Landscape Analysis


The landscape analysis represents the first step of the ARISE project in exploring the drivers of routine immunization (RI) system performance in sub-Saharan Africa. It reports on a systematic examination of written documentation of RI performance and existing performance data, and it summarizes interviews with implementers and technical and development partners to improve understanding of the drivers of system performance.

The overall purpose of the analysis was to generate a broad range of ideas about the drivers of RI system performance in Africa for further exploration in a second phase of the project.


  1. Begin to build an evidence base to improve understanding of the drivers of the RI system performance.
  2. Guide the framing of questions and issues to be examined through ARISE in-depth country studies.
  3. Identify countries and country experiences for investigation in ARISE in-depth studies.

 Summary of Methods

Three data streams fed into ARISE’s landscape analysis: a) the document review; b) the key informant screening and key informant in-depth interviews; and c) secondary data analysis. The document review followed systematic review techniques, and focused on published (including non–peer reviewed) and unpublished literature from 1995 to the present. In total, 757 documents were identified for review. After screening for relevance, researchers extracted data from 150 documents. Researchers conducted 46 screening interviews with a range of key informants and 13 additional in-depth interviews in Ghana. Secondary data were reviewed for existing measures of routine immunization (RI) system performance. These data were used to explore performance trends in sub-Saharan Africa and to guide the selection of in-depth study countries for the next phase of the project. Researchers examined a series of indicators, including coverage and equity, over the past decade.

 Identification of Drivers

Researchers also identified potential drivers of RI system performance from data extracted from written sources and key informant interviews, using the following criteria:

  1. Association with RI system performance—is there improved system performance or improved outcomes?
  2. Frequency of the evidence or occurrence of the driver—how often does this driver area emerge?
  3. Triangulation of data sources—is this driver emerging from one or more data source?
  4. Strength of evidence—is the evidence categorized as 3 or 4 (using ARISE definitions)?
  5. How driver resonates with the experience of technical team—is it plausible or known to be important for positive performance?

Researchers recorded each potential driver that emerged in a “driver journal,” which documented evidence and project thinking about the particular driver area. From that exhaustive list, a short list of drivers that have both a theoretical basis, suggesting importance, and those that that emerged from a larger and more rigorous evidence base, was derived.

Preliminary Identification of Drivers

  1. Multidimensional interventions involving systems change
  2. Country commitment to health
  3. Strong country ownership of routine immunization
  4. Immunization financing
  5. Use of community health workers/special cadres of RI
  6. Overall supply of health workers
  7. Availability and use of data for action
  8. Role of RI system in creating and sustaining demand
  9. Adaptation of RI strategies to context


The landscape analysis represents a discovery period and sets the stage for future exploration of drivers in specific contexts. The large number of potential drivers identified during this process enriches understanding of the many factors that practitioners, researchers, and program managers report as important influences on RI system performance. However, for the most part, the data fails to clearly link drivers to performance, either for changes in essential system functions or for system outcomes (e.g., coverage, equity).

Furthermore, because most reports lacked detail or failed to substantiate findings, the data did not allow researchers to prioritize any one of the emerging drivers over another. Finally, none of the data gathered for this analysis allowed researchers to assess the full picture of country experience or to review enough examples of proposed drivers to make generalizations about driver behavior or the capacity to improve performance.

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