Dealing with multiple regression details of my dataset
summary(lm(visits ~ health1 + age, data = Medicaid1986))

But it gives this output:

health1:The first principal component (divided by 1000) of three health-status variables: functional limitations, acute conditions, and chronic conditions.



You appear to be proceeding on a false premise about your variable
Based on the description of the dataset in the package, it is far from clear that a higher value of health1 represents better health.  This variable is stated to be "[t]he first principal component (divided by 1000) of three health-status variables: functional limitations, acute conditions, and chronic conditions".  All of these underlying health-status variables appear to represent worse health, and the process of taking a principal component would give some linear combination of these health-status variables.  Unless the principal component involves negative weighting on each of the underlying health-status variables, it does not unambiguously represent better health.
