Systematic review and assessment of validated case definitions for depression in administrative data.

BMC Psychiatry

Department of Community Health Sciences & Institute for Public Health, University of Calgary, 3280 Hospital Drive NW, Calgary T2N4Z6, Alberta, Canada.

Published: October 2014

AI Article Synopsis

  • - The study aims to review how administrative data can be used to identify depression, focusing on validated case definitions using ICD codes, which are less resource-intensive than traditional surveys.
  • - The systematic review analyzed 2,014 abstracts, eventually narrowing it down to three relevant studies that utilized ICD-9 and ICD-10 codes for depression, while also validating both existing and enhanced case definitions across a hospital database.
  • - Although the new case definitions demonstrated high specificity and positive predictive value, their sensitivity was lower, indicating a need for improved strategies to accurately identify depression in administrative healthcare data.

Article Abstract

Background: Administrative data are increasingly used to conduct research on depression and inform health services and health policy. Depression surveillance using administrative data is an alternative to surveys, which can be more resource-intensive. The objectives of this study were to: (1) systematically review the literature on validated case definitions to identify depression using International Classification of Disease and Related Health Problems (ICD) codes in administrative data and (2) identify individuals with and without depression in administrative data and develop an enhanced case definition to identify persons with depression in ICD-coded hospital data.

Methods: (1) Systematic review: We identified validation studies using ICD codes to indicate depression in administrative data up to January 2013. (2) VALIDATION: All depression case definitions from the literature and an additional three ICD-9-CM and three ICD-10 enhanced definitions were tested in an inpatient database. The diagnostic accuracy of all case definitions was calculated [sensitivity (Se), specificity (Sp), positive predictive value (PPV) and negative predictive value (NPV)].

Results: (1) Systematic review: Of 2,014 abstracts identified, 36 underwent full-text review and three met eligibility criteria. These depression studies used ICD-9 and ICD-10 case definitions. (2) VALIDATION: 4,008 randomly selected medical charts were reviewed to assess the performance of new and previously published depression-related ICD case definitions. All newly tested case definitions resulted in Sp >99%, PPV >89% and NPV >91%. Sensitivities were low (28-35%), but higher than for case definitions identified in the literature (1.1-29.6%).

Conclusions: Validating ICD-coded data for depression is important due to variation in coding practices across jurisdictions. The most suitable case definitions for detecting depression in administrative data vary depending on the context. For surveillance purposes, the most inclusive ICD-9 & ICD-10 case definitions resulted in PPVs of 89.7% and 89.5%, respectively. In cases where diagnostic certainty is required, the least inclusive ICD-9 and -10 case definitions are recommended, resulting in PPVs of 92.0% and 91.1%. All proposed case definitions resulted in suboptimal levels of sensitivity (ranging from 28.9%-35.6%). The addition of outpatient data (such as pharmacy records) for depression surveillance is recommended and should result in improved measures of validity.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4201696PMC
http://dx.doi.org/10.1186/s12888-014-0289-5DOI Listing

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