# Should I be controlling for all independent variables in my logistic regression model?

I'm working on a project in R where I'm looking at California's census tract-level demographic data in an explanatory logistic regression model. I have 6 demographic variables of interest: percent below 150% poverty line, percent minority, percent unemployed, percent disabled, percent with no high school diploma, and percent without health insurance. I also want to control for population density since it varies so much amongst census tracts. My binary exposure variable is if the census tract contains a large animal farming operation (1=yes, 0=no). Here's an example of the model I coded in R:

cali_logit <- glm(exposure ~ percent_unemployed + percent_minority +
percent_no_diploma + percent_uninsured + percent_under150 +
percent_disabled + pop_density, family = "binomial",
data = cali_cafos)


I checked all variables' variance inflation factors and all are under 5, so multicollinearity is not a problem. From my (long ago) stats classes, I know that when we add all of our data into a model we are adjusting the model to control for potential confounding effects. However, do I need to "control" for census tract level data? It's not quite clicking with me how percentages of other categories are confounders or need to be adjusted for in the model. If I want to see the odds of being in an exposed tract given a 1% increase in x,y,z demographic of interest, and only control for population density, should I just do an individual glm for each variable with only pop_density included? What would be the reason for adding all variables into a logistic regression model with census tract data?