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Daniel Hsiang-Te Tsai,1,2 J Simon Bell,3 Shahab Abtahi,4 Brenda N Baak,5 Marloes T Bazelier,4 Ruth Brauer,6 Adrienne YL Chan,6– 9 Esther W Chan,9– 12 Haoqian Chen,13 Celine SL Chui,9,14,15 Sharon Cook,16 Stephen Crystal,16 Poonam Gandhi,13 Sirpa Hartikainen,17 Frederick K Ho,18 Shao-Ti Hsu,1 Jenni Ilomäki,3 Ju Hwan Kim,19 Olaf H Klungel,4 Marjaana Koponen,17 Wallis CY Lau,6,8,9 Kui Kai Lau,20,21 Terry YS Lum,22 Hao Luo,22 Kenneth KC Man,6,8,9 Jill P Pell,18 Soko Setoguchi,13,23 Shih-Chieh Shao,1,24 Chin-Yao Shen,1 Ju-Young Shin,19,25,26 Patrick C Souverein,4 Anna-Maija Tolppanen,17 Li Wei,6,9 Ian CK Wong,6,8,9,27 Edward Chia-Cheng Lai1 1School of Pharmacy, Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan; 2Centre for Neonatal and Paediatric Infection, St George’s University of London, London, UK; 3Centre for Medicine Use and Safety, Faculty of Pharmacy and Pharmaceutical Sciences, Monash University, Melbourne, Australia; 4Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, the Netherlands; 5PHARMO Institute for Drug Outcomes Research, Utrecht, the Netherlands; 6Research Department of Practice and Policy, UCL School of Pharmacy, London, UK; 7Groningen Research Institute of Pharmacy, Unit of Pharmacotherapy, ‐Epidemiology and ‐Economics, University of Groningen, Groningen, the Netherlands; 8Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, Special Administrative Region, People’s Republic of China; 9Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Hong Kong, Special Administrative Region, People’s Republic of China; 10Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, Special Administrative Region, People’s Republic of China; 11Department of Pharmacy, the University of Hong Kong-Shenzhen Hospital, Shenzhen, People’s Republic of China; 12The University of Hong Kong Shenzhen Institute of Research and Innovation, Shenzhen, People’s Republic of China; 13Center for Pharmacoepidemiology and Treatment Science (PETS), Institute for Health, Rutgers University, New Brunswick, NJ, USA; 14School of Nursing, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, Special Administrative Region, People’s Republic of China; 15School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, Special Administrative Region, People’s Republic of China; 16Center for Health Services Research, Rutgers University, New Brunswick, NJ, USA; 17Kuopio Research Centre of Geriatric Care and School of Pharmacy, University of Eastern Finland, Kuopio, Finland; 18School of Health and Wellbeing, University of Glasgow, Glasgow, UK; 19School of Pharmacy, Sungkyunkwan University, Suwon, South Korea; 20Division of Neurology, Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, Special Administrative Region, People’s Republic of China; 21State Key Laboratory of Brain and Cognitive Sciences, University of Hong Kong, Hong Kong, Special Administrative Region, People’s Republic of China; 22Department of Social Work and Social Administration, University of Hong Kong, Hong Kong, Special Administrative Region, People’s Republic of China; 23Department of Medicine, Rutgers Robert Wood Johnson Medical School and Pharmacoepidemiology and Treatments Science, Institute for Health, Rutgers University, New Brunswick, NJ, USA; 24Department of Pharmacy, Keelung Chang Gung Memorial Hospital, Keelung, Taiwan; 25Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, South Korea; 26Department of Biohealth Regulatory Science, Sungkyunkwan University, Seoul, South Korea; 27Aston Pharmacy School, Aston University, Birmingham, UKCorrespondence: Edward Chia-Cheng Lai, School of Pharmacy and Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan, Email edward_lai@mail.ncku.edu.twPurpose: To describe and categorize detailed components of databases in the Neurological and Mental Health Global Epidemiology Network (NeuroGEN).Methods: An online 132-item questionnaire was sent to key researchers and data custodians of NeuroGEN in North America, Europe, Asia and Oceania. From the responses, we assessed data characteristics including population coverage, data follow-up, clinical information, validity of diagnoses, medication use and data latency. We also evaluated the possibility of conversion into a common data model (CDM) to implement a federated network approach. Moreover, we used radar charts to visualize the data capacity assessments, based on different perspectives.Results: The results indicated that the 15 databases covered approximately 320 million individuals, included in 7 nationwide claims databases from Australia, Finland, South Korea, Taiwan and the US, 6 population-based electronic health record databases from Hong Kong, Scotland, Taiwan, the Netherlands and the UK, and 2 biomedical databases from Taiwan and the UK.Conclusion: The 15 databases showed good potential for a federated network approach using a common data model. Our study provided publicly accessible information on these databases for those seeking to employ real-world data to facilitate current assessment and future development of treatments for neurological and mental disorders.Keywords: meta-data, data repository, Neurological and Mental Health Global Epidemiology Network, NeuroGEN

Anne Paakinaho,1 Miia Tiihonen,1 Heikki Koskela,2,3 Marjaana Koponen,1,4 Jari Tiihonen,5,6 Sirpa Hartikainen,1 Anna-Maija Tolppanen1 1School of Pharmacy, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland; 2Unit for Medicine and Clinical Research, Pulmonary Division, Kuopio University Hospital, Kuopio, Finland; 3School of Medicine, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland; 4Centre for Medicine Use and Safety, Faculty of Pharmacy and Pharmaceutical Sciences, Monash University, Parkville, Victoria, Australia; 5Department of Forensic Psychiatry, University of Eastern Finland, Niuvanniemi Hospital, Kuopio, Finland; 6Department of Clinical Neuroscience, Karolinska Institutet, and Center for Psychiatry Research, Stockholm City Council, Stockholm, SwedenCorrespondence: Anne Paakinaho, School of Pharmacy, Faculty of Health Sciences, University of Eastern Finland, P.O. Box 1627, Kuopio, FI-70211, Finland, Tel +358 405408490, Email anne.paakinaho@uef.fiIntroduction: Although β 2-adrenoceptor (β 2AR) agonists have been associated with a lower risk of Parkinson’s disease (PD), the findings are inconclusive and may reflect confounding by indication. We studied the association between inhaled β 2AR agonists and risk of PD in persons with asthma or chronic obstructive pulmonary disease (COPD).Methods: The nested case-control study was conducted within a register-based Finnish Parkinson’s disease study (FINPARK) and included 1406 clinically verified PD cases diagnosed during 1999– 2015, who also had asthma/COPD > 3 years before PD. PD cases were matched with up to seven controls by age, sex, duration of asthma/COPD, pulmonary diagnosis, and region (N = 8630). Cumulative and average annual exposure to short- and long-acting β 2AR agonists before a 3-year lag period was assessed with quartiles of defined daily doses (DDDs). Adjusted odds ratios (aORs) were calculated with 95% confidence intervals (CIs) using conditional logistic regression.Results: Cumulative exposure to either short- or long-acting β 2AR agonists was not associated with a risk of PD. With average annual exposure, a decreased risk was observed only for the highest quartile of long-acting β 2AR agonists (aOR 0.75; 95% CI 0.58– 0.97). In the stratified analysis the lowest risk estimates were observed among those with both asthma and COPD diagnoses. The suggestion of an inverse association was seen for the highest quartile of long-acting β 2AR agonists in asthma.Discussion: Higher levels of exposure to β 2AR agonists were not consistently associated with a reduced risk of PD. The inverse association in the highest category of average annual exposure to long-acting β 2AR agonists may be explained by unmeasured confounding, such as disease severity or smoking.Keywords: Parkinson disease, adrenergic beta-2 receptor agonists, asthma, chronic obstructive pulmonary disease, risk factors

Aleksi Hamina,1,* Ingvild Odsbu,2,* Petter Christian Borchgrevink,3,4 Li-Chia Chen,5 Thomas Clausen,1 Ketil Arne Espnes,6,7 Kine Gjesdal,8,9 Marte Handal,1,10 Sirpa Hartikainen,11 Vidar Hjellvik,10 Marianne Therese Smogeli Holter,12 Trond Høibø,13 Geana Paula Kurita,14– 16 Harald Christian Langaas,7 Torgeir Gilje Lid,8,9 Torunn Hatlen Nøst,3,17 Per Sjøgren,15 Svetlana Skurtveit1,2 On behalf of the POINT study group1Norwegian Centre for Addiction Research (SERAF), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; 2Department of Mental Disorders, Division of Mental and Physical Health, the Norwegian Institute of Public Health, Oslo, Norway; 3Department of Pain and Complex Disorders, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway; 4Institute of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway; 5Division of Pharmacy and Optometry, School of Health Sciences, University of Manchester, Manchester, UK; 6Department of Clinical Pharmacology, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway; 7Regional Medicines Information and Pharmacovigilance Centre (RELIS), Department of Clinical Pharmacology, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway; 8Faculty of Health Sciences, University of Stavanger, Stavanger, Norway; 9Center for Alcohol and Drug Research (KORFOR), Stavanger University Hospital, Stavanger, Norway; 10Department of Chronic Diseases, Division of Mental and Physical Health, the Norwegian Institute of Public Health, Oslo, Norway; 11School of Pharmacy, University of Eastern Finland, Kuopio, Finland; 12Department of Psychology, Faculty of Social Sciences, University of Oslo, Oslo, Norway; 13Research Unit for General Practice, NORCE Norwegian Research Centre, Bergen, Norway; 14Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; 15Department of Oncology, Rigshospitalet Copenhagen University Hospital, Copenhagen, Denmark; 16Department of Anaesthesiology, Pain and Respiratory Support, Rigshospitalet Copenhagen University Hospital, Copenhagen, Denmark; 17Department of Mental Health, Norwegian University of Science and Technology, Trondheim, Norway*These authors contributed equally to this workCorrespondence: Ingvild Odsbu, Department of Mental Disorders, Division of Mental and Physical Health, Norwegian Institute of Public Health, PO Box 222 Skøyen, Oslo, 0213, Norway, Tel +47 41454379, Email ingvild.odsbu@fhi.noAim: The POINT project aims to provide evidence to optimise chronic pain management, prevent adverse consequences of opioids, and improve chronic pain patients’ pain relief, functional capacity, and quality of life. We describe the outline of the project and its work packages. More specifically, we describe a cohort of persons with chronic pain and a cohort of long-term opioid users identified from a national registry linkage.Data Sources: The project utilises data from nationwide healthcare and population registers in Norway. Using the Norwegian Prescription Database, we identified a cohort of persons who have been dispensed drugs reimbursed for chronic pain and a cohort of persons who used opioids long term from 2010 to 2019. Data from the Norwegian Registry for Primary Health Care and the Norwegian Patient Registry (2008– 2019), Cancer Registry (1990– 2018) Cause of Death Registry (2010– 2019) and demographic and socioeconomic registers from Statistics Norway (2010– 2019) were linked to the cohorts.Study Population: There were 568,869 participants with chronic pain. Sixty-three percent of the cohort was women, and the mean age was 57.1 years. There were 336,712 long-term opioid users (58.6% women; 60.9 years). In chronic pain and long-term opioid user cohorts, the most frequent musculoskeletal diagnosis was back pain diagnosed in primary care (27.6% and 30.7%). Psychiatric diagnoses were also common.Main Variables: Upcoming studies will utilise psychiatric and somatic diagnoses from the patient registers, drug use from the prescription register, causes of death, demographics, and socioeconomic status (eg, education, income, workability, immigrant status) as exposures or outcomes.Conclusion and Future Plans: The two cohorts have numerous pain-related diagnoses, especially in the musculoskeletal system, and noticeably frequent somatic and psychiatric morbidity. The POINT project also includes later work packages that explore prescriber and patient perspectives around safe and effective treatment of chronic pain.Keywords: opioids, chronic pain, healthcare registers

Marjaana Koponen,1– 3 Anne Paakinaho,1,2 Julian Lin,1,2 Sirpa Hartikainen,1,2 Anna-Maija Tolppanen1,2 1School of Pharmacy, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland; 2Kuopio Research Centre of Geriatric Care, University of Eastern Finland, Kuopio, Finland; 3Center for Medicine Use and Safety, Faculty of Pharmacy and Pharmaceutical Sciences, Monash University, Parkville, Victoria, AustraliaCorrespondence: Anna-Maija Tolppanen, School of Pharmacy, Faculty of Health Sciences, University of Eastern Finland, P.O. Box 1627, Kuopio, 70211, Finland, Tel +35850 575 9530, Email anna-maija.tolppanen@uef.fiIntroduction: Drugs for other indications may be repurposable as disease-modifying drugs for Parkinson’s disease (PD). A systematic hypothesis-free approach can enable identification of candidates for repurposing. We applied a hypothesis-free systematic approach to identify drugs associated with lower risk of PD to discover candidates with potential for repurposing as disease-modifying drugs for PD and to illustrate challenges in observational studies that simultaneously investigate multiple repurposing candidates.Methods: The Finnish Parkinson’s disease study (FINPARK), a nationwide nested case-control study, was randomized to screening (10,183 cases, 67,849 controls) and replication (10,184 cases, 67,754 controls) samples, including cases diagnosed in 1998– 2015. After screening all univariable associations of register-derived exposure to individual-drug, group- and subgroup level since 1995 (exposure ≥ 3 years before outcome, threshold P = 0.1), different exposure periods were used in confounder-adjusted replication analyses.Results: In screening stage, the group-level (antipsoriatics and antigout preparations) and subgroup-level (cicatrizants, topical antipsoriatics, antigout preparations and mydriatics and cycloplegics) associations were mainly due to individual drugs. Seven other drugs (eg methotrexate, drugs for chronic obstructive pulmonary disease, COPD and/or asthma) were associated with lower risk. Associations of antigout preparations and antipsoriatics were replicated. COPD/asthma drugs, methotrexate and diabetes drugs were studied in separate, indication-restricted designs.Discussion: The results reflect the known risk factors and the implied role of the immune system in PD pathogenesis and spurious associations. They underline the importance of controlling for confounding by indication, which is challenging to apply to systematic screening.Keywords: Parkinson’s disease, pharmacoepidemiology, drug repurposing, case–control study, indication bias

Federica Pisa,1– 3 Jonas Reinold,1– 3 Piia Lavikainen,1,2 Marjaana Koponen,1,2,4 Heidi Taipale,1,2,5,6 Antti Tanskanen,5– 7 Jari Tiihonen,5,6 Sirpa Hartikainen,1,2 Anna-Maija Tolppanen1,2 1School of Pharmacy, University of Eastern Finland, Kuopio, Finland; 2Kuopio Research Centre of Geriatric Care, University of Eastern Finland, Kuopio, Finland; 3Department of Clinical Epidemiology, Leibniz Institute for Prevention Research and Epidemiology – BIPS, Bremen, Germany; 4Centre for Medicine Use and Safety, Faculty of Pharmacy and Pharmaceutical Sciences, Monash University, Melbourne, Victoria, Australia; 5Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden; 6Department of Forensic Psychiatry, Niuvanniemi Hospital, University of Eastern Finland, Kuopio, Finland; 7Impact Assessment Unit, National Institute for Health and Welfare, Helsinki, FinlandCorrespondence: Federica PisaBayer, Müllerstrasse 178, Berlin, 13353, GermanyEmail federica.pisa@bayer.comObjective: To determine the risk of hip fracture in persons with Alzheimer´s disease (AD) who initiated antiepileptic drugs (AEDs).Methods: In the Medication use and AD (MEDALZ) cohort of 70,719 Finnish community dwellers with clinically verified incident AD diagnosis in 2005– 2011, we identified all incident users of AEDs using national Prescription register. AEDs were classified as older (valproate, carbamazepine, clonazepam, phenytoin, levetiracetam, primidone) or newer (pregabalin, gabapentin, oxcarbazepine, lamotrigine, topiramate). We matched each user to 2 non-users. Incident hip fractures until 2015 were identified from the Care register for health care. We calculated inverse probability of treatment weighted hazard ratios (HR), with 95% confidence intervals, using Cox regression.Results: Altogether 5522 incident users were identified and matched to 11,044 non-users (in both groups, women: 65%; median age: 81 years). Altogether 53.3% of users initiated with newer AEDs (pregabalin 79.8%, gabapentin 10.2%) while 46.7% initiated with older AEDs (valproate 67.6%, carbamazepine 13.0%). Age- and sex-adjusted IR of hip fracture per 100 person-years was 1.8 (95% CI 1.6– 1.9) in non-users and 2.0 (95% CI 1.8– 2.2) in users. Increased risk of hip fracture was observed in users (HR 1.17, 95% CI 1.05– 1.30) compared with non-users. The risk was higher for short duration of use (< 14 weeks, HR 3.64, 95% CI 2.90– 4.58) than for medium duration (14 to < 64 weeks, HR 1.74, 95% CI 1.48– 2.05) or ≥ 64 weeks’ use (HR 1.23, 95% CI 1.08– 1.40), compared to non-users with same follow-up time. Older AEDs had HR of 1.46 (1.03– 2.08) compared with newer AEDs.Conclusion: Our results imply that AED use is associated with an increased risk of hip fracture in people with AD. These findings prompt careful consideration before prescribing AEDs to persons with AD. Persons with AD treated with antiepileptics should be carefully monitored due to their increased risk of falling and fractures.Keywords: antiepileptic drugs, hip fracture, Alzheimer´s disease, pharmacoepidemiology

Heidi Taipale,1–4,* Antti Tanskanen,3–5,* Marjaana Koponen,1,2 Anna-Maija Tolppanen,2,6 Jari Tiihonen,3,4 Sirpa Hartikainen1,2,7 1Kuopio Research Centre of Geriatric Care, University of Eastern Finland, 2School of Pharmacy, University of Eastern Finland, Kuopio, Finland; 3Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden; 4Department of Forensic Psychiatry, Niuvanniemi Hospital, University of Eastern Finland, Kuopio, 5National Institute for Health and Welfare, Helsinki, 6Research Centre for Comparative Effectiveness and Patient Safety (RECEPS), University of Eastern Finland, 7Department of Psychiatry, Kuopio University Hospital, Kuopio, Finland *These authors contributed equally to this work. Background: PRE2DUP is a modeling method that generates drug use periods (ie, when drug use started and ended) from drug purchases recorded in dispensing-based register data. It is based on the evaluation of personal drug purchasing patterns and considers hospital stays, possible stockpiling of drugs, and package information. Objective: The objective of this study was to investigate person-level agreement between self-reported drug use in the interview and drug use modeled from dispensing data with PRE2DUP method for various drug classes used by older persons. Methods: Self-reported drug use was assessed from the GeMS Study including a random sample of persons aged ≥75 years from the city of Kuopio, Finland, in 2006. Drug purchases recorded in the Prescription register data of these persons were modeled to determine drug use periods with PRE2DUP modeling method. Agreement between self-reported drug use on the interview date and drug use calculated from register-based data was compared in order to find the frequently used drugs and drug classes, which was evaluated by Cohen’s kappa. Kappa values 0.61–0.80 were considered to represent good and 0.81–1.00 as very good agreement.Results: Among 569 participants with mean age of 82 years, the agreement between interview and register data was very good for 75% and very good or good for 93% of the studied drugs or drug classes. Good or very good agreement was observed for drugs that are typically used on regular bases, whereas “as needed” drugs represented poorer results. Conclusion: PRE2DUP modeling method validly describes regular drug use among older persons. For most of drug classes investigated, PRE2DUP-modeled register data described drug use as well as interview-based data which are more time-consuming to collect. Further studies should be conducted by comparing it with other methods and in different drug user populations. Keywords: Prescription register, pharmacoepidemiology, drug utilization, validation studies

Piia Lavikainen,1,2 Esko Leskinen,3 Sirpa Hartikainen,1,2 Jyrki Möttönen,4 Raimo Sulkava,5 Maarit J Korhonen6 1Kuopio Research Centre of Geriatric Care, University of Eastern Finland, Kuopio, Finland; 2School of Pharmacy, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland; 3Department of Mathematics and Statistics, University of Jyväskylä, Jyväskylä, Finland; 4Department of Social Research, University of Helsinki, Helsinki, Finland; 5Department of Geriatrics, Institute of Public Health and Clinical Nutrition, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland; 6Department of Pharmacology, Drug Development and Therapeutics, University of Turku, Turku, Finland Abstract: Longitudinal studies typically suffer from incompleteness of data. Attrition is a major problem in studies of older persons since participants may die during the study or are too frail to participate in follow-up examinations. Attrition is typically related to an individual’s health; therefore, ignoring it may lead to too optimistic inferences, for example, about cognitive decline or changes in polypharmacy. The objective of this study is to compare the estimates of level and slope of change in 1) cognitive function and 2) number of drugs in use between the assumptions of ignorable and non-ignorable missingness. This study demonstrates the usefulness of latent variable modeling framework. The results suggest that when the missing data mechanism is not known, it is preferable to conduct analyses both under ignorable and non-ignorable missing data assumptions. Keywords: attrition, latent variable modeling, longitudinal, Mini-Mental State Examination, number of drugs, older persons

Anna-Maija Tolppanen,1 Heidi Taipale,2,3 Marjaana Koponen,2,3 Piia Lavikainen,2 Antti Tanskanen,4,5 Jari Tiihonen,4–6 Sirpa Hartikainen2,3 1Institute of Clinical Medicine – Neurology, 2Kuopio Research Centre of Geriatric Care, 3School of Pharmacy, University of Eastern Finland, Kuopio, Finland; 4Karolinska Institutet, Department of Clinical Neuroscience, Stockholm, Sweden; 5National Institute for Health and Welfare, Helsinki, Finland; 6University of Eastern Finland, Department of Forensic Psychiatry, Niuvanniemi Hospital, Kuopio, Finland Abstract: Memory diseases are the most important determinant of health care service use and quality of life among older individuals. Adverse effects of medication are common among older people, but this age group is underrepresented in clinical trials. Finnish statutory health care and prescription registers, together with personal identification numbers (PINs) and a tax-supported public health plan covering all citizens provide excellent opportunities for epidemiological research. We used routinely collected data from the Finnish health care system to establish the MEDication use among persons with Alzheimer’s disease (MEDALZ-2005) cohort. This cohort study will be used to assess medication use and its effects on health status and hospitalization among persons with Alzheimer’s disease (AD). The cohort includes all community-dwelling persons who had a clinically verified diagnosis of AD, resided in Finland, and were alive on December 31, 2005 and a matched comparison person for each affected individual. Data on purchased prescription medicines (1995–2009), inpatient hospital admissions (1972–2009), outpatient visits (1995–2009), details on diagnosed cancers (1972–2009), and mortality (until October 2010) are available for the whole cohort. This paper describes how this data can be utilized in etiological research and the assessment of health care service use, drug utilization, and associated adverse outcomes in a particularly vulnerable group that is often underrepresented in clinical trials. Keywords: drug utilization, pharmacoepidemiology, epidemiology, cohort

In Estonia, warfarin is widely prescribed by general practitioners to prevent and treat thromboembolic diseases. To date, there has been no systematic analysis of the potential risk of warfarin interactions with other drugs in the outpatient population.Objective: The aim of the study was to analyze the incidence of potential interactions in prescription schemes in Estonia in a cohort of outpatients receiving warfarin treatment.Methods: The retrospective study population included 203,646 outpatients aged 50 years or older of whom 7,175 received warfarin therapy. Patients who had used at least one prescription drug for a minimum period of 7 days concomitantly with warfarin were analyzed. Potential drug interactions were analyzed using Epocrates online, Stockley’s Drug Interactions and domestic drug interaction databases.Results: The average number of drugs used concomitantly with warfarin was 4.8 (SD=1.9) (males: 4.7 SD=2.0, females: 4.9 SD=2.0). No potential interactions in treatment regimens were found in 38% of patients, one potential interaction was observed in 29% and two or more potential interactions were observed in 33% of patients. The mean number of all potential interactions was 1.2 per patient and about the same in men and women. Potential interactions were associated with the number of drugs. Warfarin-related interactions were detected in 57% of patients, and the number of interactions related to warfarin per patient varied from 1 to 5. Most frequent were use of warfarin with NSAIDs (14%), followed by simvastatin (9%) and amiodarone (7%).Conclusion: This study shows that 57% of outpatients in Estonia receiving warfarin have drugs potentially interacting with warfarin in their treatment schemes. Most interactions (14%) with warfarin are associated with the prescription of NSAIDs.