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. 2024 Sep;14(3):1289-1299.
doi: 10.1007/s44197-024-00286-6. Epub 2024 Aug 26.

Impact of High Covid-19 Vaccination Rate in an Aging Population: Estimating Averted Hospitalizations and Deaths in the Basque Country, Spain Using Counterfactual Modeling

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Impact of High Covid-19 Vaccination Rate in an Aging Population: Estimating Averted Hospitalizations and Deaths in the Basque Country, Spain Using Counterfactual Modeling

Carlo Delfin S Estadilla et al. J Epidemiol Glob Health. 2024 Sep.

Abstract

COVID-19 vaccines have demonstrated significant efficacy in reducing severe symptoms and fatalities, although their effectiveness in preventing transmission varies depending on the population's age profile and the dominant variant. This study evaluates the impact of the COVID-19 vaccination campaign in the Basque Country region of Spain, which has the fourth highest proportion of elderly individuals worldwide. Using epidemiological data on hospitalizations, ICU admissions, fatalities, and vaccination coverage, we calibrated four versions of an ordinary differential equations model with varying assumptions on the age structure and transmission function. Counterfactual no-vaccine scenarios were simulated by setting the vaccination rate to zero while all other parameters were held constant. The initial vaccination rollout is estimated to have prevented 46,000 to 75,000 hospitalizations, 6,000 to 11,000 ICU admissions, and 15,000 to 24,000 deaths, reducing these outcomes by 73-86%. The most significant impact occurred during the third quarter of 2021, coinciding with the Delta variant's dominance and a vaccination rate exceeding 60%. Sensitivity analysis revealed that vaccination coverage had a more substantial effect on averted outcomes than vaccine efficacy. Overall, the vaccination campaign in the Basque Country significantly reduced severe COVID-19 outcomes, aligning with global estimates and demonstrating robustness across different modeling approaches.

Keywords: Averted outcomes; Basque country; COVID-19; Counterfactual analysis; Vaccination impact; Vaccine.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Diagram for the SHARUCDV model with age structure. The compartments for the < 50-year-old age group are labeled with a subscript 1 while the compartments for the  50-year-old age group are labeled with a subscript 2. The arrows indicate the progression from the susceptible (S) compartment to the hospitalized (H), mild and asymptomatic (A), ICU (U), recovered (R), and deceased (D) compartments. Orange arrows and boxes represent the dynamics among the vaccinated (Sv, Hv, Uv). Compartments CA1, CH1, CU1, CA2, CH2, CU2 (not shown in the figure) respectively record the cumulative < 50 y/o mild/asymptomatic cases, < 50 y/o hospitalized, < 50 y/o ICU admitted, 50 + y/o mild/asymptomatic cases, 50 + y/o hospitalized, and 50 + y/o ICU admitted
Fig. 2
Fig. 2
Estimates of hospitalizations, ICU admissions, and deaths averted by COVID-19 vaccination in the Basque Country region of Spain in 2021. The left graph shows the number of outcomes in thousands while the right graph shows the percentage of outcomes averted, computed as (T − D)/T, where T is the total outcomes in the counterfactual scenario without vaccination and D is the observed data. The four models have varying assumptions on the time-varying transmission rate and age dynamics
Fig. 3
Fig. 3
Estimates of daily hospitalizations, ICU admissions, and deaths in a no-vaccine scenario in the Basque Country region of Spain in 2021. The four models have varying assumptions ont he time-varying transmission rate and age dynamics
Fig. 4
Fig. 4
Sensitivity (Partial Rank Correlation Coefficients [PRCC]) and the corresponding p values of the parameters of the SHARUCDV model with weekly constant transmission rate and no age-structure (Model 4) to total averted hospitalizations, ICU admissions, and deaths during the initial stages of vaccination. Each bar represents the PRCC and corresponding p value every ten days over a 90-day period starting January 1, 2021. The results for other models are available in the Online Resource
Fig. 5
Fig. 5
Estimates of deaths averted by COVID-19 vaccination as a percentage of total population (y-axis), coverage of vaccination by the end of 2021 (x-axis, top graph) [4, 34], and proportion of the population aged 65 and above [1, 2], 2021 (x-axis, bottom graph) from He et al. (2022) [19], this study, and selected countries from Watson et al. (2022) [18]

References

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