How to use Wald Ch-Sq test Statistic in a GAM to determine which smooth term has the largest impact on the response?

Question

I am tasked with determining, from a summary function of a generalized linear model, which smooth term is most impactful to the response. Intuitively I understand that to be the smooth term with the largest Chi-Sq test stat listed in the summary, so it would be s(tests) in this case below:

Family: poisson 
Link function: log 
Formula:
daily_confirmed_cases ~ s(tests, k = 18) + s(vaccines, k = 18) + 
    s(people_fully_vaccinated, k = 18) + s(hosp) + s(icu) + s(ndate, 
    k = 18)

Parametric coefficients:
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) 7.405421   0.001489    4973   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Approximate significance of smooth terms:
                              edf Ref.df Chi.sq p-value    
s(tests)                   16.793  16.98  14076  <2e-16 ***
s(vaccines)                16.982  17.00   9744  <2e-16 ***
s(people_fully_vaccinated) 16.923  17.00   7337  <2e-16 ***
s(hosp)                     8.988   9.00   6893  <2e-16 ***
s(icu)                      8.985   9.00   7246  <2e-16 ***
s(ndate)                   16.674  16.96  11156  <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

R-sq.(adj) =  0.965   Deviance explained = 97.7%
fREML =  19764  Scale est. = 1         n = 460

Is this approach true, and if so can you explain some rational as to why a high Chi-Sq stat indicates the greater impact on the GAM?

Thanks