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Identification of hospitalizations for intentional self‐harm when E‐codes are incompletely recorded
Author(s) -
Patrick Amanda R.,
Miller Matthew,
Barber Catherine W.,
Wang Philip S.,
Canning Claire F.,
Schneeweiss Sebastian
Publication year - 2010
Publication title -
pharmacoepidemiology and drug safety
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.023
H-Index - 96
eISSN - 1099-1557
pISSN - 1053-8569
DOI - 10.1002/pds.2037
Subject(s) - medicine , diagnosis code , medical prescription , harm , psychiatry , medical diagnosis , context (archaeology) , algorithm , population , psychology , social psychology , paleontology , environmental health , pathology , pharmacology , biology , computer science
Context Suicidal behavior has gained attention as an adverse outcome of prescription drug use. Hospitalizations for intentional self‐harm, including suicide, can be identified in administrative claims databases using external cause of injury codes (E‐codes). However, rates of E‐code completeness in US government and commercial claims databases are low due to issues with hospital billing software. Objective To develop an algorithm to identify intentional self‐harm hospitalizations using recorded injury and psychiatric diagnosis codes in the absence of E‐code reporting. Methods We sampled hospitalizations with an injury diagnosis (ICD‐9 800–995) from two databases with high rates of E‐coding completeness: 1999–2001 British Columbia, Canada data and the 2004 US Nationwide Inpatient Sample. Our gold standard for intentional self‐harm was a diagnosis of E950‐E958. We constructed algorithms to identify these hospitalizations using information on type of injury and presence of specific psychiatric diagnoses. Results The algorithm that identified intentional self‐harm hospitalizations with high sensitivity and specificity was a diagnosis of poisoning, toxic effects, open wound to elbow, wrist, or forearm, or asphyxiation; plus a diagnosis of depression, mania, personality disorder, psychotic disorder, or adjustment reaction. This had a sensitivity of 63%, specificity of 99% and positive predictive value (PPV) of 86% in the Canadian database. Values in the US data were 74, 98, and 73%. PPV was highest (80%) in patients under 25 and lowest those over 65 (44%). Conclusions The proposed algorithm may be useful for researchers attempting to study intentional self‐harm in claims databases with incomplete E‐code reporting, especially among younger populations. Copyright © 2010 John Wiley & Sons, Ltd.