4.5 Article

Diagnostic and genetic overlap of three common mental disorders in structured interviews and health registries

Journal

ACTA PSYCHIATRICA SCANDINAVICA
Volume 137, Issue 1, Pages 54-64

Publisher

WILEY
DOI: 10.1111/acps.12829

Keywords

alcoholism; anxiety disorders; depressive disorder; genetics; behavioural; registries

Categories

Funding

  1. Research Council of Norway (RCN) [240061]
  2. RCN [196148]
  3. National Institutes of Health [MH-068643]
  4. National Institute on Drug Abuse [1R01DA037558-01A1]
  5. NATIONAL INSTITUTE OF MENTAL HEALTH [R01MH068643] Funding Source: NIH RePORTER
  6. NATIONAL INSTITUTE ON DRUG ABUSE [R01DA037558] Funding Source: NIH RePORTER

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Objective: To investigate whether diagnostic data from structured interviews, primary care and specialist care registries on major depressive disorder (MDD), anxiety disorders (AD) and alcohol use disorder (AUD) identify the same individuals, yield comparable comorbidity estimates and reflect the same genetic influences. Methods: Registry data from primary and specialist care were available for 11727 twins and diagnostic interview data for 2271 of these. We used logistic regression analyses and biometric modelling to investigate the overlap between the data sources. Results: Most individuals meeting diagnostic criteria at interview were not registered with a corresponding diagnosis. The rates of registration were higher for MDD (36% in primary care and 15% in specialist care) and AD (21% and 18%) than for AUD (3% and 7%). Comorbidity estimated as odds ratios, but not as polychoric correlations, was higher in the registries than in the interviews. Genetic influences on the disorders were highly correlated across data sources (median r=0.81), bordering unity for MDD and AD. Conclusion: Prevalence and comorbidity estimates differ between registries and population-based assessment. Nevertheless, diagnoses from health registries reflect the same genetic influences as common mental disorders assessed in the general population, indicating generalizability of aetiological factors across data sources.

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