Understanding the intercept in an unadjusted logistic regression

Question

I have performed an unadjusted logistic regression using weights (obtained via genetic matching) as below. I am using the survey package to make working with the weights easier:

fit <- svyglm(outcome ~ group, design = match_df_svy, 
        family = quasibinomial(link = "logit"))
ShowRegTable(fit)

            exp(coef) [confint] p     
(Intercept) 0.11 [0.09, 0.14]   &lt;0.001
groupTRUE   1.14 [0.88, 1.46]    0.323

group represents my intervention. Am I interpreting this correctly that since this is an unadjusted regression, the intercept in this case would represent the odds ratio when group = FALSE?

This doesn't seem to be the case by just looking at the incidence of the outcome in both groups:

          group     outcomeFALSE      outcomeTRUE     se.outcomeFALSE     se.outcomeTRUE
    FALSE FALSE        0.8980632       0.1019368         0.009810129        0.009810129
    TRUE   TRUE        0.8858308       0.1141692         0.007180766        0.007180766

A rough calculation of the odds ratio from this 2x2 table results in similar point estimates and CIs:

epitools::oddsratio(c(898, 101, 885, 114))
    $data
              Outcome
    Predictor  Disease1 Disease2 Total
      Exposed1      898      101   999
      Exposed2      885      114   999
      Total        1783      215  1998
$measure
          odds ratio with 95% C.I.
Predictor  estimate     lower    upper
  Exposed1 1.000000        NA       NA
  Exposed2 1.144994 0.8623838 1.522286

$p.value
          two-sided
Predictor  midp.exact fisher.exact chi.square
  Exposed1         NA           NA         NA
  Exposed2  0.3492472    0.3863462  0.3479743

$correction
[1] FALSE

attr(,"method")
[1] "median-unbiased estimate & mid-p exact CI"

I assume therefore that I am interpreting what the intercept actually means incorrectly and am just looking for some basic guidance.