Recommendations for estimating and reporting vaccine effectiveness by time since vaccination: a COVID-19 case study - Summary - MDSpire

Recommendations for estimating and reporting vaccine effectiveness by time since vaccination: a COVID-19 case study

  • By

  • Esther Kissling

  • Baltazar Nunes

  • Mariëtte Hooiveld

  • Iván Martínez-Baz

  • Susana Monge

  • Chris Robertson

  • Mirjam Knol

  • Noémie Sève

  • Ivan Mlinarić

  • Lisa Domegan

  • Ausenda Machado

  • Heather Whitaker

  • Mihaela Lazar

  • Adam Meijer

  • Theresa Enkirch

  • Itziar Casado

  • Gloria Pérez-Gimeno

  • Naoma William

  • Vincent Enouf

  • Sanja Kurečić Filipović

  • Adele McKenna

  • Ana Paula Rodrigues

  • Simon de Lusignan

  • Olivia-Carmen Timnea

  • Neus Latorre-Margalef

  • Jesús Castilla

  • Francisco Pozo

  • Mark Hamilton

  • Shirley Masse

  • Maja Ilić

  • Luca Basile

  • Joan O’Donnell

  • Raquel Guiomar

  • Maximilian Riess

  • Rodica-Manuela Popescu

  • Angela M C Rose

  • Nick Andrews

  • Sabrina Bacci

  • Lucia Pastore Celentano

  • Marta Valenciano

  • Alain Moren

  • Philippe Beutels

  • Niel Hens

  • on behalf of I-MOVE-COVID-19 and ECDC primary care study teams

  • November 17, 2025

  • 0 min

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Objective:

To provide recommendations for estimating and reporting COVID-19 vaccine effectiveness (VE) by time since vaccination (TSV) in case-control studies, emphasizing its importance for public health decisions.

Key Findings:
  • VE estimates decline over time, influenced by variant and outcome, highlighting the need for timely data.
  • TSV is crucial for accurate VE comparisons across different studies, ensuring valid conclusions.
  • Proper definitions and calculations of TSV enhance the quality of VE estimates, reducing potential biases.
Interpretation:

Understanding VE by TSV is essential for evaluating vaccine performance and making informed public health decisions, particularly in response to emerging variants.

Limitations:
  • Observational studies may be subject to bias, such as selection bias or confounding factors.
  • Missing data can affect the precision of VE estimates, leading to potential misinterpretations.
Conclusion:

The guidelines aim to improve the quality and comparability of VE estimates by TSV, encouraging ongoing refinement through collaborative input from the research community.

Original Source(s)

Recommendations for estimating and reporting vaccine effectiveness by time since vaccination: a COVID-19 case study

  • by Esther Kissling, Baltazar Nunes, Mariëtte Hooiveld, Iván Martínez-Baz, Susana Monge, Chris Robertson, Mirjam Knol, Noémie Sève, Ivan Mlinarić, Lisa Domegan, Ausenda Machado, Heather Whitaker, Mihaela Lazar, Adam Meijer, Theresa Enkirch, Itziar Casado, Gloria Pérez-Gimeno, Naoma William, Vincent Enouf, Sanja Kurečić Filipović, Adele McKenna, Ana Paula Rodrigues, Simon de Lusignan, Olivia-Carmen Timnea, Neus Latorre-Margalef, Jesús Castilla, Francisco Pozo, Mark Hamilton, Shirley Masse, Maja Ilić, Luca Basile, Joan O’Donnell, Raquel Guiomar, Maximilian Riess, Rodica-Manuela Popescu, Angela M C Rose, Nick Andrews, Sabrina Bacci, Lucia Pastore Celentano, Marta Valenciano, Alain Moren, Philippe Beutels, Niel Hens, on behalf of I-MOVE-COVID-19 and ECDC primary care study teams

  • November 17, 2025

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