I'm testing the hypothesis that the the variance observed in the number of medical consultations felt from 2009 through 2013 is due to the variance in user fees price, and not due to something else. I have 3 time series (from Jan. 2009 to Dec. 2013, measured quarterly) corresponding to the following variables (same population):
- User fees price
- Ratio of exempt consultations by exempt population
- Ratio of not exempt consultations by not exempt population
In theory, with the exception of the user fee price, the variables that influence 2 and 3 are the same. I've already established that there is a strong correlation between 1 and 3 (Pearson r = -.83; df = 18; p < 0.01). Also, between 1 and 2 I've found a weak, insignificant correlation (r = -.4).
Question: Is this enough to statistically prove my theory? If not, how should I approach this?
Healthcare demand is influenced by several variables, some of them easily measurable (such as population ageing, number of doctors, mean price of consultation, etc.), but others not so much (such as technology advances in healthcare), which makes it difficult to create a good regression model.
In this context, a user fee is a small fixed contribution payed by users for the medical consultation. Some people are exempt from this fee (the poor, children, patients with chronic diseases, pregnant women, ...).