tetano
Editor, Senior Moderator
Epidemiol Infect
. 2024 Oct 7:152:e119.
doi: 10.1017/S0950268824001249. Spatio-temporal distributions of COVID-19 vaccine doses uptake in the Netherlands: a Bayesian ecological modelling analysis
Haoyi Wang[SUP] 1 2 [/SUP], Tugce Varol[SUP] 1 3 [/SUP], Thomas Gültzow[SUP] 1 4 [/SUP], Hanne M L Zimmermann[SUP] 1 [/SUP], Robert A C Ruiter[SUP] 1 [/SUP], Kai J Jonas[SUP] 1 [/SUP]
Affiliations
In the transitioning era towards the COVID-19 endemic, there is still a sizable population that has never been vaccinated against COVID-19 in the Netherlands. This study employs Bayesian spatio-temporal modelling to assess the relative chances of COVID-19 vaccination uptake - first, second, and booster doses - both at the municipal and regional (public health services) levels. Incorporating ecological regression modelling to consider socio-demographic factors, our study unveils a diverse spatio-temporal distribution of vaccination uptake. Notably, the areas located in or around the Dutch main urban area (Randstad) and regions that are more religiously conservative exhibit a below-average likelihood of vaccination. Analysis at the municipal level within public health service regions indicates internal heterogeneity. Additionally, areas with a higher proportion of non-Western migrants consistently show lower chances of vaccination across vaccination dose scenarios. These findings highlight the need for tailored national and local vaccination strategies. Particularly, more regional efforts are essential to address vaccination disparities, especially in regions with elevated proportions of marginalized populations. This insight informs ongoing COVID-19 campaigns, emphasizing the importance of targeted interventions for optimizing health outcomes during the second booster phase, especially in regions with a relatively higher proportion of marginalized populations.
Keywords: Bayesian spatio-temporal analysis; COVID-19; prevention; small area estimation; vaccination uptake.
. 2024 Oct 7:152:e119.
doi: 10.1017/S0950268824001249. Spatio-temporal distributions of COVID-19 vaccine doses uptake in the Netherlands: a Bayesian ecological modelling analysis
Haoyi Wang[SUP] 1 2 [/SUP], Tugce Varol[SUP] 1 3 [/SUP], Thomas Gültzow[SUP] 1 4 [/SUP], Hanne M L Zimmermann[SUP] 1 [/SUP], Robert A C Ruiter[SUP] 1 [/SUP], Kai J Jonas[SUP] 1 [/SUP]
Affiliations
- PMID: 39370683
- DOI: 10.1017/S0950268824001249
In the transitioning era towards the COVID-19 endemic, there is still a sizable population that has never been vaccinated against COVID-19 in the Netherlands. This study employs Bayesian spatio-temporal modelling to assess the relative chances of COVID-19 vaccination uptake - first, second, and booster doses - both at the municipal and regional (public health services) levels. Incorporating ecological regression modelling to consider socio-demographic factors, our study unveils a diverse spatio-temporal distribution of vaccination uptake. Notably, the areas located in or around the Dutch main urban area (Randstad) and regions that are more religiously conservative exhibit a below-average likelihood of vaccination. Analysis at the municipal level within public health service regions indicates internal heterogeneity. Additionally, areas with a higher proportion of non-Western migrants consistently show lower chances of vaccination across vaccination dose scenarios. These findings highlight the need for tailored national and local vaccination strategies. Particularly, more regional efforts are essential to address vaccination disparities, especially in regions with elevated proportions of marginalized populations. This insight informs ongoing COVID-19 campaigns, emphasizing the importance of targeted interventions for optimizing health outcomes during the second booster phase, especially in regions with a relatively higher proportion of marginalized populations.
Keywords: Bayesian spatio-temporal analysis; COVID-19; prevention; small area estimation; vaccination uptake.