Hello,

I have a model that predicts a smoking outcome using a manually splined education exposures variables. I am specifically interested in the effect of the eduhigh variable. From my logistic model's eduhigh coefficient, I can interpret that for each additional year of education after 11 years of education *, a person has 0.92 times the odds of ever smoking, compared to a person with one less year of education:

Code:
. qui svyset secu [pweight=wt_1992], singleunit(certainty) strata(stratum) vce(linearized)

. global basemodel_conf "c.myrs i. female i.race i.bplace c.birthyr_c i.myrs_mi c.fyrs i.fyrs_mi
> "

. svy: logistic smokeever c.edulow c.eduhigh i.edu11 $basemodel_conf if firstiw==1992
(running logistic on estimation sample)

note: 0.myrs_mi omitted because of collinearity
note: 0.fyrs_mi omitted because of collinearity

Survey: Logistic regression

Number of strata   =        52                 Number of obs     =       5,851
Number of PSUs     =       104                 Population size   =  14,556,027
                                               Design df         =          52
                                               F(  12,     41)   =       33.06
                                               Prob > F          =      0.0000

----------------------------------------------------------------------------------
                 |             Linearized
       smokeever | Odds Ratio   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
          edulow |   1.062162    .038769     1.65   0.105     .9871466    1.142877
         eduhigh |   .9233532   .0196973    -3.74   0.000     .8846617    .9637369
         1.edu11 |   .5742586   .0768288    -4.15   0.000     .4390507    .7511046
            myrs |   1.025851   .0124483     2.10   0.040     1.001173    1.051137
        1.female |   .4006192   .0200339   -18.29   0.000     .3623694    .4429064
                 |
            race |
          black  |   .9421785   .1014996    -0.55   0.583     .7590149    1.169543
       hispanic  |   .7825791    .090535    -2.12   0.039     .6204532     .987069
other / missing  |   .8619439   .1529093    -0.84   0.406     .6037801    1.230493
                 |
          bplace |
 southern birth  |   .8329578   .0763799    -1.99   0.051     .6929648    1.001232
      immigrant  |   .6140813   .0722154    -4.15   0.000     .4849993    .7775183
                 |
       birthyr_c |   1.001843   .0149788     0.12   0.902     .9722321    1.032355
       0.myrs_mi |          1  (omitted)
            fyrs |   1.003104   .0113082     0.27   0.784      .980667    1.026054
       0.fyrs_mi |          1  (omitted)
           _cons |   4.826659    .657321    11.56   0.000     3.672521      6.3435
----------------------------------------------------------------------------------
Note: _cons estimates baseline odds.


However, odds are difficult to interpret, and I would like to use a more easily interpretable measure, like prevalence ratio... Normally I would use margins command to calculate average causal effects, but here the predictor I am interested in (eduhigh) is continuous and not categorical. How can I best convert the odds ratio reported from this logistic model to a probability ratio or a non-odds effect measure, given a continuous exposure variable?

Thank you for the support!


* For completeness, my education variables are as followed: "edulow" (continuous variable representing 0-11 years of education), "eduhigh" (continuous variable representing12-17 years of education), and "edu11" (a binary discontinuity term, split at 11 years of education).