I'm attempting a 'difference-in-differences' analysis of a health policy intervention.
Scenario: Health clinics are paid for the percentage of eligible patients who they give the right treatment to. The clinics are measured/paid separately for different treatment indicators (e.g. treatment for diabetes, for high blood pressure).
One year, they stopped payments for some of the indicators (but others continued). I want to measure if stopping the payment had an impact on performance (relative to the control group).
I have performance data for every single health clinic in the country - for the intervention and control group (both pre- and after- intervention.
My questions are:
If I have data for every single health clinic in the country (it's not a sample), should I still calculate the Standard Error and confidence intervals for the difference-in-differences (DiD) estimator?
How best should I calculate the SE and CI for the DiD estimator? Should I use just the mean average, or can I factor in the individual pairs of measurements (for each clinic)? There is a pre- and post- measurement, for the control and the intervention, for every single clinic.
Each clinic has varying numbers of eligible patients (I have the numerator/denominator for each); can I factor that into the estimation of the impact?
I'm doing the analysis in STATA.