I am working on a research project for my Masters in Public Health which compares suicide rates across different time periods. I have done all my analysis in R and from what I can tell everything is fine, but my supervisor has suggested that my adjusted model outputting narrower CI's than my crude model is somehow suspicious. I don't think I have a deep enough understanding of CI's to engage him directly, but I'm pretty sure I've seen other studies where the results look this way.

Is this something I should be worried about? What would determine whether adjusting for confounders would narrow or widen CI's for estimates?

For reference: the models are fitted as below.

adjusted_model = glm.nb(n ~ year+Age_Group+Sex+Day_of_Week+offset(log(population)), control = glm.control(maxit = 100), data = data2)```

I am working on a research project for my Masters in Public Health which compares suicide rates across different time periods. I have done all my analysis in R and from what I can tell everything is fine, but my supervisor has suggested that my adjusted model outputting narrower CI's than my crude model is somehow suspicious. I don't think I have a deep enough understanding of CI's to engage him directly, but I'm pretty sure I've seen other studies where the results look this way.

Is this something I should be worried about? What would determine whether adjusting for confounders would narrow or widen CI's for estimates?

For reference: the models are fitted as below.

crude_model = glm.nb(n ~ year+offset(log(population)), control = glm.control(maxit = 100), data = data2)

adjusted_model = glm.nb(n ~ year+Age_Group+Sex+Day_of_Week+offset(log(population)), control = glm.control(maxit = 100), data = data2)