> zinf.poiss <- glmmadmb(resp ~ var1 + var2 - 1 + (1|var3),
                   data = exampledata, family = 'poisson', zeroInflation = TRUE)

> summary(zinf.poiss)

Call:
glmmadmb(formula = resp ~ var1 + var2 - 1 + (1 | var3), data = exampledata,
    family = "poisson", zeroInflation = TRUE)

AIC: 2124.5

Coefficients:
         Estimate Std. Error z value Pr(>|z|)
var1D      -0.552      0.197   -2.80   0.0051 **
var1J      -0.314      0.207   -1.52   0.1295
var22016   -0.516      0.162   -3.19   0.0014 **
var22017   -0.279      0.127   -2.20   0.0281 *
var22018   -0.387      0.132   -2.93   0.0034 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Number of observations: total=1365, var3=12
Random effect variance(s):
Group=var3
            Variance StdDev
(Intercept)    0.267 0.5167

Zero-inflation: 0.2634  (std. err.:  0.052798 )

Log-likelihood: -1055.25
Warning message:
In .local(x, sigma, ...) :
  'sigma' and 'rdig' arguments are present for compatibility only: ignored

> zinf.poissref <- as.list(ref_grid(zinf.poiss))
> zinf.poiss.em <- as.emmGrid(zinf.poissref)
> estimates.zinf.poiss <- confint(zinf.poiss.em, adjust = 'none', level = 0.95)

> estimates.zinf.poiss
    var1 var2 prediction        SE  df  asymp.LCL  asymp.UCL
 D    2015 -0.5518259 0.1970900 Inf -0.9381152 -0.1655366
 J    2015 -0.3136274 0.2068700 Inf -0.7190851  0.0918304
 D    2016 -1.0682002 0.2963979 Inf -1.6491294 -0.4872710
 J    2016 -0.8300017 0.3561272 Inf -1.5279982 -0.1320052
 D    2017 -0.8309125 0.2366763 Inf -1.2947895 -0.3670354
 J    2017 -0.5927140 0.2452413 Inf -1.0733781 -0.1120498
 D    2018 -0.9386216 0.2334296 Inf -1.3961352 -0.4811081
 J    2018 -0.7004231 0.2461742 Inf -1.1829157 -0.2179306

Results are given on the log (not the response) scale.
Confidence level used: 0.95