Hi Statalisters

I'm doing a difference-in-difference (DID) analysis and I wonder whether I'm correctly specifying my model. Specifically, my questions are

1. Should treatment status variable be constructed as a binary variable when multiple units receive treatment?
2. Should all data be aggregated to treatment status level?

First, a brief description of the data: I have a region-quarter panel with 6 regions and 24 time units per region which amount to a total of 144 observations. Of the 6 regions, 4 have received treatment and 2 are controls. I aggregated monthly data to quarterly data as my outcome is suicide rate so I had to correct the rate for potential instability induced by a numerator less than 20 ("rule of twenty" in epidemiology).

I've contructed a binary treatment status variable:
Code:
gen intervention = inlist(region, 2, 4, 5, 6)
label def intervention 1 "Intervention" 0 "Control"
label val intervention intervention
I've not aggregated data to the treatment variable level. Instead I've used regional level data. I've seen some argue that all data should be aggregated to treatment variable level, such as here in Wing et al. (2018): "In most cases, it makes sense to aggregate the data so that outcomes are measured at the same level as the treatment variable ... ." However, I've also seen applications that use the same approach as me, i.e. assigning treatment value = (0,1) to multiple units and then run the analyses with several units.

My main model is:
Code:
outcome i.treatment##i.post i.quarter control_variable1 control_variable2, fe
After reading Angrist & Pischke (2014, part 5.2), I see that this DID model can - and perhaps should - be modelled with a multi-region approach instead of a binary treatment variable, although I'm uncertain about the practical implementation of this. For instance,
Code:
outcome i.region##i.post i.quarter control_variable1 control_variable2, fe
does not seem like a valid approach nor a traditional DID-design to me.

Hopefully, some of you know how to approach this binary/multi-group problem.

Best
Tarjei

References
Angrist & Pischke (2014) Mastering 'Metrics.
Wing et al. (2018) Designing Difference in Difference Studies: Best Practices for Public Health Policy Research. Annual Review of Public Health. 39: 453-69.