Vastused

Saatja: "Kaia Kask" <[email protected]> Saaja: "Carmen Mäe - SOM" <[email protected]>, "Info - SOM" <[email protected]> Teema: RE: Loa küsimine uuringule “Elanikkonnas aja jooksul toimunud muutused erinevate vähivormide levimuses, esinemissageduses ja patsientide ellujäämises - uuring OHDSI andmevõrgustikul” Kuupäev: 2026-06-19 11:53 Tähelepanu! Tegemist on välisvõrgust saabunud kirjaga. Tundmatu saatja korral palume linke ja faile mitte avada. Tere! Saadan vastused varem esitatud küsimustele ja manusesse on lisatud uuringu protokoll ja andmekoosseis. 1) Millised taotluses viidatud analüüsid (nt raviteekonnad, majanduslik mõju, sotsiaalne staatus, metoodikate arendamine) kuuluvad käesoleva konkreetse uuringu ulatusse ning millised mitte? Vastus: Vastavalt uuringu “EST-Health-30 - Eesti terviseandmete väärindamine” taotlusele peame iga lisauuringu ja küsimuse jaoks eraldi loa küsima. “Elanikkonnas aja jooksul toimunud muutused erinevate vähivormide levimuses, esinemissageduses ja patsientide ellujäämises - uuring OHDSI andmevõrgustikul” näol on tegemist uue uuringu ja uute küsimustega, mida esialgses taotluses ei ole kajastatud. Esitasime selle uuringu eetikakomiteele hindamiseks ning saime positiivse vastuse. 2) Millised konkreetsed andmeväljad on uuringu põhieesmärkide (vähi levimus, esinemissagedus ja elulemus) saavutamiseks vältimatult vajalikud, eriti nende andmekategooriate osas, kus on märgitud „kõik andmed“? Vastus: Lisan kaasa andmekoosseisu, kus on välja toodud andmeväljad ja nende täpsustatud põhjendus. 3) Kas ja millises ulatuses on võimalik piirata andmekoosseisu, välistades andmed, millel puudub otsene seos uuringu põhieesmärkidega (nt vabatekstilised väljad, täielik anamnees, muu mitteseotud terviseinfo)? Vastus: Vabatekstidest kasutame ainult seda informatsiooni, mis on sealt eraldatud (näiteks suitsetamise staatus, mis antud uuringus oluline näitaja), mis on tihti just anamneesis, aga ka muudes tekstides kirjas. Need andmepunktid on standardiseeritud ja viidud OMOP andmemudelisse. Uuringus ei kasutata täielikke tekste ning neid tekste, kus puudub vajalik informatsioon, vaid ainult seda fakti, mis on tekstist leitud (nt inimene on suitsetaja või inimese seisundit kirjeldav ECOG kood on 2). See faktide eraldamine tehakse EST-Health-30 - Eesti terviseandmete väärindamine uuringu 1 etapis (andmete standardiseerimine). Konkreetne uuringu näol on tegemist rahvusvahelise uuringuga, mille esimeses sammus eraldatakse erinevate vähidiagnoosiga isikud andmebaasist (uuritavad) ning enamus edasised analüüsid tehakse nende isikute peal. Üks uurimisküsimus on kirjeldada ka vähile eelnevat perioodi kõigi erinevate meditsiinisündmuste osas, seega me ei saa välistada erinevaid andmeväljasid kuna see looks kallutatud vaate võrreldes teiste riikide andmetega. Kuna arvutatakse ka esmahaigestumise sagedust (väljendab uute haigusjuhtude arvu rahvaarvu suhtes mingil ajavahemikul) ja üldist esinemissagedust andmebaasist, mille korral on vaja kasutada kogu andmestiku. Tulemuste väljastamisel tagame, et uuringus kasutatud andmed vastavad protokollis esitatud andmepunktidele. 4) Kas kulutõhususe, sotsiaalse seisundi või muude täiendavate analüüside teostamine kuulub käesoleva uuringu eesmärkide hulka? Vastus: Käesoleva uuringu eesmärkide hulka ei kuulu kulutõhususe, sotsiaalse seisundi või muude täiendavate analüüside teostamine. 5) Kas ja millises ulatuses kasutatakse käesoleva uuringu raames andmeid metoodikate arendamiseks, sh tehisintellekti või masinõppe meetodite rakendamiseks? Vastus: “Elanikkonnas aja jooksul toimunud..” uuringus olevaid andmeid ei kasutata tehisintellekti metoodika arendamiseks (sh tehisintellekti või masinõppe meetodite rakendamiseks). 6) Milliste kriteeriumide alusel on hinnatud andmete vajalikkust ning kuidas on tagatud, et töödeldakse üksnes uuringu eesmärgi saavutamiseks vältimatult vajalikku andmestikku? Palun esitada täpsustused ja vajadusel korrigeeritud andmekoosseis, mis vastab andmete minimeerimise põhimõttele. Vastus: Käesolev uuring on osa rahvusvahelisest OMOP-võrgustiku uuringust, kus meie roll on andmepartnerina käivitada uuringu protokollile vastav standardiseeritud analüüsikood oma OMOP andmemudelis (CDM) olevatel EST-Health-30 andmetel. Nii on andmete minimeerimine tagatud juba uuringu disainiga. Töödeldavate andmete koosseisu ei määra kohapealne valik, vaid uuringu protokoll, mis on eetikakomitee poolt heaks kiidetud, ja sellega kaasnev analüüsikood. Kood pöördub üksnes protokollis määratletud uuringukohordi ja andmeväljade poole, mis on uurimisküsimustele vastamiseks vältimatult vajalikud. Andmete vajalikkuse hindamise kriteerium on seega tunnuse seos protokollis defineeritud analüüsiga. Patsiendi tasemel andmete minimeerimine ja privaatsus on tagatud järgnevate meetoditega: Antud uuringus on patsiendi tasemele andmetele turvalises andmetöötluskeskkonnas ligipääs ainult uurijatel, kes on, lisaks käesolevas uuringus vastutava uurija või uuringu läbiviijana kirjas olemisele, kantud ka antud andmestiku loomiseks esitatud taotlusele “EST-Health-30 - Eesti terviseandmete väärindamine”. Patsiendi tasemel andmed ei välju turvalisest andmetöötluskeskkonnast, analüüs viiakse läbi lokaalselt. Uuringu koordinaatorile edastatakse üksnes protokollis ette nähtud agregeeritud, isikustamata tulemused. Enne tulemuste väljasaatmist kontrollime, et väljund sisaldaks ainult uuringu eesmärgi saavutamiseks vajalikke agregeeritud tulemusi ning et alla 5 inimese jäävad väikesed sagedused oleksid summutatud. Sellises hajusanalüüsi mudelit järgival uuringul ei oleks tervest CDM andmebaasist protokollis nimetatud tunnustega kitsendatud andmekogu eelnev väljavõtmine põhjendatud: analüüsikood pöördub niigi üksnes vajalike andmete poole ning eraldi koopia loomine üksnes suurendaks isikuandmete jalajälge, vähendamata seejuures andmesubjektide privaatsusriski. Andmete minimeerimine on käesolevas mudelis tagatud sellega, et turvalisest andmetöötluskeskkonnnast väljuvad üksnes agregeeritud ja vältimatult vajalikud statistilised tulemused, mitte patsiendi tasemel andmed. Head soovides Kaia Kask From: Carmen Mäe - SOM <[email protected]> Sent: Tuesday, June 2, 2026 4:23 PM To: Kaia Kask <[email protected]>; Info - SOM <[email protected]> Cc: Raivo Kolde <[email protected]>; Marek Oja <[email protected]> Subject: RE: Loa küsimine uuringule “Elanikkonnas aja jooksul toimunud muutused erinevate vähivormide levimuses, esinemissageduses ja patsientide ellujäämises - uuring OHDSI andmevõrgustikul” Tere! Täname teid esitatud taotluse ning põhjaliku andmekoosseisu kirjelduse eest! Olen taotluse läbi vaadanud ning taotluse hindamisel ilmnes siiski vajadus täpsustada esitatud andmete mahtu ja põhjendatust, eelkõige seoses andmete minimeerimise põhimõttega ning uuringu eesmärgi ja taotletud andmekoosseisu vastavusega. Taotluses on mitmes kohas viidatud andmete kasutamisele laiemate analüüside jaoks (nt raviteekondade analüüs, metodoloogiate arendamine, sh tehisintellekti meetodid, ning majandusliku ja sotsiaalse mõju hindamine), mis tulenevad EST‑Health‑30 uuringu üldistest eesmärkidest. Samas ei nähtu esitatud taotlusest piisava selgusega, kas ja millises ulatuses nimetatud analüüsid kuuluvad käesoleva konkreetse uuringu raamesse, mille põhieesmärk on kirjeldada vähivormide levimust, esinemissagedust ja elulemust. Lisaks juhin tähelepanu, et mitmete andmeväljade puhul on nende vajalikkust põhjendatud üldise väitega, et need on vajalikud tervisesündmuste ja nende järjekorra tuvastamiseks või raviteekondade analüüsimiseks. Esitatud põhjendused on valdavalt üldised ning ei võimalda hinnata konkreetsete andmeväljade vältimatut vajalikkust käesoleva uuringu eesmärkide saavutamiseks. Eriti puudutab see andmekategooriaid, kus on märgitud vajadus kasutada „kõiki andmeid“ (nt anamnees, objektiivne leid, vabatekstilised märkused jms), kuid ei ole eristatud, millised andmed on otseselt vajalikud ning millised üksnes potentsiaalselt kasulikud. Samuti esineb vastuolu seoses tehisintellekti kasutamisega: taotluses on märgitud, et käesolevas uuringus tehisintellekti meetodeid ei kasutata, kuid samas on andmete vajadust osaliselt põhjendatud EST‑Health‑30 raames kavandatava metoodika arendamisega, mis hõlmab ka tehisintellekti meetodeid. Sellest tulenevalt ei ole üheselt arusaadav, kas ja millises ulatuses kasutatakse käesoleva taotluse alusel töödeldavaid andmeid ka metoodikate arendamise eesmärgil. Täiendavalt märgin, et taotluses sisalduv üldine kinnitus andmete kasutamise kohta üksnes uuringu eesmärkide saavutamiseks vajalikus mahus ei ole täielikult kooskõlas esitatud andmekoosseisuga, kus mitmete andmekategooriate puhul on taotletud „kõiki andmeid“ ilma konkreetse väljade tasandi põhjenduseta. Eeltoodust tulenevalt palun teil esitada täpsustused järgmistes küsimustes: * Millised taotluses viidatud analüüsid (nt raviteekonnad, majanduslik mõju, sotsiaalne staatus, metoodikate arendamine) kuuluvad käesoleva konkreetse uuringu ulatusse ning millised mitte? * Millised konkreetsed andmeväljad on uuringu põhieesmärkide (vähi levimus, esinemissagedus ja elulemus) saavutamiseks vältimatult vajalikud, eriti nende andmekategooriate osas, kus on märgitud „kõik andmed“? * Kas ja millises ulatuses on võimalik piirata andmekoosseisu, välistades andmed, millel puudub otsene seos uuringu põhieesmärkidega (nt vabatekstilised väljad, täielik anamnees, muu mitteseotud terviseinfo)? * Kas kulutõhususe, sotsiaalse seisundi või muude täiendavate analüüside teostamine kuulub käesoleva uuringu eesmärkide hulka? * Kas ja millises ulatuses kasutatakse käesoleva uuringu raames andmeid metoodikate arendamiseks, sh tehisintellekti või masinõppe meetodite rakendamiseks? * Milliste kriteeriumide alusel on hinnatud andmete vajalikkust ning kuidas on tagatud, et töödeldakse üksnes uuringu eesmärgi saavutamiseks vältimatult vajalikku andmestikku? Palun esitada täpsustused ja vajadusel korrigeeritud andmekoosseis, mis vastab andmete minimeerimise põhimõttele. Lugupidamisega Carmen Mäe Nõunik Sotsiaalministeerium Suur-Ameerika 1 10122 Tallinn www.sm.ee <http://www.sm.ee> From: Kaia Kask <[email protected] <mailto:[email protected]> > Sent: Friday, May 15, 2026 4:43 PM To: Info - SOM <[email protected] <mailto:[email protected]> > Cc: Raivo Kolde <[email protected] <mailto:[email protected]> >; Marek Oja <[email protected] <mailto:[email protected]> > Subject: Loa küsimine uuringule “Elanikkonnas aja jooksul toimunud muutused erinevate vähivormide levimuses, esinemissageduses ja patsientide ellujäämises - uuring OHDSI andmevõrgustikul” Tähelepanu! Tegemist on välisvõrgust saabunud kirjaga. Tundmatu saatja korral palume linke ja faile mitte avada. Lp Sotsiaalministeerium Eesti bioeetika ja inimuuringute nõukogu (EBIN) on andnud loa uuringu “Elanikkonnas aja jooksul toimunud muutused erinevate vähivormide levimuses, esinemissageduses ja patsientide ellujäämises - uuring OHDSI andmevõrgustikul” jaoks (24. märts 2026 nr 1.1-12/837). Uuringu eesmärk on analüüsida Eesti elanikkonna erinevate vähivormide (va mittemelanoomne nahavähk) levimust ja esinemissagedust ning hinnata patsientide lühi- ja pikaajalist ellujäämist. Uuring toimub OHDSI võrgustikus viies riigis, sh Eestis. Kasutatakse EST-Health-30 andmeid, et kirjeldada erinevate vähivormide (va mittemelanoomne nahavähk) levimust ja esinemissagedust ning hinnata patsientide lühi- ja pikaajalist ellujäämist Eestis 2012–2026. EST-Health-30 andmestiku aluseks on 30% juhuvalim Eestis alates 2012. Aastast tervishoiuteenuseid tarbinud Eesti elanikest (andmesubjektid), mille suurus on hetkel ~488 000 inimest. Uuringu läbiviimiseks kasutatakse EST-Health-30 uuringu andmestikku. Vastavalt EST-Health-30 uuringu eetikakomitee taotlusele (1.1-12/2793) ja Sotsiaalministeeriumi loale (1.5-20/2833-2) on meil lisaks eetikakomitee loale kohustus küsida EST-Health-30 uuringu puhul täiendavate uurimisküsimuste uurimiseks luba ka Sotsiaalministeeriumilt. Käesolevaga palume Sotsiaalministeeriumilt luba ülaltoodud uuringu läbiviimiseks. Eetikakomitee otsus, mis sisaldab m.h uuringu taotlust, on lisatud käesolevale kirjale (digidoc konteiner manuses). Uuringu läbiviimine ei vaja täiendavaid andmeväljastusi ega täiendavat EBIN luba, vaid justnimelt andmeomaniku ehk Sotsiaalministeeriumi luba. Kinnitame, et andmete taotlemisel oleme lähtunud eesmärgipärasuse ja võimalikult väheste andmete kogumise põhimõtetest. Tegemist on rahvusvahelise uuringuga, mille raames viime läbi analüüsi standardiseeritud uuringukoodiga: meile antakse uuringukood, mille käivitame oma andmetel ning valideerime tulemuste vastavust protokollile. Käesoleva uuringu raames väljastatakse ainult protokollis ette nähtud informatsiooni protokollis defineeritud uuritavate kohta. Vastavalt uuringu protokollile on vajalikud järgmised andmeallikad: * Epikriisid (ambulatoorne, statsionaarne, kodu- ja iseseisva statsionaarse õenduse, koduõenduse ning päevaravi epikriisid), saatekirjad ja saatekirja vastused sisaldavad kliiniliselt detailset infot diagnooside, vähi staadiumi ja leviku (nt TNM-klassifikatsiooni), analüüside ja protseduuride tulemuste ning raviskeemide kohta. See info on oluline nii uuringugruppide defineerimisel kui ka kirjeldamisel. * Retseptide ja immuniseerimise andmeid kasutatakse uuritavate ravimikasutuse kirjeldamiseks ja uuringugruppide võrdlemiseks. * Vähiregistri andmed on vajalikud vähi diagnoosi tuvastamiseks. * Surma põhjuste registri andmed on vajalikud surma fakti ja põhjuse tuvastamiseks. Uurijate nimel Kaia Kask, PhD Juhiabi/Assistant Andmeteaduse õppetool/Chair of Data Science Arvutiteaduse instituut/ Institute of Computer Science Tartu Ülikool/ University of Tartu Narva mnt 18 51009 Tartu ESTONIA RESEARCH PROTOCOL: Time trends in prevalence, incidence and survival of cancer in the OHDSI Network Version: 3.0 Date: 19th November 2024 Acknowledgement: The analysis is based in part on work from the Observational Health Sciences and Informatics collaborative. OHDSI (http://ohdsi.org) is a multi-stakeholder, interdisciplinary collaborative to create open-source solutions that bring out the value of observational health data through large-scale analytics. 1 Table of contents 1 List of Abbreviations ....................................................................................................... 3 2 Responsible Parties ......................................................................................................... 3 2.1 Investigators .............................................................................................................. 3 3 Abstract ............................................................................................................................ 4 5 Milestones ........................................................................................................................ 5 6 Rationale and Background .............................................................................................. 6 7 Study Objectives .............................................................................................................. 6 8 Research Methods ........................................................................................................... 7 8.1 Study Design and Study Period................................................................................ 7 8.2 Data Sources ............................................................................................................. 7 8.3 Study Population ....................................................................................................... 7 8.4 Cancer prevalence, incidence and survival ............................................................. 8 8.5 Stratifications............................................................................................................. 8 8.6 Other variables .......................................................................................................... 8 8.7 Data quality assessment ........................................................................................... 9 8.8 Analysis...................................................................................................................... 9 9 Sample Size and Study Power ...................................................................................... 11 10 Strengths and Limitations ........................................................................................... 11 10.1 Strengths................................................................................................................ 11 10.2 Limitations ............................................................................................................. 11 11 Protection of Human Subjects .................................................................................... 11 13 Plans for Disseminating and Communicating Study Results ................................... 12 References ........................................................................................................................ 12 Appendix 1. Preliminary code list of cancer ................................................................... 14 Appendix 2. Preliminary code list of conditions ............................................................. 14 Appendix 3. Preliminary code list of medications for large-scale characterizaton ...... 18 2 1 List of Abbreviations AAPC Average Annual Percent Change APC Annual Percent Change CDM Common Data Model COPD Chronic Obstructive Pulmonary Disease EHDEN European Health Data and Evidence Network HIV Human Immunodeficiency Virus HPV Human Papillomavirus Infection IRB Institutional Review Board IR Incidence Rate IRR Incidence Rate Ratio OMOP Observational Medical Outcomes Partnership OHDSI Observational Health Data Science and Informatics PR Prevalence Rate SIDIAP Information System for Reseach in Primary Care SNOMED Systematized Nomenclature of Medicine WHO World Health Organization 2 Responsible Parties 2.1 Investigators Investigator/Author Institution/Affiliation Talita Duarte-Salles* Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands 3 Edward Burn University of Oxford Asieh Golozar Odysseus Data Services, Inc, Cambridge, MA USA, OHDSI Center at the Northeastern University, Boston, MA USA Irene López Sánchez Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain Laura Pérez Crespo Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain Agustina Giuliodori Picco Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain Berta Raventós Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands Anna Palomar Cros Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain Anton Barchuk Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands OHDSI Oncology WG *Principal Investigator Authorship in scientific manuscripts will follow ICMJE authorship criteria (http://www.icmje.org/recommendations/browse/roles-and-responsibilities/defining-the-role- of-authors-and-contributors.html). The Responsible Parties involved in this protocol take accountability for the overarching protocol, package development, providing assistance to sites running the analysis and ensuring site-specific governance is adhered to in all publications generated from this protocol. 3 Abstract Objectives: The main aim of this project is to estimate time trends in prevalence and incidence rates, and short- and long-term survival of site-specific cancers in the OHDSI network. Design: This study will be a multinational observational cohort study and will be conducted using a network of large real world data sources that have been mapped to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). 4 Setting: Population-based, electronic health records, claims and registry data from primary and secondary care. Participants: Individuals with no prior history of cancer (for incidence and survival analyses only), and who have been on the database for at least 1 year before study entry. Outcomes: Prevalent and incident cancer diagnoses and overall as well as 1-, 5-, and 10- year survival of site-specific cancers. Data analyses: The OHDSI Cohort Diagnostics package will be used to assess the fitness of use of cancer data on each database. We will calculate prevalence (PR) and incidence rates (IR) with 95% confidence intervals (95%CI) for each year and study period by dividing the number of ever and first recorded cases of cancer, respectively, by 1,000 person-years of follow-up, overall and stratified by demographics and relevant comorbidities. The overall and 1-, 5-, and -10-year survival rates will be calculated as the percentage of people who have been diagnosed with cancer and are still alive during the study period as well as one or five years after diagnosis, respectively, per year and stratified by pre-defined subgroups. To assess the incidence trend over time, we will calculate the IRs in 5 year periods and then calculate the incidence rate ratios (IRRs) and their corresponding 95%CI to analyze the differences in incidence between the defined time periods. 4 Amendments and Updates Number Date Section of Amendment or Reason study update protocol Version 2.0 13/06/2023 All Update Update to incorporate team comments and suggestions Version 3.0 19/11/2024 All Amendment Amendment to incorporate a new objective to the study 5 Milestones Milestone Planned date Final version of study protocol 20/11/2024 Create phenotype definitions 30/11/2024 Study package release 30/01/2025 5 Data analyses 01/03/2025 Writing of scientific paper(s) 01/06/2025 6 Rationale and Background Cancer is currently a leading cause of morbidity and mortality worldwide. Estimates from the World Health Organization (WHO) in 2019 showed that cancer is the first or second leading cause of death before the age of 70 years in 112 of 183 countries and ranks third or fourth in a further 23 countries.1 The burden of cancer incidence and mortality is rapidly growing worldwide, reflecting both aging and growth of the population as well as changes in the prevalence and distribution of the main risk factors for cancer. 2,3 Worldwide, female breast cancer is the most commonly diagnosed cancer, followed by lung, colorectal, prostate, and stomach cancers, while lung cancer is the leading cause of cancer death, followed by colorectal, liver, stomach, and female breast cancers.4 The continuous surveillance and monitoring of trends in cancer incidence and survival are needed for the development, implementation and evaluation of health policies aiming to reduce the burden of disease. Multiple factors are attributed to cancer incidence and cancer survival. Some of the main cancer risk factors include age, race, smoking, alcohol consumption, cardiometabolic conditions (e.g.: cardiovascular diseases, obesity, hypertension, type 2 diabetes mellitus), infectious diseases (e.g.: hepatitis B/C, human immunodeficiency virus (HIV)), among others. 5,6 Survival rates are known to vary by cancer type as well as being influenced by cancer stage at detection and treatment. It can also depend on an individual’s health, presence of comorbidities and other tumour-related factors. Recent advances in screening programmes and treatments have improved the survival rates for some cancer sites. 7 However, important disparities still exist in healthcare systems and countries. 7,8 Understanding the complexities of individual factors related to cancer incidence and survival, whilst investigating the population subgroups that are more prone to be at risk, is important for being able to plan future population-based interventions. 9 Real world data from healthcare services promise to vastly expand clinical research, providing data for large-scale studies that would not be feasible with traditional research data collection methods. The Observational Health Data Sciences and Informatics (OHDSI; www.ohdsi.org) is a multi-stakeholder and interdisciplinary international network that generates open science through large-scale analytics of real world data. 10 This data network offers a unique opportunity to study cancer incidence and survival as it includes more than 100 healthcare databases from 20 countries including over 1.5 billion individual records with longitudinal data. OHDSI analyses are based around the use of the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), which is used to standardize otherwise disparate datasets. In this project, we will estimate time trends in prevalence, incidence rates and survival of cancers in the OHDSI network. 7 Study Objectives 6 The main aim of this project is to estimate time trends in prevalence, incidence rates and short- and long-term survival of site-specific cancers in the OHDSI network. The specific study objectives include: 1) To estimate prevalence rates of site-specific cancers by calendar year, age, sex, and comorbidities. 2) To estimate incidence rates of site-specific cancers by calendar year, age, sex, and comorbidities. 3) To describe demographic, clinical characteristics and medications of individuals with cancer at the time of diagnosis. 4) To estimate overall, short- and long-term survival of site-specific cancers by calendar year, age, sex, and comorbidities. 8 Research Methods 8.1 Study Design and Study Period This study will be a multinational observational cohort study. The study period will start on 1st January 2000 or one year after the earliest date of available data in each database and span up to the 31st December 2024 (or the last date of available data in each database). 8.2 Data Sources The study will be conducted using a network of large real world data sources that have been mapped to the OMOP Common Data Model in collaboration with the OHDSI and European Health Data and Evidence Network (EHDEN) initiatives. The OMOP Common Data Model (https://github.com/OHDSI/CommonDataModel/wiki) includes a standard representation of health care experiences (such as information related to drug utilization and condition occurrence), as well as common vocabularies for coding clinical concepts, and enables consistent application of analyses across multiple disparate data sources.10 The present study will be conducted in multiple databases in the OHDSI network willing to participate. Databases that have already committed to participate include: the Information System for Research in Primary Care (SIDIAP; Spain), the Integrated Primary Care Information (IPCI, The Netherlands), The Netherlands Cancer Registry (IKNL, The Netherlands), the Clinical Practice Research Datalink (CPRD, UK). 8.3 Study Population All individuals registered in each of the data sources will be eligible for inclusion in the study. However, study participants will be required to have a year of prior history observed in the database before contributing observation time. Participants with prior history of a diagnosis of cancer (any, excluding non-melanoma skin cancer) any time prior to their index date will be excluded (for incidence and survival estimation). For incidence rates, study participants will begin contributing person time on the respective date of the latest of the following: 1) study start date (1st January 2000 or the earliest date of available data in in each of the data sources), 2) date at which they have sufficient prior 7 history (defined as 365 days), 3) date at which they reach a minimum age (where age strata are being considered). Participants will stop contributing person time at the earliest date of the following: 1) study end date (end of available data in each of the data sources), 2) date at which their observation period ends, 3) the last day in which they have the maximum age (where age strata are being considered). Where there are multiple age strata, study participants will contribute to each strata while they satisfy the conditions of that strata (i.e. when they reach the limit of one age strata they will begin contributing to the next). 8.4 Cancer prevalence, incidence and survival Cancer prevalence and incidence will be defined as the occurrence for ever or the first time in the person’s history, respectively. Conditions in the OMOP CDM use the Systematized Nomenclature of Medicine (SNOMED) as the standard vocabulary for diagnosis codes. The complete list of codes used to define each cancer site are available in Appendix 1. For those databases with available data on date and/or cause of death, overall, 1-, 5-, and 10-year survival will be estimated. We will also report overall mortality, cancer-specific mortality, and other-cause mortality when possible. 8.5 Stratifications Each target cohort will be analysed in full and stratified on sex (male, female), age (0-9; 10- 19; 20-29; 30-39; 40-49; 50-59; 60-69; 70-79; 80-89; 90-99; 100 and over), smoking status, asthma, type 2 diabetes mellitus, hypertension, cardiovascular diseases, chronic obstructive pulmonary disease (COPD), obesity, human immunodeficiency virus (HIV), human papillomavirus infection (HPV), viral hepatitis, chronic kidney disease, autoimmune conditions, and depression. These stratifications will only be performed in those databases with available information on the variables listed above. The definition of these conditions can be found in Appendix 2. Female breast cancer will be stratified by menopausal status which will be defined based on age as pre- (less than 50 years) and post-menopausal (50 years or above). All stratum are pending meeting minimum reportable cell counts (as specified by data owners). 8.6 Other variables Large-scale patient-level characterization will be conducted at the time of diagnosis. Medical history and medications use history will be assessed at any time prior cancer diagnosis and up to 366 days before index date, for 365 days to 31 days before index date, for 30 to 1 day before index date and at index date. We will also report medication use for 1 to 90 days post index-date. A list of pre-specified comorbidities and medications will be described. These will include: • Medical History: Anemia, Anxiety, Asthma, Arterial fibrillation, Cerebrovascular disease, Chronic kidney disease, Chronic liver disease, Chronic obstructive pulmonary disease (COPD), Coronary arteriosclerosis, Chron’s Disease, Dementia, Depressive disorder, Gastro-oesophageal reflux disease (GERD), GI-Bleeding, Heart failure, Human 8  Immunodeficiency Virus (HIV), Human papillomavirus infection (HPV), Hyperlipidaemia, Hypertension, Hypothyroidism, Ischemic Heart Disease, Myocardial Infraction, Non- alcoholic Fatty-acid disease (NAFLD), Obesity, Osteoarthritis, Osteoporosis, Pancreatitis, Peripheral vascular disease, Pneumonia, Psoriasis, Pulmonary embolism, Renal impairment, Stroke, Type 1 Diabetes, Type 2 Diabetes, Ulcerative colitis, UTI, Venous thromboembolism, Viral Hepatitis • Medications: Agents acting on the renin-angiotensin system, Antibacterials for systemic use, Antidepressants, Antiemetics, Antiepileptics, Anti-inflammatory and anti-rheumatic drugs, Antineoplastics, Anti-psoriatic, Antithrombotic agents, Antivirals HCV, antivirals HIV, Beta-blocking agents, Calcium channel blockers, Contraceptives, Diuretics, Drugs for acid related disorders, Drugs for obstructive airway diseases, Drugs used in diabetes, Immunosuppressants, Iron preparations, Lipid-modifying agents, Opioids, Psycholeptics and Psychostimulants. 8.7 Data quality assessment Overall data quality of the OHDSI Network databases are assessed by each data partner using the data quality dashboard after data mapping. 10 We will assess the created phenotypes including cancer events using a comprehensive cohort characterization tool, the OHDSI’s CohortDiagnostic package (https://ohdsi.github.io/CohortDiagnostics/). For any cohort and data source mapped to OMOP CDM, this package systematically generates incidence rates (stratified by age, gender, calendar year, and database), cohort characteristics (all comorbidities, drug use, procedures, health utilization) and the actual codes found in the data triggering the various rules in the cohort definitions. The CohortDiagnostics package works in two steps: 1) Generate the utilization results and diagnostics against a data source and 2) Explore the generated utilization and diagnostics in a user-friendly graphical interface R-Shiny app. These diagnostics provide a consistent methodology to evaluate cohort definitions/phenotype algorithms across a variety of observational databases, allowing to compare the overlap between alternative definitions. This can allow researchers and stakeholders to understand the heterogeneity of source coding for exposures and health outcomes as well as the impact of various inclusion criteria on overall cohort counts. Data quality of cancer diagnoses will be assessed by comparing the calculated incidence rates of events in a specific database with the incidence reported in national/regional cancer registries. 8.8 Analysis All analyses will be performed using code developed for the OHDSI Methods library and executed by data owners using R and stratified by database. 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. The code for this study will be made publicly available at https://github.com/ohdsi-studies/. To answer the study objectives, a diagnostic package, built off the OHDSI Cohort Diagnostics (https://ohdsi.github.io/CohortDiagnostics/) library, will be included in the base package as a preliminary step to assess the fitness of use of phenotypes on each database. 9 If a database passes cohort diagnostics (as described in section 8.6), the full study package will be executed. To address objectives 1 and 2, we will first summarize the socio-demographics characteristics and overall follow-up of individuals included in the study, with counts and percentages for categorical variables and median and interquartile ranges (IQR) for continuous variables. Incidence rates (IR) with 95% CI will be calculated using the R package IncidencePrevalence (https://cran.r- project.org/web/packages/IncidencePrevalence/index.html). IR will be computed for each year and study period by dividing first-recorded diagnoses by person-years at risk for the whole population or pre-defined subgroups (section 8.5). The first recorded for each outcome category will be included as an incident episode. Each outcome will be assessed separately and therefore, individuals will be able to contribute to one the incidence counts for more than one outcome. To exclude prevalent cases from incidence calculations, individuals diagnosed with any cancer will not be considered as eligible incident cases in future cohorts after the date of their first diagnosis. To assess the incidence trend over time, we will calculate the IRs in 5 year periods and then calculated the incidence rate ratios (IRRs) and their corresponding 95% confidence intervals (95% CIs) to analyze the differences in incidence between the defined time periods (to be defined depending on data availability). We will conduct a large-scale patient-level characterization to address objective 3. For this part of the analysis, we will use the package CohortCharacteristics (https://cran.r- project.org/web/packages/CohortCharacteristics/index.html). Essentially, we will use the function summariseLargeScaleCharacteristics, which is used to summarize the large-scale characteristics of a cohort table. Medical history and medications will be assessed for any time prior cancer diagnosis and up to 366 days before index date, for 365 days to 31 days before index date, for 30 to 1 day before index date and at index date (see appendix 2 and 3). Medications will also be assessed 1 to 90 days post-index date. We will also compare these characteristics in our cohorts of interest to the proportion of these diseases in a random subsample of SIDIAP. To address objective 4, the 1-, 5-, and 10-year survival rates will be calculated as the percentage of people who have been diagnosed with cancer and are still alive one, five, or ten years after diagnosis, respectively, per year, overall and also stratified by pre-defined subgroups. Survival will be calculated using data on time at risk of death from any cause and the Kaplan-Meier method. Results will be reported as plots of the estimated survival curves as well as the estimated probability of survival at years 1, 5, and 10. This analysis will be conducted only for databases that collect data on mortality. We will also perform Kaplan- Meier analyses to assess time from cancer diagnosis to cancer-specific survival, and other cause-mortality-free survival. KM curves will be further stratified by study periods (to be defined depending on data availability). We will use joinpoint modeling to examine the overall trends in cancer incidence and survival (to see if this can be implemented using the OMOP CDM).11 The model involves fitting a series of joined straight lines on a logarithmic scale to the trends in the annual rates. The 10 direction and magnitude of the resulting trends are described by the annual percent change (APC), the linear slope, across each line segment between two joinpoints. The average annual percent change (AAPC) summarizes the overall trend over time using a weighted average of the APCs within the specified period. The default maximum number of joinpoints allowed will be set at 4. In describing the change, the term increase or the term decrease will be used when the APC or the AAPC is statistically significant (p< 0.05); otherwise, the term stable was used.12 9 Sample Size and Study Power Since this study will be undertaken using population-based data, we will include all patients meeting the eligibility criteria described above. No prior sample calculation will be performed but we will do feasibility counts before running the stratified analyses. 10 Strengths and Limitations 10.1 Strengths The main strengths of this study are the sample size and the real-world nature of the data. Our study will provide data from broadly representative and heterogeneous settings and geographical regions. Another strength of this study is its longitudinal design including prospectively collected data spanning up to 2021. In addition, the use of pre-existing standardised analytics and tools across the OHDSI network will facilitate the development of the study across different data sources. 10.2 Limitations One potential limitation of the present study is misclassification of cancer diagnosis and/or date of death. However, cancer diagnosis has been already validated in some of the participating databases, and additionally, we will use the OHDSI Cohort Diagnostics tool to assess quality of cancer incidence in the participating databases and only include high quality data. The lack and/or missingness of information on relevant stratification factors; such as race, smoking habits, alcohol consumption, stage of tumor at diagnosis or cancer treatment; might also be a limitation of the study. Although we already anticipate that stratification for these factors will not be possible in all participating databases, we will be able to do it in a few databases that are part of the OHDSI network. 11 Protection of Human Subjects The study uses only de-identified data. Confidentiality of patient records will be maintained at all times. All data partners executing the study within their data sources will have received institutional review board (IRB) approval or waiver for participation in accordance with their institutional governance prior to execution. Data custodians will remain in full control of executing the analysis and packaging results. The study will be executed across a federated and distributed data network, where analysis code is sent to participating data partners and only aggregate summary statistics are returned, with no sharing of patient-level data between organizations. There will be no transmission of patient-level data at any time during 11 these analyses. Study packages will contain minimum cell count parameters to obscure any cells which fall below allowable reportable limits. 13 Plans for Disseminating and Communicating Study Results Open science aims to make scientific research, including its data process and software, and its dissemination, through publication and presentation, accessible to all levels of an inquiring society, amateur or professional and is a governing principle of the present study. Open science delivers reproducible, transparent and reliable evidence. All aspects of study (except private patient data) will be open and we will actively encourage other interested researchers, clinicians and patients to participate. We will share the study protocol with the OHDSI community for feedback. This protocol will link to open source code for all steps to generating the study results, which will also be made publicly available at data.ohdsi.org. We will deliver presentations at scientific venues including the annual OHDSI symposium. We will also prepare scientific publications for international scientific peer-review journals. We will publish the results of this study following the International Committee of Medical Journal Editors (ICMJE) authorship guidelines, and will report the results following the appropriate Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist. The main findings of this project will be shared with the general public through social media channels. With dedicated support from the OHDSI communications specialist, we will deliver regular press releases at key project stages, distributed via the extensive media networks of the study partners. References 1. International Agency for Research on Cancer, GLOBOCAN 2018 accessed via Global Cancer Observator. Accessed March 21, 2023. 2. World Health Organization (WHO). Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. WHO; 2020. Accessed January 10, 2022. who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe- leading-causes-of-death. 3. Gersten, O. & Barbieri, M. Evaluation of the Cancer Transition Theory in the US, Select European Nations, and Japan by Investigating Mortality of Infectious- and Noninfectious-Related Cancers, 1950-2018. JAMA Netw. Open 4, e215322 (2021). 4. Gersten, O. & Wilmoth, J. R. The Cancer Transition in Japan since 1951. Demogr. Res. 7, 271–306 (2002). 5. Sung, H. et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA. Cancer J. Clin. 71, 209–249 (2021). 6. Stein, C. J. & Colditz, G. A. Modifiable risk factors for cancer. Br. J. Cancer 90, 299– 303 (2004). 12 7. Wu, S., Powers, S., Zhu, W. & Hannun, Y. A. Substantial contribution of extrinsic risk factors to cancer development. Nature 529, 43–47 (2016). 8. Arnold, M. et al. Progress in cancer survival, mortality, and incidence in seven high- income countries 1995–2014 (ICBP SURVMARK-2): a population-based study. Lancet Oncol. 20, 1493–1505 (2019). 9. Allemani, C. et al. Global surveillance of trends in cancer survival 2000–14 (CONCORD-3): analysis of individual records for 37 513 025 patients diagnosed with one of 18 cancers from 322 population-based registries in 71 countries. The Lancet 391, 1023–1075 (2018). 10. Srinivasan, S. et al. Small is essential: importance of subpopulation research in cancer control. Am. J. Public Health 105 Suppl 3, S371-373 (2015). 11. Observational Health Data Sciences and Informatics. The Book of OHDSI [Internet]. 2019. Accessed January 10, 2022. https://ohdsi.github.io/TheBookOfOhdsi/. 12. Kim, H. J., Fay, M. P., Feuer, E. J. & Midthune, D. N. Permutation tests for joinpoint regression with applications to cancer rates. Stat. Med. 19, 335–351 (2000). 13. Culp, M. B., Soerjomataram, I., Efstathiou, J. A., Bray, F. & Jemal, A. Recent Global Patterns in Prostate Cancer Incidence and Mortality Rates. Eur. Urol. 77, 38–52 (2020). 13 Appendix 1. Preliminary code list of cancer Attached as a standalone document Appendix 2. Preliminary code list of conditions Conditions Concept id (Included) Concept id (Excluded) Anemia 439777 434701, 43022052 Anxiety 441542 Asthma 317009, 4235703, 4279553 Autoimmune condition 4137275, 81893, 40484648, 201254, 4063582, 134442, 257628, 254443, 4035611, 4083556, 438688, 45772123, 4262578, 2107559, 2107558, 2107560, 80809, 4145240, 46273478, 81931, 140168, 4102493, 46270482, 432295, 2108721, 4105026, 4107913, 432893, 76685, 374919, 4105005, 255304, 441928, 4331739, 46272236, 46274082, 46269999, 135215, 4058299, 4232076, 46269952, 46273369, 435216, 2107561, 2107572, 46273442, 4334806, 4297650, 201606, 46273477, 46269889, 194992, 4116142, 443394, 75614 Atrial fibrillation 313217 Cardiovascular disease 4329847, 316139, 432923, 4148906, 314666, 315295, 42535425, 43530727, 43530674, 443454, 437894 439847, 376713, 441874, 375557, 372924, 4132309, 4317150, 442289, 321042, 4048809 Cerebrovascular disease 381591 Chronic kidney disease 194385, 46271022, 192279, 4263367, 45769152, 195289, 261071, 201313, 4103224, 193253, 195737, 43530912, 195314, 192359, 45768812 37116834, 195014, 197930, 197320, 4066005 Chronic liver disease 4212540 COPD 255573, 258780 Coronary arteriosclerosis 317576 Chron’s disease 201606, 46269889, 46269999 Dementia 37312036, 37312035, 4041685, 37116464, 37017549, 37312031, 37312030, 35608576, 4244346, 377788, 4092747, 4182210, 37311665, 372610, 37017247 4043378, 45765480, 45765477, 37311890, 37312577, 4059191 Depressive disorder 440383 438727, 436665, 40481798, 435520, 4224940 14 GERD 318800 GI-Bleeding 192671 Heart Failure 316139 315295 HIV 4276586, 44783356, 439727 4013105, 432554 HPV 40480043, 4084948, 4116193, 4080771, 4294441, 4291601, 619210, 760929, 4219870, 4266804, 4269876, 4084816, 44810559, 4304732, 4164483, 4129543, 4175989, 198075, 4080330, 4147672, 4078931, 4291605, 37116426, 4347555, 4177636, 4345817, 4148102, 4345473, 36713662, 37206940, 45757380, 36717114, 45757381, 441788, 4333885, 35610330, 36715556, 35610522, 4270602, 37109025, 42535207, 4084817, 4080770, 36716153, 4291602, 4306683, 4300215, 4081909, 4142828, 4185025, 4182586, 4200132, 3657814, 3657815, 4080331, 760906, 760907, 36685421, 44810378, 4302049, 36715819, 4345474, 4145196, 4028324, 137785, 140641, 40490394, 40491348, 40490302, 40489357, 4291600, 4294439, 4300214, 4294440, 4291603, 4130346, 36716496, 4296065, 3656108, 4111926, 4289145 Hyperlipidemia 432867 Hypertension 316866, 4322024, 42709887 4167493 Hyperthyroidism 140673 Ischemic heart disease 4185932 Myocardial infarction 4329847 314666 NAFLD 4026131, 40484532 Obesity 4060985, 4256640, 45766204, 433736, 4176962, 4081038 Osteoarthritis 80180 Osteoporosis 80502 Pancreatitis 4192640 Peripheral vascular 321052 disease Pneumonia 4050869, 255848 45770911, 4001167, 4049965, 36712839, 252552 Psoriasis 140168 Pulmonary embolism 440417 Renal impairment 4030518 15 Rheumatoid arthritis 80809 Stroke 42535426, 4048784, 4045735, 4031045, 761110, 372924, 4110189, 443454, 762951, 765515, 43530683, 762933, 762937, 4111714, 4108356, 45772786, 4110190, 762935, 763015, 46273649, 35610084, 46270031, 762934, 43531607, 35610085, 46270381, 4110192, 45767658, 44782773, 46270380, 37110678, 37110679, 381316, 35609033, 4046362, 4131383, 4046237, 4119140, 4043731, 439847, 4141405, 37116473, 4144154, 4111709, 4077086, 4046359, 4319146, 4043732, 4146185, 36717605, 43530727, 4148906, 43530728, 432923, 4108952, 4111708, 4142739, 4046358, 36684840 Type 1 Diabetes 36715571, 45769891, 37016767, 45763585, 4128019, 4225656, 45773688, 45773576, 45771075, 45769902, 45769903, 45769837, 35626765, 45769832, 45757674, 435216, 42538169, 42535539, 377821, 37016353, 42689695, 45769904, 43531565, 4221344, 4223303, 37017429, 765533, 37016348, 45757432, 443592, 201531, 42535540, 45757393, 45771067, 45769876, 4228112, 45757362, 3046418, 4047906, 4102018, 45757073, 439770, 4224254, 4143857, 35626069, 45757535, 37016179, 43530660, 37016180, 4225055, 45769829, 45769830, 37312218, 45768456, 45763583, 45769834, 36713094, 318712, 37018566, 4222687, 4222553, 37017431, 4063042, 43531008, 43531009, 45763584, 45757604, 200687, 45757266, 4227210, 45770986, 45771533, 45773567, 45769833, 765373, 46269764, 4143689, 45769873, 201254, 40484648, 40484649, 4152858, 443412, 4099214, 45766051, 45757507, 45769892, 37312201, 45770902, 37312200, 45757074, 4224709, 765650. Type 2 Diabetes 4321756, 36717156, 43531588, 45769888, 4196141, 37016768, 609103, 609106, 609114, 609117, 16  602345, 45763582, 40483315, 4221495, 43531578, 43531559, 45769901, 43531566, 43531653, 43531577, 43531562, 37309630, 45769894, 43531616, 45757474, 36684827, 37018912, 443732, 43531597, 443733, 376065, 43531564, 45757280, 45769906, 4177050, 4223463, 43530690, 4222876, 37018728, 45772019, 604741, 37016349, 45770880, 201530, 4215719, 45757392, 45771064, 45757447, 45757446, 45757445, 45757444, 45757363, 45772060, 36714116, 608884, 45769875, 4130162, 45757075, 765375, 45771072, 443734, 4228443, 4140466, 45770830, 35626070, 45769905, 45757435, 609099, 609101, 43531651, 45770881, 609104, 609105, 4222415, 37162626, 45769828, 760989, 761063, 43531563, 45757450, 37312203, 37312202, 45770883, 37016354, 43530656, 609096, 609095, 45769836, 443729, 43530689, 45757278, 4221487, 4223739, 37017432, 3192767, 3191208, 3194332, 4063043, 43530685, 609116, 609119, 45770831, 45757499, 443731, 45770928, 4226121, 45769872, 45769835, 761053, 609109, 609112, 36712670, 46274058, 4142579, 45770832, 45773064, 201826, 45757508, 4230254, 4304377, 40485020, 4193704, 4200875, 4099651, 45769890, 37312205, 36712686, 45757277, 37312204, 36712687, 45757449, 43531608, 4099216, 761062 Ulcerative colitis 81893 UTI 81902 Venous thromboembolism 762047, 762148, 761444, 35616028, 35615035, 761416, 35615031, 43531681, 35616027, 35615034, 761415, 35615030, 44782746, 44782751, 762008, 760875, 765155, 762017, 762417, 762020, 765546, 762004, 44782742, 44782747, 762015, 765541, 44782748, 44782752, 762009, 760876, 765540, 17  765922, 762418, 765537, 44782767, 46270071, 762022, 44782743, 762021, 762010, 760877, 762013, 762018, 762419, 762005, 44782745, 44782744, 762026, 765156, 44782421, 764016, 44782766, 4120091, 45768439, 45768888, 762048, 45757410, 762049, 36712892, 44782762, 37109253, 40478951, 4042396, 4046884, 4133004, 4181315, 45773536, 763942, 761980, 443537, 4133975, 40480555, 4322565, 763941, 761928, 4207899, 4028057, 435565, 40481089, 4309039, 4119760, 762808, 40480461, 4124856, 4096099, 440738, 4281689, 4284538, 4309333, 4108681, 46285905, 440417, 37109911, 37016922, 43530605, 254662, 4253796, 4121618, 4119610, 46271900, 4236271, 36713113, 35615055, 4033521, 4119607, 4055089, 4327889, 320741, 439838, 4230403, 4069561, 761831, 761830, 761808, 761832, 761809, 4221821, 440750, 4176614, 761821, 761819, 444097, 761820, 761818, 4110339, 4111868, 4110343, 439314, 4109877, 4112171, 4112172, 4250765, 42538533, 44811347, 765049, 4317289, 4203836, 4175649, 4149782, 4153353, 46285904, 444247, 77310, 4189004 Viral Hepatitis 4291005 Appendix 3. Preliminary code list of medications for large-scale characterizaton Medications Concept id (Included) Concept id (Excluded) Agents acting on the renin-angiotensin 21601782 system Antibacterials for systemic use 21602796 Antidepressants 21604686 Antiemetics 21600490 Antiepileptics 21604389 Anti-inflammatory and Antirheumatic 21603933, 21602722 products Antineoplastics 21601387 Antipsoriatics 21602028 18 Antithrombotic agents 21600961 Antivirals for HCV 1501761 Antivirals for HIV 21603180 Beta blocking agents 21601664 Calcium channel blockers 21601744 Diuretics 21601461 Drugs for acid related disorders 21600046 Drugs used in addictive disorders 21604816 Drugs for obstructive airway diseases 21603248 Drugs for diabetes 21600713, 21600744 Hormonal Contraceptives 21602472 Immunosuppressants 21603891 Iron preparations 21601078 21601119 Lipid modifying agents 21601853 Opioids 21604254 Psycholeptics 21604489 Psychostimulants 21604752 19