Study Protocol
P3-C2-002
DARWIN EU® Drug Utilisation Study of
prescription opioids
11/02/2025
Version 2.0
This document represents the views of the DARWIN EU® Coordination Centre only and cannot be interpreted as reflecting those of
the European Medicines Agency or the European Medicines Regulatory Network.
P3-C2-002 Study Protocol
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Contents
LIST OF ABBREVIATIONS ......................................................................................................................... 4
1. TITLE .............................................................................................................................................. 5
2. RESPONSIBLE PARTIES – STUDY TEAM ............................................................................................. 5
3. ABSTRACT ...................................................................................................................................... 6
4. AMENDMENTS AND UPDATES......................................................................................................... 8
5. MILESTONES ................................................................................................................................... 9
6. RATIONALE AND BACKGROUND ...................................................................................................... 9
7. RESEARCH QUESTION AND OBJECTIVES ......................................................................................... 10
8. RESEARCH METHODS .................................................................................................................... 12
8.1 Study design....................................................................................................................................... 12
8.2 Study Setting ...................................................................................................................................... 12
8.3 Variables ............................................................................................................................................ 17
8.4 Data sources ...................................................................................................................................... 21
8.5 Study size ........................................................................................................................................... 25
8.6 Data analysis ...................................................................................................................................... 25
8.7 Evidence synthesis ............................................................................................................................. 30
9. DATA MANAGEMENT ................................................................................................................... 30
10. QUALITY CONTROL ...................................................................................................................... 31
11. LIMITATIONS OF THE RESEARCH METHODS.................................................................................. 31
12. GOVERNANCE BOARD ................................................................................................................. 31
13. MANAGEMENT AND REPORTING OF ADVERSE EVENTS/ADVERSE REACTIONS .............................. 32
14. PLANS FOR DISSEMINATING AND COMMUNICATING STUDY RESULTS .......................................... 32
14.1 Study report ..................................................................................................................................... 32
15. OTHER ASPECT ............................................................................................................................ 32
16. REFERENCES................................................................................................................................ 32
17. ANNEXES .................................................................................................................................... 33
Appendix I: Lists with preliminary concept definitions for exposure ...................................................... 34
Appendix II: Feasibility counts .............................................................................................................. 36
Appendix III: ENCePP checklist .............................................................................................................. 38
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Study Title DARWIN EU® - Drug utilisation study of prescription opioids
Protocol version V2.0
Date 11 February 2025
EU PAS number EUPAS1000000479
Active substances Opioids (substances listed in ATC classes N01AH, N02A and R05DA),
namely:
acetyldihydrocodeine, alfentanil, anileridine, bezitramide,
butorphanol, buprenorphine, codeine, dezocine, dimemorfan,
dextromethorphan, dextromoramide, dextropropoxyphene,
dihydrocodeine, ethylmorphine, fentanyl, hydrocodone,
hydromorphone, ketobemidone, meptazinol, meperidine (pethidine),
methadone, morphine, nicomorphine, normethadone, nalbuphine,
noscapine, oliceridine, opium, oxycodone, oxymorphone,
papaveretum, pentazocine, phenazocine, phenoperidine, pholcodine,
pirinitramide, propoxyphene, remifentanil, sufentanil, tapentadol,
thebacon, tilidine, tramadol;
naloxone;
buprenorphine/naloxone,
oxycodone/naloxone,pentazocine/naloxone, tilidine/naloxone
Medicinal product N/A
Research question This study aims to assess the incidence and prevalence of prescription
and objectives opioids for the period 2012-2024, stratified by history of cancer/no
history of cancer and age, sex, calendar year and country, as well as
characterisation of new users, indications and treatment duration
overall and in people with history of cancer/no history of cancer
stratified by calendar year and country
Countr-ies of study Estonia, Belgium, The Netherlands, France, Spain, Denmark, Norway
AuthorAuthors Amy Lam, Annika Jödicke
1
This is a routine repeated study from P2-C1-002 (EUPAS105641).
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LIST OF ABBREVIATIONS
Acronyms/terms Description
ACI VARHA Auria Clinical Informatics VARHA
CDM Common Data Model
CDWBORDEAUX Bordeaux University Hospital
DA Disease Analyzer
DARWIN EU® Data Analysis and Real World Interrogation Network
DK-DHR Danish Data Health Registries
DUS Drug Utilisation Study
EBB Estonian Biobank
EGCUT Estonian Genome Center at the University of Tartu
EHR Electronic Health Records
EMA European Medicines Agency
GP General Practitioner
ID Index date
IMASIS Institut Municipal Assistència Sanitària Information System
IPCI Integrated Primary Care Information Project
NLHR Norwegian Linked Health Registry
OHDSI Observational Health Data Sciences and Informatics
OMOP Observational Medical Outcomes Partnership
SIDIAP Sistema d’Informació per al Desenvolupament de la Investigació en Atenció Primària
WHO World Health Organisation
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1. TITLE
DARWIN EU® - Drug Utilisation Study of prescription opioids
2. RESPONSIBLE PARTIES – STUDY TEAM
Table 1 shows a description of the Study team by role, name and organization.
Table 1. Description of study team.
Study team Role Names Organisation
Principal Investigator(s) Amy Lam University of Oxford
Data Scientist(s) Mike Du University of Oxford
Edward Burn
Clinical Epidemiologist Annika Jödicke University of Oxford
Junqing (Frank) Xie
Data Partner* Names Organisation
Local Study Coordinator/Data Gargi Jadhav IQVIA
Analyst Isabella Kaczmarczyk
Akram Mendez
Dina Vojinovic
Talita Duarte Salles IDIAP JGol
Irene López Sánchez
Agustina Giuliodori Picco
Anna Palomar Cros
Raivo Kolde University of Tartu
Marek Oja
Ami Sild
Katia Verhamme Erasmus MC
Romain Griffier CHU Bordeaux
Guillaume Verdy
Claus Møldrup Danish Medicines Agency
Elvira Bräuner
Susanne Bruun
Monika Roberta Korcinska
Handest
Juan Manuel Ramírez-Anguita Consorci Mar Parc de Salut
Angela Leis Barcelona
Miguel-Angel Mayer
Saeed Hayati University of Oslo
Nhung Trinh
Hedvig Nordeng
Maren Mackenzie Olson
*Data partners’ role is only to execute code at their data source, review and approve their results. They do not
have an investigator role. Data analysts/programmers do not have an investigator role and thus declaration of
interests (DOI) for them is not needed.
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3. ABSTRACT
Title
DARWIN EU® - Drug Utilisation Study of prescription opioids.
Rationale and Background
Prescription opioids, while effective for managing severe pain, have led to a public health crisis due to
misuse, addiction, and overdose, particularly in the US. Recently, concerns have been growing in Europe
due to increasing opioid use and related mortality. Factors such as chronic pain, mental health disorders,
and advanced age can exacerbate misuse and the development of dependence. Given the potential for
global spread of this issue, enhanced surveillance and in-depth research into opioid utilisation patterns are
imperative. A drug utilisation study using a Common Data Model (CDM) is a promising approach to
supplement European opioid monitoring systems, providing more granular data to inform evidence-based
decisions on this complex topic.
Research question and Objectives
The objectives of this study are
(i) To investigate the annual incidence and annual period prevalence of use of opioids (overall,
active drug substance, strength (weak/strong opioids) and route (oral, transdermal or
parenteral), stratified by history of cancer/no history of cancer and for calendar year, age, sex
and country/database during the study period.
(ii) To determine duration of prescription opioid use, as well as characteristics of new users and
indication for opioid prescribing/dispensing overall and in people with history of cancer/no
history of cancer, all stratified by calendar year and country/database.
Research Methods
Study design
• Population level cohort study (Objective 1, Population-level drug utilisation study on opioids)
• New drug user cohort study (Objective 2, Patient-level drug utilisation analyses regarding summary
characterisation, duration, and indication of opioid use)
Population
Population-level utilisation of opioids: All people registered in the respective databases on 1st of January of
each year in the period 2012-2024 (or the latest available, whatever comes first), with at least 1 year of
prior data availability, will participate in the population-level analysis (period prevalence calculation in
Objective 1). Therefore, children aged <1 year will be excluded.
New users of opioids in the period between 1/1/2012 and 31/12/2024 (or latest date available, whatever
comes first), with at least 1 year of data availability, and no use of the respective opioid in the previous 12
months, will be included for incidence rate calculations in Objective 1.
Patient-level drug utilisation: New users of opioids in the period between 1/1/2012 and 31/12/2024 (or latest
date available, whatever comes first), with at least 1 year of data availability, and no use of the respective
opioid in the previous 12 months, will be included for patient-level drug utilisation analyses.
Variables
Drug of interest: Opioids (substances listed in ATC classes N01AH, N02A and R05DA); naloxone; and fixed
naloxone-opioid combinations.
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Data sources
1. Estonian Biobank (EBB), Estonia
2. IQVIA LBD Belgium, Belgium
3. Integrated Primary Care Information Project (IPCI), The Netherlands
4. The Information System for Research in Primary Care (SIDIAP), Spain
5. Clinical Data Warehouse for Bordeaux University Hospital (CDWBORDEAUX), France
6. Danish Data Health Registries (DK-DHR), Denmark
7. Institut Municipal Assistència Sanitària Information System (IMASIS), Spain
8. Norwegian Linked Health Registry (NLHR), Norway
Sample size
No sample size has been calculated.
Data analyses
Population-level drug utilisation will be conducted in all databases. Patient-level DUS analyses will be
conducted in all databases. No duration will be calculated for EBB.
Population-level opioid use: Annual period prevalence of opioid use and annual incidence rates per 100,000
person years will be estimated.
Patient-level opioid use: Summary patient-level characterisation by list of pre-defined
conditions/medications of interest will be conducted at index date, including patient demographics, and
history of comorbidities and comedication. Frequency of indication at index date, and in the immediate
time before will be calculated. Cumulative treatment duration will be estimated for the first treatment era
and the minimum, p25, median, p75, and maximum will be provided.
For all analyses a minimum cell count of 5 will be used when reporting results, with any smaller counts will
be noted as <5.
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4. AMENDMENTS AND UPDATES
Number Date Section of Amendment or Reason
study protocol
update
Version 06/02/2025 N/A Update from initial study This is a routine-
1.0 protocol (P2-C1-002, repeated study.
EUPAS105641)
Comparison with Previous Protocols
P2-C1-002 P3-C2-002
(EUPAS105641) (Current study protocol)
Study period 2012-2022 2012-2024
Data partner
EBB [Estonia] * *
IQVIA DA Germany [Germany] *
IQVIA LBD Belgium [Belgium] * *
SIDIAP [Spain] * *
IPCI [The Netherlands] * *
CDWBORDEAUX [France] * *
ACI VARHA [Finland] *
DK-DHR [Denmark] *
IMASIS [Spain] *
NLHR [Norway] *
Reference study protocol N/A P2-C1-002 (EUPAS105641)
Changes from reference study N/A - Exposure: Add opioid use with history of
protocol cancer/no history of cancer
- Patient-level DUS: change large scale
characterisation to pre-defined list of
conditions and medications
- Indication: consider procedures for
possible indication in hospital database
- Sensitivity analysis: remove 6-month
washout period
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5. MILESTONES
Study deliverable Timeline
Draft Study Protocol 17/01/2025
Final Study Protocol 31/01/2025
Creation of Analytical code February 2025
Execution of Analytical Code on the data February 2025
Draft Study Report March 2025
Final Study Report To be confirmed
*Planned dates are dependent on obtaining approvals from the internal review boards of the data sources.
6. RATIONALE AND BACKGROUND
Prescription opioids are important medications recommended to treat acute and chronic moderate to
severe pain but can lead to complex and interconnecting health and social issues related to misuse, abuse,
dependence, addiction, overdose, and drug diversion. Abuse of prescription opioids, in particular, is an
ongoing public health crisis in the US. By 2016 of all patients with a fatal overdose, 25% were due to
prescription opioids1. This alarming trend has manifested through distinct waves of opioid-related
challenges over several decades, with the most recent wave starting around 2013. Within this latest wave,
synthetic opioids, particularly the illicit production of fentanyl, have emerged as a primary focal point of
concern and investigation in the US2.
While no similar concern was observed in Europe by 2015, recent studies in Europe, suggest an increasing
trend in the use of prescription opioids and opioid-use related mortality. Given that drug markets are
increasingly global, the insufficient surveillance of these trends could potentially overlook the indicators of
burgeoning issues.3
Clinical use of prescription opioids may also lead to some of the concerns above. Patients with chronic pain
may develop dependence and addiction due to prolonged prescription opioid exposure leading to drug
tolerance and a need for increased dose or opioid strength4. Similarly, patients with mental health
disorders are at increased risk of initiation and prolonged opioid treatments and their consequences.
Moreover, older adults are more susceptible to the adverse effects of opioids, yet they typically have more
pain management requirements due to accumulating a range of chronic disorders leading to painful
conditions5. There is an imperative need for further investigation to describe the utilisation patterns of
opioids among this demographic6.
A drug utilisation study of prescription opioids based on a Common Data Model (CDM) will provide useful
information on the trends of prescription opioids and the characteristics of prescription opioid users in
Europe. By supplementing the conventional European monitoring systems for aggregated opioid
consumption, this study will offer detailed data on these drugs incl. their strength and route of
administration, thereby enabling well-informed, evidence-based decision-making in addressing this
multifaceted topic.
Following the completion of P2-C1-002 (EUPAS105641, https://catalogues.ema.europa.eu/node/3796),
EMA requested a routine repeated study to include additional databases and more recent data.
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7. RESEARCH QUESTION AND OBJECTIVES
Table 2. Primary and secondary research questions and objectives.
A. Primary research question and objective
Objective:
To investigate the annual incidence and annual period prevalence of
use of opioids (overall, active drug substance, strength (weak/strong
opioids), route (oral, transdermal or parenteral)), stratified by history
of cancer and calendar year, age, sex and country/database during the
study period.
Hypothesis: Not applicable
Population (mention key inclusion- All people registered in the respective databases on 1st of January of
exclusion criteria): each year in the period 2012-2024 (or the latest available, whatever
comes first), with at least 1 year of prior data availability, will participate
in the population-level analysis (period prevalence calculation in
Objective 1). Therefore, children aged <1 year will be excluded.
New users of opioids in the period between 1/1/2012 and 31/12/2024
(or latest date available, whatever comes first), with at least 1 year of
data availability, and no use of the respective opioid in the previous 12
months, will be included for incidence rate calculations in Objective 1.
Exposure: Opioids (substances listed in ATC classes N01AH, N02A and R05DA), as
well as naloxone, and fixed combinations (i.e. buprenorphine and
naloxone, oxycodone and naloxone)
Comparator: None
Outcome: None
Time (when follow up begins and Follow-up will start on a pre-specified calendar time point, namely 1st
ends): of January for each calendar year between 2012-2024 for the
calculation of annual incidence/prevalence rates.
End of follow-up will be defined as the earliest of loss to follow-up,
end of data availability, death, or end of study period, whatever comes
first.
Setting: Inpatient and outpatient setting using data from the following 8 data
sources: EBB [Estonia], IQVIA LBD Belgium [Belgium], SIDIAP [Spain],
IPCI [The Netherlands], CDWBORDEAUX [France], DK-DHR [Denmark],
IMASIS [Spain], NLHR [Norway]
Main measure of effect: Incidence and prevalence of opioid use
B. Secondary research question and objective
Objective: To determine the duration of the first treatment era of opioid use,
as well as characteristics of new users and indication for opioid
prescribing/dispensing overall and in people with history of
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cancer/no history of cancer, all stratified calendar year and
country/database.
Hypothesis: Not applicable
Population (mention key inclusion- New users of opioids overall and in people with history of cancer/no
exclusion criteria): history of cancer in the period between 1/1/2012 and 31/12/2024
(or latest date available, whatever comes first), with at least 1 year
of prior data availability, and no use of the respective opioid in the
previous 12 months, will be included for patient-level drug
utilisation analyses.
Exposure: Opioids (substances listed in ATC classes N01AH, N02A and
R05DA), as well as naloxone, and fixed combinations (i.e.
buprenorphine and naloxone, oxycodone and naloxone )
Comparator: None
Outcome: None
Time (when follow up begins and ends): Follow-up will start on the date of incident opioid prescription
and/or dispensation (index date).
End of follow-up will be defined as the earliest of loss to follow-up,
end of data availability or death, or end of study period, whatever
comes first.
Setting: Inpatient and outpatient setting using data from the following 8
data sources: EBB [Estonia], IQVIA LBD Belgium [Belgium], SIDIAP
[Spain], IPCI [The Netherlands], CDWBORDEAUX [France], DK-DHR
[Denmark], IMASIS [Spain], NLHR [Norway]
Main measure of effect: Duration of opioid use (first treatment era) expressed as
minimum, p25, median, p75, and maximum days
Summary patient-level characterisation by list of pre-defined
conditions/medications of interest for new opioid users overall
and in people with history of cancer/no history of cancer (1)
overall, (2) for the 10 most frequent opioids in each database, (3)
by strength, (4) by route.
Indications, based on a high-level approach considering the most
frequent conditions and procedures recorded in the month/week
before/at the date of treatment start.
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8. RESEARCH METHODS
8.1 Study design
A cohort study will be conducted using routinely-collected health data from 8 databases. The study will
comprise two consecutive parts:
1. A population-based cohort study will be conducted to address objective 1, assessing the prevalence
and incidence of the respective opioids of interest.
2. A new drug user cohort will be used to address objective 2; to characterise individual-level opioid
utilisation in terms of summary patient characteristics, indication and duration of use.
8.2 Study Setting
8.2.1 Study population
The study cohort will comprise all individuals present in the database during the study period (2012-2024)
and with at least 365 days of data availability before the day they become eligible for study inclusion.
Therefore, children aged <1 year will be excluded.
Additional eligibility criteria will be applied for the calculation of incidence rates and patient-level drug
utilisation analyses: New users will have a first prescription of opioids in the period between 1/1/2012 and
31/12/2024 (or latest date available, whatever comes first), with at least 1 year of prior data availability,
and no use of the respective opioid in the previous 12 months.
8.2.2 Study period and follow-up
The study period will be from the 1st of January 2012 until the earliest of either 31st December 2024 or the
respective latest date of data availability of the respective databases.
For the population-level analyses for incidence and prevalence, individuals will contribute person-time from
the date they have reached at least 365 days of data availability.
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Table 3. Operational Definition of Time 0 (index date) and other primary time anchors.
Study population Time Anchor Description Number of Type of entry Washout Care Code Diagnosis Incident with Measure Source of
name(s) (e.g., time 0) entries window Setting1 Type2 position respect to… ment algorith
characte m
ristics/
validatio
n
All patients from the Patient present in the Multiple Prevalent n/a IP n/a n/a Overall, n/a n/a
database eligible for database during the study and substance,
the study – Analysis period and with at least 1 OP strength,
of Prevalent Use year of valid database history route
All patients from the Patient present in the Multiple Incident [-365 to IP and OP n/a n/a Overall, n/a n/a
database eligible for database during the study ID] substance,
the study – Analysis period and with at least 1 strength,
of incident use year of valid database history route
1 IP = inpatient, OP = outpatient, n/a = not applicable, ID = index date
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Both incidence and prevalence require an appropriate denominator population and their contributed
observation time to first be identified. Study participants in the denominator population will begin
contributing person time on the respective date of the latest of the following: 1) study start date (1st
January 2012), 2) date at which they have a year of prior history recorded. Participants will stop
contributing person time at the earliest date of the following: 1) study end date (31st December 2024) or 2)
end of available data in each of the data sources or 3) date at which the observation period of the specific
person ends.
An example of entry and exit into the denominator population is shown in Figure 1. In this example, person
ID 1 has already sufficient prior history before the study start date and observation period ends after the
study end date, so will contribute during the complete study period. Person ID 2 and 4 enter the study only
when they have sufficient prior history. Person ID 3 leaves when exiting the database (the end of
observation period). Lastly, person ID 5 has two observation periods in the database. The first period
contributes time from study start until end of observation period, the second starts contributing time again
once sufficient prior history is reached and exits at study end date.
Figure 1. Included observation time for the denominator population.
8.2.3 In- and exclusion criteria
8.2.3.1 Population-level Utilisation of opioids
The study cohort will comprise all individuals present in the period 2012-2024 (or the latest available), with
at least 365 days of data availability before the day they become eligible for study inclusion.
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Additional eligibility criteria will be applied for the calculation of incidence rates: New users will have a first
prescription of opioids in the period between 1/1/2012 and 31/12/2024 (or latest date available, whatever
comes first), with at least 1 year of prior data availability, and no use of the respective opioid in the
previous 12 months.
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8.2.3.2 Patient-level Utilisation of opioids
All new users of opioids, after 365 days of no use of the specific opioid /substance /strength/ route, in the period between 1/1/2012 and 31/12/2024 (or
latest date available), with at least 365 days of visibility prior to the date of their first opioid prescription.
Table 4. Operational definitions of inclusion criteria.
Criterion Details Order of Assessment Care Settings Code Diagnosis Applied to Measurement Source for
application window Type position study characteristics/ algorithm
populations: validation
Observation period All individuals present N/A N/A primary care, N/A N/A All individuals N/A N/A
in the database in the period 2012- secondary care within the
during the period 2024 (or the latest (i.e in- and selected
2012-2024 (or the available) outpatient databases
latest available) specialist care)
Prior database Study participants will After 1 year primary care, N/A N/A All individuals N/A N/A
history of 1 year be required to have a secondary care within the
year of prior history (i.e in- and selected
observed before outpatient databases
contributing specialist care)
observation time
Washout period New users will be After 365 days primary care, N/A N/A All individuals N/A N/A
required to have not secondary care within the
used opioids/ the (i.e in- and selected
specific opioid outpatient databases
substance /strength/ specialist care)
route 365 days before a
“new” prescription
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8.3 Variables
8.3.1 Exposure
For this study, the exposure of interest is use (during study period) of opioids, naloxone and fixed opioid-
naloxone combinations.
Opioids will be grouped
(1) Overall
(2) by drug substance (incl. combinations and products for all indications)
(3) by strength (weak/potent opioids) for those opioids where strength is labelled by the WHO
(4) by route (oral, transdermal or parenteral) for overall opioids
This list of opioids is described in Table 5. Details of exposure are described in Table 6.
Table 5. Exposure of interest.
Substance Name Strength* No record counts Substance Name Strength* No record counts
in databases in databases
expected based expected based
on feasibility on feasibility
acetyldihydrocodeine noscapine
alfentanil oliceridine X
anileridine X opium
bezitramide X oxycodone potent
butorphanol X oxymorphone potent X
buprenorphine potent papaveretum
codeine weak pentazocine
dezocine X phenazocine
dimemorfan phenoperidine X
dextromethorphan pholcodine
dextromoramide pirinitramide
dextropropoxyphene X propoxyphene
dihydrocodeine remifentanil
ethylmorphine sufentanil
fentanyl potent tapentadol potent
hydrocodone weak thebacon
hydromorphone potent tilidine
ketobemidone tramadol weak
meptazinol
meperidine (pethidine) naloxone
methadone potent
morphine potent buprenorphine/naloxone
nicomorphine oxycodone/naloxone
normethadon X pentazocine/naloxone
nalbuphine tilidine/naloxone
*Drug strength has been assigned bases on the WHO analgesic ladder (https://www.ncbi.nlm.nih.gov/books/NBK554435/):
weak opioids (hydrocodone, codeine, tramadol),
potent opioids (morphine, methadone, fentanyl, oxycodone, buprenorphine, tapentadol, hydromorphone, oxymorphone)
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Table 6. Exposure details.
Exposure Details Washout Assessme Care Code Diagnosis Applied Incident Measure Source of
group window nt Setting Type position to study with ment algorithm
name(s) Window populatio respect characteri
ns: to… stics/
validation
Overall Preliminary code lists [-365 to ID] Calendar Biobank, RxNorm N/A All Previous N/A N/A
opioids, provided in Table 5. year primary individuals opioid use
substance, and present in
strength, secondary the
route care database
during the
study
period
Opioid use Preliminary code lists [-365 to ID] Calendar Biobank, RxNorm N/A All Previous N/A N/A
(overall, provided in Table 5. History year primary individuals opioid use
strength, of cancer defined as cancer- and present in
route) with related observation or secondary the
history of condition within 1 year care database
cancer/no before index date or use of during the
history of antineoplastic treatment study
cancer within 1 year before index period
date.
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8.3.2 Outcomes
None.
8.3.3 Other covariates, including confounders, effect modifiers and other variables (where
relevant)
8.3.3.1 Covariates for stratification in population-level drug utilisation study:
• Calendar year
• Age: 10-year age bands will be used: 1-10, 11-20, 21-20 […] , and >80
• Sex: male or female
• History of cancer: yes or no
8.3.3.2 Covariates for patient-level drug utilisation study:
Baseline characteristics given by the list of pre-defined conditions/medications of interest: the operational
definition of the included covariates are as follows: anxiety, asthma, autoimmune disease, chronic kidney
disease, chronic liver disease, chronic obstructive pulmonary disease, dementia, depressive disorder,
diabetes, gastro-oesophageal reflux disease, heart failure, HIV, hypertension, hypothyroidism,
inflammatory bowel disease, malignant neoplastic disease, lung cancer, colorectal cancer, prostate cancer,
pancreatic cancer, ovarian cancer, leukemia, multiple myeloma, breast cancer, endometrial cancer,
Hodgkin lymphoma, non-Hodgkin lymphoma, myocardial infarction, osteoporosis, pneumonia, rheumatoid
arthritis, stroke, venous thromboembolism. Covariates for the baseline medications will be pre-defined as
follows: agents acting on the renin-angiotensin system, antibacterials for systemic use, antidepressants,
antiepileptics, anti-inflammatory and antirheumatic products, antineoplastic agents, antithrombotic agents,
beta blocking agents, calcium channel blockers, diuretics, drugs for acid related disorders, drugs for
obstructive airway diseases, drugs used in diabetes, hormonal contraceptives, immunosuppressants, lipid
modifying agents, psycholeptics, psychostimulants. Index date is the start of the (first) incident prescription
during the study period.
Indication: We will use a high-level approach considering the most frequent conditions (all databases) and
procedures (hospital database only) recorded in the month/week before/at the date of treatment start. The
top 10 most frequent co-morbidities from large-scale patient characterisation recorded (1) at index date
[primary definition] and (2) in the week before index date, (2) in the month before index date [sensitivity
analyses] will be provided as proxies for indication.
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Table 7. Operational definitions of covariates.
Characteristic Details Type of Assessment Care Settings¹ Code Type2 Diagnosis Applied to Measurement Source for
variable window Position3 study characteristic algorithm
populations: s/
validation
Indication of Top 10 most Counts At index date Biobank, SNOMED N/A Persons with N/A N/A
Use frequent co- and as primary and new use
morbidities and sensitivity secondary care during the
procedures analyses in study period
from large-scale windows
patient around index
characterisation date (ID): [-7,
ID] and [-30, ID]
Summary Patient-level Counts Demographics, Biobank, SNOMED, N/A Persons with N/A N/A
characteristics characterisation co-morbidities primary and RxNorm new use
of new users with regard to and co- secondary care during the
by list of pre- baseline co- medication at study period
defined variates by pre- index date (ID),
conditions/me defined and within
dications of conditions/medi anytime to 366
interest cations of days before ID,
interest. 365 to-181 days
before ID, and
180 to 1 day
before ID
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8.4 Data sources
This study will be conducted using routinely collected data from 8 databases from 7 European countries. All
databases were previously mapped to the OMOP CDM.
1. Estonian Biobank (EBB), Estonia
2. IQVIA LBD Belgium, Belgium
3. Integrated Primary Care Information Project (IPCI), The Netherlands
4. The Information System for Research in Primary Care (SIDIAP), Spain
5. Clinical Data Warehouse of Bordeaux University Hospital (CDWBordeaux), France
6. Danish Data Health Registries (DK-DHR), Denmark
7. Institut Municipal Assistència Sanitària Information System (IMASIS), Spain
8. Norwegian Linked Health Registry (NLHR), Norway
Information on the data source(s) with a justification for their choice in terms of ability to capture the
relevant data is described below and in a Table 8.
Fit for purpose: This study will be repeated in 5 out of the 7 databases from the initial study P2-C1-002 and
will include 3 additional databases. The selection of databases for this study was performed based on data
reliability and relevance for the research question and feasibility counts.
6 databases include records from primary care and outpatient specialist care where opioids are expected to
be prescribed. 2 databases are covering in-and outpatient records from hospitals, where opioids are
expected to be initiated and prescribed for outpatient use following hospital discharge.
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Table 8. Description of data sources.
Country Name of Justification for Health Care setting Type of Number of Feasibility Data lock for the
Database Inclusion Data active count of last update
subjects exposure (if
relevant)
The Netherlands IPCI Database covers Primary care EHR 1.25 million Please see 21/10/2024
primary care where Appendix
opioid prescriptions are
issued.
France CDWBORDEA Database covers Secondary care (in EHR 0.2 million 22/02/2024
UX hospital care setting and outpatients)
where opioid may be
initiated
Spain SIDIAP Databases covers Primary care EHR 6.0 million 30/06/2023
primary care /
Belgium IQVIA LBD outpatient specialist Primary care, EHR 0.2 million 30/09/2024
Belgium care setting where outpatient
opioid prescriptions are specialist care
issued.
Estonia EBB Database covers Biobank Claims data 0.2 million 01/06/2023
primary care setting
where opioid
prescriptions are
issued.
Denmark DK-DHR Database covers Community EHR 5.96 million 21/5/2024
secondary care pharmacy,
specialist setting where secondary care
opioid prescriptions are specialist
issued.
Norway NLHR Database covers Primary care, Registries, 6.95 million 29/10/2024
primary care and secondary care EHR
secondar care specialist, hospital
specialists where opioid inpatient care
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Country Name of Justification for Health Care setting Type of Number of Feasibility Data lock for the
Database Inclusion Data active count of last update
subjects exposure (if
relevant)
prescription are issued.
Spain IMASIS Database covers Secondary care EHR 0.1 million 13/07/2024
secondary care specialist, hospital
specialists where opioid inpatient
prescription are issued.
IPCI = Integrated Primary Care Information Project; CDWBORDEAUX= Bordeaux University Hospital, SIDIAP = Sistema d’Informació per al Desenvolupament de la Investigació en Atenció
Primària, DA = Disease Analyzer, EBB = Estonian Biobank, EHR = Electronic Heath record, DK-DHR = Danish Data Health Registries, NLHR = Norwegian Linked Health Registry data, IMASIS =
Institut Municipal Assistència Sanitària Information. Exposure is based on prescription data.
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Integrated Primary Care Information Project (IPCI), The Netherlands
IPCI is collected from electronic health records (EHR) of patients registered with their general practitioners
(GPs) throughout the Netherlands.7 The selection of 374 GP practices is representative of the entire
country. The database contains records from 3.0 million (as of 01-2025) patients out of a Dutch population
of 17M starting in 19967. The median follow-up is 4.6 years as of 01/2025. The observation period for a
patient is determined by the date of registration at the GP and the date of leave/death. The observation
period start date is refined by many quality indicators, e.g. exclusion of peaks of conditions when
registering at the GP. All data before the observation period is kept as history data. Drugs are captured as
prescription records with product, quantity, dosing directions, strength and indication. Drugs not
prescribed in the GP setting might be underreported. Indications are available as diagnoses by the GPs and,
indirectly, from secondary care providers but the latter might not be complete. Approval needs to be
obtained for each study from the Governance Board7.
Bordeaux University Hospital (CDWBORDEAUX), France
The clinical data warehouse of the Bordeaux University Hospital comprises electronic health records on
more than 2 million patients with data collection starting in 2005. The hospital complex is made up of three
main sites and comprises a total of 3,041 beds (2021 figures). The database currently holds information
about the person (demographics), visits (inpatient and outpatient), conditions and procedures (billing
codes), drugs (outpatient prescriptions and inpatient orders and administrations), measurements
(laboratory tests and vital signs) and dates of death (in or out-hospital death).8
Information System for Research in Primary Care (SIDIAP), Spain (IDIAP Jordi Gol)
SIDIAP is collected from EHR records of patients receiving primary care delivered through Primary Care
Teams (PCT), consisting of GPs, nurses and non-clinical staff9. The Catalan Health Institute manages 286 out
of 370 such PCT with a coverage of 5.6M patients, out of 7.8M people in the Catalan population (74%). The
database started to collect data in 2006. The mean follow-up is 15.5 years as of 01/2025. The observation
period for a patient can be the start of the database (2006), or when a person is assigned to a Catalan
Health Institute primary care centre. Date of exit can be when a person is transferred-out to a primary care
centre that does not pertain to the Catalan Health Institute, or date of death, or date of end of follow-up in
the database. Drug information is available from prescriptions and from dispensing records in pharmacies.
Drugs not prescribed in the GP setting might be underreported; and disease diagnoses made at specialist
care settings are not included. Studies using SIDIAP data require previous approval by both a Scientific and
an Ethics Committee.
Longitudinal Patient Database (LPD) Belgium, Belgium (IQVIA)
LPD Belgium is a computerised network of GPs who contribute to a centralised database of anonymised
data of patients with ambulatory visits. Currently, around 300 GPs from 234 practices are contributing to
the database covering 1.1M patients from a total of 11.5M Belgians (10.0%). The database covers time
from 2005 through the present. Observation time is defined by the first and last consultation dates. Drug
information is derived from GP prescriptions. Drugs obtained over the counter by the patient outside the
prescription system are not reported. No explicit registration or approval is necessary for drug utilisation
studies.
Estonian Biobank – University of Tartu (Estonia)
The Estonian Biobank (EBB) is a population-based biobank of the Estonian Genome Center at the University
of Tartu (EGCUT). Its cohort size is currently close to 200,000 participants (“gene donors” >= 18 years of
age) which closely reflects the age, sex and geographical distribution of the Estonian adult population.
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Genomic GWAS analysis have been performed on all gene donors. The database also covers health
insurance claims, digital prescriptions, discharge reports, information about incident cancer cases and
causes of death from national sources for each donor.
Danish Data Health Registries (DK-DHR), Denmark
Danish health data is collected, stored and managed in national health registers at the Danish Health Data
Authority and covers the entire population which makes it possible to study the development of diseases
and their treatment over time. There are no gaps in terms of gender, age and geography in Danish health
data due to mandatory reporting on all patients from cradle to grave, in all hospitals and medical clinics.
Personal identification numbers enable linking of data across registers. High data quality due to
standardisation, digitisation and documentation means that Danish health data is not based on
interpretation. The present database has access to the following registries for the entire Danish population
of 5.9 million persons from 1/1/1995: the Central Person Registry, the National Patient Registry, the
Register of Pharmaceutical Sales, the National Cancer Register, the Cause of Death registry, the Clinical
Laboratory Information Register, COVID-19 test and Vaccination Registries, and the complete vaccination
registry. The median follow-up is 21.7 years (as of 01/2025).
Norwegian Linked Health Registry data (NLHR), Norway
Norway has a universal public health care system consisting of primary and specialist health care services
covering a population of approximately 5.4 million inhabitants. Many population-based health registries
were established in the 1960s with use of unique personal identifiers facilitating linkage between registries.
Data from registries includes information about the pregnancy, diagnosis in secondary care (e.g., hospital),
diagnosis and contact in primary care (e.g, GPs and outpatient specialists), all medications dispensed
outside of hospitals, test results of communicable diseases (e.g., Sars-Cov-2), and records on vaccinations.
The median follow-up is 16 years (as of 01/2025).
Institut Municipal Assistència Sanitària Information System (IMASIS), Spain
The Institut Municipal Assistència Sanitària Information System (IMASIS) is the Electronic Health Record
(EHR) system of Parc de Salut Mar Barcelona (PSMar) which is a complete healthcare services organisation.
The information system includes and shares the clinical information of two general hospitals (Hospital del
Mar and Hospital de l’Esperança), one mental health care centre (Centre Dr. Emili Mira) and one social-
healthcare centre (Centre Fòrum) including emergency room settings, that are offering specific and
different services in the Barcelona city area (Spain). At present, IMASIS includes clinical information from
around 1 million patients with at least one diagnosis and who have used the services of this healthcare
system since 1990 and from different settings such as admissions, outpatients, emergency room and major
ambulatory surgery. The average follow-up period per patient is 6.4 years.
8.5 Study size
No sample size has been calculated as this is a descriptive study. Prevalence and Incidence of opioid use
among the study population will be estimated as part of Objective 1. Feasibility counts are provided in the
Appendix.
8.6 Data analysis
This section describes the details of the analysis approach and rationale for the choice of analysis, with
reference to the D1.3.8.3 Complete Catalogue of Data Analysis which describes the type of analysis in
function of the study type.
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The analysis will include calculation of population-based incidence rates and prevalence, as described in
section 9.7.5.1 – Population-level drug utilisation study, characterisation of patient-level baseline
covariates for opioid users, percentages of indications, and descriptive statistics of treatment duration
of opioid, as described in section 9.7.5.2 – Individual-level drug utilisation study.
8.6.1 Federated network analyses
Analyses will be conducted separately for each database. Before study initiation, test runs of the analytics
are performed on a subset of the data sources or on a simulated set of patients and quality control checks
are performed. Once all the tests are passed, the final package is released in the version-controlled Study
Repository for execution against all the participating data sources.
The data partners locally execute the analytics against the OMOP-CDM in R Studio and review and approve
the by default aggregated results before returning them to the Coordination Centre. Sometimes multiple
execution iterations are performed, and additional fine tuning of the code base is needed. A service desk
will be available during the study execution for support.
The study results of all data sources are checked after which they are made available to the team in the
Digital Research Environment and the Dissemination Phase can start. All results are locked and
timestamped for reproducibility and transparency.
8.6.2 Patient privacy protection
Cell suppression will be applied as required by databases to protect people’s privacy. Cell counts < 5 will be
reported as <5.
8.6.3 Statistical model specification and assumptions of the analytical approach considered
R-packages
We will use the R package “DrugUtilization” for the patient-level drug utilisation analyses including patient-
level characterisation, and “IncidencePrevalence package”11 for the population-level estimation of drug
utilisation.
Drug exposure calculations
Drug eras will be defined as follows: Exposure starts at date of the first prescription, e.g., the index date the
person entered the cohort. For each prescription, the estimated duration of use is retrieved from the drug
exposure table in the CDM, using start and end date of the exposure. Subsequent prescriptions will be
combined into continuous exposed episodes (drug eras) using the following specifications:
Two drug eras will be merged into one continuous drug era if the distance in days between end of the first
era and start of the second era is ≤ 7 days. The time between the two joined eras will be considered as
exposed by the first era as shown in Figure 2, first row. Note: dose is not considered for this study.
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Gap era Dose in Cumulative dose Cumulative
Schematics
joint mode between time
“first” 𝑑1 𝑑1 ⋅ (𝑥1 + 𝑥12 ) + 𝑑2 ⋅ 𝑥2 𝑥1 + 𝑥12 + 𝑥2
“second” 𝑑2 𝑑1 ⋅ 𝑥1 + 𝑑2 ⋅ (𝑥2 + 𝑥12 ) 𝑥1 + 𝑥12 + 𝑥2
“zero” 0 𝑑1 ⋅ 𝑥1 + 𝑑2 ⋅ 𝑥2 𝑥1 + 𝑥12 + 𝑥2
“join” NA 𝑑1 ⋅ 𝑥1 + 𝑑2 ⋅ 𝑥2 𝑥1 + 𝑥2
Figure 2. Gap era joint mode.
If two eras start at the same date, the overlapping period will be considered exposed by both. We will not
consider repetitive exposure.
New user cohorts
New users will be selected based on their first prescription of the respective drug of interest after the start
of the study. For each patient, at least 365 days of data availability will be required prior to that
prescription. New users will be required to not have been exposed to the drug of interest for at least 365
days prior the current prescription. If the start date of a prescription does not fulfil the exposure washout
criteria of 365 days of no use, the whole exposure is eliminated.
8.6.4 Methods to derive parameters of interest
Calendar time
Calendar time will be based on the calendar year of the index prescription.
Age
Age at index date will be calculated using January 1st of the year of birth as proxy for the actual birthday.
We will use 10-year age bands for stratification for population-level analyses: 1-10,11-20, 21-20 […] and
>80
Sex
Results for population-level analyses will be presented stratified by sex.
Indication
Indications will be assessed based on a high-level approach considering the most frequent conditions (all
databases) and procedures (hospital database only) recorded at the date of treatment start/ in the
week/month before treatment start.
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Characterisation of patient-level features
Patient characterisation by pre-defined conditions/medications of interest before/on index date (= date of
prescription) will be provided for different classifications for opioids [as introduced in section 9.3.1
“Exposures”] overall and in patients with history of cancer/no history of cancer, namely for (1) opioids
overall, (2) for the 10 most frequent opioids in each database, (3) weak/potent opioids and (4)
transdermal/oral/parenteral opioids, stratified for database/country. Co-variates will be extracted for the
following time intervals: Concepts in the “condition” and “drug” domain will be assessed for anytime to -
366 days [conditions only], -365 days to -181 days, -180 to -1 day before index date, and at index date. List
of pre-defined conditions/medications of interest will be given in section 9.3.3.2 “Covariates for patient-
level drug utilisation study”
8.6.5 Methods planned to obtain point estimates with confidence intervals of measures of
occurrence
8.6.5.1 Population-level drug utilisation study
Prevalence and incidence calculations will be conducted separately for (1) opioids overall, (2) by drug
substance (incl. combinations and products for all indications), (3) by strength (weak/potent opioids) for
those opioids where strength is labelled by the WHO, (4) by route (oral, transdermal or parenteral) for
overall opioids and stratified by history of cancer.
Prevalence calculations
Prevalence will be calculated as annual period prevalence which summarises the total number of
individuals who use the drug of interest during a given year divided by the population at risk of getting
exposed during that year. Therefore, period prevalence gives the proportion of individuals exposed at any
time during a specified interval. Binomial 95% confidence intervals will be calculated.
Opioid use
Figure 3. Period prevalence example.
An illustration of the calculation of period prevalence is shown below in Figure 3. Between time t+2 and
t+3, two of the five study participants are opioid users giving a prevalence of 40%. Meanwhile, for the
period t to t+1 all five also have some observation time during the year with one of the five study
participants being an opioid user, giving a prevalence of 20%.
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Incidence calculations
Annual incidence rates of the opioid of interest will be calculated as the of number of new users after
356 days (180 days) of no use per 100,000 person-years of the population at risk of getting exposed
during the period for each calendar year. Any study participants with use of the medication of interest
prior to the date at which they would have otherwise satisfied the criteria to enter the denominator
population (as described above) will be excluded. Those study participants who enter the denominator
population will then contribute time at risk up to their first prescription during the study period. Or if
they do not have a drug exposure, they will contribute time at risk up as described above in section
9.2.2 (study period and end of follow-up). Incidence rates will be given together with 95% Poisson
confidence intervals.
An illustration of the calculation of incidence of opioid use is shown below in Figure 4. Patient ID 1 and
4 contribute time at risk up to the point at which they become incident users of opioid. Patient ID 2 and
5 are not seen to use opioid and so contribute time at risk but no incident outcomes. Meanwhile,
patient ID 3 first contributes time at risk starting at the day when the washout period of a previous
exposure, before study start, has ended before the next exposure of opioid is starting. A second period
of time at risk again starts after the washout period. For person ID 4, only the first and third exposures
of opioid count as incident use, while the second exposure starts within the washout period of the first
exposure. The time between start of the first exposure until the washout period after the second
exposure is not considered as time at risk.
Opioid use
Figure 4. Incidence example.
8.6.5.2 Patient-level drug utilisation study
New drug user patient-level characteristics on/before index date
For each concept extracted before/at index date, the number of persons (N, %) with a record within the
pre-specified time windows will be provided.
Indication
Indications will be assessed based on a high-level approach considering the 10 most frequent
conditions (all databases) and procedures (hospital database only) recorded at the date of treatment
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start/ in the week/month before treatment start. The number of persons (N, %) with a record of the
respective indication will be provided.
Treatment duration
Treatment duration will be calculated as the duration of the first treatment era of the opioid of interest
during the study period. Treatment duration will be summarised providing the minimum, p25, median, p75,
and maximum treatment duration. For databases, where duration cannot be calculated due to e.g. missing
information on quantity or dosing, treatment duration will not be provided.
8.6.6 Description of sensitivity analyses.
Table 9. Sensitivity analyses – rationale, strengths and limitations.
What is being Why? Strengths of the Limitations of the
varied? How? (What do you sensitivity analysis sensitivity analysis
expect to learn?) compared to the compared to the
primary primary
Window to Indication of use will Indication of use Proportion of patients Potential
assess be explored at index might not always be with an indication of misclassification of
indication of date (ID), and in a recorded on the use might increase. indication of use if the
use period of [-30 to ID] date of prescription disease code registered
days of the index of the opioid of in the week/month
date and in a period interest before has nothing to
from [-7 to ID] days do with prescription of
before index date the opioid of interest
8.7 Evidence synthesis
Results from analyses described in Section 9.7 will be presented separately for each database and no
pooling of results will be conducted.
9. DATA MANAGEMENT
All databases will have been mapped to the OMOP common data model. This enables the use of
standardised analytics and tools across the network since the structure of the data and the terminology
system is harmonised. The OMOP CDM is developed and maintained by the Observational Health Data
Sciences and Informatics (OHDSI) initiative and is described in detail on the wiki page of the CDM:
https://ohdsi.github.io/CommonDataModel and in The Book of OHDSI: http://book.ohdsi.org. This analytic
code for this study will be written in R. Each data partner will execute the study code against their database
containing patient-level data and will then return the results set which will only contain aggregated data.
The results from each of the contributing data sites will then be combined in tables and figures for the
study report.
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10. QUALITY CONTROL
General database quality control
A number of open-source quality control mechanisms for the OMOP CDM have been developed (see
Chapter 15 of The Book of OHDSI http://book.ohdsi.org/DataQuality.html). In particular, it is expected that
data partners will have run the OHDSI Data Quality Dashboard tool
(https://github.com/OHDSI/DataQualityDashboard). This tool provides numerous checks relating to the
conformance, completeness and plausibility of the mapped data. Conformance focuses on checks that
describe the compliance of the representation of data against internal or external formatting, relational, or
computational definitions, completeness in the sense of data quality is solely focused on quantifying
missingness, or the absence of data, while plausibility seeks to determine the believability or truthfulness of
data values. Each of these categories has one or more subcategories and are evaluated in two contexts:
validation and verification. Validation relates to how well data align with external benchmarks with
expectations derived from known true standards, while verification relates to how well data conform to
local knowledge, metadata descriptions, and system assumptions.
Study specific quality control
When defining cohorts for drugs, a systematic search of possible codes for inclusion will be identified using
CodelistGenerator R package (https://github.com/darwin-eu/CodelistGenerator). A pharmacist will review
the codes of the opioids of interest. This software allows the user to define a search strategy and using this
will then query the vocabulary tables of the OMOP common data model so as to find potentially relevant
codes. In addition, DrugExposureDiagnostics12 will be run if needed to assess the use of different codes
across the databases contributing to the study.
The study code will be based on two R packages currently being developed to (1) estimate Incidence and
Prevalence and (2) characterise drug utilisation using the OMOP common data model. These packages will
include numerous automated unit tests to ensure the validity of the codes, alongside software peer review
and user testing. The R package will be made publicly available via GitHub.
11. LIMITATIONS OF THE RESEARCH METHODS
The study will be informed by routinely collected health care data and so data quality issues must be
considered. In particular, a recording of a prescription or dispensation does not mean that the patient
actually took the drug. In addition, assumptions around the duration of drug use will be unavoidable. For
databases, where duration cannot be calculated due to e.g. missing information on quantity, dosing or end
date, treatment duration will not be provided.
In addition, the recording of events used for patient characterisation and identification of the (potential)
indication may vary across databases and recording of indication may be incomplete.
12. GOVERNANCE BOARD
EBB, SIDIAP, IMASIS and CDWBordeaux will require to undergo their respective ethical approvals.
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13. MANAGEMENT AND REPORTING OF ADVERSE EVENTS/ADVERSE
REACTIONS
In agreement with the new guideline on good pharmacovigilance practice (EMA/873138/2011), there will
be no requirement for expedited reporting of adverse drug reactions as only secondary data will be used in
this study.
14. PLANS FOR DISSEMINATING AND COMMUNICATING STUDY
RESULTS
14.1 Study report
A PDF report including an executive summary, and the specified tables and/or figures will be submitted to
EMA by the DARWIN EU® CC upon completion of the study, and made available at EUPAS
An interactive dashboard incorporating all the results (tables and figures) will be provided alongside the pdf
report. The full set of underlying aggregated data used in the dashboard will also be made available if
requested.
15. OTHER ASPECT
None.
16. REFERENCES
1. Seth P, Scholl L, Rudd RA, Bacon S. Overdose Deaths Involving Opioids, Cocaine, and
Psychostimulants - United States, 2015-2016. MMWR Morb Mortal Wkly Rep. 2018;67(12):349-358.
doi:10.15585/mmwr.mm6712a1. .
2. Centers for Disease Control and Prevention, National Center for Injury Prevention and Control,
Department of Health and Human Services. Annual Surveillance Report Of Drug-related Risks And
Outcomes. https://www.cdc.gov/drugoverdose/pdf/pubs/2019-cdc-drug-surveillance-report.pdf.
Accessed May 16, 2023. .
3. van Amsterdam J, van den Brink W. The Misuse of Prescription Opioids: A Threat for Europe?
Current drug abuse reviews. 2015;8(1):3-14. doi:10.2174/187447370801150611184218. .
4. Kennedy J, Wood EG, Wu C-H. Factors associated with frequent or daily use of prescription opioids
among adults with chronic pain in the United States. The Journal of international medical research.
2023;51(1):3000605221149289. doi:10.1177/03000605221149289. .
5. Sullivan MD, Edlund MJ, Zhang L, Unützer J, Wells KB. Association between mental health disorders,
problem drug use, and regular prescription opioid use. Arch Intern Med. 2006;166(19):2087-2093.
doi:10.1001/archinte.166.19.2087. .
6. Dufort A, Samaan Z. Problematic Opioid Use Among Older Adults: Epidemiology, Adverse Outcomes
and Treatment Considerations. Drugs & Aging. 2021;38(12):1043-1053. doi:10.1007/s40266-021-
00893-z. .
7. Vlug A, van der Lei J, Mosseveld B, van Wijk M, van der Linden P, MC S. Postmarketing surveillance
based on electronic patient records: the IPCI project. Methods of information in medicine.
1999;38:339-44.
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Author(s): A. Lam, A. Jödicke Version: V2.0
Dissemination level: Public
8. Brat GA, Weber GM, Gehlenborg N, et al. International electronic health record-derived COVID-19
clinical course profiles: the 4CE consortium. NPJ Digit Med. 2020;3:109. doi:10.1038/s41746-020-
00308-0
9. Garcia-Gil Mdel M, Hermosilla E, Prieto-Alhambra D, Fina F, Rosell M, Ramos R. Construction and
validation of a scoring system for the selection of high-quality data in a Spanish population primary
care database (SIDIAP). Informatics in primary care. 2011;19(3):135-45.
10. Rathmann W, Bongaerts B, Carius H, Kruppert S, Kostev K. Basic characteristics and
representativeness of the German Disease Analyzer database. Int J Clin Pharmacol Ther.
2018;56(10):459-466.
11. Edward Burn etal. IncidencePrevalence: Estimate Incidence and Prevalence using the OMOP
Common Data Model.Version: 0.3.0. Published: 2023-05-07 https://cran.r-
project.org/web/packages/IncidencePrevalence/index.html.
12. Ger Inberg et al. Diagnostics for OMOP Common Data Model Drug Records: Package
‘DrugExposureDiagnostics’. Version 0.4.1. Date/Publication 2023-03-13. https://cran.r-
project.org/web//packages//DrugExposureDiagnostics/DrugExposureDiagnostics.pdf.
17. ANNEXES
Appendix I: Lists with preliminary concept definitions for exposure
Appendix II: Feasibility counts
Appendix III: ENCePP checklist for study protocols
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APPENDIX I: Lists with preliminary concept definitions for exposure
Prescriptions will be identified based on the relevant ingredient. Non-systemic products will be excluded
from the code list.
Substance Name Concept Id No record counts in databases expected
based on feasibility
acetyldihydrocodeine 21603407
alfentanil 19059528
anileridine 19032662 X
bezitramide 37493802 X
butorphanol 1133732 X
buprenorphine 1133201
codeine 1201620
dezocine 19088393 X
dimemorfan 36852751
dextromethorphan 1119510
dextromoramide 19021940
dextropropoxyphene 1153664 X
dihydrocodeine 1189596
ethylmorphine 19050414
fentanyl 1154029
hydrocodone 1174888
hydromorphone 1126658
ketobemidone 40798904
meptazinol 19003010
meperidine (pethidine) 1102527
methadone 1103640
morphine 1110410
nicomorphine 37493805
normethadon 19015787 X
nalbuphine 1114122
noscapine 19021930
oliceridine 37002667 X
opium 923829
oxycodone 1124957
oxymorphone 1125765 X
papaveretum 19129648
pentazocine 1130585
phenazocine 19132884
phenoperidine 19132889 X
pholcodine 19024213
pirinitramide 19134009
propoxyphene 1153664
remifentanil 19016749
sufentanil 19078219
tapentadol 19026459
thebacon 40799139
tilidine 19002431
tramadol 1103314
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Substance Name Concept Id No record counts in databases expected
based on feasibility
naloxone 1114220
buprenorphine/naloxone 45776270, 37498350, 40015149, 1970413
oxycodone/naloxone 21160441, 41017321, 45774941, 36269469
pentazocine/naloxone 40063474
40063477, 43799912, 41298261, 36272016,
tilidine/naloxone 40063476, 36264356
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APPENDIX II: Feasibility counts
Table 1. Feasibility record counts per database.
Concept Id Name Bordeaux IPCI# IQVIA Belgium# SIDIAP# Estonian DK-DHR# NLHR# IMASIS#
University Biobank#
Hospital#
19059528 alfentanil 100 100 32,800
1133201 buprenorphine 4,000 26,500 7,300 80,200 400 473,000 236,100 1,500
1201620 codeine 16,300 809,900 192,100 2,884,800 100,700 2,883,800 2,589,900 5,900
1119510 dextromethorphan 200 9,200 151,400 962,900 157,900 100 1,400
19021940 dextromoramide 100 300
dimemorfan
35197951 phosphate 656,400*
1189596 dihydrocodeine 200 93,900 8,600 3,200 200
19050414 ethylmorphine 100 29,000 22,100 1,773,000
1154029 fentanyl 2,800 77,800 24,200 283,600 600 264,500 52,600 149,000
1174888 hydrocodone 1,400
1126658 hydromorphone 200 400 500 8,200 2,200 200 200
40798904 ketobemidone 141,400 50,700
1102527 meperidine 200 700 100 108,000 3,800 800
19003010 meptazinol
1103640 methadone 2,600 5,100 100 3,900 500 131,700 8,000 3,500
1110410 morphine 172,000 64,200 3,700 108,500 1,300 1,662,900 67,500 76,300
1114122 nalbuphine 16,200
Nicomorphine
37493800 hydrochloride 201,700*
19021930 noscapine 47,100 5,300 17,300 32,500 15,500
923829 opium 29,300 200 100 1,879,900 4,000
1124957 oxycodone 58,600 240,100 19,700 71,000 6,600 1,061,100 507,600 3,200
19129648 papaveretum
1130585 pentazocine 100 100 5,200 100
19132884 phenazocine
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Concept Id Name Bordeaux IPCI# IQVIA Belgium# SIDIAP# Estonian DK-DHR# NLHR# IMASIS#
University Biobank#
Hospital#
19024213 pholcodine 100 10,600
19134009 pirinitramide 200 300
1153664 propoxyphene 900 200 100 113,600
19016749 remifentanil 600 100 16,500
19078219 sufentanil 1,300 100 200 16,100
19026459 tapentadol 4,500 900 124,500 19,300 55,800 3,500
40799139 thebacon 100
19002431 tilidine 13,100
1103314 tramadol 275,100 562,800 255,000 2,873,700 90,200 5,105,800 1,801,700 113,100
# Drug era record counts unless otherwise specified, *Drug exposure record counts.
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APPENDIX III: ENCePP checklist
ENCePP Checklist for Study Protocols (Revision 4)
Adopted by the ENCePP Steering Group on 15/10/2018
Study title: DARWIN EU® - Drug utilisation study of prescription opioids.
.
EU PAS Register® number: EUPAS1000000479
Study reference number: P3-C2-002
Section 1: Milestones Yes No N/A Section
Number
1.1 Does the protocol specify timelines for
1.1.1 Start of data collection1
1.1.2 End of data collection2
Overview and
1.1.3 Progress report(s)
5
1.1.4 Interim report(s)
1.1.5 Registration in the EU PAS Register®
1.1.6 Final report of study results.
Comments:
Section 2: Research question Yes No N/A Section
Number
2.1 Does the formulation of the research question and
objectives clearly explain:
2.1.1 Why the study is conducted? (e.g. to address an
important public health concern, a risk identified in the risk
management plan, an emerging safety issue) 6, 7
2.1.2 The objective(s) of the study?
2.1.3 The target population? (i.e. population or subgroup
to whom the study results are intended to be generalized)
1
Date from which information on the first study is first recorded in the study dataset or, in the case of secondary use of data, the date from which
data extraction starts.
2
Date from which the analytical dataset is completely available.
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Section 2: Research question Yes No N/A Section
Number
2.1.4 Which hypothesis(-es) is (are) to be tested?
2.1.5 If applicable, that there is no a priori
hypothesis?
Comments:
Section 3: Study design Yes No N/A Section
Number
3.1 Is the study design described? (e.g., cohort, case- 8.1
control, cross-sectional, other design)
3.2 Does the protocol specify whether the study is
based on primary, secondary or combined data 8.4
collection?
3.3 Does the protocol specify measures of occurrence? 8.1 and
(e.g., rate, risk, prevalence) 8.7.5.1
3.4 Does the protocol specify measure(s) of
association? (e.g., risk, odds ratio, excess risk, rate ratio,
hazard ratio, risk/rate difference, number needed to harm
(NNH))
3.5 Does the protocol describe the approach for the
collection and reporting of adverse events/adverse
reactions? (e.g. adverse events that will not be collected in
case of primary data collection)
Comments:
Section 4: Source and study populations Yes No N/A Section
Number
4.1 Is the source population described? 8.4
4.2 Is the planned study population defined in terms
8.2.1
of:
4.2.1 Study time period
4.2.2 Age and sex
4.2.3 Country of origin
4.2.4 Disease/indication
4.2.5 Duration of follow-up
4.3 Does the protocol define how the study population
will be sampled from the source population? 8.2.3
(e.g., event or inclusion/exclusion criteria)
Comments:
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Section 5: Exposure definition and measurement Yes No N/A Section
Number
5.1 Does the protocol describe how the study exposure
is defined and measured? (e.g. operational details for 8.3.1
defining and categorizing exposure, measurement of dose and
duration of drug exposure)
5.2 Does the protocol address the validity of the
exposure measurement? (e.g., precision, accuracy, use of
validation sub-study)
5.3 Is exposure categorized according to time
8.3.1
windows?
5.4 Is intensity of exposure addressed?
8.7.3
(e.g., dose, duration)
5.5 Is exposure categorized based on biological
mechanism of action and taking into account the
pharmacokinetics and pharmacodynamics of the
drug?
5.6 Is (are) (an) appropriate comparator(s) identified?
Comments:
Section 6: Outcome definition and measurement Yes No N/A Section
Number
6.1 Does the protocol specify the primary and
secondary (if applicable) outcome(s) to be
investigated?
6.2 Does the protocol describe how the outcomes are
defined and measured?
6.3 Does the protocol address the validity of outcome
measurement? (e.g. precision, accuracy, sensitivity,
specificity, positive predictive value, use of validation sub-
study)
6.4 Does the protocol describe specific outcomes
relevant for Health Technology Assessment?
(e.g. HRQoL, QALYs, DALYS, health care services utilisation,
burden of disease or treatment, compliance, disease
management)
Comments:
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Section 7: Bias Yes No N/A Section
Number
7.1 Does the protocol address ways to measure
confounding? (e.g., confounding by indication)
7.2 Does the protocol address selection bias? (e.g.
healthy user/adherer bias)
7.3 Does the protocol address information bias?
(e.g. misclassification of exposure and outcomes, time-related
bias)
Comments:
Section 8: Effect measure modification Yes No N/A Section
Number
8.1 Does the protocol address effect modifiers?
(e.g., collection of data on known effect modifiers, sub-group
analyses, anticipated direction of effect)
Comments:
Section 9: Data sources Yes No N/A Section
Number
9.1 Does the protocol describe the data source(s) used
in the study for the ascertainment of:
9.1.1 Exposure? (e.g., pharmacy dispensing, general
practice prescribing, claims data, self-report, face-to-face 8.4
interview)
9.1.2 Outcomes? (e.g., clinical records, laboratory markers
or values, claims data, self-report, patient interview
including scales and questionnaires, vital statistics)
9.1.3 Covariates and other characteristics? 8.4 and
8.3.3
9.2 Does the protocol describe the information
available from the data source(s) on:
9.2.1 Exposure? (e.g. date of dispensing, drug quantity, 8.4 and
dose, number of days of supply prescription, daily dosage,
prescriber)
8.7.3
9.2.2 Outcomes? (e.g. date of occurrence, multiple event,
severity measures related to event)
9.2.3 Covariates and other characteristics? 8.4 and
(e.g., age, sex, clinical and drug use history, co-morbidity,
co-medications, lifestyle)
8.7.3
9.3 Is a coding system described for:
9.3.1 Exposure? (e.g. WHO Drug Dictionary, Anatomical 8.4
Therapeutic Chemical (ATC) Classification System)
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Section 9: Data sources Yes No N/A Section
Number
9.3.2 Outcomes? (e.g., International Classification of
Diseases (ICD), Medical Dictionary for Regulatory Activities
(MedDRA))
9.3.3 Covariates and other characteristics? 9.4
9.4 Is a linkage method between data sources
described? (e.g. based on a unique identifier or other)
Comments:
Section 10: Analysis plan Yes No N/A Section
Number
10.1 Are the statistical methods and the reason for their
8.7
choice described?
10.2 Is study size and/or statistical precision estimated?
10.3 Are descriptive analyses included? 8.7
10.4 Are stratified analyses included? 8.7
10.5 Does the plan describe methods for analytic control
of confounding?
10.6 Does the plan describe methods for analytic control
of outcome misclassification?
10.7 Does the plan describe methods for handling
missing data?
10.8 Are relevant sensitivity analyses described? 8.7.6
Comments:
Section 11: Data management and quality control Yes No N/A Section
Number
11.1 Does the protocol provide information on data
storage? (e.g., software and IT environment, database 8.8
maintenance and anti-fraud protection, archiving)
11.2 Are methods of quality assurance described? 8.8
11.3 Is there a system in place for independent review
of study results?
Comments:
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Section 12: Limitations Yes No N/A Section
Number
12.1 Does the protocol discuss the impact on the study
8.9
results of:
12.1.1 Selection bias?
12.1.2 Information bias?
12.1.3 Residual/unmeasured confounding?
(e.g., anticipated direction and magnitude of such biases,
validation sub-study, use of validation and external data,
analytical methods).
12.2 Does the protocol discuss study feasibility?
(e.g. study size, anticipated exposure uptake, duration of
follow-up in a cohort study, patient recruitment, precision of the
estimates)
Comments:
Section 13: Ethical/data protection issues Yes No N/A Section
Number
13.1 Have requirements of Ethics Committee/
9
Institutional Review Board been described?
13.2 Has any outcome of an ethical review procedure 9
been addressed?
13.3 Have data protection requirements been 9
described?
Comments:
Section 14: Amendments and deviations Yes No N/A Section
Number
14.1 Does the protocol include a section to document
4
amendments and deviations?
Comments:
Section 15: Plans for communication of study Yes No N/A Section
results Number
15.1 Are plans described for communicating study
11
results (e.g., to regulatory authorities)?
15.2 Are plans described for disseminating study results
11
externally, including publication?
Comments:
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Name of the main author of the protocol: Amy Lam
Date: 06/02/2025
Signature: A. Lam
DARWIN EU® Coordination Centre 44/44
Saatja: "Raivo Kolde" <
[email protected]>
Saaja:
[email protected]
Teema: DARWIN kordusuuringu protokoll kooskõlastamiseks EBIN-le
Kuupäev: 2025-02-17 11:20
Tähelepanu! Tegemist on välisvõrgust saabunud kirjaga.
Tundmatu saatja korral palume linke ja faile mitte avada.
Tähelepanu! Tegemist on välisvõrgust saabunud kirjaga.
Tundmatu saatja korral palume linke ja faile mitte avada.
Tere
Sooviksime EMA DARWIN projekti raames (raamtaotluse otsus nr 1.1-12/1832),
kiirkooskõlastada järgmise uuringu protokolli. Tegu on kordusuuringuga
uuringule P2-C1-002 (EBIN kooskõlastus 1.1-12/3087), milles uuritakse
opioidide kasutust üle euroopa. Uuringusse on lisandunud andmestikke
(väljastpoolt eestit) ning pikenenud ajaperiood.
Lugupidamisega,
Raivo Kolde