Vaccination is one of the main public health strategies to counter the spread of transmissible and potentially deadly diseases. The COVID-19 pandemic has put a strain on healthcare systems around the world, also adversely affecting vaccine administration, leading many parents to postpone even mandatory vaccinations. In this study, we aim to analyze how adherence to the vaccine schedule has changed for the cohort of children born in the three-year period 2019–2021. In particular, the coverage of the hexavalent vaccine—whose cycle must be completed in the first 24 months of life—and of the trivalent vaccine—whose the first dose must be administered in the first 24 months of life—in the Local Health Authority Benevento territory in southern Italy was studied. Using the DMAIC cycle of the Lean Six Sigma methodology we first analyze the vaccination coverage of those born in 2019, also using indicators defined by the Italian Ministry of Health, and then statistically compared it with those born in 2020 and 2021. For those born in 2020, following corrective actions implemented after the COVID-19 outbreak, overall vaccination coverage improved compared to those born in 2019 but worsened compared to those born in 2021. A particularly critical finding was shown for children not resident but domiciled in the territory for whom the adherence rate in 2021 drops to 76%.This work provides an excellent methodological basis for extending the analysis by considering a longer time and a larger number of variables.

Using DMAIC Approach to Study Vaccination Adherence in the COVID-19 Era: A Single-Center Study / Scala, Arianna; Grandito, Fabio; Rosa, Angelo; Volpe, Gennaro; De Fazio, Marco; Fossi, Elena; Bozza, Alessandro; Galdo, Valentina; Triassi, Maria; Improta, Giovanni. - (2025), pp. 1-11. ( 1st International Conference Logistics and Lean Engineering for Advanced Healthcare Methodologies Modelling, LLEAHMM 2024 ita 2024) [10.1007/978-3-031-82923-9_1].

Using DMAIC Approach to Study Vaccination Adherence in the COVID-19 Era: A Single-Center Study

Arianna Scala;Fabio Grandito;Valentina Galdo;Maria Triassi;Giovanni Improta
2025

Abstract

Vaccination is one of the main public health strategies to counter the spread of transmissible and potentially deadly diseases. The COVID-19 pandemic has put a strain on healthcare systems around the world, also adversely affecting vaccine administration, leading many parents to postpone even mandatory vaccinations. In this study, we aim to analyze how adherence to the vaccine schedule has changed for the cohort of children born in the three-year period 2019–2021. In particular, the coverage of the hexavalent vaccine—whose cycle must be completed in the first 24 months of life—and of the trivalent vaccine—whose the first dose must be administered in the first 24 months of life—in the Local Health Authority Benevento territory in southern Italy was studied. Using the DMAIC cycle of the Lean Six Sigma methodology we first analyze the vaccination coverage of those born in 2019, also using indicators defined by the Italian Ministry of Health, and then statistically compared it with those born in 2020 and 2021. For those born in 2020, following corrective actions implemented after the COVID-19 outbreak, overall vaccination coverage improved compared to those born in 2019 but worsened compared to those born in 2021. A particularly critical finding was shown for children not resident but domiciled in the territory for whom the adherence rate in 2021 drops to 76%.This work provides an excellent methodological basis for extending the analysis by considering a longer time and a larger number of variables.
2025
9783031829222
9783031829239
Using DMAIC Approach to Study Vaccination Adherence in the COVID-19 Era: A Single-Center Study / Scala, Arianna; Grandito, Fabio; Rosa, Angelo; Volpe, Gennaro; De Fazio, Marco; Fossi, Elena; Bozza, Alessandro; Galdo, Valentina; Triassi, Maria; Improta, Giovanni. - (2025), pp. 1-11. ( 1st International Conference Logistics and Lean Engineering for Advanced Healthcare Methodologies Modelling, LLEAHMM 2024 ita 2024) [10.1007/978-3-031-82923-9_1].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/1007039
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