Root Causes of Poor Immunisation Data Quality and Proven Interventions: A Systematic Literature Review

Effective allocation of resources and investments heavily rely on good quality data. As global investments in vaccines increases, particularly by organisations such as Gavi, The Vaccine Alliance, Switzerland, the demand for data which is accurate and representative is urgent. Understanding what causes poor immunisation data and how to address these problems are therefore key in maximizing investments, improving coverage and reducing risks of outbreaks.

Identify the root causes of poor immunisation data quality and proven solutions for guiding future data quality interventions.

Qualitative systematic review of both scientific and grey literature using key words on immunisation and health information systems. Once screened, articles were classified either as identifying root causes of poor data quality or as an intervention to improve data quality. A total of 8,646 articles were initially identified which were screened and reduced to 26. Results were heterogeneous in methodology, settings and conclusions with a variation of outcomes. Key themes were underperformance in health facilities and limited Human Resource (HR) capacity at the peripheral level leading to data of poor quality. Repeated reference to a "culture" of poor data collection, reporting and use in low-income countries was found implying that it is the attitudes and subsequent behaviour of staff that prevents good quality data. Documented interventions mainly involved implementing Information Communication Technology (ICT) at the district level. However, without changes in HR capacity the skills and practices of staff remain a key impediment to reaching its full impact.

There was a clear incompatibility between identified root causes, mainly being behavioural and organizational factors, and interventions introducing predominantly technical factors. More emphasis should be placed on interventions that build on current practices and skills in a gradual process in order to be more readily adopted by health workers. Major gaps in the literature exist mainly in the lack of assessment at central and intermediate levels and association between inaccurate target setting from outdated census data and poor data quality as well as limited documentation of interventions that target behaviour change and policy change. This prevents the ability to make informed decisions on best methodology for improving data quality.