Agreement Between Medico-Administrative Database Algorithms and Survey-Based Diagnoses for Depression and Anxiety in Older Adults
<b>Objectives</b>: This study aimed to assess the concordance between depression and anxiety case definitions derived from algorithms based on medico-administrative data and structured interviews aligned with the fourth edition of the Diagnostic and Statistical Manual of Mental Disorders...
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MDPI AG
2025-06-01
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| Series: | Pharmacoepidemiology |
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| Online Access: | https://www.mdpi.com/2813-0618/4/2/12 |
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| author | Giraud Ekanmian Carlotta Lunghi Helen-Maria Vasiliadis Line Guénette |
| author_facet | Giraud Ekanmian Carlotta Lunghi Helen-Maria Vasiliadis Line Guénette |
| author_sort | Giraud Ekanmian |
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| description | <b>Objectives</b>: This study aimed to assess the concordance between depression and anxiety case definitions derived from algorithms based on medico-administrative data and structured interviews aligned with the fourth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) criteria in older adults. <b>Methods</b>: We analyzed data from 1405 primary care older adults (≥65 years) from the <i>Étude sur la Santé des Aînés</i> (ESA)-Services cohort (2011–2013) in Quebec, Canada, who had available survey and medico-administrative data. Cases of depression and anxiety were identified using algorithms incorporating combinations of hospitalization records, physician-visit claims, and medication claims for antidepressants or anxiolytics. The agreement was assessed with the kappa statistics (κ), and the algorithms’ sensitivity, specificity, and positive and negative predictive values were calculated using the case definitions derived from the DSM-IV-aligned ESA-Services interviews as the gold standard. <b>Results</b>: Agreements between the algorithms and the interviews were fair (κ: 0.06–0.22) for depression gooand slight (κ: 0.02–0.09) for anxiety. The algorithms had low sensitivity (2–39.7% for depression and 1.4–39.9% for anxiety) but high specificity (84.5–99.6% for depression and 73–99.2% for anxiety), depending on the algorithm. <b>Conclusions</b>: The agreement between algorithms based on administrative data and DSM-IV-aligned interviews for anxiety or depressive disorders was low. The two methods identified older adults with different characteristics. Despite these discrepancies, algorithms with high specificity provide valuable insights into healthcare utilization patterns associated with these disorders. |
| format | Article |
| id | doaj-art-4459fe9ae1a74c5b8df074db6035ca26 |
| institution | DOAJ |
| issn | 2813-0618 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Pharmacoepidemiology |
| spelling | doaj-art-4459fe9ae1a74c5b8df074db6035ca262025-08-20T03:16:35ZengMDPI AGPharmacoepidemiology2813-06182025-06-01421210.3390/pharma4020012Agreement Between Medico-Administrative Database Algorithms and Survey-Based Diagnoses for Depression and Anxiety in Older AdultsGiraud Ekanmian0Carlotta Lunghi1Helen-Maria Vasiliadis2Line Guénette3Faculty of Pharmacy, Laval University, Quebec City, QC G1V 5C3, CanadaFaculty of Pharmacy, Laval University, Quebec City, QC G1V 5C3, CanadaDépartement des Sciences de la Santé Communautaire, Faculté de Médecine, Université de Sherbrooke, Sherbrooke, QC J1H 5H3, CanadaFaculty of Pharmacy, Laval University, Quebec City, QC G1V 5C3, Canada<b>Objectives</b>: This study aimed to assess the concordance between depression and anxiety case definitions derived from algorithms based on medico-administrative data and structured interviews aligned with the fourth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) criteria in older adults. <b>Methods</b>: We analyzed data from 1405 primary care older adults (≥65 years) from the <i>Étude sur la Santé des Aînés</i> (ESA)-Services cohort (2011–2013) in Quebec, Canada, who had available survey and medico-administrative data. Cases of depression and anxiety were identified using algorithms incorporating combinations of hospitalization records, physician-visit claims, and medication claims for antidepressants or anxiolytics. The agreement was assessed with the kappa statistics (κ), and the algorithms’ sensitivity, specificity, and positive and negative predictive values were calculated using the case definitions derived from the DSM-IV-aligned ESA-Services interviews as the gold standard. <b>Results</b>: Agreements between the algorithms and the interviews were fair (κ: 0.06–0.22) for depression gooand slight (κ: 0.02–0.09) for anxiety. The algorithms had low sensitivity (2–39.7% for depression and 1.4–39.9% for anxiety) but high specificity (84.5–99.6% for depression and 73–99.2% for anxiety), depending on the algorithm. <b>Conclusions</b>: The agreement between algorithms based on administrative data and DSM-IV-aligned interviews for anxiety or depressive disorders was low. The two methods identified older adults with different characteristics. Despite these discrepancies, algorithms with high specificity provide valuable insights into healthcare utilization patterns associated with these disorders.https://www.mdpi.com/2813-0618/4/2/12depressionanxietyalgorithmcase definitionsensitivityspecificity |
| spellingShingle | Giraud Ekanmian Carlotta Lunghi Helen-Maria Vasiliadis Line Guénette Agreement Between Medico-Administrative Database Algorithms and Survey-Based Diagnoses for Depression and Anxiety in Older Adults Pharmacoepidemiology depression anxiety algorithm case definition sensitivity specificity |
| title | Agreement Between Medico-Administrative Database Algorithms and Survey-Based Diagnoses for Depression and Anxiety in Older Adults |
| title_full | Agreement Between Medico-Administrative Database Algorithms and Survey-Based Diagnoses for Depression and Anxiety in Older Adults |
| title_fullStr | Agreement Between Medico-Administrative Database Algorithms and Survey-Based Diagnoses for Depression and Anxiety in Older Adults |
| title_full_unstemmed | Agreement Between Medico-Administrative Database Algorithms and Survey-Based Diagnoses for Depression and Anxiety in Older Adults |
| title_short | Agreement Between Medico-Administrative Database Algorithms and Survey-Based Diagnoses for Depression and Anxiety in Older Adults |
| title_sort | agreement between medico administrative database algorithms and survey based diagnoses for depression and anxiety in older adults |
| topic | depression anxiety algorithm case definition sensitivity specificity |
| url | https://www.mdpi.com/2813-0618/4/2/12 |
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