Long-term Opioid Therapy, Misuse and Mortality in Patients With Chronic Non-cancer Pain in Germany
Conditions: Pain
Sponsor: LinkCare GmbH

 
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CONFIDENTIAL 
LinkCare GmbH 
 
Cover Page 
 
Title 
Long-term Opioid Therapy, Misuse and 
Mortality in Patients with Chronic Non-
cancer Pain in Germany 
Protocol date 
28 November 2018 
NCT number 
N/A 
Protocol status 
Final 
Therapeutic area 
Chronic Noncancer Pain 
Active Substance 
N/A 
Medicinal Product 
N/A 
Product Reference 
N/A 
Research Question and 
Objectives 
This study aims to examine the mortality, risk 
of addiction/abuse and hospital admissions 
among opioid users with chronic 
noncancer pain compared to patients with 
non-opioid pain medication. 
Country(-ies) of Study 
Germany 
Data used in study 
German health claims data 
Author and Contact of 
study protocol 
Tino Schubert 
LinkCare GmbH 
Leitzstr. 45 
70469 Stuttgart 
0172/8819171 
 
 
The acquisition of data and the data analysis by LinkCare is financially 
supported by Grünenthal. The sponsor has no role in the design of the 
study, in the collection, analyses and interpretation of the data, in the 
writing of the manuscript, and in the decision to submit the paper for 
publication. The manuscript with the data analyses will be made 
available to the sponsor after acceptance of the manuscript. 
 

 
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CONFIDENTIAL 
LinkCare GmbH 2018 
 
Study Design Schema 
Figure 1: Study design schema 
 
Primary endpoint: 
§
Death by any course
Secondary endpoints:
§
Hospitalization due to any course
§
Hospitalization due to misuse, addiction diagnosis
§
Outpatient diagnosis of misuse or addiction
§
Death in hospitals
201
0
2012
2014
2015
2016
2017
2012
Opioid
Group
Non-opioid
Group
2013
§
At least one prescription of 
opioid medication in at least 3 
quarters between 01.01.2013 
and 31.12.2017
§
At least one prescription of 
non-opioid medication (in at 
least 3 quarters between
01.01.2013 and 31.12.2017
Baseline Assessment
Inclusion criteria
§
No prescription of 
opioid and non-
opioid medication 
between 01.01.2012 
and 31.12.2012
Complete study
population
§
At least one diagnosis: R 51 or R52*; M00* – 99*; G43*-G44*; G62*; G50.0 or G50.1; F45.4*; E10.4*-E14.4 plus G63.3 in 
at least 3 quarters between 01.01.2013 and 31.12.2017, at least one before first claim of study medication
§
At least 18 years old
§
Following diagnoses: C00-C97 with chemotherapy or radiation therapy or documented palliative care between 
01.01.2013 and first claim of study medication
§
Opioid substitution therapy (Z51.83) in study period
Exclusion criteria
Propensity 
Score 
Matching
Exposure Time between 01.01.2013 and 31.12.2017
Inclusion criteria

 
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1. 
Table of Contents 
 
Summary Table of Study Protocol .......................... Fehler! Textmarke nicht definiert. 
Study Design Schema .................................................................................................... 2 
1. 
Table of Contents .................................................................................................. 3 
2. 
List of Abbreviations ............................................................................................... 6 
3. 
Responsible Parties ................................................................................................ 7 
4. 
Abstract .................................................................................................................. 9 
5. 
Protocol amendments and Updates ................................................................ 13 
6. 
Milestones ............................................................................................................. 13 
7. 
Rationale and Background ................................................................................ 13 
7.1 
Diseases and Therapeutic Area ............................................................. 13 
7.2 
Rationale ................................................................................................... 14 
8. 
Research Question and Objectives .................................................................. 15 
8.1 
Primary objectives .................................................................................... 15 
8.2 
Secondary objectives .............................................................................. 15 
9. 
Research Methods ............................................................................................... 16 
9.1 
Study Design ............................................................................................. 16 
9.2 
Setting and Study Population ................................................................. 17 
9.2.1 
Study Period .............................................................................. 20 
9.2.2 
Patient Eligibility ........................................................................ 21 
9.3 
Risk adjustment ......................................................................................... 27 
9.3.1 
Propensity Score Matching ..................................................... 27 
9.3.2 
Other risk adjustment ............................................................... 29 
9.4 
Variables ................................................................................................... 32 
9.4.1 
Exposure Assessment ............................................................... 32 
9.4.2 
Outcome Assessment .............................................................. 34 
9.5 
Validity and Reliability ............................................................................. 36 
9.6 
Data Management ................................................................................. 37 
9.6.1 
Obtaining Data Files ................................................................ 37 
9.6.2 
Linking Data Files ...................................................................... 37 
9.6.3 
Review and Verification of Data Quality .............................. 37 

 
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9.7 
Data Analysis ............................................................................................ 37 
9.7.1 
Planned Analyses ..................................................................... 38 
9.8 
Subgroup and Sensitivity Analysis ........................................................... 38 
9.8.1 
Subgroups for addidional descriptive analyses ................... 38 
9.8.2 
Sensitivity analysis ..................................................................... 39 
9.9 
Quality Control ......................................................................................... 39 
9.10 
Limitations of the Research Methods .................................................... 39 
9.10.1 
Internal Validity of Study Design ............................................. 39 
9.10.2 
External Validity of Study Design ............................................ 41 
9.10.3 
Analysis Limitations ................................................................... 42 
9.10.4 
Limitations Due to Missing Data and/or 
Incomplete Data ..................................................................... 42 
9.11 
Other Aspects ........................................................................................... 42 
10. 
Protection of Human Subjects ............................................................................ 43 
11. 
Plans for Disseminating and Communicating Study Results ........................... 43 
12. 
Contributions to improve health care ............................................................... 44 
13. 
Innovative strength of the project ..................................................................... 44 
14. 
References ........................................................................................................... 46 
 
 

 
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List of Tables 
Table 5-1: Protocol amendments and updates ........................................................ 13 
Table 6-1: Milestone plan ............................................................................................. 13 
Table 9-1: List of ATC codes of study opioid and non-opioids ................................. 24 
Table 9-2: List of included ICD-10 diagnoses for both cohorts ................................ 25 
Table 9-3: Exclusion criteria for cancer patients ........................................................ 26 
Table 9-4: Charlson Comorbidity Index ...................................................................... 31 
Table 9-5:: Standard psychiatric doses as threshold for low/high dosage ............ 33 
 
List of Figures 
Figure 1: Study design schema ..................................................................................... 2 
Figure 2: Inclusion and exclusion criteria .................................................................... 22 
 
 

 
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2. 
List of Abbreviations 
Term 
Definition 
ATC 
Anatomical Therapeutic Chemical (ATC) classification 
CCI 
Charlson Comorbidity Index 
GM 
German Modification 
HIV 
Human Immunodeficiency Virus 
d 
Day  
EBM 
Einheitlicher Bewertungsmaßstab 
GP 
General practitioner 
Mg 
Milligram  
Max 
Maximum 
Min 
Minimum  
N/A 
Not Applicable 
NSAIDs 
Nonsteroidal anti-inflammatory drugs 
OPS 
Operationen- und Prozedurenschlüssel, an official 
classification of operational procedures, German 
version of international Classification of Procedures in 
M di i
(ICPM)
PZN 
 
Pharmazentralnummer / Identification code for 
pharmacy products 
PS 
Propensity Score 
SD 
Standard Deviation 
SHI 
Statutory Health Insurance 
 

 
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3. 
Responsible Parties 
Name 
Institution 
Contact 
Role 
Prof. Dr. med. 
Winfried Häuser 
Klinikum 
Saarbrücken 
gGmbH 
Winterberg 1, 66119 Saarbrücken 
Email: whaeuser@klinikum-
saarbruecken.de 
Author of study protocol, 
interpretation of data  
Prof. Dr. med. 
Christoph Maier 
Ruhr-Universität 
Bochum 
 
Akademiestr. 42, 44789 Bochum 
Email: christoph.maier@rub.de 
Author of study protocol, 
interpretation of data  
Prof. Dr. med. 
Dr. rer. nat. 
Thomas R. Tölle 
Klinikum rechts 
der Isar, 
Technische 
Universität 
Muenchen 
Ismaninger Str. 22, 81675 Muenchen 
Email: toelle@lrz.tu-muenchen.de 
Author of study protocol, 
interpretation of data  

 
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Tino Schubert 
LinkCare GmbH 
 
Leitzstraße 45, 70469 Stuttgart 
Phone: 0172 8819171 
Email: ts@link-care.de 
Study coordinator, author of 
study protocol, data acquisition 
and data analysis 
Grünenthal 
GmbH 
 
Steinfeldstr. 2, 52222 Stolberg 
Sponsor of the study 
 

 
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4. 
Abstract 
Study Title: Long-term Opioid Therapy, Misuse and Mortality in Patients 
with Chronic Non-cancer Pain in Germany 
Study Background and Rationale: Among patients receiving opioids for 
noncancer pain, recent research in North America showed a strong 
association between doses and opioid-related mortality, especially at 
dosages exceeding thresholds recommended in recent guidelines. 
However, the focus on over-dosage may underestimate overall opioid-
related mortality and data on death associated with opioid use in a 
population-based cohort of chronic noncancer pain patients in 
Europe is scarce. Especially comparative studies studying the safety of 
long-term opioid therapy in a real-world setting relative to non-opioid 
medication for chronic noncancer pain are needed in a European 
context. 
Research Question and Objective(s): The primary objective is to 
investigate the association between mortality among patients with 
chronic noncancer pain with long-term opioid-therapy compared to 
non-opioid pain medication. The secondary objectives are to 
determine the risk of addiction/abuse and hospital admissions among 
opioid users with chronic non-cancer pain compared to patients with 
non-opioid pain medication. 
Study Design/Type: This is a cross-sectional observational cohort study 
between 2012 and 2017 of patients with chronic noncancer pain.  
Study Population and Data Resource: The data will be retrieved from 
an anonymized German health claims database including 4,00,000 
persons insured by 69 German statutory health insurances. The data set 
includes 5.0% of the population covered by statutory health insurances 

 
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from January 1, 2012, to December 31, 2017. Only anonymized and 
aggregated data (no directly or indirectly identifying data) will be 
extracted.  
Summary of Patient Eligibility Criteria: From the data extraction, the 
cohort will be analysed according to the following criteria: 
Inclusion criteria: At least one diagnosis of pain R51 (headache), R52* 
(pain, not elsewhere classified), M00 – M99 (Diseases of the 
musculoskeletal system and connective tissue), G43* (migraine), 
G44*(other headache syndromes), G50.0 (trigeminal neuralgia), G50.1 
(atypical facial pain), F45.4* (persistent somatoform pain disorder), 
G62* (polyneuropathies), E10.4*-E14.4 plus G63.3 (diabetes mellitus with 
neurological complications) in at least 3 quarters between 01.01.2012 
and their first opioid claim AND opioid medication (Cohort A) OR non-
opioid medication (Cohort B) between 01.01.2012 and first pain 
medication claim. 
Exclusion criteria: Patients with previous prescription of opioid 
medication or non-opioid medication between 01.01.2012 and index 
treatment will be excluded. Patients with cancer treatment, palliative 
care or opioid substitution after index treatment will be excluded. 
Follow-up: Patients’ follow-up period include 5 years after start of 
opioid resp. non-opioid treatment and chronic pain diagnosis between 
01.01.2013 and 31.12.2017. Each patient will be censored at death 
(death date), switching of study group (opioid --> non-opioid or vice 
versa), 12 months without treatment or followed-up 5 years until last 
known record for the patient in the German health claims database, 
latest at 31.12. 2017 (end of latest data cut), whichever happens first. 
Variables:  

 
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Outcome Variables include death by any course as primary endpoint 
and hospitalization due to any course, hospitalization with misuse, 
addiction diagnoses, outpatient diagnosis of misuse or addiction and 
treatment procedures as secondary endpoints. 
Exposure Variables are pain treatments including opioid and non-
opioid treatments. 
Other Covariates will be collected for assessments over the course of 
the study:  Age and gender (sex: male, female), charlson comorbidity 
index. Comorbidities, comedications, region will be collected for 
Propensity Score Matching. 
Data Analysis: The main analysis will focus on each of the 2 main 
cohorts with chronic pain patients as defined at inclusion and: 
• with opioid treatment between 01.01.2013 and 31.12.2017 (named 
cohort A) 
• with non-opioid treatment between 01.01.2013 and 31.12.2017 
(named cohort B) 
To address the group comparisons adequately, a propensity score 
matching will be conducted. The main analyses will include a 
multivariate Cox proportional hazards regression. Additional stratified 
analyzes will be carried out as follows: Subgroup analyses for each 
following pain conditions will be conducted: headache (R 51*); pain, 
not elsewhere classified (R52*); persistent somatoform pain disorder 
(F45.4*); polyneuropathy in diabetes (E10.4*-E14.4 plus G63.3), kyphosis 
and lordosis (M40), scoliosis (M41), spinal osteochondrosis (M42), other 
deforming dorsopathies (M43), ankylosing spondylitis (M45), other 
inflammatory spondylopathies (M46), spondylosis (M47), other 
spondylopathies (M48), and spondylopathies in diseases classified 

 
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elsewhere (M49), Cervical disc disorders (M50), thoracic, 
thoracolumbar, and lumbosacral intervertebral disc disorders (M51), 
Other and unspecified dorsopathies, not elsewhere classified, and 
dorsalgia (M54). 
 

 
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5. 
Protocol amendments and Updates 
Table 5-1: Protocol amendments and updates 
Version 
Description 
Name / Author 
Date 
V.01 
First draft 
LinkCare GmbH 
10 September2018 
V.02 
Amendments 
following TCs and 
mails with the 
other authors 
LinkCare GmbH 
31 October 2018 
v.03 
Ammendments 
following e-mail of 
Prof. Haeuser 
LinkCare GmbH 
28 November 2018 
6. 
Milestones 
Table 6-1: Milestone plan 
Milestone 
Planned date 
Finalization of study design 
30. September 2018 
Writing of a detailed analytical protocol/ 
study plan 
15. November 2018 
Contracting with German health 
insurances 
15.  November 2018 
Data obtaining, quality assurance and 
data analysis 
31. January 2019 
Writing of publication 
28. February 2019 
Submission and peer-review process 
31. March 2019 
Publication 
31. July 2019 (dependent on 
journal) 
7. 
Rationale and Background 
7.1 
Diseases and Therapeutic Area 
Chronic non-cancer pain describes back, musculoskeletal, abdominal, 
headache and other neurologic pain without an evidence of cancer1. 
                                               
1 Dowell et al., 2016 

 
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Today, chronic pain is one of major health care problems in Europe. For 
instance, a large telephone survey in 15 European countries and Israel 
showed that chronic non-cancer pain of moderate to severe intensity has 
a prevalence of 19 % affecting the quality of patient’s social and working 
lives2. Common treatment of moderate to severe chronic pain includes 
opioids, analgesic anticonvulsants3 and low-dose cyclic antidepressants4. 
Analgesic anticonvulsants and low-dose cyclic antidepressants can have 
serious adverse effects5. But also the tendency of increased prescriptions 
of opioid analgesics leads to a discussion about potential harms of this 
medication6. 
7.2 
Rationale 
Among patients receiving opioids for treatment of noncancer pain, 
recent research in North America showed a strong association between 
doses and opioid-related mortality, especially at dosages exceeding 
thresholds recommended in recent guidelines7. However, the focus on 
over-dosage may underestimate overall opioid-related mortality and 
data on death associated with opioid use in a population-based cohort 
of chronic non-cancer pain patients in Europe is scarce. Especially 
comparative studies studying the safety of long-term opioid therapy 
relative to non-opioid medication for chronic noncancer pain are 
needed in a European context. 
 
                                               
2 Breivik et al., 2006 
3 Moore et al., 2011 
4 Carragee et al., 2005 
5 Ray et al., 2004 
6 Okie et al., 2010 
7 Ray et al., 2016 

 
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8. 
Research Question and Objectives 
8.1 
Primary objectives 
Objective of this study is to investigate the association between mortality 
among patients with chronic noncancer pain with long-term opioid-
therapy compared to non-opioid pain medication (nonsteroidal anti-
inflammatory drugs (NSAIDs, Metamizole), anticonvulsants, 
antidepressants). 
In order to address the primary objective, following hypotheses will be 
examined in the study:  
§ How many patients with chronic noncancer pain on long-term 
opioid therapy and patients with non-opioid pain medication died 
in time period of five years? 
§ Which demographic factors, comorbidities and comedication 
predict mortality and time to mortality among patients with chronic 
noncancer pain on long-term opioid therapy and of patients with 
non-opioid pain medication? 
§ Does mortality differ between patients with chronic noncancer pain  
on long-term opioid therapy compared to patients with non-opioid 
pain medication in time period of five years? 
 
8.2 
Secondary objectives 
In addition, present study aims to determine the risk of addiction/ abuse 
and hospital admissions among opioid users with chronic non-cancer pain 
compared to patients with non-opioid pain medication. 
The secondary objectives will be addresses with following hypotheses:  

 
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• How often do patients on long-term opioid therapy and patients 
with non-opioid pain medication being hospitalized and how 
long do they stay in hospital? 
• Does the number of hospitalizations and length of stay in hospital  
differ between patients on long-term opioid therapy compared 
to patients with non-opioid pain medication in time period of 
five years? 
• How many misuse and addiction diagnoses were coded in 
outpatient sector for patients on long-term opioid therapy and 
patients with non-opioid pain medication in time period of five 
years? 
• Does the number of outpatient misuse and addiction diagnoses 
differ between patients on long-term opioid therapy compared 
to patients with non-opioid pain medication in time period of 
five years? 
• How many sick leave days do patients on long-term opioid 
therapy and patients with non-opioid pain medication have in 
time period of five years? 
 
9. 
Research Methods 
9.1 
Study Design 
This is a retrospective observational cohort study based on an 
anonymized German health claims database. This database contains 
data on  around 4 million insured people from 69 German statutory health 
insurances (SHI). This research database is a complete, longitudinal claims 
dataset of approx. 7 million individual patients (ca. 10% of the statutory 
insured population of Germany) between 2012 and 2017. The analyses for 
this study will be done on a 4 million patients subset that is stratified by age 

 
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and gender based on the 2013 population structure of Germany as 
reported by the German Federal Statistical Office DeStatis.  
This study consists of two cohorts: opioid and non-opioid patients. Only 
patients who started their therapy between 2013 and 2017 and had no 
therapy in 2012 (therapy naïve patients) will be included in the study. 
Exposure time is defined as max. 5 years after the initial prescription for 
each patient, so that exposure time is max. 60 months per patient. 
Exposure time may end before the end of study if patient died, stopped 
treatment (end of follow-up after 1 year without treatment with opioids / 
other pain medication), changed treatment group (from opioids to other 
pain medication or vice versa) or was lost to follow up due to other 
reasons (e.g. change of insurance), whichever occurs first. 
 
9.2 
Setting and Study Population 
The database contains data on around 4 million insured people from 69 
German statutory health insurances (SHI). German SHI system comprises of 
approximately 120 independent health insurance companies. Funds paid 
by these SHI to any provider of health care (e.g. hospitals, physicians, 
pharmacies, psychotherapists and physiotherapists) represent almost the 
complete picture of the total health care costs of individual patients. All 
health insurance data can be linked to patients’ demographics including 
age and gender. Health care data in the database include: 
• Patient demographics: 
o age 
o gender 
o date of death 

 
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o region of residence 
o insurance status 
o date of insurance start and end 
• Hospital care: 
o main diagnosis (ICD-10-GM codes) and additional diagnoses 
o procedures and surgeries 
o date of hospital admission 
o reason for admission (e.g. accident, emergency, normal) 
o date of end of hospital stay 
o reason of end of hospital stay (e.g. regular discharge from 
hospital, death) 
o DRG-Code 
o costs 
• Outpatient care: 
o diagnoses (ICD-10-GM codes) 
o quarter in which the diagnosis was documented 
o procedures performed (EBM codes, e.g. laboratory tests, 
radiology, echocardiography) and date of procedure 
o type of specialist that documented the diagnosis and 
performed 
the 
procedure 
(e.g. 
cardiologist, 
general 
practitioner) 
o costs 
• Pharmaceutical claims:  

 
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o drug dispensed (PZN code, which identifies medications at the 
package level) – this is mapped to ATC codes and DDDs 
o quantity dispensed 
o day of prescription 
o day of dispensing 
o type of specialist prescribing the medication (e.g. general 
practitioner) 
o costs of drugs dispensed from the perspective of the statutory 
health 
insurance 
(without 
individual 
rebates 
between 
insurance and pharmaceutical companies) 
• Physical therapy and technical aids:  
o type of therapy (e.g. massage, occupational therapy, walker, 
wheel chair) 
o quantity prescribed 
o type of care provider 
o start and end date 
o costs 
• Sick leave: 
o diagnoses that cause sick leave 
o duration of sick leave 
o start date of sick leave 
o end date of sick leave 

 
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o type of specialist that filled out the sick leave form (e.g. general 
practitioner) 
o costs of sick leave 
To note, data are adjusted within the database to Germany’s typical age 
and sex distribution in accordance with the Federal Statistical Office 
based on the year 2013 (https://www-genesis.destatis.de/genesis/online) 
to foster its overall representativeness in regards to the population in 
Germany. Privately insured patients in Germany are not included in this 
database. 
All data used are explicitly approved by the involved statutory health 
insurances for health care research usage according to § 287 or § 75 of 
the German social security law (SGB V).  
Source population of the study are patients covered by the German 
Health Claims database over 2012-2017 years. 
Study population are patients from the source population meeting 
inclusion (eligibility) criteria (see section 9.2.2). 
9.2.1 
Study Period 
Due to data protection regulations in Germany, only an extract of 
maximum 6 years of data coverage could be available for the research 
purposes: 1st January 2012 to 31st December 2017.  
The baseline period is defined as the period prior to the date of first 
diagnosis of diagnoses or first opioid or non-opioid treatment initiation as 
described in inclusion criteria (Chapter 9.2.2.1). The baseline assessment 
period endures between January 01, 2012 and first study medication 
prescription.  

 
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Index treatment is definded as the date of first opioid or non-opioid 
treatment prescription between 1st January 2013 and 31st December 2017. 
Exposure time is defined as time between index treatment and December 
31, 2017. Considering data availability of a maximum of 6 years and 
prescription in a required time period of 60 months, exposure time endures 
maximum five years. Thus, patients can be censored at death (death 
date in the linked records), last known record for the patient in the 
German health claims database, switching of treatment groups (from 
opioid group to non-opioid group or vice versa), 12 months without 
opioid/non-opioid treatment or 31st December 2017 (end of latest data 
cut), whichever comes first. 
The outcomes will be assessed between index treatment and 31.12.2017. 
 
9.2.2 
Patient Eligibility 
9.2.2.1 
Inclusion Criteria  
For the purposes of this study, the requested extraction of the database 
consists of population of patients with chronic non-cancer pain 
corresponding to the following criteria. Inclusion and exclusion criteria are 
also provided in Figure 2. 

 
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Figure 2: Inclusion and exclusion criteria 
 
 
Cohort A 
• Patients are included in the long-term opioid group if they started 
an opioid-therapy between 2013 and 2017 and received 
consecutive prescriptions for opioid medications over a minimum of 
3 quarters, over a 60-month period between January 1, 2013, and 
December 31, 2017. The treatment will be assessed using the ATC 
codes of opioid treatment N02AA01, N02AA05, N02AB03, N02AE01, 
N02AX02, N02AX01, N02AX06 see also Table 9-1) reported in 
reimbursed medicines to patients. 
Opioid Group
Non-Opioid Group
Inclusion criteria
Exclusion criteria
Prescription of opioid or non-opioid treatment in 2012 (table 9-1)
Following diagnoses of pain in at least three quarters in 2013: R51, R52*, 
M00* - M99*, G43*-G44*, G50.0 or G50.1, G92*, F45.4* or E10.4*-E14.4 plus 
G63.3
Prescription of opioid treatment 
in at least 3 quarters, over a 60-
month period  2013-2017 
(table 9-1)
Prescription of non-opioid 
treatment in at least 3 quarters, 
over a 60-month period  2013-
2017 (table 9-1)
Following before index treatment: C00-C97 if chemotherapy or 
radiation therapy is coded between 2013 and index treatment;
palliative care.
Following in study period: opioid substitution treatment (Z51.83)
AND
+
+
+
+
At least 18 years old
+

 
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• Patients are only included if they have been diagnosed in at least 
three quarters in the study period with one of the following 
diagnoses: R51, R52*, M00*-M99*, G43*-G44*, G50.0 or G50.1, F45.4*, 
G62*, or E10.4*-E14.4 plus G63.3. At least one diagnoses must be 
between 1 January 2012 and index treatment and main and 
secondary hospital diagnoses (i.e. Haupt- und Nebendiagnosen) 
will be used to include the patients. 
Cohort B 
• Patients with non-opioid pain medication are included, if they 
received a medication therapy with anticonvulsants (gabapentin, 
pregabalin, carbamazepine), antidepressants  or non-opioid 
analgesics (NSAIDs, Metamizole) over a minimum of 3 quarters, over 
a 60-month period between January 1, 2013, and December 31, 
2017. The non-opioid treatment will be assessed using the ATC 
codes (N03AX12, N03AX16, N03AF01, see also Table 9-1) of 
treatment reported in reimbursed medicines to patients.  
• Patients are only included if they have been diagnosed in at least 
three quarters in 2012 with one of the following diagnoses: R51, R52*, 
M00*-M99*, G43*-G44*, G50.0 or G50.1, F45.4*, G62*, or E10.4*-E14.4 
plus G63.3.At least one diagnoses must be between 1 January 2012 
and index treatment and main and secondary hospital diagnoses 
(i.e. Haupt- und Nebendiagnosen) will be used to include the 
patients. 
All diagnoses that lead to patient inclusion when coded between 
01.01.2012 and index treatment and in at least three quarters within the 
study period are provided in Table 9-2 below. 

 
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Table 9-1: List of ATC codes of study opioid and non-opioids 
Name 
ATC Code 
Study opioids 
Morphine 
N02AA01 
Oxycodone 
N02AA05 
Fentanyl 
N02AB03 
Buprenorphine 
N02AE01 
Tramadol 
N02AX02 
Tilidine 
N02AX01 
Tapentadol 
N02AX06 
Study non-opioids (anticonvulsive) 
Gabapentin  
N03AX12 
Pregabalin  
N03AX16 
Carbamazepine  
N03AF01 
Study non-opioids (antidepressants) 
Non-selective monoamine reuptake inhibitors 
N06AA* 
Selective serotonin reuptake inhibitors 
N06AB* 
Monoamine oxidase inhibitors, non-selective 
N06AF* 
Monoamine oxidase A inhibitors 
N06AG* 
Other antidepressants 
N06AX* 
Study non-opioids (Nonsteroidal anti-inflammatory drugs) 
Butylpyrazolidines 
M01AA* 
Acetic acid derivatives and related substances 
M01AB* 
Oxicams 
M01AC* 
Propionic acid derivatives 
M01AE* 
Fenamates 
M01AG* 
Coxibs 
M01AH* 

 
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Other antiinflammatory and antirheumatic agents, 
non-steroids 
M01AX* 
Study non-opioids (others) 
Metamizole 
N02BB02 
 
Table 9-2: List of included ICD-10 diagnoses for both cohorts 
Name 
ICD-10 Code 
Diseases of the musculoskeletal system and connective tissue 
M00* - M99* 
Headache 
R51 
Pain, not elsewhere classified 
R52* 
Migraine  
G43* 
Other headache syndromes 
G44* 
Trigeminal neuralgia  
G50.0 
Atypical facial pain 
G50.1 
Persistent somatoform pain disorder 
F45.4* 
Other and unspecified polyneuropathies 
G62* 
Diabetes mellitus with neurological complications 
AND 
Polyneuropathy in diseases classified elsewhere 
E10.4-E14.4 
 
G63.3 
 
9.2.2.2 
Exclusion Criteria 
Following exclusion criteria will be used to extract the eligible study 
population (see also Figure 2): 
Cohort A and B: 

 
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• Patients with present opioid and non-opioid pain medication 
prescriptions in 2012 are excluded from analysis (therapy-naïve 
patients only).  
• Cancer patients will be excluded if the cancer diagnosis is 
accompanied by at least one of the following treatments in the 
same quarter: radiation therapy or chemotherapy all defined by 
the codes in Table 9-3. Diagnoses will be assessed via ICD-10 
diagnoses during the inclusion period from 1 January 2012 until 
index treatment and main and secondary hospital diagnoses (i.e. 
Haupt- und Nebendiagnosen) will be taken into account. 
• Palliative care, coded by ICD-10 code Z51.5 or OPS code 8-982*, 8-
98e*, 8-98h* before index date is excluded 
• Opioid substitution treatment with ICD-10 code Z51.83 in the study 
period is excluded. 
Table 9-3: Exclusion criteria for cancer patients 
Name 
ICD-10 Code 
Malignant neoplasms 
C00-C97 
Plus at least one the following in the same quarter 
Name 
Code 
Type of Code 
Palliative care 
Z51.5 
ICD-10 
Chemo therapy 
Z51.1 
ICD-10 
Radiation therapy 
Z51.0 
ICD-10 
Chemo therapy 
8-541* 
8-542* 
8-543* 
8-544* 
8-546* 
OPS Code 

 
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Radiation therapy 
8-52* 
OPS Code 
Palliative care 
8-982* 
8-98e* 
8-98h* 
OPS Code 
 
9.3 
Risk adjustment 
9.3.1 
Propensity Score Matching 
For the planned analyses, patients with opioid and non-opioid treatment 
need to be comparable. Mortality rates are expected around 100 and 
200 per 10,000 persons years8 thus limiting the number of covariates for 
risk-adjustment to avoid over-fitting (10:1 rule of thumb: Not more than 1 
predictor/covariate for 10 events). To avoid this overfitting problem but still 
be able to adjust for a large number of covariates, a propensity score (PS) 
matching will be conducted before the acutual Cox regression.  
PS-Matching will be based on logistic regression and reflect the probability 
of getting an opioid treatment considering the following risk factors. The 
choice of covariates is based on a previous retrospective cohort study 
about opioid dependency and mortality in the US9 (see Apendix A, for the 
list of study covariates based on Ray et al.) 
• region (i.e. ‘Bundesland’) 
• comorbidities: obstructive sleep apnoea, depression and other 
comordidities  
• comedication 
with 
other 
anticonvulsants 
than 
gabapentin, 
pregabalin or carbamazepine, comedication with neuroleptics, 
NSAR, tranquilizer or muscle relaxants, metamizole, comedication 
                                               
8 Numbers from Ray et al. (2016) 
9 Ray et al., 2016 

 
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with cannabinoid drugs, year of index treatment and drug dosage 
of the opioids/non-opioids as defined in section 9.4.  
• medical procedures based on OPS-codes assessed as a number of 
procedures (see Appendix B, for the list of OPS codes), e.g. dialysis 
PS matching will be based on a logistic regression (logit model) and 
reflects the likelihood that a patient will be prescribed by opioid 
medication if certain risk factors/comornbidities are considered. All binary 
predictors are included in the model as dummy variables (1 vs. 0). The 
influence of each variable is given as odds ratios with 95% confidence 
intervals.  
The following equation will be employed for PS matching: 
 
 
The dependent variable is the treatment and will be dummy coded as 1 
(opioid treatment in index year) and 0 (no opioid treatment) (Pi). 
Age and gender will also be uses for risk adjustment and will be assessed 
using national insurance data, for age by date of birth and is calculated 
based on the index treatment. For age, a variance of 5 years is allowed, i. 
the age can vary by plus / minus 5 years when matching. 
Finally, based on the propensity scores, matching will be performed using 
the GenMatch function of the R statistical package. The probability values 
pi of the matched patients may vary by ± 0.2 * standard deviation. This 
range is often used in PS matching using logistic regression, provides a 
balance of included covariates, and thus no large number of patients are 
lost because they are still treated as comparable for small deviations10. 
                                               
10 Austin et al., 2011 

 
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The frequency matching will be performed by dividing the study cohort 
into 1 % centiles according to the long-acting opioid propensity score 
distribution11. Within each centile, one patient that had a prescription of 
non-opioid treatment will be randomly selected to increase the likelihood 
that all matches have equal quality and permit an analysis with 
unmatched data.  
 
9.3.2 
Other risk adjustment 
The following Cox proportional hazards time to event regression model 
describes the method of another risk adjustment: 
λ(tXj) = λ0(t) * exp (β1Xj1+ β2Xj2+...+ βmXjm) 
This expression gives the hazard rate at time t for person j  
• λ(tXj) is the hazard for outcome at time t for person j  
• where exp is a mathematical constant, Euler's number (e = 2,71828)  
• X1, …, Xm are the values of exposure variables and covariates 
• β1, …, βm are estimated  regression coefficients that indicate the 
importance of exposure variables and covariates.  
• λ0(t) is the baseline hazard indicating hazard for the outcome if all 
exposure variables are zero (similar meaning “intercept”).  
Following covariates will be used in the Cox proportional hazards time to 
event regression: 
• Gender (sex – male, female) will be assessed via indicated sex in the 
social insurance register 
                                               
11 Ray et al., 2016 

 
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• Age will be assessed using national insurance data by date of birth 
and  calculated based on the ‘index treatment’ (the date of first 
study medication claim between 1st January 2012 and 31st 
December 2017).  
• Charlson Comorbidity Index (CCI) will base on the the modified CCI 
according to Quan (2005) and Quan (2011). Using modified CCI is 
preferebale because is was validated by ICD-10 while, for example, 
the Romano-CCI was validated using ICD-912. The modified CCI 
predicts the one-year mortality for a patient who may have a range 
of comorbid conditions (e.g. AIDS, or cancer) with a total of 16 
conditions13. Each condition is assigned a score between 0 (no risk) 
and 6 (high risk), depending on the risk of dying associated with 
conditions. Age and gender are defined as hazard ratios. The scores 
are summed up to provide a total score to predict mortality. CCI will 
be added as a covariate in the Cox regression model (Table 9-4).  
• Duration of the opioid /non-opioid treatment will be used as a one of 
the predictors of opioid treatment in be added as a covariate in the 
Cox regression model and assessed as the difference between the 
last opioid or non-opioid prescription based on ATC codes and the 
first opioid or non-opioid prescription in months, between January 1st 
2012 and December 31st 2016. The prescription will be dummy coded 
as 1 (prescription available) or 0 (no prescription available). 
• Quarter of index treatment: Quarter in which index treatment was 
initiated, wheras Q1/2012 is quarter=1, Q2/2012 is quarter=2 and so 
on.  
• Propsensity Score as calculated before 
                                               
12 Yurkovich et al 2015 
13 Quan et al 2011 

 
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Table 9-4: Charlson Comorbidity Index 
Conditin 
Scale 
Points 
ICD-10-GM Code 
Myocardial infarction  
No 
0 
  
yes 
1 
I21*-I22*, I25.2 
Congestive Heart Failure No 
0 
  
yes 
1 
I50* 
Peripheral vascular 
disease 
No 
0 
  
yes 
1 
I73*, I74.2, I74.3, I74.4, I71.3, I71.5, I71.4, I71.6 
Cerebrovascular  
accidents 
No 
0 
  
yes 
1 
I60*-I69* 
Dementia 
  
No 
0 
  
yes 
1 
F00*-F03*, F05.1 
Chronic obstructive 
pulmonary disease 
No 
0 
  
yes 
1 
J44* 
Rheumatic disease 
No 
0 
  
yes 
1 
M05*, M06*, M31.5, M32–M34, M35.3, M45*,  
Peptic ulcer disease 
No 
0 
  
yes 
1 
K25*, K26*, K27*, K28* 
Liver disease  
  
No 
0 
  
Mild  
1 
K70.1-K70.3, K70.9, K71-K71.0, K71.2-K71.8, K73*, 
K74.0, K74.2, K74.6, K74.3, K74.4, K74.5 
Severe  
3 
K70.4, K71.1, K72*, K76.3 
Diabetes mellitus 
  
  
  
No  
0 
  
Un-
complicated  
1 
E10.9*, E11.9*, E13.9*, E14.9*, E10.1*, E11.1*, E13.1*, 
E14.1*  
Complicated 
/ end organ 
damage  
2 
E10.2*, E11.2*, E13.2*, E14.2* E10.3*, E11.3*, E13.3*, 
E14.3* E10.4*, E11.4*, E13.4*, E14.4*, E10.5*, E11.5*, 
E13.5*, E14.5* 
Hemiplegia 
No 
0 
  
yes 
2 
G81*  
Kidney diseases 
No 
0 
  
yes 
2 
N17*-N19* 
Cancer  
No 
0 
 
Solid   
2 
C00*-C76*, C80*, C81*, C82*, C83*, C84*, C88.3, 
C88.7, C88.9, C96*, C90.1, C91*-C95*, C81*, C82*, 
C83*, C84*, C85*, C86*, C88* 
Metastatic 
6 
C77*, C78*, C79*, C80* 
AIDS / HIV 
  
No 
0 
  
yes 
6 
B20* - B24* 
Demographics as risk factors 
  
Hazard Ratio  
age (> 65 years) 
4,40 
gender (male) 
1,28 
 

 
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The Cox proportional hazards model will use treatment dosage as time-
varying covariates, re-calculating it annually during follow up. The model 
will also be adjusted for age, gender, CCI at the baseline. Covariates will 
not be treated as time varying variables.  
 
9.4 
Variables 
The following variables will be collected for assessments over the course of 
the study. 
 
9.4.1 
Exposure Assessment  
Opioid and non-opioid treatments will be assessed using ATC and PZN 
codes (see Table 9-1) as described below: 
• Opioid Treatment received between January 1st 2012 and 
December 31st 2016 in at least 3 quarters, over a 60-month period 
based on ATC code N02A%. 
• Non-Opioid Treatment (NSAIDs, anticonvulsants, antidepressants) 
received between January 1st 2012 and December 31st 2016 in at 
least 3 quarters, over a 60-month period, based on ATC code  in 
Table 9-1. 
• Treatment Dosage will be assessed based on DDD assessment per 
ATC code using DIMDI data. Dosage will thus be calculated as 
medication dose per day based on the average prescription per 
year of the year. The number of medication claums, the number of 
drug packages and DDD per package are used to estimate the daily 
dose.  

 
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o Treatment dosage of opioid treatment will be defined based 
on morphine equivalence. The low dosage is thus defined as  
less than 60mg/d morphine equivalence. The intermediate 
dosage will be defined as a dosage between 60mg/d and 
120mg/d morphine equivalence. The high dosage will be 
defined as 120mg/d morphine equivalence or higher.  
o Treatment dosage for non-opioid treatment is defined as 
follows: 
§ Anticonvulsive: based on gabapentin equivalence. The 
low dosage is thus defined as a daily dosage of less than 
600mg/d gabapentin equivalents and a high dosage of 
600mg/d or more gabapentin equivalents.  
§ Antidepressants: 
via 
standard 
psychiatric 
dose 
according to AKDÄ (2006) recommendations: daily 
doses below the minmum standard psychiatric dose will 
be defined as “low”, dosages above as “high”. The 
standard psychiatric dose per ATC can be seen in Table 
9-5.  
Table 9-5:: Standard psychiatric doses as threshold for 
low/high dosage 
Active substance 
ATC code 
Minimum standard psychatric 
dose 
Amitriptylin 
N06AA09 
100 
Amitriptylinoxid 
N06AA09 
100 
Clomipramin 
N06AA04 
100 
Desipramin 
N06AA01 
100 
Dibenzepin 
N06AA08 
240 
Doxepin 
N06AA12 
100 
Imipramin 
N06AA02 
100 
Maprotilin 
N06AA21 
100 
Nortriptylin 
N06AA10 
50 

 
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Trimipramin 
N06AA06 
100 
Citalopram 
N06AB04 
20 
Escitalopram 
N06AB10 
10 
Fluoxetin 
N06AB03 
20 
Fluvoxamin 
N06AB08 
100 
Paroxetin 
N06AB05 
20 
Sertralin 
N06AB06 
50 
Moclobemid 
N06AG02 
300 
Tranylcypromin 
N06AF04 
20 
Venlafaxin 
N06AX16 
75 
Duloxetin 
N06AX21 
60 
Reboxetin 
N06AX18 
8 
Mianserin 
N06AX03 
60 
Mirtazapin 
N06AX11 
15 
Trazodon 
N06AX05 
200 
 
• Duration of treatment will be operationalized as time in months 
between the last and first prescription of an opioid or non-opioid 
treatment between between 01.01.2013 and 31.12.2017. Duration 
might endure at least 9 months because 3 quarters of prescription 
are the inclusion criterion. The duration of treatment might be 60 
months as a Maximum (5 years of exposure time).   
9.4.2 
Outcome Assessment 
The study will assess the following main outcomes during the follow-up 
period. 
9.4.2.1 
Primary endpoint 
Primary endpoint is death by any course in the study follow-up. Death will 
be assessed during the follow-up period by number of patients who 
deceased during follow-up period (2013 – 2017) via the date of death in 
German claims data base. Additionally, mortality rate will be estimated as 
number of patients who died during the follow-up divided by person-time 

 
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of follow-up, censoring at time of death, last known record for the patient 
in the database, or 31st December 2017 (end of latest data cut), whichever 
is earlier. 
9.4.2.2 
Secondary endpoints 
Secondary endpoints are following: 
• Hospitalization due to any course. Hospitalization will be assessed 
via the number of hospitalisations (calculated by counting the 
number of discharge dates (KH_Fall. Entlassungsdatum) in the year 
of interest, hospitalization rate, and length of stay in the hospital 
(KH_Fall. Verweildauer). The rate of hospitalization will be estimated 
as the number of patients with at least one hospitalization during 
the follow-up period divided by person-time of follow-up, censoring 
at time of death, last known record for the patient in the database, 
or 31st December 2017 (end of latest data cut), whichever is earlier. 
• Intensive care unit (ICU-stay) stay is defined as patient’s stay based 
on specialty of departement in hospital (KH_OPS.Fachabteilung = 
3600). 
• Hospitalization with to misuse or addiction.  The hospitalization with 
misuse or addiction will be analysed, if principal (main) diagnosis at 
discharge date between January 1st 2013 and December 31st 2017 
contains at least one ICD-10 diagnosis of misuse/addiction: F10* or 
F11* or F13* or F19* or T40* (see also Appendix B in Fehler! 
Verweisquelle konnte nicht gefunden werden.). 
• Hospital admission diagnoses.  We report the hospital admission 
diagnoses based on ICD-10 codes between January 1st 2013 and 
December 31st 2017. 
• Coding of procedures in hospitals that indicate number of patients 
that were hospitalized with severe diseases such as cardiovascular 

 
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diseases will be assessed via OPS-Codes provided in Appendix B as 
an excelsheet.  
• Coding of a misuse or addiction diagnosis in the outpatient sector 
will be assessed via number of codings of misuse/addiction (ICD 
Codes = F10* or F11* or F13* or F19* or T40*) in outpatient sector by 
general practitioner or a specialist (e.g. psychiatrist), as ensured 
diagnosis (Diagnosesicherheit=’G’). Additionally, the number of 
patients with a least one coding of ICD 10 codes of an ensured 
misuse/addiction in outpatient sector by general practitioner or a 
specialist (e.g. psychiatrist) will be assessed.  
• Sick leave will be assessed as an absolute number and relative 
frequencies of patients with an attest of inability to work (patients 
with attest of inability to work as a percentage of all patients) in 
2013-2017 and number of days being off work. 
• Hospital deaths will be assessed via reason of discharge = death 
(Entlassgrund = Tod) 
• Main- and secondary discharge diagnoses from hospital deaths will 
be assessed as the TOP 20 diagnoses of patients who deceased 
during a hospital stay 
 
9.5 
Validity and Reliability 
The external validity of the used data set is checked against the national 
reference data from official resources: Information System of the Federal 
Health Monitoring for the total German population and from the German 
Drug Prescription Report for the German population insured within the SHI 
system (Anderson et al., 2016). The reliability of each outcome variable 
(i.e., diagnoses) will be warranted due to the use of only “ensured” 

 
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(instead of suspected diagnosis) outpatient diagnoses or inpatients 
diagnoses.    
9.6 
Data Management 
9.6.1 
Obtaining Data Files 
The data authority of the involved statutory health insurance (SHI) will 
obtain the data based on this analytical protocol. There will be an 
approval process of the data used and the methods applied. The 
involved SHIs will therefore get a copy of this analytical protocol to review 
whether the issues addressed are for health care research only. 
 
9.6.2 
Linking Data Files 
Not applicable. 
 
9.6.3 
Review and Verification of Data Quality 
The frequency  of missing data will be provided for each variable in the 
outputput tables. Range (min and max) will be reported in order to find 
inplausible low or high values for each continuous variable in the analysis. 
 
9.7 
Data Analysis 
All analysis will be conducted using R (version 3.3.1 or subsequent versions) 
amd the SAS Statistical Package 9.4 . 
Main analysis will focus on each of the 2 main cohorts as defined at 
inclusion: 
• Patients with pain diagnosis and opioid treatment between 1st 
January 2013 and 31st December 2017 

 
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• Patients with pain diagnosis and non-opioid treatment between 1st 
January 2013 and 31st December 2017 
Stratified analyzes will be carried out for each of the 2 main sub-cohorts 
and each following pain conditions will be conducted: headache (R 51*); 
pain, not elsewhere classified (R52*); persistent somatoform pain disorder 
(F45.4*); polyneuropathy in diabetes (E10.4*-E14.4 plus G63.3). 
 
9.7.1 
Planned Analyses 
To adress the main research questions, descriptive analyses, Cox 
proportional hazards regression and test for examining group differences 
(T-test and Chi2 test) will be provided.  
Descriptive analyses using mean, median, standard deviation, absolut 
and relative frequency, minimum and maximum will be reported. 
To address the research questions about the differences between opioid 
and non-opioid treatment, inductive analyses using Chi2 Tests and t-test 
with critical value at the 5% significance level will be employed. 
 
9.8 
Subgroup and Sensitivity Analysis 
9.8.1 
Subgroups for addidional descriptive analyses 
The following main sub-groups of patients will be distinguished to stratify 
during descriptive analyses while assessing study objectives: 
Subgroup analyses for each following pain conditions will be conducted:  
 
Subgroup 
Headache (R 51*) 
Pain, not elsewhere 
classified (R52*) 

 
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Persistent somatoform pain 
disorder (F45.4*) 
Polyneuropathy in Diabetes 
(E10.4*-E14.4 plus G63.3) 
Back pain (M54*) 
Arthrosis (M15*-M19*) 
9.8.2 
 Sensitivity analysis  
The following main sensitivity analysis will be conducted:  
 
Sensitivity analysis 
No cancer patients: Exclude all patients with at 
least one C00 – C96 diagnosis before index date 
 
9.9 
Quality Control 
Assessment of availability and completeness of variables, including 
missing data and implausible ranges, as described in previous sections, will 
be conducted. 
 
9.10 
Limitations of the Research Methods 
 
9.10.1 
Internal Validity of Study Design   
The statutory health insurances database uses billing data which are 
designed for invoices and not for research activities, and therefore lack 
detailed clinical information and lab data. About 10% of German 
inhabitants are members of the private health insurance companies. 
Because the access to a private health insurance companies in Germany 
has special requirements, among others income, there are differences 
between insured patients of statutory and private health insurance 

 
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companies in terms of their socioeconomic status and health (care) 
behavior. Therefore the study sample may not be completely 
representative of the general German population in terms of 
socioeconomic status. 
Given the data structure of the German health care system, the exact 
treatmnet dosage of a medication has to be estimated and might differ 
from the real dosage. There is also no clinical patient information and we 
do not know whether the dispensed drugs were used immediately, saved 
for later use, diverted or not used at all. The assumption that all drugs were 
used at a time close to dispensation would probably lead to an 
overestimation of drug use.  
 
9.10.1.1 
Measurement Error(s)/Misclassification(s) 
Due to administrative nature of the database, the possibility of 
misclassification and miscoded data exists. This study is using prescription 
data to assess possession, which is only a proxy for patients taking their 
medication.  There may be missing data due to limitations of the linkage 
between different datasets, with fully linked data available for subsets of 
patients.   
 
9.10.1.2 
Information Bias 
There is a potential for misreporting of information in the existing health 
systems from where data are retrieved (medical records and national 
health registries). However, due to a large sample size in present study, an 
error in classification of a condition would unlikely to have a significant 
effect on final results. The current study focuses on deaths and 

 
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hospitalisations as key outcome measures that are less likely to be missing 
and less likely to be subject to bias. 
 
9.10.1.3 
Selection Bias 
All eligible patients who meet inclusion criteria will be considered for the 
analysis. The selection bias is less likely in claims data of health insurances 
data. 
 
9.10.1.4 
Confounding 
Confounding occurs when a covariate is independently associated with 
the exposure and the outcome of interest. The size of the datasets should 
allow analysis to explore and control this bias satisfactory. Subgroup 
analyses of subpopulations with different chronic pain diagnoses will be 
considered. However, unmeasured confounding cannot be excluded. 
  
9.10.2 
External Validity of Study Design 
External validity describes the degree to which findings are able to be 
generalizable to the total population. Due to the retrospective 
observation design, the present study shows benefits of interventions 
under real conditions and increases the external validity. Insurees 
contributing to the German statutory health insurances are broadly 
representative of the German general population, although given these 
were originally employer based sickness funds, it is possible there are some 
residual differences in the demographic profile. Also, as indicated in 
section 9.9.1, private insurance patients (about 10% of German 
population) are not covered in this database.  The demographic profile of 

 
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covered patients will be compared with general population 
demographics as far as data allow. 
 
9.10.3 
Analysis Limitations 
Some of the subgroups might be too small and thus the interpretation of 
results should report the sample size and possible limitations of 
interpretation.  
There might be other unknown predictors or confounders of the outcomes 
that limit the interpretation of final results. 
 
9.10.4 
Limitations Due to Missing Data and/or Incomplete Data 
Missing data are a frequent feature of any real-world study using 
secondary databases, and depending on its nature and extent, could 
have a significant impact on the conclusions that can be drawn from the 
data.  
The present study includes a limited number of exposure variables in a 
large sample size that makes the impact of random missing data less 
important than in a study with a small sample size. Descriptive statistics of 
all variables will provide an insight into the extent of missing data. Missing 
data on covariates is not expected to be problematic in this study, but 
should we need to address missing data for an unanticipated reason, we 
will use the method of multiple imputations and also consider casewise 
deletion. Imputation will not be used for the outcome variables. 
 
9.11 
Other Aspects 
Not applicable. 

 
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10. 
Protection of Human Subjects 
The planned analysis is based solely on claims data of health insurances. 
All data in the database are anonymized, i.e. there are no identifiable 
patients in the dataset (i.e. by date of birth, name, or others). Furthermore, 
all data are analysed on an aggregated level, not on a patient-individual 
level. Cohorts of patients smaller 5 will not be reported.  
All data used are explicitly approved by the involved statutory health 
insurances for health care research usage according to the German 
social security law (§ 287 SGB V or § 75 SGB X). 
 
11. 
Plans for Disseminating and Communicating Study Results 
The study results will be reported as a manuscript in a peer-reviewed 
journal. Planned submission is in QII – QIV 2019. 
Authorship of any publications resulting from this study will be determined 
on the basis of the International Committee of Medical Journal Editors 
(ICJME) Recommendations for the Conduct, Reporting, Editing and 
Publication of Scholarly Work in Medical Journals, which states: 
• Authorship credit should be based on (1) substantial contributions to 
conception and design, acquisition of data, or analysis and 
interpretation of data; (2) drafting the article or revising it critically for 
important intellectual content; (3) final approval of the version to be 
published and (4) agreement to be accountable for all aspects of the 
work in ensuring that questions related to the accuracy or integrity of 
any part of the work are appropriately investigated and resolved.  
Authors should meet conditions 1, 2, and 3 and 4. 
• When a large, multicenter group has conducted the work, the group 
should identify the individuals who accept direct responsibility for the 
manuscript. These individuals should fully meet the criteria for 
authorship defined above. 

 
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• Acquisition of funding, collection of data, or general supervision of the 
research group, alone, does not justify authorship. 
• All persons designated as authors should qualify for authorship, and all 
those who qualify should be listed. 
• Each author should have participated sufficiently in the work to take 
public responsibility for appropriate portions of the content. 
 
Grünenthal as study sponsor has no role in the design of the study, in the 
collection, analyses and interpretation of the data, in the writing of the 
manuscript, and in the decision to submit the paper for publication. The 
manuscript with the data analyses will be made available to the sponsor 
after acceptance of the manuscript. 
12. 
Contributions to improve health care 
In order to further improve the care of patients with chronic noncancer 
pain, a close knowledge of the current care of these patients and the 
therapies used is essential. The present study will help to characterize the 
differences between opioid and non-opioid patients with chronic 
noncancer pain regarding mortality, hospitalizations and misuse. The data 
from the SHI research database are the ideal data basis, as they represent 
the care of patients in Germany in a representative way. 
 
13. 
Innovative strength of the project 
In an increasingly complex health care environment, valid and 
representative data are essential to understand if and how the treatment 
of chronic non-cancer pain patients can be improved. Previous studies 
showed strong association between doses and opioid-related mortality, 
esp. at doses exceeding thresholds recommended in recent guidelines, 
but the data on death associated with opioid use is scarce. Especially 
comparative studies are needed in a European context. Thus, this study 

 
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will use the innovative approach of obtaining the data directly from SHI's 
representative database. This approach allows for accurate conclusions 
about the mortality of opioid and non-opioid patients with non-cancer 
pain in Germany. 
 

 
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14. 
References 
 
Breivik H et al. Survey of chronic pain in Europe: prevalence, impact on 
daily life, and treatment. European journal of pain 10.4 (2006): 287-
333. 
Carragee EJ. Persistent low back pain. N Engl J Med. 2005;352(18):1891-
1898. 
Dowell D, Haegerich TM, Chou R. CDC guideline for prescribing opioids for 
chronic pain–United States, 2016. JAMA. 2016;315(15): 1624-1645. 
Kuss O, Blettner M, Börgermann J. Propensity Score–eine alternative 
Methode zur Analyse von Therapieeffekten. Dtsch Arztebl 113 
(2016): 35-36. 
Moore RA, Wiffen PJ, Derry S, McQuay HJ. Gabapentin for chronic 
neuropathic pain and fibromyalgia in adults. Cochrane Database 
Syst Rev. 2011;(3):CD007938. 
Okie S. A flood of opioids, a rising tide of deaths. N Engl J Med. 2010;363 
(21)1981-1985. 
Quan H et al. Updating and Validating the Charlson Comorbidity Index 
and Score for Risk Adjustment in Hospital Discharge Abstracts Using 
Data From 6 Countries. American Journal of Epidemiology, 2011: 
Vol. 173, No. 6 
Ray WA, et al. Prescription of long-acting opioids and mortality in patients 
with chronic noncancer pain. Jama 315 .22 (2016): 2415-2423. 
Ray, WA, et al. Cyclic antidepressants and the risk of sudden cardiac 
death. Clin Pharmacol Ther. 2004; 75(3):234-241. 

 
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Rubin DB, Thomas N. Matching using estimated propensity scores: relating 
the-ory to practice. Biometrics 1996; 52: 249–64.  
Yurkovich M et al. A systematic review identifies valid comorbidity indices 
derived from administrative health data. Journal of clinical 
epidemiology 68.1 (2015): 3-14.