Obsidian | Level 7

## Calculating treatment adherence/persistence from individual level drug data

Hi everyone,

I have some individual-level prescription redemption data and I would like to calculate the point prevalence of drug use, defined as the percentage of the population being treated at monthly intervals one year before and after to the initiation of an intervention (intervention starts at different time points for each individual, but every individual has 1 year-follow-up in both directions).

My current data looks something like this:

Table 1

 id drug_atc date_of_redemption grace_period days_supply last_day_of_supply intervention_date cov_by_last intervention 1 AA10A 23/12/2020 30 30 21/02/2021 01/02/2021 - 0 1 AA10A 31/01/2021 30 30 01/04/2021 01/02/2021 1 0 1 AA10A 04/02/2021 30 30 05/04/2021 01/02/2021 1 1 1 AA10A 07/03/2021 30 30 06/05/2021 01/02/2021 1 1 1 AA10A 15/05/2021 30 30 14/07/2021 01/02/2021 0 1 2 AA10A 02/04/2021 30 60 01/07/2021 01/06/2021 - 0 2 AA10A 05/08/2021 30 60 03/11/2021 01/06/2021 0 1 2 AA10A 06/09/2021 30 30 05/11/2021 01/06/2021 1 1

Last_day_of_supply is the days_supply + grace_period.

Cov_by_last denotes if the prescription was filled before the last_day_of_supply of the previous prescription (yes=1, no=0).

Intervention denotes if the prescription was filled before (0) or after (1) the intervention.

I also have a shell table with each id, the date of intervention and date of the monthly intervals since intervention:

Table 2

 ID drug_atc month_since_int intervention_date intervention date_since_inv 1 AA10A -12 01/02/2021 0 01/02/2020 1 AA10A -11 01/02/2021 0 01/03/2020 1 AA10A -10 01/02/2021 0 01/04/2020 1 AA10A -9 01/02/2021 0 01/05/2020 1 AA10A -8 01/02/2021 0 01/06/2020 1 AA10A -7 01/02/2021 0 01/07/2020 1 AA10A -6 01/02/2021 0 01/08/2020 1 AA10A -5 01/02/2021 0 01/09/2020 1 AA10A -4 01/02/2021 0 01/10/2020 1 AA10A -3 01/02/2021 0 01/11/2020 1 AA10A -2 01/02/2021 0 01/12/2020 1 AA10A -1 01/02/2021 0 01/01/2021 1 AA10A 0 01/02/2021 1 01/02/2021 1 AA10A 1 01/02/2021 1 01/03/2021 1 AA10A 2 01/02/2021 1 01/04/2021 1 AA10A 3 01/02/2021 1 01/05/2021 1 AA10A 4 01/02/2021 1 01/06/2021 1 AA10A 5 01/02/2021 1 01/07/2021 1 AA10A 6 01/02/2021 1 01/08/2021 1 AA10A 7 01/02/2021 1 01/09/2021 1 AA10A 8 01/02/2021 1 01/10/2021 1 AA10A 9 01/02/2021 1 01/11/2021 1 AA10A 10 01/02/2021 1 01/12/2021 1 AA10A 11 01/02/2021 1 01/01/2022 1 AA10A 12 01/02/2021 1 01/02/2022 2 AA10A -12 01/06/2021 0 01/06/2020 2 AA10A -11 01/06/2021 0 01/07/2020 2 AA10A -10 01/06/2021 0 01/08/2020

I would like to create a table like this,

table 3

 ID drug_atc month_since_int in_treatment intervention 1 AA10A -12 1 0 1 AA10A -11 1 0 1 AA10A -10 1 0 1 AA10A -9 1 0 1 AA10A -8 1 0 1 AA10A -7 1 0 1 AA10A -6 1 0 1 AA10A -5 1 0 1 AA10A -4 1 0 1 AA10A -3 1 0 1 AA10A -2 1 0 1 AA10A -1 1 0 1 AA10A 0 1 1 1 AA10A 1 1 1 1 AA10A 2 1 1 1 AA10A 3 0 1 1 AA10A 4 1 1 1 AA10A 5 0 1 1 AA10A 6 1 1 1 AA10A 7 0 1 1 AA10A 8 1 1 1 AA10A 9 0 1 1 AA10A 10 1 1 1 AA10A 11 0 1 1 AA10A 12 0 1 2 AA10A -12 1 0 2 AA10A -11 1 0 2 AA10A -10 0 0

where in_treatment denotes whether the individual was covered by a days_supply of the drug on the day of the start of the month_since_int.

Eventually, the final table should look something like this, which contains the percentage of individuals in treatment at each month in relation to the intervention.

 month_since_int drug_atc in_treat_percent -12 AA10A 70.0% -11 AA10A 60.0% -10 AA10A 80.0% -9 AA10A 70.0% -8 AA10A 60.0% -7 AA10A 60.0% -6 AA10A 70.0% -5 AA10A 70.0% -4 AA10A 60.0% -3 AA10A 80.0% -2 AA10A 60.0% -1 AA10A 70.0% 0 AA10A 60.0% 1 AA10A 60.0% 2 AA10A 50.0% 3 AA10A 40.0% 4 AA10A 50.0% 5 AA10A 30.0% 6 AA10A 30.0% 7 AA10A 20.0% 8 AA10A 20.0% 9 AA10A 30.0% 10 AA10A 20.0%

The key step I am struggling with is creating table 3, but I happy to receive input to any of the steps, it does not have to be the total solution.

Thanks

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