Study Protocol Phase II C1-00 2 25 / 05 /202 3 Version 1 . 0 Table of c ontents TOC \o "1-3" \h \z \u Document History PAGEREF _Toc135314826 \h 4 LIST OF ABBREVIATIONS PAGEREF _Toc135314827 \h 6 1. Title PAGEREF _Toc135314828 \h 7 2. Marketing Authorisation Holder PAGEREF _Toc135314829 \h 7 3. Responsible parties – study team PAGEREF _Toc135314830 \h 7 4. ABSTRACT (Stand alone summary of the study protocol) PAGEREF _Toc135314831 \h 8 5. AMENDMENTS AND UPDATES PAGEREF _Toc135314832 \h 9 6. MILESTONES PAGEREF _Toc135314833 \h 10 7. RATIONALE AND BACKGROUND PAGEREF _Toc135314834 \h 10 8. RESEARCH QUESTION AND OBJECTIVES PAGEREF _Toc135314835 \h 11 9. RESEARCH METHODS PAGEREF _Toc135314836 \h 13 9.1 Study Design PAGEREF _Toc135314837 \h 13 9.2 Study Setting PAGEREF _Toc135314838 \h 13 9.2.1 Study population PAGEREF _Toc135314839 \h 13 9.2.2 Study period and follow-up PAGEREF _Toc135314840 \h 13 9.2.3 In- and exclusion criteria PAGEREF _Toc135314841 \h 15 9.3 Variables PAGEREF _Toc135314842 \h 17 9.3.1 Exposure PAGEREF _Toc135314843 \h 17 9.3.2 Outcomes PAGEREF _Toc135314844 \h 19 9.3.3 Other covariates, including confounders, effect modifiers and other variables (where relevant) PAGEREF _Toc135314845 \h 19 9.4 Data sources PAGEREF _Toc135314846 \h 21 9.5 Study size PAGEREF _Toc135314847 \h 24 9.6 Data Management PAGEREF _Toc135314848 \h 24 9.7 Data Analysis PAGEREF _Toc135314849 \h 24 9.7.1 Federated Network Analyses PAGEREF _Toc135314850 \h 25 9.7.2 Patient privacy protection PAGEREF _Toc135314851 \h 25 9.7.3 Statistical model specification and assumptions of the analytical approach considered PAGEREF _Toc135314852 \h 25 9.7.4 Methods to derive parameters of interest PAGEREF _Toc135314853 \h 26 9.7.5 Methods planned to obtain point estimates with confidence intervals of measures of occurrence PAGEREF _Toc135314854 \h 27 9.7.6 Description of sensitivity analyses. PAGEREF _Toc135314855 \h 29 9.8 Quality Control PAGEREF _Toc135314856 \h 29 9.9 Limitation of the research methods PAGEREF _Toc135314857 \h 30 9.10 Evidence synthesis PAGEREF _Toc135314858 \h 30 10. PROTECTION OF HUMAN SUBJECTS PAGEREF _Toc135314859 \h 30 11. MANAGEMENT AND REPORTING OF ADVERSE EVENTS/ADVERSE REACTIONS PAGEREF _Toc135314860 \h 30 12. PLANS FOR DISSEMINATING AND COMMUNICATING STUDY RESULTS PAGEREF _Toc135314861 \h 31 12.1 Study Report PAGEREF _Toc135314862 \h 31 13. REFERENCES PAGEREF _Toc135314863 \h 32 14. ANNEXES PAGEREF _Toc135314864 \h 35 Appendix I: Lists with Preliminary concept definitions for exposure PAGEREF _Toc135314865 \h 36 Appendix II – ENCePP checklist PAGEREF _Toc135314866 \h 37 Document History Version Date Description V1.0 25 / 05 /202 3 S ubmission to EMA Study Title DARWIN EU® - D rug utilization study of prescription opioids . Protocol version identifier V 1 Date of last version of protocol 2 5 th May 2023 EU PAS register number To be completed after registration Active substance s Opioids ( substances listed in ATC classes N01AH, N02A and R05DA) incl. naloxone and fixed naloxone-opioid combinations , namely: acetyldihydrocodeine, a lfentanil, 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 and objectives This study aims to assess the incidence and prevalence of prescription opioids for the period 2012-202 2 , stratified by age, sex, calendar year and country , as well as characteris ation of new users , indications and treatment duration stratified by country Country(-ies) of study Estonia, Germany, Belgium, T he Netherlands, Fran ce , Spain , Finland Author Junqing Xie, Annika Jödicke LIST OF ABBREVIATIONS Acronyms/terms Description ACI VARHA Auria Clinical Informatics VARHA CDM Common Data Model CHUBX Bordeaux University Hospital DA Disease Analyzer DARWIN EU® Data Analysis and Real World Interrogation Network DUS Drug Utilization 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 IPCI Integrated Primary Care Information Project 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 Title DARWIN EU® Drug Utilisation Study of prescription opioids . Marketing Authorisation Holder N/A Responsible parties – study team Table 1 shows a description of the Study team by role, name and organization . Table SEQ Table \* ARABIC 1 : Description of Study Team Study team Role Names Organi z ation Principal Investigator (s) Annika J ö dicke Junqing (Frank) Xie University of Oxford Data Scientist (s) Marti Catala Sabate Yuchen Guo Mike Du University of Oxford Clinical Epidemiologist Daniel Prieto Alhambra University of Oxford Statistician NA Data Manager NA Data Partner* Names Organi z ation Local Study Coordinator/Data Analyst James Brash IQVIA Talita Duarte Salles IDIAP JGol Raivo Kolde University of Tartu Mees Mosseveld Erasmus MC Tommi Kauko Varha Romain Griffier CHU Bordeaux *Data partners’ role is only to execute code at their data source. These people do not have an investigator role. ABSTRACT (Stand alone summary of the study protocol) Title DARWIN EU® Drug Utilisation Study o f 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 utilization patterns are imperative. A drug utilization study using the 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 problem. Research question and Objectives The objectives of this study are To investigate the annual incidence and annual period prevalence of use of opioids ( overall , active drug substance , stre ngth (weak/strong opioids ) and route (oral, transdermal or parenteral) ), stratified by calendar year, age , sex and country/database during the study period 2012-202 2 . To determine duration of prescription opioid use , as well as c haracteristics of new users and indication for opioid prescribing/dispensing , all s tratified by country/database. Research Methods Study design Population level cohort study (Objective 1, Population-level drug utili z ation study on opioid s ) New drug user cohort study (Objective 2, Patient -level drug utili z ation analys e s regarding summary characterisation, duration , and indication of opioid use ) Population Population-level utili z ation of opioid s : All people registered in the respective databases on 1 st of January of each year in the period 2012-2022 (or the latest available), with at least 1 year of data availability, will participate in the population-level analysis (period prevalence calculation in Objective 1). Therefore, children aged <1year will be excluded. New users of opioids in the period between 1/1/2012 and 31/12/2022 (or latest date available), with at least 1 year of data visibility, and no use of the respective opioid in the previous 6 months or 12 months, will be included for incidence rate calculations in Objective 1. Patient -level drug utili z ation: New users of opioids in the period between 1/1/2012 and 31/12/2022 (or latest date available), with at least 1 year of data visibility, and no use of the respective opioid in the previous 6 months or 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) incl. naloxone and fixed naloxone-opioid combinations . (further details see section 9.3.1) Data sources Estonian Biobank (EBB) , Estonia IQVIA Disease Analyzer Germany (IQVIA DA Germany), Germany IQVIA LBD Belgium, Belgium Integrated Primary Care Information Project (IPCI), The Netherlands The Information System for Research in Primary Care (SIDIAP), Spain Clinical Data Warehouse of Bordeaux University Hospital ( CHUBX ) , France Auria Clinical Informatics VARHA (ACI Varha) , Finland Sample size No sample size has been calculated. Data analyses Population-level drug utilisation will be conducted in all databases , with ACI VARHA not contributing to the prevalence analyses. Patient - level DUS analyses will be conducted in all databases, with ACI VARHA not contributing to the analysis of duration of opioid prescriptions . Population-level opioid use: Annual period prevalence of opioid use and annual incidence rates per 100,000 person years will be estimated as described in section 9.7.5.1 – Population-level drug utili z ation study . Patient -level opioid use : Large-scale patient -level characteri z ation will be conducted. Index date will be the date of the first prescription of the specific opioid for each person. Frequency of indication at index date , and in the immediate time before will be assessed. Cumulative t reatment duration will be estimated for the first treatment era and the minimum, p25, median, p75, and maximum will be provided. See for further description section 9.7.5.2 – Patient- level drug utili z ation study. For all analyses a minimum cell count of 5 will be used when reporting results, with any smaller counts obscured. AMENDMENTS AND UPDATES Number Date Section of study protocol Amendment or update Reason MILESTONES STUDY SPECIFIC DELIVERABLE TIMELINE Draft Study Protocol 19/05/2023 Final Study Protocol 25/05/2023 Creation of Analytical code Execution of Analytical Code on the data Interim Study Report (if applicable) NA Draft Study Report Final Study Report 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 opioids ADDIN EN.CITE <EndNote><Cite><RecNum>559</RecNum><DisplayText><style face="superscript">1</style></DisplayText><record><rec-number>559</rec-number><foreign-keys><key app="EN" db-id="awfexxfak92550e9pvrvta0lfseprt0pstfx" timestamp="1684421357">559</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors></contributors><titles><title>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. </title></titles><dates></dates><urls></urls></record></Cite></EndNote> 1 . 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 ADDIN EN.CITE <EndNote><Cite><RecNum>560</RecNum><DisplayText><style face="superscript">2</style></DisplayText><record><rec-number>560</rec-number><foreign-keys><key app="EN" db-id="awfexxfak92550e9pvrvta0lfseprt0pstfx" timestamp="1684421376">560</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors></contributors><titles><title>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. </title></titles><dates></dates><urls></urls></record></Cite></EndNote> 2 . 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. ADDIN EN.CITE <EndNote><Cite><RecNum>561</RecNum><DisplayText><style face="superscript">3</style></DisplayText><record><rec-number>561</rec-number><foreign-keys><key app="EN" db-id="awfexxfak92550e9pvrvta0lfseprt0pstfx" timestamp="1684421395">561</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors></contributors><titles><title>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. </title></titles><dates></dates><urls></urls></record></Cite></EndNote> 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 strength ADDIN EN.CITE <EndNote><Cite><RecNum>562</RecNum><DisplayText><style face="superscript">4</style></DisplayText><record><rec-number>562</rec-number><foreign-keys><key app="EN" db-id="awfexxfak92550e9pvrvta0lfseprt0pstfx" timestamp="1684421408">562</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors></contributors><titles><title>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. </title></titles><dates></dates><urls></urls></record></Cite></EndNote> 4 . Si milarly, 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 opioid s , yet they typical ly have more pain management requirements due to accumulating a range of chronic disorders leading to painful conditions ADDIN EN.CITE <EndNote><Cite><RecNum>563</RecNum><DisplayText><style face="superscript">5</style></DisplayText><record><rec-number>563</rec-number><foreign-keys><key app="EN" db-id="awfexxfak92550e9pvrvta0lfseprt0pstfx" timestamp="1684421427">563</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors></contributors><titles><title>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. </title></titles><dates></dates><urls></urls></record></Cite></EndNote> 5 . There is an imperative need for further investigation to elucidate the utilization patterns of opioids among this demographic ADDIN EN.CITE <EndNote><Cite><RecNum>564</RecNum><DisplayText><style face="superscript">6</style></DisplayText><record><rec-number>564</rec-number><foreign-keys><key app="EN" db-id="awfexxfak92550e9pvrvta0lfseprt0pstfx" timestamp="1684421439">564</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors></contributors><titles><title>Dufort A, Samaan Z. Problematic Opioid Use Among Older Adults: Epidemiology, Adverse Outcomes and Treatment Considerations. Drugs &amp; Aging. 2021;38(12):1043-1053. doi:10.1007/s40266-021-00893-z. </title></titles><dates></dates><urls></urls></record></Cite></EndNote> 6 . A drug utili z ation study of prescription opioids based on the 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 the se drug s incl. their strength and route of administration , thereby enabling well-informed, evidence-based decision-making in addressing this multifaceted issue. RESEARCH QUESTION AND OBJECTIVES Table SEQ Table \* ARABIC 2 : Primary and secondary research questions and objective 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) and route (oral, transdermal or parenteral) ), stratified by calendar year, age, sex and country/database during the study period 2012-2022. Hypothesis: N ot ap plicable Population (mention key inclusion-exclusion criteria): All people registered in the respective databases on 1 st of January of each year in the period 2012-2022 (or the latest available), with at least 1 year of data availability, will participate in the population-level analysis (period prevalence calculation in Objective 1). Therefore, children aged <1year will be excluded. New users of opioids in the period between 1/1/2012 and 31/12/2022 (or latest date available), with at least 1 year of data visibility, and no use of the respective opioid in the previous 6 months or 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 ends): Follow-up will start on a pre-specified calendar time point e.g., 1st of January for each calendar year between 20 1 2 -202 2 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 ( e .g. 31 st December 202 2 ) Setting: Inpatient and outpatient setting using data from the following 7 data sources: E BB [Estonia] , IQVIA DA Germany [Germany], IQVIA LBD Belgium [Belgium], SIDIAP [Spain], IPCI [The Netherlands], CHUBX [France] , ACI VARHA [Finland] Main measure of effect: Incidence and prevalence of opioid use Secondary research question and objective Objective: To determine the duration of the first treatment era of opioid use , as well as c haracteristics of new users and indication for opioid prescribing/dispensing, all s tratified by country/database. Hypothesis: N ot ap plicable Population (mention key inclusion-exclusion criteria): New users of opioids in the period between 1/1/2012 and 31/12/2022 (or latest date available), with at least 1 year of data visibility, and no use of the respective opioid in the previous 6 months or 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 (31 st December 202 2 ) Setting: Inpatient and outpatient setting using data from the following 7 data sources: EBB [Estonia] , IQVIA DA Germany [Germany], IQVIA LBD Belgium [Belgium], SIDIAP [Spain], IPCI [The Netherlands], CHUBX [France], ACI VARHA [Finland] Main measure of effect: Duration of opioid use ( first treatment era) expressed as minimum, p25, median, p75, and maximum days Large - scale characterisation for new opioid users (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 recorded in the month / week before/at the date of treatment start. RESEARCH METHODS S tudy Design Retrospective cohort studies will be conducted using routinely-collected health data from 7 databases. The study will comprise two consecutive parts: A population-based cohort study will be conducted to address objective 1, assessing the prevalence and incidence of the respective opioids of interest . A new drug user cohort will be used to address objective 2; to characteri z e individual -level opioid utili z ation in terms of summary patient characteristics, indication and duration of use. Study Setting S tudy population The study cohort will comprise all individuals present in the database during the study period (2012- 2022 ) and with at least 365 days of data availability before the day they become eligible for study inclusion. Therefore, children aged <1year will be excluded. Additional eligibility criteria will be applied for the calculation of incidence rates and patient-level drug utilisation analyses where the observation time of users of the op i oid of interest is excluded during use and 6 months (12months) afterwards. S tudy period and follow-up The study period will be from the 1st of January 2012 until the earliest of 31 st December 2022 or the respective latest date of data availability of the respective databases. Follow-up will start from the date people have reached at least 365 days of data availability . Table SEQ Table \* ARABIC 3 : Operational Definition of Time 0 (index date) and other primary time anchors Study population name(s) Time Anchor Description ( e.g., time 0) Number of entries Type of entry Washout window Care Setting 1 Code Type 2 Diagnosis position Incident with respect to… Measurement characteristics/ validation Source of algorithm All patients from the database eligible for the study – Analysis of Prevalent Use Patient present in the database during the study period (2012- 2022 ) and with at leas t 1 year of valid database history Multiple Prevalent n/a IP and OP n/a n/a Overall, s ubstance, strength, route n/a n/a All patients from the database eligible for the study – Analysis of incident use Patient present in the database during the study period (2012-202 2 ) and with at least 1 year of valid database history Multiple Incident [ -365, - 180 ] IP and OP n/a n/a Overall, substance, strength, route n/a n/a 1 IP = inpatient, OP = outpatient, n/a = not applicable 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 (31 st December 2022 ) 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 exit ing the database (the end of observation period). Lastly, person ID 5 has two observation periods in the database. T he first period contribute s time from study start until end of observation period , the second start s contributing time again once sufficient prior history is reached and exit s at study end date . Figure SEQ Figure \* ARABIC 1 : Included observation time for the denominator population I n - and exclusion criteria Population-level Utilisation of opioids The study cohort will comprise all individual s present in the period 2012- 2022 (or the latest available), with at least 365 days of data availability before the day they become eligible for study inclusion. Additional eligibility criteria will be applied for the calculation of incidence rates: The observation time of users of the opioid of interest is excluded during use and 180 days (365days) afterwards. Patient -level Utilisation of opioids All new users of opioid s , after 180 days (365days) of no use of the specific opioid /substance /strength/ route , in the period between 01/01/201 2 and 31/12/ 2022 (or latest date available), with at least 365 days of visibility prior to the date of their first antibiotic prescription . Table SEQ Table \* ARABIC 4 : Operational Definitions of Inclusion Criteria Criterion Details Order of application Assessment window Care Settings Code Type Diagnosis position Applied to study populations: Measurement characteristics/ validation Source for algorithm O bservation period in the database during the period 201 2 - 202 2 (or the latest available) A ll individuals present in the period 2012-2022 (or the latest available) N/A N/A p rimary care , secondary care (i.e in- and outpatient specialist care) N/A N/A All individuals within the selected databases N/A N/A Prior database history of 1 year Study participants will be required to have a year of prior history observed before contributing observation time After 1 year primary care, secondary care (i.e in- and outpatient specialist care) N/A N/A All individuals within the selected databases N/A N/A Washout period New users will be required to have not used opioid s/ the specific opioid substance /strength/ route 180days (365days) before a “new” prescription After 180days, 365days primary care, secondary care (i.e in- and outpatient specialist care) N/A N/A All individuals within the selected databases N/A N/A Variables 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 O verall by drug substance (incl. combinations and products for all indications) by strength (weak/ potent opioids) for those opioids where strength is labelled by the WHO by route (oral, transdermal or parenteral) for overall opioids. This list of opioid s is described in Table 5 . Details of exposure are described in Table 6. Table SEQ Table \* ARABIC 5 : Exposure of interest S ubstance Name Strength* No record counts in databases expected based on feasibility S ubstance Name Strength* No record counts in databases expected based on feasibility acetyldihy d rocodeine 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 X phenoperidine X dextromethorphan pholcodine dextromoramide pirinitramide dextropropoxyphene X propoxyphene dihydrocodeine remifentanil ethylmorphine sufentanil fentanyl potent tapentadol potent hydrocodone weak X thebacon hydromorphone potent tilidine ketobemidone tramadol weak meptazinol meperidine (pethidine) naloxone methadone potent morphine potent buprenorphine/naloxone n icomorphine X oxycodone/naloxone normethadon X p entazocine /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) Table SEQ Table \* ARABIC 6 : Exposure details Exposure group name(s) Details Washout window Assessment Window Care Setting Code Type Diagnosis position Applied to study populations: Incident with respect to… Measurement characteristics/ validation Source of algorithm Overall opioids , substance, strength, route Preliminary c ode lists provided in Table 5 [- 180 , - 365 ] Calendar year Biobank, p rimary and secondary care RxNorm N/A All individuals present in the database during the study period Previous opioid use N/A N/A Outcome s N / A Other covariates, including confounders, effect modifiers and other variables (where relevant) Covariates for stratification in population-level drug utili z ation study: Calendar year Age: clinical-informed age bands will be used : 1 -18 , 1 9 - 60 , 6 1 -85 , and >85 Sex : male or female Covariates for patient -level drug utili z ation study: Large-scale characterisation of baseline characteristics : the operational definition of the covariates is described in the Table 7 below. 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 recorded in the month / week before/at the date of treatment start. The t op 10 most frequent c o-morbidities fr om large-scale patient characteri z ation 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. Table SEQ Table \* ARABIC 7 : Operational Definitions of Covariates Characteristic Details Type of variable Assessment window Care Settings¹ Code Type 2 Diagnosis Position 3 Applied to study populations: Measurement characteristics/ validation Source for algorithm Indication of Use Top 10 most frequent co-morbidities from large-scale patient characterization Counts At index date and as sensitivity analyses in windows around index date (ID): [-7, ID] and [-30, ID] Biobank, primary and secondary care SNOMED N/A Persons with new use during the study period N/A N/A Large-scale summary characteristics of new users Large-scale patient-level characterization with regard to baseline co -variates Counts Demographics, c o - morbidities and co - medication a t index date (ID), and within anytime to 36 6 days before ID, 365 to -18 1days before ID , and 180 to 1 day before ID Biobank, primary and secondary care SNOMED N/A Persons with new use during the study period N/A N/A Data sources This study will be conducted using routinely collected data from 7 databases in 6 European countries ( 5 EU countries and United Kingdom). All databases were previously mapped to the OMOP CDM. Estonian Biobank (EBB), Estonia IQVIA Disease Analyzer Germany (IQVIA DA Germany), Germany IQVIA LBD Belgium, Belgium Integrated Primary Care Information Project (IPCI), The Netherlands The Information System for Research in Primary Care (SIDIAP), Spain Clinical Data Warehouse of Bordeaux University Hospital (CHUBX), France Auria Clinical Informatics VARHA (ACI VARHA ) , Finland Information on the data source(s) with a justification for their choice in terms of ability to capture the relevant data is described in a Table 8 . Table SEQ Table \* ARABIC 8 : Description of data sources Country Name of Database Justification for Inclusion Health Care setting Type of Data Number of active subjects Feasibility count of disease (if relevant) Data lock for the last update The Netherlands IPCI Database covers primary care / outpatient specialist care setting where opioid prescriptions are issued. Primary care EHR 1.39 million N/A 1/1 2 /2022 France CHUBX Database covers hospital care setting where opioid may be initiated Secondary care (in and outpatients) EHR 2.13 million N/A 1/ 3 /202 3 Spain SIDIAP Databases covers primary care / outpatient specialist care setting where opioid prescriptions are issued. Primary care EHR 5.8 million N/A 1 / 6 / 2022 Belgium IQVIA LBD Belgium outpatient specialist care EHR 0.4 million N/A 1/1/2022 Germany IQVIA DA Germany outpatient specialist care EHR 8.5 million N/A 1/9/ 2022 Finland ACI VARHA Database covers hospital care setting where opioid may be initiated Secondary care (in and outpatients) EHR 0.7 million N/A 31 /12/2021 Estonia EBB Database covers primary care setting where opioid prescriptions are issued. Biobank Claims data 0.2 million N/A 31/3/ 2021 IPCI = Integrated Primary Care Information Project; CHUBX= 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. Exposure is based on prescription data . 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. ADDIN EN.CITE <EndNote><Cite><Author>Vlug</Author><Year>1999</Year><RecNum>512</RecNum><DisplayText><style face="superscript">7</style></DisplayText><record><rec-number>512</rec-number><foreign-keys><key app="EN" db-id="awfexxfak92550e9pvrvta0lfseprt0pstfx" timestamp="1659957575">512</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Vlug, AE</author><author>van der Lei, J</author><author>Mosseveld, BM</author><author>van Wijk, MA</author><author>van der Linden, PD</author><author>Sturkenboom MC</author></authors></contributors><titles><title>Postmarketing surveillance based on electronic patient records: the IPCI project</title><secondary-title>Methods of information in medicine</secondary-title></titles><periodical><full-title>Methods of information in medicine</full-title></periodical><pages>339-44</pages><volume>38</volume><dates><year>1999</year></dates><urls></urls></record></Cite></EndNote> 7 The selection of 374 GP practices is representative of the entire country. The database contains records from 2.8 million patients out of a Dutch population of 17M starting in 1996 ADDIN EN.CITE <EndNote><Cite><Author>Vlug</Author><Year>1999</Year><RecNum>512</RecNum><DisplayText><style face="superscript">7</style></DisplayText><record><rec-number>512</rec-number><foreign-keys><key app="EN" db-id="awfexxfak92550e9pvrvta0lfseprt0pstfx" timestamp="1659957575">512</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Vlug, AE</author><author>van der Lei, J</author><author>Mosseveld, BM</author><author>van Wijk, MA</author><author>van der Linden, PD</author><author>Sturkenboom MC</author></authors></contributors><titles><title>Postmarketing surveillance based on electronic patient records: the IPCI project</title><secondary-title>Methods of information in medicine</secondary-title></titles><periodical><full-title>Methods of information in medicine</full-title></periodical><pages>339-44</pages><volume>38</volume><dates><year>1999</year></dates><urls></urls></record></Cite></EndNote> 7 . The median follow-up is 4.7 years. 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 Board ADDIN EN.CITE <EndNote><Cite><Author>Vlug</Author><Year>1999</Year><RecNum>512</RecNum><DisplayText><style face="superscript">7</style></DisplayText><record><rec-number>512</rec-number><foreign-keys><key app="EN" db-id="awfexxfak92550e9pvrvta0lfseprt0pstfx" timestamp="1659957575">512</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Vlug, AE</author><author>van der Lei, J</author><author>Mosseveld, BM</author><author>van Wijk, MA</author><author>van der Linden, PD</author><author>Sturkenboom MC</author></authors></contributors><titles><title>Postmarketing surveillance based on electronic patient records: the IPCI project</title><secondary-title>Methods of information in medicine</secondary-title></titles><periodical><full-title>Methods of information in medicine</full-title></periodical><pages>339-44</pages><volume>38</volume><dates><year>1999</year></dates><urls></urls></record></Cite></EndNote> 7 . Bordeaux University Hospital (CHUBX), 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). PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5CcmF0PC9BdXRob3I+PFllYXI+MjAyMDwvWWVhcj48UmVj TnVtPjExPC9SZWNOdW0+PERpc3BsYXlUZXh0PjxzdHlsZSBmYWNlPSJzdXBlcnNjcmlwdCI+ODwv c3R5bGU+PC9EaXNwbGF5VGV4dD48cmVjb3JkPjxyZWMtbnVtYmVyPjExPC9yZWMtbnVtYmVyPjxm b3JlaWduLWtleXM+PGtleSBhcHA9IkVOIiBkYi1pZD0icnZzMHo5dHJqYTB0cjZlczJ6b3g1cjly OXN4d3c5ZnoyZjU5IiB0aW1lc3RhbXA9IjE2Njc0MjU1NTEiPjExPC9rZXk+PC9mb3JlaWduLWtl eXM+PHJlZi10eXBlIG5hbWU9IkpvdXJuYWwgQXJ0aWNsZSI+MTc8L3JlZi10eXBlPjxjb250cmli dXRvcnM+PGF1dGhvcnM+PGF1dGhvcj5CcmF0LCBHLiBBLjwvYXV0aG9yPjxhdXRob3I+V2ViZXIs IEcuIE0uPC9hdXRob3I+PGF1dGhvcj5HZWhsZW5ib3JnLCBOLjwvYXV0aG9yPjxhdXRob3I+QXZp bGxhY2gsIFAuPC9hdXRob3I+PGF1dGhvcj5QYWxtZXIsIE4uIFAuPC9hdXRob3I+PGF1dGhvcj5D aGlvdmF0bywgTC48L2F1dGhvcj48YXV0aG9yPkNpbWlubywgSi48L2F1dGhvcj48YXV0aG9yPldh aXRtYW4sIEwuIFIuPC9hdXRob3I+PGF1dGhvcj5PbWVubiwgRy4gUy48L2F1dGhvcj48YXV0aG9y Pk1hbG92aW5pLCBBLjwvYXV0aG9yPjxhdXRob3I+TW9vcmUsIEouIEguPC9hdXRob3I+PGF1dGhv cj5CZWF1bGlldS1Kb25lcywgQi4gSy48L2F1dGhvcj48YXV0aG9yPlRpYm9sbG8sIFYuPC9hdXRo b3I+PGF1dGhvcj5NdXJwaHksIFMuIE4uPC9hdXRob3I+PGF1dGhvcj5ZaSwgUy4gTC48L2F1dGhv cj48YXV0aG9yPktlbGxlciwgTS4gUy48L2F1dGhvcj48YXV0aG9yPkJlbGxhenppLCBSLjwvYXV0 aG9yPjxhdXRob3I+SGFuYXVlciwgRC4gQS48L2F1dGhvcj48YXV0aG9yPlNlcnJldC1MYXJtYW5k ZSwgQS48L2F1dGhvcj48YXV0aG9yPkd1dGllcnJlei1TYWNyaXN0YW4sIEEuPC9hdXRob3I+PGF1 dGhvcj5Ib2xtZXMsIEouIEouPC9hdXRob3I+PGF1dGhvcj5CZWxsLCBELiBTLjwvYXV0aG9yPjxh dXRob3I+TWFuZGwsIEsuIEQuPC9hdXRob3I+PGF1dGhvcj5Gb2xsZXR0LCBSLiBXLjwvYXV0aG9y PjxhdXRob3I+S2xhbm4sIEouIEcuPC9hdXRob3I+PGF1dGhvcj5NdXJhZCwgRC4gQS48L2F1dGhv cj48YXV0aG9yPlNjdWRlbGxlciwgTC48L2F1dGhvcj48YXV0aG9yPkJ1Y2FsbywgTS48L2F1dGhv cj48YXV0aG9yPktpcmNob2ZmLCBLLjwvYXV0aG9yPjxhdXRob3I+Q3JhaWcsIEouPC9hdXRob3I+ PGF1dGhvcj5PYmVpZCwgSi48L2F1dGhvcj48YXV0aG9yPkpvdWhldCwgVi48L2F1dGhvcj48YXV0 aG9yPkdyaWZmaWVyLCBSLjwvYXV0aG9yPjxhdXRob3I+Q29zc2luLCBTLjwvYXV0aG9yPjxhdXRo b3I+TW9hbCwgQi48L2F1dGhvcj48YXV0aG9yPlBhdGVsLCBMLiBQLjwvYXV0aG9yPjxhdXRob3I+ QmVsbGFzaSwgQS48L2F1dGhvcj48YXV0aG9yPlByb2tvc2NoLCBILiBVLjwvYXV0aG9yPjxhdXRo b3I+S3Jhc2thLCBELjwvYXV0aG9yPjxhdXRob3I+U2xpeiwgUC48L2F1dGhvcj48YXV0aG9yPlRh biwgQS4gTC4gTS48L2F1dGhvcj48YXV0aG9yPk5naWFtLCBLLiBZLjwvYXV0aG9yPjxhdXRob3I+ WmFtYmVsbGksIEEuPC9hdXRob3I+PGF1dGhvcj5Nb3dlcnksIEQuIEwuPC9hdXRob3I+PGF1dGhv cj5TY2hpdmVyLCBFLjwvYXV0aG9yPjxhdXRob3I+RGV2a290YSwgQi48L2F1dGhvcj48YXV0aG9y PkJyYWRmb3JkLCBSLiBMLjwvYXV0aG9yPjxhdXRob3I+RGFuaWFyLCBNLjwvYXV0aG9yPjxhdXRo b3I+RGFuaWVsLCBDLjwvYXV0aG9yPjxhdXRob3I+QmVub2l0LCBWLjwvYXV0aG9yPjxhdXRob3I+ QmV5LCBSLjwvYXV0aG9yPjxhdXRob3I+UGFyaXMsIE4uPC9hdXRob3I+PGF1dGhvcj5TZXJyZSwg UC48L2F1dGhvcj48YXV0aG9yPk9ybG92YSwgTi48L2F1dGhvcj48YXV0aG9yPkR1YmllbCwgSi48 L2F1dGhvcj48YXV0aG9yPkhpbGthLCBNLjwvYXV0aG9yPjxhdXRob3I+SmFubm90LCBBLiBTLjwv YXV0aG9yPjxhdXRob3I+QnJlYW50LCBTLjwvYXV0aG9yPjxhdXRob3I+TGVibGFuYywgSi48L2F1 dGhvcj48YXV0aG9yPkdyaWZmb24sIE4uPC9hdXRob3I+PGF1dGhvcj5CdXJndW4sIEEuPC9hdXRo b3I+PGF1dGhvcj5CZXJuYXV4LCBNLjwvYXV0aG9yPjxhdXRob3I+U2FuZHJpbiwgQS48L2F1dGhv cj48YXV0aG9yPlNhbGFtYW5jYSwgRS48L2F1dGhvcj48YXV0aG9yPkNvcm1vbnQsIFMuPC9hdXRo b3I+PGF1dGhvcj5HYW5zbGFuZHQsIFQuPC9hdXRob3I+PGF1dGhvcj5HcmFkaW5nZXIsIFQuPC9h dXRob3I+PGF1dGhvcj5DaGFtcCwgSi48L2F1dGhvcj48YXV0aG9yPkJvZWtlciwgTS48L2F1dGhv cj48YXV0aG9yPk1hcnRlbCwgUC48L2F1dGhvcj48YXV0aG9yPkVzdGV2ZSwgTC48L2F1dGhvcj48 YXV0aG9yPkdyYW1mb3J0LCBBLjwvYXV0aG9yPjxhdXRob3I+R3Jpc2VsLCBPLjwvYXV0aG9yPjxh dXRob3I+TGVwcm92b3N0LCBELjwvYXV0aG9yPjxhdXRob3I+TW9yZWF1LCBULjwvYXV0aG9yPjxh dXRob3I+VmFyb3F1YXV4LCBHLjwvYXV0aG9yPjxhdXRob3I+VmllLCBKLiBKLjwvYXV0aG9yPjxh dXRob3I+V2Fzc2VybWFubiwgRC48L2F1dGhvcj48YXV0aG9yPk1lbnNjaCwgQS48L2F1dGhvcj48 YXV0aG9yPkNhdWNoZXRldXgsIEMuPC9hdXRob3I+PGF1dGhvcj5IYXZlcmthbXAsIEMuPC9hdXRo b3I+PGF1dGhvcj5MZW1haXRyZSwgRy48L2F1dGhvcj48YXV0aG9yPkJvc2FyaSwgUy48L2F1dGhv cj48YXV0aG9yPktyYW50eiwgSS4gRC48L2F1dGhvcj48YXV0aG9yPlNvdXRoLCBBLjwvYXV0aG9y PjxhdXRob3I+Q2FpLCBULjwvYXV0aG9yPjxhdXRob3I+S29oYW5lLCBJLiBTLjwvYXV0aG9yPjwv YXV0aG9ycz48L2NvbnRyaWJ1dG9ycz48YXV0aC1hZGRyZXNzPkRlcGFydG1lbnQgb2YgQmlvbWVk aWNhbCBJbmZvcm1hdGljcywgSGFydmFyZCBNZWRpY2FsIFNjaG9vbCwgQm9zdG9uLCBNQSBVU0Eu Z3JpZC4zODE0Mi4zYzAwMDAwMDA0MTkzNjc1NFgmI3hEO0lSQ0NTIElDUyBNYXVnZXJpLCBQYXZp YSwgSXRhbHkuJiN4RDtEZXBhcnRtZW50IG9mIEludGVybmFsIE1lZGljaW5lIGFuZCBNZWRpY2Fs IFRoZXJhcHksIFVuaXZlcnNpdHkgb2YgUGF2aWEsIFBhdmlhLCBJdGFseS5ncmlkLjg5ODIuYjAw MDAgMDAwNCAxNzYyIDU3MzYmI3hEO1VBQiBJbmZvcm1hdGljcyBJbnN0aXR1dGUsIEJpcm1pbmdo YW0sIEFMIFVTQS5ncmlkLjI2NTg5Mi4yMDAwMDAwMDEwNjM0NDE4NyYjeEQ7RGVwYXJ0bWVudCBv ZiBJbnRlcm5hbCBNZWRpY2luZSwgRGl2aXNpb24gb2YgTWVkaWNhbCBJbmZvcm1hdGljcywgVW5p dmVyc2l0eSBvZiBLYW5zYXMgTWVkaWNhbCBDZW50ZXIsIEthbnNhcyBDaXR5LCBLUyBVU0EuZ3Jp ZC40MTIwMTYuMDAwMDAgMDAwMSAyMTc3IDYzNzUmI3hEO0RlcGFydG1lbnQgb2YgQ29tcHV0YXRp b25hbCBNZWRpY2luZSAmYW1wOyBCaW9pbmZvcm1hdGljcywgVW5pdmVyc2l0eSBvZiBNaWNoaWdh biwgQW5uIEFyYm9yLCBNSSBVU0EuZ3JpZC4yMTQ0NTguZTAwMDAwMDAwODY4MzczNzAmI3hEO0lu c3RpdHV0ZSBmb3IgQmlvbWVkaWNhbCBJbmZvcm1hdGljcywgVW5pdmVyc2l0eSBvZiBQZW5uc3ls dmFuaWEgUGVyZWxtYW4gU2Nob29sIG9mIE1lZGljaW5lLCBQaGlsYWRlbHBoaWEsIFBBIFVTQS5n cmlkLjI1ODc5LjMxMDAwMCAwMDA0IDE5MzYgODk3MiYjeEQ7RGVwYXJ0bWVudCBvZiBCaW9zdGF0 aXN0aWNzLCBFcGlkZW1pb2xvZ3ksIGFuZCBJbmZvcm1hdGljcywgVW5pdmVyc2l0eSBvZiBQZW5u c3lsdmFuaWEgUGVyZWxtYW4gU2Nob29sIG9mIE1lZGljaW5lLCBQaGlsYWRlbHBoaWEsIFBBIFVT QS5ncmlkLjI1ODc5LjMxMDAwMCAwMDA0IDE5MzYgODk3MiYjeEQ7RGVwYXJ0bWVudCBvZiBOZXVy b2xvZ3ksIE1hc3NhY2h1c2V0dHMgR2VuZXJhbCBIb3NwaXRhbCwgQm9zdG9uLCBNQSBVU0EuZ3Jp ZC4zMjIyNC4zNTAwMDAgMDAwNCAwMzg2IDk5MjQmI3hEO0RlcGFydG1lbnQgb2YgRWxlY3RyaWNh bCBDb21wdXRlciBhbmQgQmlvbWVkaWNhbCBFbmdpbmVlcmluZywgVW5pdmVyc2l0eSBvZiBQYXZp YSwgUGF2aWEsIEl0YWx5LmdyaWQuODk4Mi5iMDAwMCAwMDA0IDE3NjIgNTczNiYjeEQ7RGVwYXJ0 bWVudCBvZiBQZWRpYXRyaWNzLCBVbml2ZXJzaXR5IG9mIE1pY2hpZ2FuIE1lZGljYWwgU2Nob29s LCBBbm4gQXJib3IsIE1JIFVTQS5ncmlkLjIxNDQ1OC5lMDAwMDAwMDA4NjgzNzM3MCYjeEQ7RGVw YXJ0bWVudCBvZiBNZWRpY2luZSwgRGF2aWQgR2VmZmVuIFNjaG9vbCBvZiBNZWRpY2luZSBhdCBV Q0xBLCBMb3MgQW5nZWxlcywgQ0EgVVNBLmdyaWQuMTkwMDYuM2UwMDAwIDAwMDAgOTYzMiA2NzE4 JiN4RDtDb21wdXRhdGlvbmFsIEhlYWx0aCBJbmZvcm1hdGljcyBQcm9ncmFtLCBCb3N0b24gQ2hp bGRyZW4mYXBvcztzIEhvc3BpdGFsLCBCb3N0b24sIE1BIFVTQS5ncmlkLjI1MTUuMzAwMDAgMDAw NCAwMzc4IDg0MzgmI3hEO0RlcGFydG1lbnQgb2YgTWVkaWNpbmUsIE1hc3NhY2h1c2V0dHMgR2Vu ZXJhbCBIb3NwaXRhbCwgQm9zdG9uLCBNQSBVU0EuZ3JpZC4zMjIyNC4zNTAwMDAgMDAwNCAwMzg2 IDk5MjQmI3hEO1NjaWVudGlmaWMgRGlyZWN0aW9uLCBJUkNDUyBDYSZhcG9zOyBHcmFuZGEgT3Nw ZWRhbGUgTWFnZ2lvcmUgUG9saWNsaW5pY28gZGkgTWlsYW5vLCBNaWxhbm8sIEl0YWx5LiYjeEQ7 QklPTUVSSVMgKEJJT01lZGljYWwgUmVzZWFyY2ggSW5mb3JtYXRpY3MgU29sdXRpb25zKSwgUGF2 aWEsIEl0YWx5LiYjeEQ7QmlvbWVkaWNhbCBJbmZvcm1hdGljcyBDZW50ZXIsIE1lZGljYWwgVW5p dmVyc2l0eSBvZiBTb3V0aCBDYXJvbGluYSwgQ2hhcmxlc3RvbiwgU0MgVVNBLmdyaWQuMjU5ODI4 LmMwMDAwIDAwMDEgMjE4OSAzNDc1JiN4RDtCb3JkZWF1eCBVbml2ZXJzaXR5IEhvc3BpdGFsLCBC b3JkZWF1eCwgRnJhbmNlLmdyaWQuNDIzOTkuMzUwMDAwIDAwMDQgMDU5MyA3MTE4JiN4RDtVT0Mg UmljZXJjYSwgSW5ub3ZhemlvbmUgZSBCcmFuZCBSZXB1dGF0aW9uLCBBU1NUIFBhcGEgR2lvdmFu bmkgWFhJSUksIEJlcmdhbW8sIEl0YWx5LiYjeEQ7RGVwYXJ0bWVudCBvZiBNZWRpY2FsIEluZm9y bWF0aWNzLCBVbml2ZXJzaXR5IG9mIEVybGFuZ2VuLU51cm5iZXJnLCBFcmxhbmdlbiwgR2VybWFu eS5ncmlkLjUzMzAuNTAwMDAgMDAwMSAyMTA3IDMzMTEmI3hEO0NlbnRlciBmb3IgTWVkaWNhbCBJ bmZvcm1hdGlvbiBhbmQgQ29tbXVuaWNhdGlvbiBUZWNobm9sb2d5LCBVbml2ZXJzaXR5IEhvc3Bp dGFsIEVybGFuZ2VuLCBFcmxhbmdlbiwgR2VybWFueS5ncmlkLjQxMTY2OC5jMDAwMCAwMDAwIDk5 MzUgNjUyNSYjeEQ7TmF0aW9uYWwgVW5pdmVyc2l0eSBIZWFsdGggU3lzdGVtcywgU2luZ2Fwb3Jl LCBTaW5nYXBvcmUuZ3JpZC40MTA3NTkuZTAwMDAgMDAwNCAwNDUxIDYxNDMmI3hEO0RlcGFydG1l bnQgb2YgT25jb2xvZ3ksIEFTU1QgUGFwYSBHaW92YW5uaSBYWElJSSwgQmVyZ2FtbywgSXRhbHku JiN4RDtQZW5uIE1lZGljaW5lLCBEYXRhIEFuYWx5dGljcyBDZW50ZXIsIFBoaWxhZGVscGhpYSwg UEEgVVNBLiYjeEQ7RGVwYXJ0bWVudCBvZiBCaW9tZWRpY2FsIGFuZCBIZWFsdGggSW5mb3JtYXRp Y3MsIENoaWxkcmVuJmFwb3M7cyBIb3NwaXRhbCBvZiBQaGlsYWRlbHBoaWEsIFBoaWxhZGVscGhp YSwgUEEgVVNBLmdyaWQuMjM5NTUyLmEwMDAwIDAwMDEgMDY4MCA4NzcwJiN4RDtOb3J0aCBDYXJv bGluYSBUcmFuc2xhdGlvbmFsIGFuZCBDbGluaWNhbCBTY2llbmNlcyAoTkMgVHJhQ1MpIEluc3Rp dHV0ZSwgVU5DIENoYXBlbCBIaWxsLCBDaGFwZWwgSGlsbCwgTkMgVVNBLmdyaWQuMTA2OTguMzYw MDAwMDAwMTIyNDgzMjA4JiN4RDtXSU5EIERlcGFydG1lbnQgQVBIUCBHcmVhdGVyIFBhcmlzIFVu aXZlcnNpdHkgSG9zcGl0YWwsIFBhcmlzLCBGcmFuY2UuJiN4RDtEZXBhcnRtZW50IG9mIEJpb21l ZGljYWwgSW5mb3JtYXRpY3MsIEhFR1AsIEFQSFAgR3JlYXRlciBQYXJpcyBVbml2ZXJzaXR5IEhv c3BpdGFsLCBQYXJpcywgRnJhbmNlLmdyaWQuNDE0MDkzLmImI3hEO0NsaW5pY2FsIFJlc2VhcmNo IFVuaXQsIFNhaW50IEFudG9pbmUgSG9zcGl0YWwsIEFQSFAgR3JlYXRlciBQYXJpcyBVbml2ZXJz aXR5IEhvc3BpdGFsLCBQYXJpcywgRnJhbmNlLmdyaWQuNDEyMzcwLjMwMDAwIDAwMDQgMTkzNyAx MTAwJiN4RDtTdHJhdGVneSBhbmQgVHJhbnNmb3JtYXRpb24gRGVwYXJ0bWVudCwgQVBIUCBHcmVh dGVyIFBhcmlzIFVuaXZlcnNpdHkgSG9zcGl0YWwsIFBhcmlzLCBGcmFuY2UuJiN4RDtIZWlucmlj aC1MYW56LUNlbnRlciBmb3IgRGlnaXRhbCBIZWFsdGgsIFVuaXZlcnNpdHkgTWVkaWNpbmUgTWFu bmhlaW0sIEhlaWRlbGJlcmcgVW5pdmVyc2l0eSwgTWFubmhlaW0sIEdlcm1hbnkuZ3JpZC40MTE3 NzguYzAwMDAgMDAwMSAyMTYyIDE3MjgmI3hEO0lOUklBIFNvcGhpYS1BbnRpcG9saXMtWkVOSVRI IFRlYW0sIExJUk1NLCBNb250cGVsbGllciwgRnJhbmNlLmdyaWQuNDY0NjM4LmIwMDAwIDAwMDQg MDU5OSAwNDg4JiN4RDtJbnN0aXR1dGUgb2YgTWVkaWNhbCBCaW9tZXRyeSBhbmQgU3RhdGlzdGlj cywgTWVkaWNhbCBDZW50ZXIsIFVuaXZlcnNpdHkgb2YgRnJlaWJ1cmcsIEZyZWlidXJnIGltIEJy ZWlzZ2F1LCBHZXJtYW55LmdyaWQuNTk2My45JiN4RDtDbGluaWNhbCBSZXNlYXJjaCBVbml0LCBQ YXJpcyBTYWNsYXksIEFQSFAgR3JlYXRlciBQYXJpcyBVbml2ZXJzaXR5IEhvc3BpdGFsLCBQYXJp cywgRnJhbmNlLiYjeEQ7U0VEL1NJRVJSQSwgSW5yaWEgQ2VudHJlIGRlIFBhcmlzLCBQYXJpcywg RnJhbmNlLiYjeEQ7VW5pdmVyc2l0ZSBQYXJpcy1TYWNsYXksIElucmlhLCBDRUEsIFBhcmlzLCBG cmFuY2UuZ3JpZC41NTgzLmIwMDAwIDAwMDEgMjI5OSA4MDI1JiN4RDtDbGV2eS5pbywgUGFyaXMs IEZyYW5jZS4mI3hEO1NlcXVlTCwgSW5yaWEgTGlsbGUsIFBhcmlzLCBGcmFuY2UuJiN4RDtFTlMs IFBTTCBVbml2ZXJzaXR5LCBQYXJpcywgRnJhbmNlLmdyaWQuNTYwNy40MDAwMDAwMDEyMTEwNTU0 NyYjeEQ7SW5zdGl0dXRlIG9mIERpZ2l0YWxpemF0aW9uIGluIE1lZGljaW5lLCBGYWN1bHR5IG9m IE1lZGljaW5lIGFuZCBNZWRpY2FsIENlbnRlciwgVW5pdmVyc2l0eSBvZiBGcmVpYnVyZywgRnJl aWJ1cmcgaW0gQnJlaXNnYXUsIEdlcm1hbnkuZ3JpZC41OTYzLjkmI3hEO0lSQ0NTIENhJmFwb3M7 IEdyYW5kYSBPc3BlZGFsZSBNYWdnaW9yZSBQb2xpY2xpbmljbyBkaSBNaWxhbm8sIE1pbGFubywg SXRhbHkuJiN4RDtCcmVubmVyIENoaWxkcmVuJmFwb3M7cyBIb3NwaXRhbCwgV2FrZSBGb3Jlc3Qg U2Nob29sIG9mIE1lZGljaW5lLCBXaW5zdG9uLVNhbGVtLCBOQyBVU0EuZ3JpZC4yNDExNjcuNzAw MDAgMDAwMSAyMTg1IDMzMTg8L2F1dGgtYWRkcmVzcz48dGl0bGVzPjx0aXRsZT5JbnRlcm5hdGlv bmFsIGVsZWN0cm9uaWMgaGVhbHRoIHJlY29yZC1kZXJpdmVkIENPVklELTE5IGNsaW5pY2FsIGNv dXJzZSBwcm9maWxlczogdGhlIDRDRSBjb25zb3J0aXVtPC90aXRsZT48c2Vjb25kYXJ5LXRpdGxl Pk5QSiBEaWdpdCBNZWQ8L3NlY29uZGFyeS10aXRsZT48L3RpdGxlcz48cGVyaW9kaWNhbD48ZnVs bC10aXRsZT5OUEogRGlnaXQgTWVkPC9mdWxsLXRpdGxlPjwvcGVyaW9kaWNhbD48cGFnZXM+MTA5 PC9wYWdlcz48dm9sdW1lPjM8L3ZvbHVtZT48ZWRpdGlvbj4yMDIwLzA4LzMxPC9lZGl0aW9uPjxr ZXl3b3Jkcz48a2V5d29yZD5EYXRhYmFzZXM8L2tleXdvcmQ+PGtleXdvcmQ+T3V0Y29tZXMgcmVz ZWFyY2g8L2tleXdvcmQ+PGtleXdvcmQ+VmlyYWwgaW5mZWN0aW9uPC9rZXl3b3JkPjwva2V5d29y ZHM+PGRhdGVzPjx5ZWFyPjIwMjA8L3llYXI+PC9kYXRlcz48aXNibj4yMzk4LTYzNTIgKEVsZWN0 cm9uaWMpJiN4RDsyMzk4LTYzNTIgKExpbmtpbmcpPC9pc2JuPjxhY2Nlc3Npb24tbnVtPjMyODY0 NDcyPC9hY2Nlc3Npb24tbnVtPjx1cmxzPjxyZWxhdGVkLXVybHM+PHVybD5odHRwczovL3d3dy5u Y2JpLm5sbS5uaWguZ292L3B1Ym1lZC8zMjg2NDQ3MjwvdXJsPjwvcmVsYXRlZC11cmxzPjwvdXJs cz48Y3VzdG9tMj5QTUM3NDM4NDk2PC9jdXN0b20yPjxlbGVjdHJvbmljLXJlc291cmNlLW51bT4x MC4xMDM4L3M0MTc0Ni0wMjAtMDAzMDgtMDwvZWxlY3Ryb25pYy1yZXNvdXJjZS1udW0+PC9yZWNv cmQ+PC9DaXRlPjwvRW5kTm90ZT4A ADDIN EN.CITE PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5CcmF0PC9BdXRob3I+PFllYXI+MjAyMDwvWWVhcj48UmVj TnVtPjExPC9SZWNOdW0+PERpc3BsYXlUZXh0PjxzdHlsZSBmYWNlPSJzdXBlcnNjcmlwdCI+ODwv c3R5bGU+PC9EaXNwbGF5VGV4dD48cmVjb3JkPjxyZWMtbnVtYmVyPjExPC9yZWMtbnVtYmVyPjxm b3JlaWduLWtleXM+PGtleSBhcHA9IkVOIiBkYi1pZD0icnZzMHo5dHJqYTB0cjZlczJ6b3g1cjly OXN4d3c5ZnoyZjU5IiB0aW1lc3RhbXA9IjE2Njc0MjU1NTEiPjExPC9rZXk+PC9mb3JlaWduLWtl eXM+PHJlZi10eXBlIG5hbWU9IkpvdXJuYWwgQXJ0aWNsZSI+MTc8L3JlZi10eXBlPjxjb250cmli dXRvcnM+PGF1dGhvcnM+PGF1dGhvcj5CcmF0LCBHLiBBLjwvYXV0aG9yPjxhdXRob3I+V2ViZXIs IEcuIE0uPC9hdXRob3I+PGF1dGhvcj5HZWhsZW5ib3JnLCBOLjwvYXV0aG9yPjxhdXRob3I+QXZp bGxhY2gsIFAuPC9hdXRob3I+PGF1dGhvcj5QYWxtZXIsIE4uIFAuPC9hdXRob3I+PGF1dGhvcj5D aGlvdmF0bywgTC48L2F1dGhvcj48YXV0aG9yPkNpbWlubywgSi48L2F1dGhvcj48YXV0aG9yPldh aXRtYW4sIEwuIFIuPC9hdXRob3I+PGF1dGhvcj5PbWVubiwgRy4gUy48L2F1dGhvcj48YXV0aG9y Pk1hbG92aW5pLCBBLjwvYXV0aG9yPjxhdXRob3I+TW9vcmUsIEouIEguPC9hdXRob3I+PGF1dGhv cj5CZWF1bGlldS1Kb25lcywgQi4gSy48L2F1dGhvcj48YXV0aG9yPlRpYm9sbG8sIFYuPC9hdXRo b3I+PGF1dGhvcj5NdXJwaHksIFMuIE4uPC9hdXRob3I+PGF1dGhvcj5ZaSwgUy4gTC48L2F1dGhv cj48YXV0aG9yPktlbGxlciwgTS4gUy48L2F1dGhvcj48YXV0aG9yPkJlbGxhenppLCBSLjwvYXV0 aG9yPjxhdXRob3I+SGFuYXVlciwgRC4gQS48L2F1dGhvcj48YXV0aG9yPlNlcnJldC1MYXJtYW5k ZSwgQS48L2F1dGhvcj48YXV0aG9yPkd1dGllcnJlei1TYWNyaXN0YW4sIEEuPC9hdXRob3I+PGF1 dGhvcj5Ib2xtZXMsIEouIEouPC9hdXRob3I+PGF1dGhvcj5CZWxsLCBELiBTLjwvYXV0aG9yPjxh dXRob3I+TWFuZGwsIEsuIEQuPC9hdXRob3I+PGF1dGhvcj5Gb2xsZXR0LCBSLiBXLjwvYXV0aG9y PjxhdXRob3I+S2xhbm4sIEouIEcuPC9hdXRob3I+PGF1dGhvcj5NdXJhZCwgRC4gQS48L2F1dGhv cj48YXV0aG9yPlNjdWRlbGxlciwgTC48L2F1dGhvcj48YXV0aG9yPkJ1Y2FsbywgTS48L2F1dGhv cj48YXV0aG9yPktpcmNob2ZmLCBLLjwvYXV0aG9yPjxhdXRob3I+Q3JhaWcsIEouPC9hdXRob3I+ PGF1dGhvcj5PYmVpZCwgSi48L2F1dGhvcj48YXV0aG9yPkpvdWhldCwgVi48L2F1dGhvcj48YXV0 aG9yPkdyaWZmaWVyLCBSLjwvYXV0aG9yPjxhdXRob3I+Q29zc2luLCBTLjwvYXV0aG9yPjxhdXRo b3I+TW9hbCwgQi48L2F1dGhvcj48YXV0aG9yPlBhdGVsLCBMLiBQLjwvYXV0aG9yPjxhdXRob3I+ QmVsbGFzaSwgQS48L2F1dGhvcj48YXV0aG9yPlByb2tvc2NoLCBILiBVLjwvYXV0aG9yPjxhdXRo b3I+S3Jhc2thLCBELjwvYXV0aG9yPjxhdXRob3I+U2xpeiwgUC48L2F1dGhvcj48YXV0aG9yPlRh biwgQS4gTC4gTS48L2F1dGhvcj48YXV0aG9yPk5naWFtLCBLLiBZLjwvYXV0aG9yPjxhdXRob3I+ WmFtYmVsbGksIEEuPC9hdXRob3I+PGF1dGhvcj5Nb3dlcnksIEQuIEwuPC9hdXRob3I+PGF1dGhv cj5TY2hpdmVyLCBFLjwvYXV0aG9yPjxhdXRob3I+RGV2a290YSwgQi48L2F1dGhvcj48YXV0aG9y PkJyYWRmb3JkLCBSLiBMLjwvYXV0aG9yPjxhdXRob3I+RGFuaWFyLCBNLjwvYXV0aG9yPjxhdXRo b3I+RGFuaWVsLCBDLjwvYXV0aG9yPjxhdXRob3I+QmVub2l0LCBWLjwvYXV0aG9yPjxhdXRob3I+ QmV5LCBSLjwvYXV0aG9yPjxhdXRob3I+UGFyaXMsIE4uPC9hdXRob3I+PGF1dGhvcj5TZXJyZSwg UC48L2F1dGhvcj48YXV0aG9yPk9ybG92YSwgTi48L2F1dGhvcj48YXV0aG9yPkR1YmllbCwgSi48 L2F1dGhvcj48YXV0aG9yPkhpbGthLCBNLjwvYXV0aG9yPjxhdXRob3I+SmFubm90LCBBLiBTLjwv YXV0aG9yPjxhdXRob3I+QnJlYW50LCBTLjwvYXV0aG9yPjxhdXRob3I+TGVibGFuYywgSi48L2F1 dGhvcj48YXV0aG9yPkdyaWZmb24sIE4uPC9hdXRob3I+PGF1dGhvcj5CdXJndW4sIEEuPC9hdXRo b3I+PGF1dGhvcj5CZXJuYXV4LCBNLjwvYXV0aG9yPjxhdXRob3I+U2FuZHJpbiwgQS48L2F1dGhv cj48YXV0aG9yPlNhbGFtYW5jYSwgRS48L2F1dGhvcj48YXV0aG9yPkNvcm1vbnQsIFMuPC9hdXRo b3I+PGF1dGhvcj5HYW5zbGFuZHQsIFQuPC9hdXRob3I+PGF1dGhvcj5HcmFkaW5nZXIsIFQuPC9h dXRob3I+PGF1dGhvcj5DaGFtcCwgSi48L2F1dGhvcj48YXV0aG9yPkJvZWtlciwgTS48L2F1dGhv cj48YXV0aG9yPk1hcnRlbCwgUC48L2F1dGhvcj48YXV0aG9yPkVzdGV2ZSwgTC48L2F1dGhvcj48 YXV0aG9yPkdyYW1mb3J0LCBBLjwvYXV0aG9yPjxhdXRob3I+R3Jpc2VsLCBPLjwvYXV0aG9yPjxh dXRob3I+TGVwcm92b3N0LCBELjwvYXV0aG9yPjxhdXRob3I+TW9yZWF1LCBULjwvYXV0aG9yPjxh dXRob3I+VmFyb3F1YXV4LCBHLjwvYXV0aG9yPjxhdXRob3I+VmllLCBKLiBKLjwvYXV0aG9yPjxh dXRob3I+V2Fzc2VybWFubiwgRC48L2F1dGhvcj48YXV0aG9yPk1lbnNjaCwgQS48L2F1dGhvcj48 YXV0aG9yPkNhdWNoZXRldXgsIEMuPC9hdXRob3I+PGF1dGhvcj5IYXZlcmthbXAsIEMuPC9hdXRo b3I+PGF1dGhvcj5MZW1haXRyZSwgRy48L2F1dGhvcj48YXV0aG9yPkJvc2FyaSwgUy48L2F1dGhv cj48YXV0aG9yPktyYW50eiwgSS4gRC48L2F1dGhvcj48YXV0aG9yPlNvdXRoLCBBLjwvYXV0aG9y PjxhdXRob3I+Q2FpLCBULjwvYXV0aG9yPjxhdXRob3I+S29oYW5lLCBJLiBTLjwvYXV0aG9yPjwv YXV0aG9ycz48L2NvbnRyaWJ1dG9ycz48YXV0aC1hZGRyZXNzPkRlcGFydG1lbnQgb2YgQmlvbWVk aWNhbCBJbmZvcm1hdGljcywgSGFydmFyZCBNZWRpY2FsIFNjaG9vbCwgQm9zdG9uLCBNQSBVU0Eu Z3JpZC4zODE0Mi4zYzAwMDAwMDA0MTkzNjc1NFgmI3hEO0lSQ0NTIElDUyBNYXVnZXJpLCBQYXZp YSwgSXRhbHkuJiN4RDtEZXBhcnRtZW50IG9mIEludGVybmFsIE1lZGljaW5lIGFuZCBNZWRpY2Fs IFRoZXJhcHksIFVuaXZlcnNpdHkgb2YgUGF2aWEsIFBhdmlhLCBJdGFseS5ncmlkLjg5ODIuYjAw MDAgMDAwNCAxNzYyIDU3MzYmI3hEO1VBQiBJbmZvcm1hdGljcyBJbnN0aXR1dGUsIEJpcm1pbmdo YW0sIEFMIFVTQS5ncmlkLjI2NTg5Mi4yMDAwMDAwMDEwNjM0NDE4NyYjeEQ7RGVwYXJ0bWVudCBv ZiBJbnRlcm5hbCBNZWRpY2luZSwgRGl2aXNpb24gb2YgTWVkaWNhbCBJbmZvcm1hdGljcywgVW5p dmVyc2l0eSBvZiBLYW5zYXMgTWVkaWNhbCBDZW50ZXIsIEthbnNhcyBDaXR5LCBLUyBVU0EuZ3Jp ZC40MTIwMTYuMDAwMDAgMDAwMSAyMTc3IDYzNzUmI3hEO0RlcGFydG1lbnQgb2YgQ29tcHV0YXRp b25hbCBNZWRpY2luZSAmYW1wOyBCaW9pbmZvcm1hdGljcywgVW5pdmVyc2l0eSBvZiBNaWNoaWdh biwgQW5uIEFyYm9yLCBNSSBVU0EuZ3JpZC4yMTQ0NTguZTAwMDAwMDAwODY4MzczNzAmI3hEO0lu c3RpdHV0ZSBmb3IgQmlvbWVkaWNhbCBJbmZvcm1hdGljcywgVW5pdmVyc2l0eSBvZiBQZW5uc3ls dmFuaWEgUGVyZWxtYW4gU2Nob29sIG9mIE1lZGljaW5lLCBQaGlsYWRlbHBoaWEsIFBBIFVTQS5n cmlkLjI1ODc5LjMxMDAwMCAwMDA0IDE5MzYgODk3MiYjeEQ7RGVwYXJ0bWVudCBvZiBCaW9zdGF0 aXN0aWNzLCBFcGlkZW1pb2xvZ3ksIGFuZCBJbmZvcm1hdGljcywgVW5pdmVyc2l0eSBvZiBQZW5u c3lsdmFuaWEgUGVyZWxtYW4gU2Nob29sIG9mIE1lZGljaW5lLCBQaGlsYWRlbHBoaWEsIFBBIFVT QS5ncmlkLjI1ODc5LjMxMDAwMCAwMDA0IDE5MzYgODk3MiYjeEQ7RGVwYXJ0bWVudCBvZiBOZXVy b2xvZ3ksIE1hc3NhY2h1c2V0dHMgR2VuZXJhbCBIb3NwaXRhbCwgQm9zdG9uLCBNQSBVU0EuZ3Jp ZC4zMjIyNC4zNTAwMDAgMDAwNCAwMzg2IDk5MjQmI3hEO0RlcGFydG1lbnQgb2YgRWxlY3RyaWNh bCBDb21wdXRlciBhbmQgQmlvbWVkaWNhbCBFbmdpbmVlcmluZywgVW5pdmVyc2l0eSBvZiBQYXZp YSwgUGF2aWEsIEl0YWx5LmdyaWQuODk4Mi5iMDAwMCAwMDA0IDE3NjIgNTczNiYjeEQ7RGVwYXJ0 bWVudCBvZiBQZWRpYXRyaWNzLCBVbml2ZXJzaXR5IG9mIE1pY2hpZ2FuIE1lZGljYWwgU2Nob29s LCBBbm4gQXJib3IsIE1JIFVTQS5ncmlkLjIxNDQ1OC5lMDAwMDAwMDA4NjgzNzM3MCYjeEQ7RGVw YXJ0bWVudCBvZiBNZWRpY2luZSwgRGF2aWQgR2VmZmVuIFNjaG9vbCBvZiBNZWRpY2luZSBhdCBV Q0xBLCBMb3MgQW5nZWxlcywgQ0EgVVNBLmdyaWQuMTkwMDYuM2UwMDAwIDAwMDAgOTYzMiA2NzE4 JiN4RDtDb21wdXRhdGlvbmFsIEhlYWx0aCBJbmZvcm1hdGljcyBQcm9ncmFtLCBCb3N0b24gQ2hp bGRyZW4mYXBvcztzIEhvc3BpdGFsLCBCb3N0b24sIE1BIFVTQS5ncmlkLjI1MTUuMzAwMDAgMDAw NCAwMzc4IDg0MzgmI3hEO0RlcGFydG1lbnQgb2YgTWVkaWNpbmUsIE1hc3NhY2h1c2V0dHMgR2Vu ZXJhbCBIb3NwaXRhbCwgQm9zdG9uLCBNQSBVU0EuZ3JpZC4zMjIyNC4zNTAwMDAgMDAwNCAwMzg2 IDk5MjQmI3hEO1NjaWVudGlmaWMgRGlyZWN0aW9uLCBJUkNDUyBDYSZhcG9zOyBHcmFuZGEgT3Nw ZWRhbGUgTWFnZ2lvcmUgUG9saWNsaW5pY28gZGkgTWlsYW5vLCBNaWxhbm8sIEl0YWx5LiYjeEQ7 QklPTUVSSVMgKEJJT01lZGljYWwgUmVzZWFyY2ggSW5mb3JtYXRpY3MgU29sdXRpb25zKSwgUGF2 aWEsIEl0YWx5LiYjeEQ7QmlvbWVkaWNhbCBJbmZvcm1hdGljcyBDZW50ZXIsIE1lZGljYWwgVW5p dmVyc2l0eSBvZiBTb3V0aCBDYXJvbGluYSwgQ2hhcmxlc3RvbiwgU0MgVVNBLmdyaWQuMjU5ODI4 LmMwMDAwIDAwMDEgMjE4OSAzNDc1JiN4RDtCb3JkZWF1eCBVbml2ZXJzaXR5IEhvc3BpdGFsLCBC b3JkZWF1eCwgRnJhbmNlLmdyaWQuNDIzOTkuMzUwMDAwIDAwMDQgMDU5MyA3MTE4JiN4RDtVT0Mg UmljZXJjYSwgSW5ub3ZhemlvbmUgZSBCcmFuZCBSZXB1dGF0aW9uLCBBU1NUIFBhcGEgR2lvdmFu bmkgWFhJSUksIEJlcmdhbW8sIEl0YWx5LiYjeEQ7RGVwYXJ0bWVudCBvZiBNZWRpY2FsIEluZm9y bWF0aWNzLCBVbml2ZXJzaXR5IG9mIEVybGFuZ2VuLU51cm5iZXJnLCBFcmxhbmdlbiwgR2VybWFu eS5ncmlkLjUzMzAuNTAwMDAgMDAwMSAyMTA3IDMzMTEmI3hEO0NlbnRlciBmb3IgTWVkaWNhbCBJ bmZvcm1hdGlvbiBhbmQgQ29tbXVuaWNhdGlvbiBUZWNobm9sb2d5LCBVbml2ZXJzaXR5IEhvc3Bp dGFsIEVybGFuZ2VuLCBFcmxhbmdlbiwgR2VybWFueS5ncmlkLjQxMTY2OC5jMDAwMCAwMDAwIDk5 MzUgNjUyNSYjeEQ7TmF0aW9uYWwgVW5pdmVyc2l0eSBIZWFsdGggU3lzdGVtcywgU2luZ2Fwb3Jl LCBTaW5nYXBvcmUuZ3JpZC40MTA3NTkuZTAwMDAgMDAwNCAwNDUxIDYxNDMmI3hEO0RlcGFydG1l bnQgb2YgT25jb2xvZ3ksIEFTU1QgUGFwYSBHaW92YW5uaSBYWElJSSwgQmVyZ2FtbywgSXRhbHku JiN4RDtQZW5uIE1lZGljaW5lLCBEYXRhIEFuYWx5dGljcyBDZW50ZXIsIFBoaWxhZGVscGhpYSwg UEEgVVNBLiYjeEQ7RGVwYXJ0bWVudCBvZiBCaW9tZWRpY2FsIGFuZCBIZWFsdGggSW5mb3JtYXRp Y3MsIENoaWxkcmVuJmFwb3M7cyBIb3NwaXRhbCBvZiBQaGlsYWRlbHBoaWEsIFBoaWxhZGVscGhp YSwgUEEgVVNBLmdyaWQuMjM5NTUyLmEwMDAwIDAwMDEgMDY4MCA4NzcwJiN4RDtOb3J0aCBDYXJv bGluYSBUcmFuc2xhdGlvbmFsIGFuZCBDbGluaWNhbCBTY2llbmNlcyAoTkMgVHJhQ1MpIEluc3Rp dHV0ZSwgVU5DIENoYXBlbCBIaWxsLCBDaGFwZWwgSGlsbCwgTkMgVVNBLmdyaWQuMTA2OTguMzYw MDAwMDAwMTIyNDgzMjA4JiN4RDtXSU5EIERlcGFydG1lbnQgQVBIUCBHcmVhdGVyIFBhcmlzIFVu aXZlcnNpdHkgSG9zcGl0YWwsIFBhcmlzLCBGcmFuY2UuJiN4RDtEZXBhcnRtZW50IG9mIEJpb21l ZGljYWwgSW5mb3JtYXRpY3MsIEhFR1AsIEFQSFAgR3JlYXRlciBQYXJpcyBVbml2ZXJzaXR5IEhv c3BpdGFsLCBQYXJpcywgRnJhbmNlLmdyaWQuNDE0MDkzLmImI3hEO0NsaW5pY2FsIFJlc2VhcmNo IFVuaXQsIFNhaW50IEFudG9pbmUgSG9zcGl0YWwsIEFQSFAgR3JlYXRlciBQYXJpcyBVbml2ZXJz aXR5IEhvc3BpdGFsLCBQYXJpcywgRnJhbmNlLmdyaWQuNDEyMzcwLjMwMDAwIDAwMDQgMTkzNyAx MTAwJiN4RDtTdHJhdGVneSBhbmQgVHJhbnNmb3JtYXRpb24gRGVwYXJ0bWVudCwgQVBIUCBHcmVh dGVyIFBhcmlzIFVuaXZlcnNpdHkgSG9zcGl0YWwsIFBhcmlzLCBGcmFuY2UuJiN4RDtIZWlucmlj aC1MYW56LUNlbnRlciBmb3IgRGlnaXRhbCBIZWFsdGgsIFVuaXZlcnNpdHkgTWVkaWNpbmUgTWFu bmhlaW0sIEhlaWRlbGJlcmcgVW5pdmVyc2l0eSwgTWFubmhlaW0sIEdlcm1hbnkuZ3JpZC40MTE3 NzguYzAwMDAgMDAwMSAyMTYyIDE3MjgmI3hEO0lOUklBIFNvcGhpYS1BbnRpcG9saXMtWkVOSVRI IFRlYW0sIExJUk1NLCBNb250cGVsbGllciwgRnJhbmNlLmdyaWQuNDY0NjM4LmIwMDAwIDAwMDQg MDU5OSAwNDg4JiN4RDtJbnN0aXR1dGUgb2YgTWVkaWNhbCBCaW9tZXRyeSBhbmQgU3RhdGlzdGlj cywgTWVkaWNhbCBDZW50ZXIsIFVuaXZlcnNpdHkgb2YgRnJlaWJ1cmcsIEZyZWlidXJnIGltIEJy ZWlzZ2F1LCBHZXJtYW55LmdyaWQuNTk2My45JiN4RDtDbGluaWNhbCBSZXNlYXJjaCBVbml0LCBQ YXJpcyBTYWNsYXksIEFQSFAgR3JlYXRlciBQYXJpcyBVbml2ZXJzaXR5IEhvc3BpdGFsLCBQYXJp cywgRnJhbmNlLiYjeEQ7U0VEL1NJRVJSQSwgSW5yaWEgQ2VudHJlIGRlIFBhcmlzLCBQYXJpcywg RnJhbmNlLiYjeEQ7VW5pdmVyc2l0ZSBQYXJpcy1TYWNsYXksIElucmlhLCBDRUEsIFBhcmlzLCBG cmFuY2UuZ3JpZC41NTgzLmIwMDAwIDAwMDEgMjI5OSA4MDI1JiN4RDtDbGV2eS5pbywgUGFyaXMs IEZyYW5jZS4mI3hEO1NlcXVlTCwgSW5yaWEgTGlsbGUsIFBhcmlzLCBGcmFuY2UuJiN4RDtFTlMs IFBTTCBVbml2ZXJzaXR5LCBQYXJpcywgRnJhbmNlLmdyaWQuNTYwNy40MDAwMDAwMDEyMTEwNTU0 NyYjeEQ7SW5zdGl0dXRlIG9mIERpZ2l0YWxpemF0aW9uIGluIE1lZGljaW5lLCBGYWN1bHR5IG9m IE1lZGljaW5lIGFuZCBNZWRpY2FsIENlbnRlciwgVW5pdmVyc2l0eSBvZiBGcmVpYnVyZywgRnJl aWJ1cmcgaW0gQnJlaXNnYXUsIEdlcm1hbnkuZ3JpZC41OTYzLjkmI3hEO0lSQ0NTIENhJmFwb3M7 IEdyYW5kYSBPc3BlZGFsZSBNYWdnaW9yZSBQb2xpY2xpbmljbyBkaSBNaWxhbm8sIE1pbGFubywg SXRhbHkuJiN4RDtCcmVubmVyIENoaWxkcmVuJmFwb3M7cyBIb3NwaXRhbCwgV2FrZSBGb3Jlc3Qg U2Nob29sIG9mIE1lZGljaW5lLCBXaW5zdG9uLVNhbGVtLCBOQyBVU0EuZ3JpZC4yNDExNjcuNzAw MDAgMDAwMSAyMTg1IDMzMTg8L2F1dGgtYWRkcmVzcz48dGl0bGVzPjx0aXRsZT5JbnRlcm5hdGlv bmFsIGVsZWN0cm9uaWMgaGVhbHRoIHJlY29yZC1kZXJpdmVkIENPVklELTE5IGNsaW5pY2FsIGNv dXJzZSBwcm9maWxlczogdGhlIDRDRSBjb25zb3J0aXVtPC90aXRsZT48c2Vjb25kYXJ5LXRpdGxl Pk5QSiBEaWdpdCBNZWQ8L3NlY29uZGFyeS10aXRsZT48L3RpdGxlcz48cGVyaW9kaWNhbD48ZnVs bC10aXRsZT5OUEogRGlnaXQgTWVkPC9mdWxsLXRpdGxlPjwvcGVyaW9kaWNhbD48cGFnZXM+MTA5 PC9wYWdlcz48dm9sdW1lPjM8L3ZvbHVtZT48ZWRpdGlvbj4yMDIwLzA4LzMxPC9lZGl0aW9uPjxr ZXl3b3Jkcz48a2V5d29yZD5EYXRhYmFzZXM8L2tleXdvcmQ+PGtleXdvcmQ+T3V0Y29tZXMgcmVz ZWFyY2g8L2tleXdvcmQ+PGtleXdvcmQ+VmlyYWwgaW5mZWN0aW9uPC9rZXl3b3JkPjwva2V5d29y ZHM+PGRhdGVzPjx5ZWFyPjIwMjA8L3llYXI+PC9kYXRlcz48aXNibj4yMzk4LTYzNTIgKEVsZWN0 cm9uaWMpJiN4RDsyMzk4LTYzNTIgKExpbmtpbmcpPC9pc2JuPjxhY2Nlc3Npb24tbnVtPjMyODY0 NDcyPC9hY2Nlc3Npb24tbnVtPjx1cmxzPjxyZWxhdGVkLXVybHM+PHVybD5odHRwczovL3d3dy5u Y2JpLm5sbS5uaWguZ292L3B1Ym1lZC8zMjg2NDQ3MjwvdXJsPjwvcmVsYXRlZC11cmxzPjwvdXJs cz48Y3VzdG9tMj5QTUM3NDM4NDk2PC9jdXN0b20yPjxlbGVjdHJvbmljLXJlc291cmNlLW51bT4x MC4xMDM4L3M0MTc0Ni0wMjAtMDAzMDgtMDwvZWxlY3Ryb25pYy1yZXNvdXJjZS1udW0+PC9yZWNv cmQ+PC9DaXRlPjwvRW5kTm90ZT4A ADDIN EN.CITE.DATA 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 staff ADDIN EN.CITE <EndNote><Cite><Author>Garcia-Gil Mdel</Author><Year>2011</Year><RecNum>513</RecNum><DisplayText><style face="superscript">9</style></DisplayText><record><rec-number>513</rec-number><foreign-keys><key app="EN" db-id="awfexxfak92550e9pvrvta0lfseprt0pstfx" timestamp="1659958033">513</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Garcia-Gil Mdel, M</author><author>Hermosilla, E</author><author>Prieto-Alhambra, D </author><author>Fina, F </author><author>Rosell, M</author><author>Ramos, R</author></authors></contributors><titles><title>Construction and validation of a scoring system for the selection of high-quality data in a Spanish population primary care database (SIDIAP).</title><secondary-title>Informatics in primary care</secondary-title></titles><periodical><full-title>Informatics in primary care</full-title></periodical><pages>135-45</pages><volume>19</volume><number>3</number><dates><year>2011</year></dates><urls></urls></record></Cite><Cite><Author>Garcia-Gil Mdel</Author><Year>2011</Year><RecNum>513</RecNum><record><rec-number>513</rec-number><foreign-keys><key app="EN" db-id="awfexxfak92550e9pvrvta0lfseprt0pstfx" timestamp="1659958033">513</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Garcia-Gil Mdel, M</author><author>Hermosilla, E</author><author>Prieto-Alhambra, D </author><author>Fina, F </author><author>Rosell, M</author><author>Ramos, R</author></authors></contributors><titles><title>Construction and validation of a scoring system for the selection of high-quality data in a Spanish population primary care database (SIDIAP).</title><secondary-title>Informatics in primary care</secondary-title></titles><periodical><full-title>Informatics in primary care</full-title></periodical><pages>135-45</pages><volume>19</volume><number>3</number><dates><year>2011</year></dates><urls></urls></record></Cite></EndNote> 9 . 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 10 years. 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. Disease Analyser (DA) Germany, Germany (IQVIA) DA Germany is collected from extracts of patient management software used by GPs and specialists practicing in ambulatory care settings ADDIN EN.CITE <EndNote><Cite><Author>Rathmann</Author><Year>2018</Year><RecNum>514</RecNum><DisplayText><style face="superscript">10</style></DisplayText><record><rec-number>514</rec-number><foreign-keys><key app="EN" db-id="awfexxfak92550e9pvrvta0lfseprt0pstfx" timestamp="1659958707">514</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Rathmann, W.</author><author>Bongaerts, B.</author><author>Carius, HJ.</author><author>Kruppert, S.</author><author>Kostev, K.</author></authors></contributors><titles><title>Basic characteristics and representativeness of the German Disease Analyzer database</title><secondary-title>Int J Clin Pharmacol Ther</secondary-title></titles><periodical><full-title>Int J Clin Pharmacol Ther</full-title></periodical><pages>459-466</pages><volume>56</volume><number>10</number><dates><year>2018</year></dates><urls></urls></record></Cite></EndNote> 10 . Data coverage includes more than 34M distinct person records out of at total population of 80M (42.5%) in the country and collected from 2,734 providers. Patient visiting more than one provider are not cross identified for data protection reasons and therefore recorded as separate in the system. Dates of service include from 1992 through present. Observation time is defined by the first and last consultation dates. Germany has no mandatory GP system and patient have free choice of specialist. As a result, data are collected from visits to 28.8% General, 13.4% Orthopaedic Surgery, 11.8% Otolaryngology, 11.2% Dermatology, 7.7% Obstetrics/Gynaecology, 6.2% various Neurology and Psychiatry 7.0% Paediatric, 4.6% Urology, 3.7% Cardiology, 3.5% Gastroenterology, 1.5% Pulmonary and 0.7% Rheumatology practices. Drugs are recorded as prescriptions of marketed products. No registration or approval is required for drug utili z ation studies. Auria Clinical Informatics at Turku University Hospital, Hospital District of Southwest Finland ( ACI VARHA ) The data covers the patient register at the Hospital District of Southwest Finland ( ACI VARHA ), containing Turku University Hospital, which is one of the five university hospitals in Finland. It covers the public specialist health care and most emergency health care in the area of Southwest Finland (Varsinais-Suomi) for all demographic groups (765K persons). The data is utilized for scientific research from the data lake in the ACI VARHA under the Finnish legislation (The Act on Secondary Use of Health and Social Data). The most relevant data domains are patients, visits, inpatient episodes, diagnoses, laboratory results, procedures, medication, pathology, radiology, radiotherapy and chemotherapy. 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. 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. Study size No sample size has been calculated. Prevalence and Incidence of opioid use among the study population will be estimated as part of Objective 1. Data Management All databases will have been mapped to the OMOP common data model. This enables the use of standardi z ed analytics and tools across the network since the structure of the data and the terminology system is harmoni z ed. 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. 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. The analysis will include calculation of population - based incidence rates and prevalence, as described in section 9.7.5.1 – Population-level drug utili z ation study , characteri z ation 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 utili z ation study. 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. Patient privacy protection Cell suppression will be applied as required by databases to protect people’s privacy. Cell counts < 5 will be clouded . Statistical model specification and assumptions of the analytical approach considered R-packages We will use the R package “DrugUtilization” for the patient-level drug utili z ation analyses including patient-level characteri z ation, and “ IncidencePrevalence package ” ADDIN EN.CITE <EndNote><Cite><RecNum>566</RecNum><DisplayText><style face="superscript">11</style></DisplayText><record><rec-number>566</rec-number><foreign-keys><key app="EN" db-id="awfexxfak92550e9pvrvta0lfseprt0pstfx" timestamp="1684421609">566</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors></contributors><titles><title>Edward Burn etal. IncidencePrevalence: Estimate Incidence and Prevalence using the OMOP Common Data Model.&#xD;Version: 0.3.0. Published: 2023-05-07 https://cran.r-project.org/web/packages/IncidencePrevalence/index.html</title></titles><dates></dates><urls></urls></record></Cite></EndNote> 11 for the population-level estimation of drug utili z ation. 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 show n in Figure 2 , first row . Note: d ose is not considered for this study. 682625 468160 0 0 Gap era joint mode Schematics Dose in between Cumulative dose Cumulative time “first” d 1 d 1 ⋅ x 1 + x 12 + d 2 ⋅ x 2 x 1 + x 12 + x 2 “second” d 2 d 1 ⋅ x 1 + d 2 ⋅( x 2 + x 12 ) x 1 + x 12 + x 2 “zero” 0 d 1 ⋅ x 1 + d 2 ⋅ x 2 x 1 + x 12 + x 2 “join” NA d 1 ⋅ x 1 + d 2 ⋅ x 2 x 1 + x 2 681198 68514 Figure SEQ Figure \* ARABIC 2 : Gap era joint mode 0 0 Figure SEQ Figure \* ARABIC 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 visibility 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 180 days (365days) prior the current prescription. If the start date of a prescription does not fulfil the exposure washout criteria of 1 80 days (365days) of no use, the whole exposure is eliminated. 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 1 st of the year of birth as proxy for the actual birthday. The following age groups will be used for stratification for population-level analyses : 1-18, 1 9 -60, 6 1 -85, and >85 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 recorded at the date of treatment start / in the week/month before treatment start. Characteri z ation of patient-level features Large scale patient characterisation before/on index date (= date of prescription) will be provided for different classifications for opioids, namely for (1), (2) for the 10 most frequent opioids in each database, (3) and (4), 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 -36 6 days [conditions only], -365days to -18 1 days, - 180 to - 1 day before index date, and at index date. Methods planned to obtain point estimates with confidence intervals of measures of occurrence Population-level drug utili z ation 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 and (4) by route (oral, transdermal or parenteral) for overall opioids. 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. 929005 890905 0 0 4314190 1189990 Opioid use 0 0 Opioid use 772795 3138805 Figure SEQ Figure \* ARABIC 3 : Period prevalence example Figure SEQ Figure \* ARABIC 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 a n opioid user , giving a prevalence of 20%. Incidence calculations Annual incidence rates of the opioid of interest will be calculated as the of number of new users after 180 days (365days) 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. 4606290 2304415 Opioid use 0 0 Opioid use 544195 4351020 Figure SEQ Figure \* ARABIC 4 : Incidence example Figure SEQ Figure \* ARABIC 4 : Incidence example 544195 1885988 0 0 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 with in 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. Patient -level drug utili z ation 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 recorded at the date of treatment 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 e ra of the opioid of interest during the study period . T reatment duration will be summarized 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. Description of sensitivity analyses. Table 9 describes the sensitivity analysis with regard to exploration of the indication of use. Table SEQ Table \* ARABIC 9 : Sensitivity analyses – rationale, strengths and limitations What is being varied? How? Why? (What do you expect to learn?) Strengths of the sensitivity analysis compared to the primary Limitations of the sensitivity analysis compared to the primary Window to assess indication of use Indication of use will be explored a t index date (ID) , and in a p eriod of [- 30 to ID ] days of the index date and in a period from [-7 to ID ] days before index date I ndication of use might not always be recorded on the date of prescription of the opioid of interest Proportion of patients with an indication of use might increase. Potential misclassification of indication of use if the disease code registered in the week /month before has nothing to do with prescription of the opioid of interest 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, DrugExposureDiagnostics ADDIN EN.CITE <EndNote><Cite><RecNum>565</RecNum><DisplayText><style face="superscript">12</style></DisplayText><record><rec-number>565</rec-number><foreign-keys><key app="EN" db-id="awfexxfak92550e9pvrvta0lfseprt0pstfx" timestamp="1684421520">565</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors></contributors><titles><title>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</title></titles><dates></dates><urls></urls></record></Cite></EndNote> 12 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) characteri z e drug utili z ation 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. Limitation 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 unavoi dable. For databases, where duration cannot be calculated due to e.g. missing information on quantity , dosing or end date , e.g. ACI Varha, treatment duration will not be provided. In addition, the recording of events used for patient characteri z ation and identification of the (potential) indication may vary across databases and recording of indication may be incomplete. Evidence synthesis Results from analyses described in S ection 9.7 will be presented separately for each database and no pooling of results will be conducted. PROTECTION OF HUMAN SUBJECTS For this study, participants from various EU member states will process personal data from individuals which is collected in national/regional electronic health record databases. Due to the sensitive nature of this personal medical data, it is important to be fully aware of ethical and regulatory aspects and to strive to take all reasonable measures to ensure compliance with ethical and regulatory issues on privacy. All databases used in this study are already used for pharmaco-epidemiological research and have a well-developed mechanism to ensure that European and local regulations dealing with ethical use of the data and adequate privacy control are adhered to. In agreement with these regulations, rather than combining person level data and performing only a central analysis, local analyses will be run, which generate non-identifiable aggregate summary results. The output files are stored in the DARWIN Remote Research Environment. These output files do not contain any data that allow identification of subjects included in the study. The RRE implements further security measures in order to ensure a high level of stored data protection to comply with the local implementation of the General Data Protection Regulation (GDPR) (EU) 679/20161 in the various member states. 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. PLANS FOR DISSEMINATING AND COMMUNICATING STUDY RESULTS 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. 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. REFERENCES ADDIN EN.REFLIST 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. 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 . ANNEXES Appendix I : Lists with preliminary concept definitions for exposure Appendix II : ENCePP checklist for study protocols 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. S ubstance 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 X dextromethorphan 1119510 dextromoramide 19021940 dextropropoxyphene 1153664 X dihydrocodeine 1189596 ethylmorphine 19050414 fentanyl 1154029 hydrocodone 1174888 X hydromorphone 1126658 ketobemidone 40798904 meptazinol 19003010 meperidine (pethidine) 1102527 methadone 1103640 morphine 1110410 n icomorphine 37493805 X 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 naloxone 1114220 b uprenorphine / naloxone 45776270 , 37498350 , 40015149 , 1970413 oxycodone/naloxone 21160441 , 41017321 , 45774941 , 36269469 pentazocine/naloxone 40063474 tilidine/naloxone 40063477 , 43799912 , 41298261 , 36272016 , 40063476 , 36264356 Appendix II – ENCePP checklist ENCePP Checklist for Study Protocols (Revision 4) Adopted by the ENCePP Steering Group on 15/10/2018 The European Network of Centres for Pharmacoepidemiology and Pharmacovigilance (ENCePP) welcomes innovative designs and new methods of research. This Checklist has been developed by ENCePP to stimulate consideration of important principles when designing and writing a pharmacoepidemiological or pharmacovigilance study protocol. The Checklist is intended to promote the quality of such studies, not their uniformity. The user is also referred to the ENCePP Guide on Methodological Standards in Pharmacoepidemiology , which reviews and gives direct electronic access to guidance for research in pharmacoepidemiology and pharmacovigilance. For each question of the Checklist, the investigator should indicate whether or not it has been addressed in the study protocol. If the answer is “Yes”, the section number of the protocol where this issue has been discussed should be specified. It is possible that some questions do not apply to a particular study (for example, in the case of an innovative study design). In this case, the answer ‘N/A’ (Not Applicable) can be checked and the “Comments” field included for each section should be used to explain why. The “Comments” field can also be used to elaborate on a “No” answer. This Checklist should be included as an Annex by marketing authori z ation holders when submitting the protocol of a non-interventional post-authori z ation safety study (PASS) to a regulatory authority (see the Guidance on the format and content of the protocol of non-interventional post-authorization safety studies ). The Checklist is a supporting document and does not replace the format of the protocol for PASS presented in the Guidance and Module VIII of the Good pharmacovigilance practices (GVP). Study title: DARWIN EU® - Drug utilization study of prescription opioids. . EU PAS Register ® number: Study reference number (if applicable): Section 1: Milestones Yes No N/A Section Number 1.1 Does the protocol specify timelines for Overview and 6 - milestones 1.1.1 Start of data collection FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX 1.1.2 End of data collection FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX 1.1.3 Progress report(s) FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX 1.1.4 Interim report(s) FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX 1.1.5 Registration in the EU PAS Register ® FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX 1.1.6 Final report of study results. FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX Comments: Section 2: Research question Yes No N/A Section Number 2.1 Does the formulation of the research question and objectives clearly explain: FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX 6- Background and 8 - Research questions and objectives 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) FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX 2.1.2 The objective(s) of the study? FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX 2.1.3 The target population? (i.e. population or subgroup to whom the study results are intended to be generalized) FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX 2.1.4 Which hypothesis(-es) is (are) to be tested? FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX 2.1.5 If applicable, that there is no a priori hypothesis? FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX Comments: Section 3: Study design Yes No N/A Section Number 3.1 Is the study design described? ( e.g., cohort, case-control, cross-sectional, other design) FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX 9.1 3.2 Does the protocol specify whether the study is based on primary, secondary or combined data collection? FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX 9.4 3.3 Does the protocol specify measures of occurrence? (e.g., rate, risk, prevalence) FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX 9.1 and 9.7.5.1 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)) FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX 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) FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX Comments: Section 4: Source and study populations Yes No N/A Section Number 4.1 Is the source population described? FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX 9.4 4.2 Is the planned study population defined in terms of: 9.2.1 4.2.1 Study time period FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX 4.2.2 Age and sex FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX 4.2.3 Country of origin FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX 4.2.4 Disease/indication FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX 4.2.5 Duration of follow-up FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX 4.3 Does the protocol define how the study population will be sampled from the source population? ( e.g., event or inclusion/exclusion criteria) FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX 9.2.3 Comments: 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 defining and categori z ing exposure, measurement of dose and duration of drug exposure) FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX 9.3.1 5.2 Does the protocol address the validity of the exposure measurement? ( e.g., precision, accuracy, use of validation sub-study) FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX 5.3 Is exposure categori z ed according to time windows? FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX 9.3.1 5.4 Is intensity of exposure addressed? ( e.g., dose, duration) FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX 9.7.3 5.5 Is exposure categori z ed based on biological mechanism of action and taking into account the pharmacokinetics and pharmacodynamics of the drug? FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX 5.6 Is (are) (an) appropriate comparator(s) identified? FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX 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? FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX 6.2 Does the protocol describe how the outcomes are defined and measured? FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX 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) FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX 6.4 Does the protocol describe specific outcomes relevant for Health Technology Assessment? (e.g. HRQoL, QALYs, DALYS, health care services utili z ation, burden of disease or treatment, compliance, disease management) FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX Comments: Section 7: Bias Yes No N/A Section Number 7.1 Does the protocol address ways to measure confounding? ( e.g., confounding by indication) FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX 7.2 Does the protocol address selection bias? (e.g. healthy user/adherer bias) FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX 7.3 Does the protocol address information bias? (e.g. misclassification of exposure and outcomes, time-related bias) FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX 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) FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX 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 interview) FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX 9.4 9.1.2 Outcomes? ( e.g., clinical records, laboratory markers or values, claims data, self-report, patient interview including scales and questionnaires, vital statistics) FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX 9.1.3 Covariates and other characteristics? FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX 9.4 and 9.3.3 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, dose, number of days of supply prescription, daily dosage, prescriber ) FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX 9.4 and 9.7.3 9.2.2 Outcomes? (e.g. date of occurrence, multiple event, severity measures related to event ) FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX 9.2.3 Covariates and other characteristics? ( e.g., age, sex, clinical and drug use history, co-morbidity, co-medications, lifestyle) FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX 9.4 and 9.7.3 9.3 Is a coding system described for: 9.3.1 Exposure? (e.g. WHO Drug Dictionary, Anatomical Therapeutic Chemical (ATC) Classification System) FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX 9.4 9.3.2 Outcomes? ( e.g., International Classification of Diseases (ICD), Medical Dictionary for Regulatory Activities (MedDRA)) FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX 9.3.3 Covariates and other characteristics? FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX 9.4 9.4 Is a linkage method between data sources described? (e.g. based on a unique identifier or other) FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX Comments: Section 10: Analysis plan Yes No N/A Section Number 10.1 Are the statistical methods and the reason for their choice described? FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX 9.7 10.2 Is study size and/or statistical precision estimated? FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX 10.3 Are descriptive analyses included? FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX 9.7 10.4 Are stratified analyses included? FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX 9.7 10.5 Does the plan describe methods for analytic control of confounding? FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX 10.6 Does the plan describe methods for analytic control of outcome misclassification? FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX 10.7 Does the plan describe methods for handling missing data? FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX 10.8 Are relevant sensitivity analyses described? FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX 9.7.6 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 maintenance and anti-fraud protection, archiving) FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX 9.8 11.2 Are methods of quality assurance described? FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX 9.8 11.3 Is there a system in place for independent review of study results? FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX Comments: Section 12: Limitations Yes No N/A Section Number 12.1 Does the protocol discuss the impact on the study results of: 9.9 12.1.1 Selection bias? FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX 12.1.2 Information bias? FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX 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). FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX 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) FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX Comments: Section 13: Ethical/data protection issues Yes No N/A Section Number 13.1 Have requirements of Ethics Committee/ Institutional Review Board been described? FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX 10 13.2 Has any outcome of an ethical review procedure been addressed? FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX 10 13.3 Have data protection requirements been described? FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX 10 Comments: Section 14: Amendments and deviations Yes No N/A Section Number 14.1 Does the protocol include a section to document amendments and deviations? FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX 5 Comments: Section 15: Plans for communication of study results Yes No N/A Section Number 15.1 Are plans described for communicating study results ( e.g., to regulatory authorities) ? FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX 12 15.2 Are plans described for disseminating study results externally, including publication? FORMCHECKBOX FORMCHECKBOX FORMCHECKBOX 12 Comments: Name of the main author of the protocol: Dr. Annika J ö dicke Date: 25/05/2023 Signature: J ö dicke Click or tap here to enter text.
Saatja: Raivo Kolde <
[email protected]>
Saadetud: 30.06.2023 14:13
Adressaat: <"SM Info">
Teema: Taotlus eesti bioeetika ja inimuuringute nõukogule
uurimisprotokolli kooskõlastamiseks
Manused: DARWIN_EU_D.2.2.3_Study_Protocol_P2-C1-002_v1.0_Clean.docx
Tere
Euroopa ravimiameti (EMA) DARWIN projektile on EBIN väljastanud raamloa
(otsus nr 1.1-12/1832), kuid iga uuringu jaoks on vaja kooskõlastada
kiirmenetlusega ka uuringu protokoll.
Nüüd soovib EMA uurida opioidide tarvitamise levimust, vanuse soo ja
riigi
kaupa. Vastava uuringu protokoll on manuses ja me sooviks seda
kiirmenetluse
korras kooskõlastada.
Lugupidamisega,
Raivo Kolde