Factors limiting data quality in the expanded programme on immunization in low and middle-income countries: A scoping review

Elsevier, Vaccine, Volume 38, 19 June 2020
Authors: 
Harrison K., Rahimi N., Carolina Danovaro-Holliday M.

Few public health interventions can match the immense achievements of immunization in terms of mortality and morbidity reduction. However, progress in reaching global coverage goals and achieving universal immunization coverage have stalled; with key stakeholders concerned about the accuracy of reported coverage figures. Incomplete and incorrect data has made it challenging to obtain an accurate overview of immunization coverage, particularly in low- and middle-income countries (LMIC). To date, only one literature review concerning immunization data quality exists. However, it only included articles from Gavi-eligible countries, did not go deep into the characteristics of the data quality problems, and used a narrow ‘data quality’ definition. This scoping review builds upon that work; exploring the “state of data quality” in LMIC, factors affecting data quality in these settings and potential means to improve it. Only a small volume of literature addressing immunization data quality in LMIC was found and definitions of ‘data quality’ varied widely. Data quality was, on the whole, considered poor in the articles included. Coverage numerators were seen to be inflated for official reports and denominators were inaccurate and infrequently adjusted. Numerous factors related to these deficiencies were reported, including health information system fragmentation, overreliance on targets and poor data management processes. Factors associated with health workers were noted most frequently. Authors suggested that data quality could be improved by ensuring proper data collection tools, increasing workers’ capacities and motivation through training and supervision, whilst also ensuring adequate and timely feedback on the data collected. The findings of this scoping review can serve as the basis to identify and address barriers to good quality immunization data in LMICs. Overcoming said barriers is essential if immunization's historic successes are to continue.