Hello . I am modeling costs and have balanced covariates using ebalance. The glm runs but when I call for adjusted predicted means I get an error.

GLM:
glm total_cost_2015 pcp_vs_er_index spine_surgery mental sex_rc age neck chronic_pain smoking obesity i.lumbar_surgery_history i.cervical_surgery_history i.sud ///
median_incom elixhauser_score i.married_rc i.latino i.urban i.hsvscoll ///
i.high_dp_rc i.muscle_relaxants i.nsaids i.opioid i.oral_steroid m_total_visit [pweight=_webal] if insur == 0 & education < 5 & high_dp_rc <2 ///
& married_rc <2 & latino < 2 & urban < 2, link(log) family(gamma)

Pred means:
adjust, by(pcp_vs_er_index) exp ci

ERROR: 0b: operator invalid

When I don't include the covariates and run the glm with the weights, I get the predicted costs just fine but not when I run the glm with the covariates. Below are partial variables limited by the number of accepted variables by dataex.

I am looking for help on the ERROR on getting the predicted mean costs. Thanks for your help. Please let me know if more information is needed. I consulted Stata help but am still stymied.

Code:
* Example generated by -dataex-. For more info, type help dataex
clear
input float(total_cost_2015 pcp_vs_er_index spine_surgery mental sex_rc age neck chronic_pain smoking obesity lumbar_surgery_history cervical_surgery_history sud) long median_income float elixhauser_score
 1759.808 1 0 1 0 63 0 0 1 0 0 0 1  52517 5
  2053.89 1 0 0 1 29 0 0 0 0 0 0 0  63910 0
    76.44 0 0 0 0 49 1 0 0 0 0 0 0  67037 0
        0 1 0 0 1 41 0 0 0 0 0 0 0  53324 0
 447.5552 0 0 1 0 44 0 1 0 0 0 0 0  60172 1
        0 1 0 0 1 64 0 0 0 0 0 0 0  45605 1
 1438.041 1 0 0 1 63 0 0 0 0 0 0 0  63155 0
        0 0 0 0 0 55 1 0 0 0 0 0 0  41336 2
2090.4058 0 0 0 0 50 0 0 0 0 0 0 0  56883 1
 5298.303 1 0 0 0 53 1 0 0 0 0 0 0  96005 0
        0 1 0 0 0 41 1 0 0 0 0 0 0  59393 0
3474.8455 1 0 0 1 30 0 0 0 0 0 0 0  79000 0
        0 1 0 0 0 50 0 0 1 0 0 0 1  35859 2
 6101.723 1 1 0 1 58 0 0 0 0 0 0 0  58915 0
220.09624 1 0 0 0 50 0 0 0 0 0 0 0  34350 1
    42.63 1 0 0 1 47 0 0 0 0 0 0 0  61393 0
 856.7705 0 0 0 0 23 0 0 0 0 0 0 0  44668 0
        0 1 0 0 1 46 0 0 0 0 0 0 0  70294 0
 12.09991 1 0 1 0 33 0 0 0 0 0 0 0  39460 1
160.26825 1 0 0 0 21 0 0 0 0 0 0 0  28837 1
        0 0 0 1 1 18 0 0 1 0 0 0 1  58594 0
        0 1 0 0 0 54 1 0 0 0 0 0 0  74133 1
173.51897 1 0 0 1 62 0 1 0 0 0 0 0  66019 1
 647.1835 1 0 1 0 52 0 0 1 0 0 0 1  66941 1
        0 1 0 0 1 37 0 0 0 0 0 0 0  69194 0
        0 1 0 1 1 37 0 0 0 0 0 0 0  91660 0
 3.636033 1 0 0 1 61 0 0 0 0 0 0 0  48348 0
        0 1 0 0 0 61 0 0 1 0 0 0 1  44536 1
        0 1 0 1 0 47 0 0 0 0 0 0 0  60671 0
        0 1 0 1 0 50 0 0 0 0 0 0 0  26305 3
        0 1 0 0 0 30 0 0 0 0 0 0 0  67564 0
        0 1 0 1 1 44 0 0 0 0 0 0 0  45909 1
        0 0 0 1 0 51 0 1 1 0 0 0 1  23618 6
   727.45 1 0 0 0 29 0 0 0 0 0 0 0  46624 0
 53.26788 1 0 0 0 21 0 0 0 0 0 0 0  64293 0
        0 1 0 1 0 47 0 0 0 0 0 0 0  38779 2
        0 1 0 1 1 38 0 0 0 0 0 0 0  32444 2
280.10883 1 0 1 0 59 1 1 0 0 0 0 0  46624 4
        0 1 0 0 1 46 0 0 0 0 0 0 0  61324 0
        0 1 0 1 0 22 0 0 0 0 0 0 0  49493 0
1661.5273 1 0 1 0 59 0 0 0 0 0 0 0  76442 2
304.65915 1 0 0 0 23 0 0 0 0 0 0 0  64828 0
 82.95205 1 0 1 0 33 0 0 0 1 0 0 0  71736 3
 410.8057 1 0 1 0 38 0 0 0 0 0 0 1  66941 2
    242.9 0 0 0 0 26 0 0 1 0 0 0 1  43635 0
 3020.458 1 0 0 0 55 1 0 0 0 1 1 0  63077 0
        0 1 0 1 0 47 1 0 0 0 0 0 0  42981 1
        0 0 0 0 0 39 0 0 0 0 0 0 0  71048 0
 691.6946 0 0 0 1 38 0 1 0 0 0 0 0  35966 0
 15.10061 1 0 1 0 44 1 0 0 0 0 0 0  40040 0
        0 0 0 0 0 31 1 0 0 0 0 0 0  58482 0
        0 0 0 1 0 30 0 0 0 0 0 0 0  59390 1
105.47048 0 0 0 1 59 0 1 0 0 0 0 0  49479 2
        0 1 0 0 0 41 0 0 0 0 0 0 0  67564 1
        0 1 0 1 1 35 0 1 0 1 0 0 1  64583 5
        0 1 0 0 0 54 0 0 1 0 0 0 1  43241 3
 92.43604 1 0 0 1 43 0 0 0 0 0 0 0  43024 0
180.71083 1 0 1 0 38 1 0 0 0 0 0 0  70240 1
        0 1 0 0 0 55 0 0 0 0 0 0 0  45679 1
        0 1 0 0 1 32 1 0 0 0 0 0 0  53524 0
        0 0 0 0 1 63 0 1 0 0 0 0 1  44797 5
  2275.39 1 1 1 0 43 0 0 0 0 0 0 0  63345 1
        0 1 0 0 1 18 0 0 0 0 0 0 0  91071 0
        0 1 0 0 0 57 0 0 0 0 0 0 0  64069 1
        0 1 0 0 0 33 0 0 0 0 0 0 0  51364 0
        0 1 0 0 1 36 0 0 0 0 0 0 0  81103 0
   110.09 1 0 0 0 24 0 0 1 0 0 0 0  31667 1
 832.7615 1 0 1 0 58 0 0 0 0 0 0 0  64792 1
3780.7544 0 0 0 0 47 0 0 0 0 0 0 0  62768 0
 508.4539 1 0 0 0 56 1 0 0 0 0 0 0 108294 0
   128.44 1 0 0 1 44 0 0 0 0 0 0 0  41087 0
        0 0 0 1 1 58 0 0 0 0 0 0 0  74178 1
 86.07298 1 0 0 0 23 0 0 0 0 0 0 0  48821 0
        0 1 0 0 1 63 1 0 0 0 0 0 0  97500 1
 278.7536 1 0 1 1 27 0 0 0 0 0 0 0  53478 1
 944.8231 1 0 0 1 24 0 0 0 0 0 0 0  46017 0
258.98654 1 0 0 0 62 0 0 0 0 0 0 0  53324 2
        0 1 0 1 0 57 0 0 0 0 0 0 0  68257 3
        0 0 0 1 1 38 1 0 1 0 0 0 1  33782 2
        0 1 0 0 1 47 0 0 0 0 0 0 0  83260 1
128.13986 1 0 0 1 50 1 0 0 0 0 0 0  59393 0
        0 1 0 0 1 48 1 0 0 0 0 0 0  48455 0
128.72566 1 0 0 1 41 1 0 0 0 0 0 0  55652 0
 240.3777 1 0 0 0 41 0 1 0 0 0 0 0  80795 0
        0 1 0 0 0 26 0 0 0 0 0 0 0  90341 0
        0 0 0 0 0 42 0 0 0 0 0 0 0  71402 0
        0 1 0 0 0 49 0 0 0 0 0 0 0  42891 0
        0 1 0 1 0 47 0 0 0 0 0 0 0  54368 1
        0 1 0 0 1 18 0 0 0 0 0 0 0  90208 0
  153.784 1 0 0 0 58 0 0 0 0 0 0 0  53524 5
        0 1 0 0 0 61 0 0 0 0 0 0 0  51682 2
        0 1 0 0 0 58 1 0 0 1 0 0 0  42981 7
 1432.617 1 0 0 1 40 1 0 0 0 0 0 0  43175 0
  2452.15 1 0 1 0 29 0 0 0 0 0 0 0  71048 1
        0 1 0 0 1 19 0 0 0 0 0 0 0  95106 0
 3409.565 1 0 0 0 58 0 0 0 1 0 0 0  50327 2
 4386.179 1 0 0 1 32 1 0 0 0 0 0 0  63910 0
        0 0 0 0 1 24 0 0 1 0 0 0 1  57400 0
33.596954 1 0 0 0 57 1 0 0 0 0 0 0  80795 0
   376.05 1 0 0 1 55 0 0 1 0 0 0 1  32353 0
end
label values sex_rc sexlab
label def sexlab 0 "Female", modify
label def sexlab 1 "Male", modify