# How to interpret a mediation analysis results?

I am new to mediation analysis and would like to know how to interpret the results of a mediation analysis obtained using R.

The independent variable is gender (1 Men /2 Women, categories).

The dependent variable is COPD (0 No COPD / 1 COPD, categories).

The mediator is tobacco habit (0 no tobacco habit / 1 yes tobacco habit, categories).

   ## Test for Total Effect

fit.totaleffect=lm(EPOC~SEX,Episcan_total_e1)
summary(fit.totaleffect)

Call:
lm(formula = EPOC ~ SEX, data = Episcan_total_e1)

Residuals:
Min       1Q   Median       3Q      Max
-0.03086 -0.03086  0.00000  0.00000  0.96914

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept)  0.030864   0.006477   4.765 2.29e-06 ***
SEX2        -0.030864   0.008759  -3.524 0.000452 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.1166 on 713 degrees of freedom
Multiple R-squared:  0.01712,    Adjusted R-squared:  0.01574
F-statistic: 12.42 on 1 and 713 DF,  p-value: 0.0004525

# sig
## Test for IV on Mediator - Needs to be sig. for mediation to occur

fit.mediator=lm(tabaco_mediation~SEX,Episcan_total_e1)
summary(fit.mediator)

Call:
lm(formula = tabaco_mediation ~ SEX, data = Episcan_total_e1)

Residuals:
Min      1Q  Median      3Q     Max
-0.7346 -0.4808  0.2654  0.5192  0.5192

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept)  0.73457    0.02638  27.844  < 2e-16 ***
SEX2        -0.25375    0.03567  -7.113 2.77e-12 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.4749 on 713 degrees of freedom
Multiple R-squared:  0.06626,    Adjusted R-squared:  0.06495
F-statistic: 50.59 on 1 and 713 DF,  p-value: 2.771e-12

## Test for IV and Mediator's effect on DV simultaneously

fit.dv=lm(EPOC~SEX+tabaco_mediation,Episcan_total_e1)
summary(fit.dv)

Call:
lm(formula = EPOC ~ SEX + tabaco_mediation, data = Episcan_total_e1)

Residuals:
Min       1Q   Median       3Q      Max
-0.03360 -0.03360 -0.00534  0.00495  0.97669

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept)        0.023306   0.009356   2.491   0.0130 *
SEX2              -0.028253   0.009063  -3.118   0.0019 **
tabaco_mediation1  0.010289   0.009193   1.119   0.2634
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.1166 on 712 degrees of freedom
Multiple R-squared:  0.01884,    Adjusted R-squared:  0.01609
F-statistic: 6.837 on 2 and 712 DF,  p-value: 0.001145


In overrall I summarised all values in this graph:

I would like to know how to interpret that there is a negative association and that the effect between the mediator and dependent variable is not significant.

Linked to the previous answers, I confirm that mediation does not occur. However, since the variables are dichotomous, I suggest using a generalised linear model glm with family=binomial(logit) option for a better model fitting. Lastly, be coherent between description and variable name for better understanding.