensure implementation plans are designed to serve the communities with the highest need, pilot in areas of highest need. To determine effectiveness of an intervention, pilot in areas of highest likelihood of success. | In Senegal, a pilot of facility-based IMCI in one district identified a supervision gap. () |
When considering pilot testing, consider impact of potential delayed introduction of evidence-based intervention | In Nepal, the practice of requiring pilot testing by local researchers and implementing partners led to delays of PCV and rotavirus. () | |
Customize how interventions are implemented | Prioritize interventions geographically based on local data on burden of disease | In Peru, ITN distribution and IRS targeted in high-transmission areas. () |
Assess local need when implementing with an equity lens | Bangladesh decided to implement IMCI at the community level, reflecting the needs of a predominantly rural country. () | |
Use data to evaluate impact of system constraints | Bangladesh selected PCV-10 instead of PCV-13 due to evidence of similar effectiveness but lower cold-chain requirements. () | |
Adjust continuously | During implementation, use routine program monitoring data, evaluations, and other available data sources. After implementation and during adaptation, reassess program areas of need, including adaptations to interventions and/or implementation strategies | In Peru, subnational surveillance on artemisinin resistance led to early adoption of differentiated ACT regimens. () Senegal conducted evaluations post-PCV introduction and post-rotavirus vaccine introduction () |
