From prescriptions to drug use periods - things to notice

Abstract

Background: Electronic prescription registers provide a vast data source for pharmacoepidemiological research. Prescriptions as such are not suitable for all research purposes; e.g., studying concurrent use of different drugs or adverse drug events during current use. For those purposes, data on dispensed prescriptions needs to be transformed to periods of drug use.

Methods: We used 3,828,292 dispensed prescriptions claimed between 1 January 2002 and 31 December 2009 for 28,093 persons with Alzheimer's disease. Examples of drug use histories are presented to discuss different aspects that should be noticed when using register-based data consisting of drug purchases.

Results: There is no simple method for correctly transforming dispensed prescriptions to periods of drug use that is usable for all drugs and drug users. Fixed assumptions of daily dose (in defined daily doses, tablets or other units) and fixed time windows should be used with caution and adjusted for different drug use patterns.

Conclusions: We recommend that when transforming prescription drug purchases to drug use periods personal dose, purchasing pattern and other behavioral differences between patients should be taken into account.

Figure 1

DDD _AVG distribution of temazepam (N05CD07), two most commonly used doses were 0.5 and 1.0 DDD per day.

Figure 2

A case history with a high number of purchases (n = 101) and high DDD per day dose (4–6) of lactulose (A06AD11), over six years.

Figure 3

Distribution of refill time lengths for a single package of 25 mg quetiapine, package size 100 tablets, Nordic article number 075533 in (A), and for 200 mg quetiapine, package size 100 tablets, Nordic article number 075551 in (B).

Figure 4

Two different purchase patterns of metoprolol (C07AB02); a regular in (A) and an irregular in (B) with variations in both purchased amount and intervals between purchases.

Figure 5

Increasing dosage and package size of oxycodone (N02AA05) in long-term use. The dosage is three-fold higher than the initial value by the end of the follow-up after hospital stays marked on timeline.

Figure 6

The amount of DDD purchased; escitalopram (N06AB10). Refill time length between purchases increased while the dosage remained stable. Patient has no hospital stays.

Figure 7

An individual’s purchase history for about six years, which describes the purchase of 20 different drugs. Note the long hospital stays between days 679–719, 1506–1574 and 2113–2250 (marked with grey bars). After day 2250, this person has not collected any drugs and resides in nursing home, so no information on drug use is available in the prescription register.