Christiane Gasse1,2, Andreas Aalkjaer Danielsen2,3, Marianne Giørtz Pedersen1,2, Carsten Bøcker Pedersen1,2,4, Ole Mors2,3, Jakob Christensen5. 1. National Centre for Register-based Research, Aarhus University, Aarhus, Denmark. 2. The Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH), Aarhus, Denmark. 3. Psychosis Research Unit, Aarhus University Hospital, Risskov, Denmark. 4. Centre for Integrated Register-Based Research at Aarhus University (CIRRAU), Aarhus, Denmark. 5. Department of Neurology, Aarhus University Hospital, Aarhus, Denmark.
Abstract
PURPOSE: It is not possible to fully assess intention of self-harm and suicidal events using information from administrative databases. We conducted a validation study of intention of suicide attempts/self-harm contacts identified by a commonly applied Danish register-based algorithm (DK-algorithm) based on hospital discharge diagnosis and emergency room contacts. METHODS: Of all 101 530 people identified with an incident suicide attempt/self-harm contact at Danish hospitals between 1995 and 2012 using the DK-algorithm, we selected a random sample of 475 people. We validated the DK-algorithm against medical records applying the definitions and terminology of the Columbia Classification Algorithm of Suicide Assessment of suicidal events, nonsuicidal events, and indeterminate or potentially suicidal events. We calculated positive predictive values (PPVs) of the DK-algorithm to identify suicidal events overall, by gender, age groups, and calendar time. RESULTS: We retrieved medical records for 357 (75%) people. The PPV of the DK-algorithm to identify suicidal events was 51.5% (95% CI: 46.4-56.7) overall, 42.7% (95% CI: 35.2-50.5) in males, and 58.5% (95% CI: 51.6-65.1) in females. The PPV varied further across age groups and calendar time. After excluding cases identified via the DK-algorithm by unspecific codes of intoxications and injury, the PPV improved slightly (56.8% [95% CI: 50.0-63.4]). CONCLUSIONS: The DK-algorithm can reliably identify self-harm with suicidal intention in 52% of the identified cases of suicide attempts/self-harm. The PPVs could be used for quantitative bias analysis and implemented as weights in future studies to estimate the proportion of suicidal events among cases identified via the DK-algorithm.
PURPOSE: It is not possible to fully assess intention of self-harm and suicidal events using information from administrative databases. We conducted a validation study of intention of suicide attempts/self-harm contacts identified by a commonly applied Danish register-based algorithm (DK-algorithm) based on hospital discharge diagnosis and emergency room contacts. METHODS: Of all 101 530 people identified with an incident suicide attempt/self-harm contact at Danish hospitals between 1995 and 2012 using the DK-algorithm, we selected a random sample of 475 people. We validated the DK-algorithm against medical records applying the definitions and terminology of the Columbia Classification Algorithm of Suicide Assessment of suicidal events, nonsuicidal events, and indeterminate or potentially suicidal events. We calculated positive predictive values (PPVs) of the DK-algorithm to identify suicidal events overall, by gender, age groups, and calendar time. RESULTS: We retrieved medical records for 357 (75%) people. The PPV of the DK-algorithm to identify suicidal events was 51.5% (95% CI: 46.4-56.7) overall, 42.7% (95% CI: 35.2-50.5) in males, and 58.5% (95% CI: 51.6-65.1) in females. The PPV varied further across age groups and calendar time. After excluding cases identified via the DK-algorithm by unspecific codes of intoxications and injury, the PPV improved slightly (56.8% [95% CI: 50.0-63.4]). CONCLUSIONS: The DK-algorithm can reliably identify self-harm with suicidal intention in 52% of the identified cases of suicide attempts/self-harm. The PPVs could be used for quantitative bias analysis and implemented as weights in future studies to estimate the proportion of suicidal events among cases identified via the DK-algorithm.
Authors: Rachel L Zelkowitz; Tammy Jiang; Erzsébet Horváth-Puhó; Amy E Street; Timothy L Lash; Henrik T Sørensen; Anthony J Rosellini; Jaimie L Gradus Journal: J Affect Disord Date: 2022-03-16 Impact factor: 6.533
Authors: Jaimie L Gradus; Anthony J Rosellini; Erzsébet Horváth-Puhó; Tammy Jiang; Amy E Street; Isaac Galatzer-Levy; Timothy L Lash; Henrik T Sørensen Journal: Am J Epidemiol Date: 2021-12-01 Impact factor: 4.897
Authors: Liv S Thiele; Kazi Ishtiak-Ahmed; Janne P Thirstrup; Esben Agerbo; Carin A T C Lunenburg; Daniel J Müller; Christiane Gasse Journal: Pharmaceuticals (Basel) Date: 2022-07-14
Authors: Andreas Kiesbye Øvlisen; Lasse Hjort Jakobsen; Kristian Hay Kragholm; René Ernst Nielsen; Peter de Nully Brown; Rasmus Bo Dahl-Sørensen; Henrik Frederiksen; Nikolaj Mannering; Pär Lars Josefsson; Ahmed Ludvigsen Al-Mashhadi; Judit Mészáros Jørgensen; Andriette Dessau-Arp; Michael Roost Clausen; Robert Schou Pedersen; Christian Torp-Pedersen; Marianne Tang Severinsen; Tarec Christoffer El-Galaly Journal: Am J Hematol Date: 2022-03-29 Impact factor: 13.265
Authors: Amy E Street; Tammy Jiang; Erzsébet Horváth-Puhó; Anthony J Rosellini; Timothy L Lash; Henrik T Sørensen; Jaimie L Gradus Journal: J Trauma Stress Date: 2021-05-28