tetano
Editor, Senior Moderator
JMIR Public Health Surveill
. 2024 Nov 22:10:e56926.
doi: 10.2196/56926. SARS-CoV-2 Infection Risk by Vaccine Doses and Prior Infections Over 24 Months: ProHEpiC-19 Longitudinal Study
Pere Torán-Monserrat[SUP] 1 2 3 4 [/SUP], Noemí Lamonja-Vicente[SUP] 1 [/SUP], Anna Costa-Garrido[SUP] 1 [/SUP], Lucía A Carrasco-Ribelles[SUP] 1 5 [/SUP], Bibiana Quirant[SUP] 6 7 8 [/SUP], Marc Boigues[SUP] 6 7 8 [/SUP], Xaviera Molina[SUP] 9 10 [/SUP], Carla Chacón[SUP] 1 [/SUP], Rosalia Dacosta-Aguayo[SUP] 1 [/SUP], Fernando Arméstar[SUP] 11 [/SUP], Eva María Martínez Cáceres[SUP] 6 7 8 [/SUP], Julia G Prado[SUP] 2 12 13 [/SUP], Concepción Violán[SUP] 1 5 8 14 15 [/SUP]; ProHEpiC-19 study group[SUP] 16 [/SUP]
Collaborators, Affiliations
Background: As the vaccination campaign against COVID-19 progresses, it becomes crucial to comprehend the lasting effects of vaccination on safeguarding against new infections or reinfections.
Objective: This study aimed to assess the risk of new SARS-CoV-2 infections based on the number of vaccine doses, prior infections, and other clinical characteristics.
Methods: We defined a cohort of 800 health care workers in a 24-month study (March 2020 to December 2022) in northern Barcelona to determine new infections by SARS-CoV-2. We used extended Cox models, specifically Andersen-Gill (AG) and Prentice-Williams-Peterson, and we examined the risk of new infections. The AG model incorporated variables such as sex, age, job title, number of chronic conditions, vaccine doses, and prior infections. Additionally, 2 Prentice-Williams-Peterson models were adjusted, one for those individuals with no or 1 infection and another for those with 2 or 3 infections, both with the same covariates as the AG model.
Results: The 800 participants (n=605, 75.6% women) received 1, 2, 3, and 4 doses of the vaccine. Compared to those who were unvaccinated, the number of vaccine doses significantly reduced (P<.001) the risk of infection by 66%, 81%, 89%, and 99%, respectively. Unit increase in the number of prior infections reduced the risk of infection by 75% (P<.001). When separating individuals by number of previous infections, risk was significantly reduced for those with no or 1 infection by 61% (P=.02), and by 88%, 93%, and 99% (P<.001) with 1, 2, 3, or 4 doses, respectively. In contrast, for those with 2 or 3 previous infections, the reduction was only significant with the fourth dose, at 98% (P<.001). The number of chronic diseases only increased the risk by 28%-31% (P<.001) for individuals with 0-1 previous infections.
Conclusions: The study suggests that both prior infections and vaccination status significantly contribute to SARS-CoV-2 immunity, supporting vaccine effectiveness in reducing risk of reinfection for up to 24 months after follow-up from the onset of the pandemic. These insights contribute to our understanding of long-term immunity dynamics and inform strategies for mitigating the impact of COVID-19.
Keywords: COVID-19; SARS-CoV-2; cohort; coronavirus; epidemiological; epidemiology; extended Cox models; health care workers; infectious; longitudinal; respiratory; risks; vaccinated; vaccination; vaccines.
. 2024 Nov 22:10:e56926.
doi: 10.2196/56926. SARS-CoV-2 Infection Risk by Vaccine Doses and Prior Infections Over 24 Months: ProHEpiC-19 Longitudinal Study
Pere Torán-Monserrat[SUP] 1 2 3 4 [/SUP], Noemí Lamonja-Vicente[SUP] 1 [/SUP], Anna Costa-Garrido[SUP] 1 [/SUP], Lucía A Carrasco-Ribelles[SUP] 1 5 [/SUP], Bibiana Quirant[SUP] 6 7 8 [/SUP], Marc Boigues[SUP] 6 7 8 [/SUP], Xaviera Molina[SUP] 9 10 [/SUP], Carla Chacón[SUP] 1 [/SUP], Rosalia Dacosta-Aguayo[SUP] 1 [/SUP], Fernando Arméstar[SUP] 11 [/SUP], Eva María Martínez Cáceres[SUP] 6 7 8 [/SUP], Julia G Prado[SUP] 2 12 13 [/SUP], Concepción Violán[SUP] 1 5 8 14 15 [/SUP]; ProHEpiC-19 study group[SUP] 16 [/SUP]
Collaborators, Affiliations
- PMID: 39648969
- DOI: 10.2196/56926
Background: As the vaccination campaign against COVID-19 progresses, it becomes crucial to comprehend the lasting effects of vaccination on safeguarding against new infections or reinfections.
Objective: This study aimed to assess the risk of new SARS-CoV-2 infections based on the number of vaccine doses, prior infections, and other clinical characteristics.
Methods: We defined a cohort of 800 health care workers in a 24-month study (March 2020 to December 2022) in northern Barcelona to determine new infections by SARS-CoV-2. We used extended Cox models, specifically Andersen-Gill (AG) and Prentice-Williams-Peterson, and we examined the risk of new infections. The AG model incorporated variables such as sex, age, job title, number of chronic conditions, vaccine doses, and prior infections. Additionally, 2 Prentice-Williams-Peterson models were adjusted, one for those individuals with no or 1 infection and another for those with 2 or 3 infections, both with the same covariates as the AG model.
Results: The 800 participants (n=605, 75.6% women) received 1, 2, 3, and 4 doses of the vaccine. Compared to those who were unvaccinated, the number of vaccine doses significantly reduced (P<.001) the risk of infection by 66%, 81%, 89%, and 99%, respectively. Unit increase in the number of prior infections reduced the risk of infection by 75% (P<.001). When separating individuals by number of previous infections, risk was significantly reduced for those with no or 1 infection by 61% (P=.02), and by 88%, 93%, and 99% (P<.001) with 1, 2, 3, or 4 doses, respectively. In contrast, for those with 2 or 3 previous infections, the reduction was only significant with the fourth dose, at 98% (P<.001). The number of chronic diseases only increased the risk by 28%-31% (P<.001) for individuals with 0-1 previous infections.
Conclusions: The study suggests that both prior infections and vaccination status significantly contribute to SARS-CoV-2 immunity, supporting vaccine effectiveness in reducing risk of reinfection for up to 24 months after follow-up from the onset of the pandemic. These insights contribute to our understanding of long-term immunity dynamics and inform strategies for mitigating the impact of COVID-19.
Keywords: COVID-19; SARS-CoV-2; cohort; coronavirus; epidemiological; epidemiology; extended Cox models; health care workers; infectious; longitudinal; respiratory; risks; vaccinated; vaccination; vaccines.