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Abstract Background We conducted a mixed methods evaluation to assess whether implementing a primary care quality improvement (QI) programme utilising a digital call-and-recall tool improved timely receipt and equity of first measles, mumps and rubella (MMR) and diphtheria tetanus, pertussis and polio-containing (DTaP /IPV) vaccinations. Methods 138,133 and 136,952 children were eligible to receive first MMR and DTaP/IPV respectively between 1st January 2019 and 31st January 2024 in North East London. We compared proportions with timely first MMR or DTaP/IPV receipt (by ages 18 and six months respectively) pre- and post-implementation using an interrupted time series analysis. We calculated change in the Slope Index of Inequality (SII) by an area-level deprivation measure. We conducted ‘Think Aloud’ exercises and semi-structured interviews with users. Findings The proportion of children with timely first MMR receipt increased by 5·3 % (Rate Ratio [RR]:1·053, 95 % confidence interval [CI]:1·033–1·073), equating to an absolute increase in timely MMR receipt of 4·1 % - from 77·7 % to 81·8 % - and for first DTaP/IPV by 0·9 % (RR:1·009, 95 % CI:1·003–1·015). There was no significant change in SII for either vaccine. Users reported improved recall with tool use, but identified practice-level and systemic barriers, including staff dynamics and unachievable national targets, limiting its consistent use. Interpretation In a real-world setting, a call-and-recall tool within a primary care QI programme improved timely first MMR receipt. Sustained improvement requires additional support including by incentivising achievable targets and improving staff capacity and training.

More information Original publication

DOI

https://doi.org/10.1016/j.vaccine.2024.126522

Publisher

Elsevier B.V

Publication Date

01/01/2025

Addresses

This study was funded by the National Institute for Health and Care Research (NIHR) School for Primary Care Research (Project reference 664)

Keywords

Primary care, Vaccination, Immunisation, Quality improvement, Health data science