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PHARMACOEPIDEMIOLOGY AND PRESCRIPTION
Primary non-adherence in general practice: a Danish
register study
Anton Pottegård &Rene dePont Christensen &Alae Houji &
Camilla Binderup Christiansen &Maja Skov Paulsen &
Janus Laust Thomsen &Jesper Hallas
Received: 5 August 2013 /Accepted: 31 March 2014
#Springer-Verlag Berlin Heidelberg 2014
Abstract
Purpose The aim of this study was to describe primary non-
adherence (PNA) in a Danish general practitioner (GP) set-
ting, i.e. the extent to which patients fail to fill the first
prescription for a new drug. We also assessed the length of
time between the issuing of a prescription by the GP and the
dispensing of the drug by the pharmacist. Lastly, we sought to
identify associations between PNA and the characteristics of
the patient, the drug and the GP.
Methods By linking data on issued prescriptions compiled in
the Danish General Practice Database with data on redeemed
prescriptions contained in the Danish National Prescription
Registry, we calculated the rate of PNA among Danish pa-
tients from January 2011 through to August 2012.
Characteristics associated with PNA were analysed using a
mixed effects logistic regression model.
Results A total of 146,959 unique patients were started on
307,678 new treatments during the study period. The overall
rate of PNA was 9.3 %, but it varied according to the major
groups of the Anatomical Therapeutic Chemical (ATC)
Classification System, ranging from 16.9 % for “Blood and
bloodforming organs”(ATC group B) to 4.7 % for
“Cardiovascular system”(ATC group C). Most of the patients
redeemed their prescriptions within the first week. Older age,
high incomeand a diagnosis of chronic obstructive pulmonary
disease were found to be significantly associated with lower
rates of PNA, while polypharmacy and a diagnosis of ischae-
mic heart disease were associated with higher rates of PNA.
Conclusions The overall rate of PNA among Danish residents
in a GP setting was 9.3 %. Certain drug classes and patient
characteristics were associated with PNA.
Keywords Patient adherence .Medication adherence .
General practice .Registries .Pharmacology .
Pharmacoepidemiology
Introduction
Non-adherence to medications is a well-known challenge in
pharmacological treatment. Conceptually, non-adherence can
be divided into primary and secondary non-adherence. Primary
non-adherence (PNA) occurs when the patient fails to initiate
treatment altogether, while secondary non-adherence is used to
describe a complex range of situations, such as when the patient
intentionally or unintentionally skips doses, uses lower doses
than prescribed or uses medical devices incorrectly [1].
The term non-adherence is often used in the sense of
secondary non-adherence, and most research conducted to
date has focused on secondary non-adherence. However,
when only secondary non-adherence is considered, i.e. PNA
is not taken into account, the overall rate of non-adherence is
an underestimation, as the number of patients who do not
redeem their prescription at all is not included in the analysis
[2]. Furthermore, from a clinical perspective, a patient failing
to initiate treatment, i.e. showing PNA, constitutes a challenge
that is different from that of a patient who at one point
accepted treatment but who later fails to adhere to the
A. Pottegård (*):R. d. Christensen :A. Houji :
C. B. Christiansen :J. Hallas
Clinical Pharmacology, Institute of Public Health, University of
Southern Denmark, JB Winsløwsvej 19, 2, 5000 Odense C, Denmark
e-mail: apottegaard@health.sdu.dk
A. Pottegård :J. Hallas
Department of Clinical Chemistry & Pharmacology, Odense
University Hospital, Odense C, Denmark
R. d. Christensen :M. S. Paulsen :J. L. Thomsen
Research Unit of General Practice, Institute of Public Health,
University of Southern Denmark, Odense C, Denmark
M. S. Paulsen :J. L. Thomsen
Danish Quality Unit of General Practice, Odense C, Denmark
Eur J Clin Pharmacol
DOI 10.1007/s00228-014-1677-y
agreed-upon regimen. As such, a knowledge of overall rates
of and factors associated with PNA is an important aspect of
prescribing.
When compared to the extensive research that has focused
on secondary non-adherence [1,3,4], the amount of research
done on PNA is relatively modest, albeit increasing in more
recent years. In the pivotal study by Beardon et al. [5], 5.2 %
of prescriptions issued in primary care were never filled at the
pharmacy, while newer studies have reported rates ranging
from 2.4 to 30.7 % [6–19]. Several papers have reported
variance in PNA according to patient characteristics [5,
7–14,16–19], prescriber characteristics [9,10,12,16,19],
drug type [5,7–10,13,14,16] and level of patient co-payment
[5,13,14]. However, comparisons between studies are com-
plicated by differences in setting (primary care [5,11,14,19],
secondary care [8,12,15,17]orboth[7,9,10,13,16,18])
and major differences in methodology. Follow-up times in
studies reported to date range from 2 days [15] up to 6 months
[9], and while some studies include all prescriptions [5,7,10,
11,13,14,18,19], others only consider the first prescription
for a new drug [8–10,15–17], with the latter group showing
markedly higher rates of non-adherence.
The aim of our study is to describe PNA in a Danish
general practitioner (GP) setting. Specifically:
1. To estimate the rate of PNA, overall as well as specified
by subgroups of patients and drug types;
2. To describe the timing between prescriptions being pre-
scribed by the physician and filled at the pharmacy;
3. To identify factors associated with PNA, including char-
acteristics of the patient, of the drug and of the GP.
Method
The study was a register-based study conducted in Denmark.
We calculated the rate of PNA in the primary care setting
among Danish patients by linking data on issued prescriptions
compiled in the Danish General Practice Database (DAMD)
with data on redeemed prescriptions contained in the Danish
National Prescription Registry.
Setting
All Danish residents (5.6 million) have free and direct access
to GPs, ophthalmologists, and ear, nose and throat office-
based specialists, as well as hospital emergency services.
The GPs are the gatekeepers who control further patient
access to the secondary health care system [20], and the
majority of all prescriptions are issued by GPs. In Denmark,
the Danish Health and Medicines Authority assigns reim-
bursement status to a medicine. Consequently, when a resident
purchases a prescription medicine, reimbursement is automat-
ically deducted from the price charged at the pharmacy.
Reimbursement applies to all citizens, irrespective of income.
Data sources
The DAMD was implemented in 2006 and is a database that
contains a patient’s clinical data and prescription information
related to individual consultations with a GP [21,22]. DAMD
uses a data capture module incorporated in the GP’sIT-
system. This module automatically sends information on pre-
scribed medication, diagnoses and laboratory data to DAMD
for each contact between a GP and a patient. Drugs are
categorised according to the Anatomic Therapeutic
Chemical (ATC) Classification System, which is a classifica-
tion system developed and maintained by the World Health
Organization (WHO) [23]. Diagnoses are coded according to
the International Classification of Primary Care system
(ICPC) [21]. A national agreement states, that as of April
2013, all 2,100 GP practices in Denmark are obliged to use
the data capture module and consequently contribute data to
DAMD [21].
The Danish National Prescription Registry [24]contains
data on all prescription drugs dispensed in retail pharmacies to
Danish citizens since 1994. The data include an exact account
of the dispensed pharmaceutical product, including substance,
brand name, dose unit and quantity, date of dispensing, age
and gender of the drug user and identifiers for the prescribing
physician and the dispensing pharmacy.
All data sources were linked by use of the personal identi-
fication number, a unique identifier assigned to all Danish
residents since 1968 that encodes gender and date of birth
[25]. All linkages were performed within Statistics Denmark,
a governmental institution that collects and maintains elec-
tronic records for a broad spectrum of statistical and scientific
purposes [24,26,27].
Population
Our study population consisted of all GPs contributing data to
the DAMD who were classified as “up-to-standard”in the
database throughout the entire study period, as well as all
patients aged ≥18 years assigned to these GPs. Being classi-
fied as “up-to-standard”implies ≥70 % of all consultations
were encoded with an ICPC diagnosis.
Analysis
Data were obtained for the period of January 2010 through to
December 2012. The primary study outcome was PNA, de-
fined as not having redeemed a prescription within 4 months
from the day the prescription was issued. Due to this 4-month
window, only data on prescriptions issued between 1 January
Eur J Clin Pharmacol
2011 and 31 August 2012 were included in the analysis. Only
new prescriptions were included, defined as the patient not
having filled a prescription for the same drug substance within
the last 2 years prior to the new prescription being issued,
according to the Prescription Registry. Drugs were classified
at the fifth level of the ATC system, i.e. at the level of single
drug substances. We also described the timing of the pharma-
cy visit relative to the issuing of the prescription at the GP’s
office, by calculating the cumulative proportion of prescrip-
tions having been filled each day for the first 30 days after the
prescriptions were issued. Lastly, we compared PNA across
patient characteristics, drug classes and GP characteristics:
Patient characteristics included gender, age at 1 January
2011 (categories: 18–29, 30–49, 50–69, and 70+ years),
cohabitation (married vs. not-married), total family in-
come in 2010 (<250,000 DKK
1
, 250,000–499,999 DKK
and ≥500,000 DKK), level of education by 1 January
2011 (≤10, 11–12 and 13+ years), polypharmacy (use of
0–2drugs,3–7 drugs and 8+ drugs during 2010) and the
presence of selected diagnoses at any time during the
study period: diabetes mellitus (T89–T90), chronic ob-
structive pulmonary disease (R95) and ischemic heart
disease (K75).
Drug characteristics were analyzed according to all main
ATC groups, i.e. the first level of the ATC system, one by
one. Fifteen specific subgroups representing frequently
used drugs were also selected and analysed.
The GP characteristics included were age (<50, 50–59,
and 60+ years) and number of GPs in the given practice
(solo practice, 2, and 3+ GPs). For practices with ≥2 GPs,
age corresponded to the mean age of the GPs in the given
practice.
These associations were firstly explored as subgroups, i.e.
stratifying all issued prescription by the above-mentioned
characteristics. Secondly, we estimated odds ratios (ORs) for
PNA associated with the different characteristics using logistic
regression. PNA is an individual trait, and we therefore
employed a mixed effects logistic regression model with
random effects for both the subject and the prescriber. Also,
since the rate of PNA is assumed to be fairly low, the ORs
reported are reasonable estimates of the corresponding risk-
ratios.
All calculations were performed using STATA Release
12.0 (StataCorp, College Station, TX).
The study was approved by the scientific board of Statistics
Denmark and by DAMD (project 52–13). According to
Danish law, ethical approval is not required for registry-
based studies.
Results
Eighty-three GPs were included in the study. During the study
period 307,678 new treatments were initiated among 146,959
unique patients. The characteristics of the patientsare present-
ed in Table 1.
Table 2shows the PNA within subgroups of patients. The
overall rate of PNA was 9.3 %. PNA was more frequent
among those aged 18–29 years (13.8 %) and decreased with
age, with patients aged 70+ years having the lowest rate of
PNA (7.5 %). Patients with incomes of <250,000 DKK per
year had a higher rate of PNA (10.0 %) than those in the two
higher income categories (9.3 and 8.9 %, respectively).
The numbers of unfilled prescriptions for each main ATC
group are shown in Table 3. PNA varied by main ATC group,
ranging from4.7 to 73.9 %. The rate was highest for “Vari ou s ”
(ATC group V) (73.9 %) and “Antineoplastic and
immunomodulating agents”(ATC group L) (70.1 %).
However, these two groups were rarely prescribed (119 and
197 prescriptions, respectively). Among the remaining
1
1Euro≈7.50 DKK
Tabl e 1 Demographics of patients
Characteristics N=146,959 unique patients
Gender
Male 64,673 (44.0 %)
Female 82,286 (56.0 %)
Married/cohabiting
Yes 103,694 (70.6 %)
No 43,265 (29.4 %)
Age (years)
18-29 15,941 (10.8 %)
30-49 52,072 (35.4 %)
50-69 55,773 (38.0 %)
70+ 23,173 (15.8 %)
Number of prescribed drugs
0–2 65,604 (44.6 %)
3–7 57,153 (38.9 %)
8+ 24,202 (16.5 %)
Comorbidity
Diabetes mellitus 10,753 (7.3 %)
Chronic obstructive pulmonary disease 4,998 (3.4 %)
Ischaemic heart diseases 2,709 (1.8 %)
Income (DKK per year)
<250.000 28,838 (19.6 %)
250.000–499.999 46,105 (31.4 %)
500,000+ 72,016 (49.0 %)
Education
≤10 years 28,761 (19.6 %)
11–12 years 17,861 (12.2 %)
13+ years 100,337 (68.3 %)
Eur J Clin Pharmacol
groups, the highest rate of PNA was for “Blood and
bloodforming organs”(ATC group B) (16.9 %) and the lowest
for “Systemic hormonal preparations”(ATC group H) (5.2 %)
and “Cardiovascular system”(ATC group C) (4.7 %). Among
the pre-selected drug classes, we found that PNA ranged from
9.1 % for nonsteroidal anti-inflammatory drugs (NSAIDs) to
2.4 % for dihydropyridine derivates. Patients prescribed
NSAIDs, inhaled corticosteroids and bronchodilators showed
the highest rate of PNA.
Figure 1shows the number of days between the prescribing
of a prescription by the GP and the redemption of the pre-
scription by the patient at a pharmacy. We found that 65.2 %
of the patients redeemed their prescription on the same date
that the prescription was issued and that 89.3 % patients had
redeemed their prescription by day 30. The majority of the
patients filled their prescriptions within the first week.
Table 4shows the association of the different variables with
the rate of PNA in a mixed-effect multivariable logistic re-
gression model. Age had the strongest association with PNA,
with age 70+ years associated with a lower PNA, with an OR
of 0.48 [95 % confidence interval (CI) 0.45–0.51], compared
to age 18–29 years. Similarly, having an income of > 500,000
DKK and having a diagnosis of chronic obstructive
Tabl e 2 Primary non-adherence
a
in different patient subgroups
Characteristics % Primary non-adherence
(unfilled/issued prescriptions)
Overall 9.3 (28,526/307,678)
Gender
Male 9.1 (10,758/117,646)
Female 9.4 (17,768/190,032)
Married/Cohabiting
Yes 8.8 (18,329/207,681)
No 10.2 (10,197/99,997)
Age (years)
18–29 13.8 (3,819/27,651)
30–49 10.9 (10,569/96,666)
50–69 7.8 (9,079/116,097)
70+ 7.5 (5,059/67,264)
Polypharmacy
0–2 drugs 9.5 (9,897/103,875)
3-7 drugs 9.2 (11,182/121,819)
8+ drugs 9.1 (7,447/81,984)
Comorbidity
Diabetes mellitus 8.5 (2,772/32,570)
Chronic obstructive pulmonary
disease
7.0 (1,343/19,270)
Ischaemic heart diseases 8.4 (664/7,877)
Income (DKK per year)
<250.000 10.0 (7,107/71,318)
250,000-499,999 9.3 (9,587/102,877)
500,000+ 8.9 (11,832/133,483)
Education
≤10 years 8.6 (6,152/71,153)
11–12 years 10.2 (3,949/38,710)
13+ years 9.3 (18,425/197,815)
a
Primary non-adherence (PNA) is calculated as the proportion of pre-
scriptions that were not filled within 4 months of being issued by the
general practitioner (GP)
Tabl e 3 Primary non-adherence
a
according to different drug classes
b
Drug classes % PNA (unfilled
issued prescriptions)
Main groups of ATC
Gastrointestinal and metabolism (A) 9.9 (2,339/23,598)
Blood and blood-forming organs (B) 16.9 (974/5,760)
Cardiovascular system (C) 4.7 (1,661/35,421)
Dermatologicals (D) 10.2 (3,830/37,636)
Genitourinary system (G) 12.3 (1,759/14,354)
Systemic hormonal preparations (H) 5.2 (306/5,855)
Antiinfectives (J) 6.5 (4,168/64,372)
Antineoplastic and
Immunomodulating
drugs (L)
70.1 (138/197)
Musculoskeletal system (M) 9.4 (2,314/24,601)
Nervous system (N) 9.9 (3,967/40,092)
Antiparasitic products (P) 11.4 (553/4,852)
Respiratory system (R) 11.2 (3,531/ 31,402)
Sensory organs (S) 8.8 (1,593/18,114)
Various (V) 73.9 (88/119)
Specific drug subgroups
c
Proton pump inhibitors 6.9 (695/10,056)
Antidiabetics 4.0 (133/3,340)
Low-dose acetylsalicylic
acid (ASA)
6.9 (145/2,106)
Bendroflumethiazide 3.3 (96/2,923)
Dihydropyridine derivatives 2.4 (96/3,966)
Angiotensin-converting enzyme
(ACE) inhibitors
3.3 (44/1,322)
Angiotensin II receptor
(AT-II) antagonists
2.5 (142/5,580)
Statins 6.2 (309/4,984)
β-Lactams 3.2 (295/9,091)
Nonsteroidal anti-inflammatory
drugs (NSAIDs)
9.1 (1,815/20,046)
Tramadol and Codeine 5.2 (588/11,297)
Benzodiazepines (anxiolytics) 5.9 (174/2,960)
Selective serotonin re-uptake
inhibitors (SSRIs)
6.4 (283/4,445)
Bronchodilators 8.6 (470/5,449)
Inhaled corticosteroids 8.8 (167/1,889)
a
PNA is calculated as the proportion of prescriptions that were not filled
within 4 months of being issued
b
Classes/groups of the Anatomic Therapeutic Chemical (ATC) Classifi-
cation System
c
For definition of these drug classes, see Appendix
Eur J Clin Pharmacol
pulmonary disease were also associated with lower PNA
rates, with an OR of 0.77 and 0.80, respectively.
Polypharmacy, i.e. taking more than eight drugs,
showed an OR of 1.15 (95 % CI 1.10–1.21). While
larger practice size were associated with lower rates of
PNA, these estimates did not reach statistical signifi-
cance. Lastly, having a diagnosis of ischaemic heart
disease increased the risk of PNA, with an OR of
1.22. Gender, cohabitation and education had little or
no association with the degree of PNA.
Discussion
The results of this study show that overall, 9.3 % of patientsin
Danish primary care failed to fill their prescriptions within 4
months of issue during the study period. The lowest rate of
PNA was for drugs for the “Cardiovascular system”(ATC
group C). Most of the patients redeemed their prescription
within the first week. Age was found to be the most important
factor associated with PNA.
The primary strength of the study is its high internal valid-
ity due to the highquality of the data sources used [21,24]and
the large sample size.
Our study also has a number of limitations. First, the re-
quirement that all GPs included in this study had to be classified
as “up-to-standard”might imply that the GPs included are not
representative of all Danish GPs. However, we have no reason
to believe, that “up-to-standard”GPs handle adherence prob-
lems better than other GPs in Denmark. A second potentially
important limitation is a lack of knowledge on the level of
agreement between the GP and patient regarding the treatment,
i.e. we do not know if the GP and the patient agreed on
initiating treatment the same day or 1 month after the prescrip-
tion was issued. Furthermore, the data contain no means to
uniquely identify a single prescription. Therefore, later prescrip-
tions redeemed for the same drug, issued by different pre-
scribers, would also result in the patient being classified as
adherent for the GP prescription, even though the original
prescription was never redeemed. This might decrease the
observed rate of PNA, especially among patients followed by
hospitals or other specialists. However, the rapid saturation seen
in Fig. 1, which shows that the vast majority of patients did pick
up their prescriptions within the first week of issuing indicates
that this factor did not play a major role.
In some studies, the PNA proportion was calculated for all
prescriptions [5,7,10,11,13,14,18,19] while in other
60
70
80
90
100
0102030
Primary adherence(%)
Time (days)
Fig. 1 Proportion of redeemed prescriptions during the first 30 days
following issuing of the prescription by the general practitioner
Tabl e 4 Mixed effects
a
logistic regression analysis of the dependence of
primary non-adherence on various patient- and GP-related variables
Patient- and GP-related variables Odds ratio [95 %
confidence interval]
Gender
Female (Reference)
Male 1.03 [0.99–1.06]
Cohabitation
No (Reference)
Yes 1.01 [0.96–1.05]
Age (years)
18–29 (Reference)
30–49 0.85 [0.80–0.89]
50–69 0.55 [0.52–0.58]
70+ 0.48 [0.45–0.51]
Polypharmacy
0–2 drugs (Reference)
3–7 drugs 1.05 [1.01–1.09]
8+ drugs 1.15 [1.10–1.21]
Comorbidity
Diabetes mellitus 0.98 [0.93–1.04]
Chronic obstructive
pulmonary disease
0.80 [0.74–0.86]
Ischaemic heart diseases 1.22 [1.10–1.35]
Income (DKK)
<250,000 (Reference)
250,000–499,999 0.91 [0.87–0.95]
500,000+ 0.77 [0.72–0.81]
Education
≤10 years (Reference)
11–12 years 1.01 [0.95–1.06]
13+ years 0.96 [0.92–1.00]
Practice size
Solo GP (Reference)
2 GPs 0.74 [0.55–1.00]
>3 GPs 0.83 [0.65–1.04]
GP age
<50 years (Reference)
50–59 years 1.00 [0.79–1.25]
>60 years 0.90 [0.67–1.21]
a
Random factors: Subject and prescriber
Eur J Clin Pharmacol
studies—such as our study—only the first prescription for a
new drug treatment was taken into account [8–10,15–17].
This different methodology explains some of the apparent
discrepancy in reported PNA rates; if you include the second
or later prescriptions for a given drug, the likelihood that a
patient will stop treatment, i.e. not fill the prescription, condi-
tional on several previous prescriptions, is probably lower. To
our knowledge, only three studies have used an approach
similar to ours, i.e. studying new treatments outside the hos-
pital setting, namely, the two studies by Fischer et al. [9,10]
and the study by Shin et al. [16]. The overall rate found in our
study is markedly lower than the 24–28 % reported by Fischer
et al. [9,10], but comparable to the rate of 9.8 % reported by
Shin et al. [16]. However, some differences in study design,
for example, the inclusion of specialist prescribers and chil-
dren, make the comparison difficult. Furthermore, it is likely
that the substantial differences in the structure of the health
care in the USA and Denmark explain a significant proportion
of the differences observed.
PNA varied by ATC drug class, ranging from 73.9 % for
drugs in the “Var iou s ”category (ATC group V) to 4.7 % for
drugs related to the “Cardiovascular system”category (ATC
group C). Although drugs in the categories “Vari ous ”(ATC
group V) and “Antineoplastic and immunomodulating
agents”(ATC group L) had the highest rate of PNA, they
did not affect the overall PNA, as both drug classes are
prescribed infrequently in generally practice. In addition, the
“Vari o u s”category includes drugs that are exempt from reim-
bursement, and patients might therefore buy them over-the-
counter to save prescriptions charges. Outside these two spe-
cial groups, drugs related to “Blood and bloodforming or-
gans”(ATC group B) had the highest rate of PNA (16.9 %).
The PNA for “Antiinfectives”(6.5 %) and for “β-Lactams”
(3.2 %) was lower than the overall rate of PNA, possibly due
to the former drug being used for the short-term treatment of
infectionsand patients usually needingthem urgently [14,17].
AccordingtoFig.1, most of the patients included in
this study redeemed their prescription within 1 week
after issuing. This result is confirmed in other studies
which also have reported that most prescriptions are
redeemed within 1 week [12,17]. About 90 % of the
patients who redeemed their prescriptions within
4 months redeemed their prescriptions within the first
week (data not shown). This was seen for all drug main
groups except the genitourinary system and sex hor-
mones, where only 80 % of those filling the prescrip-
tion did so within the first week. This group also
generally had a higher rate of PNA compared to the
other groups (Table 3).
Our findings have both a clinical and a general
research aspect. The clinical aspect is that GPs can
largely expect their patients to redeem their prescrip-
tions and to do so quite soon. We were also able to
identify the patients who were most likely to do so: The
embodiment of a compliant patient would be an elderly,
rich woman who is prescribed a cardiovascular drug by an
experienced doctor in a large practice. The research per-
spective is that GP’s data are very useful for
pharmacoepidemiological research, as aptly illustrated by
the tremendous success of the General Practitioners
Research Database in the UK. The ultimate measure of
interest in pharmacoepidemiological research is what the
patients have actually ingested. One may argue that data
sources based on drug dispensing, such as the pharmacy-
based system, is one step closer to the actual ingestion rate
than data sources based on drug prescribing. However, this
would usually be a matter of non-differential misclassifica-
tion of drug exposure, and the approximately 10 % dis-
crepancy found in our study would rarely be critical. In
contrast, there are epidemiological designs that are particu-
larly vulnerable to exposure misclassification and where
accurate data on the timing of drug intake is crucial, such
as the case-crossover design and its variants [28]. In such
situations, one might prefer a different data source than a
GP-based system.
Conflicts of interest None.
Appendix
Tabl e 5 ATC codes used to specify the drug subgroups presented in the
bottom half of table 3
Anatomical subgroups ATC codes
Proton pump inhibitors A02BC
Antidiabetics A10
Low-dose ASA B01AC
Bendroflumethiazide with potassium chloride C03AB01
Dihydropyridine derivates C08C
ACE-inhibitors inclusive combination preparations C09B
AT-II antagonists inclusive combination preparations C09C and C09D
Statins C10AA
β-Lactams J01CA
NSAIDs M01A excl.
M01AX
Tramadol and codeine N02AX02 and
R05DA
Benzodiazepines (anxiolytics) N05BA
SSRIs N06AB
Bronchodilators R03AC
Inhaled corticosteroids R03BA
Eur J Clin Pharmacol
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