Predictors of admission and readmission to hospital for major depression: A community cohort study of 52,990 individuals.

Hamish Innes, James Lewsey, Daniel J Smith

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Abstract

BACKGROUND: Our current knowledge about predictors of admission and re-admission to hospital as a result of major depressive disorder (MDD) is limited. Here we present a descriptive analysis of factors which are associated with MDD hospitalisations within a large population cohort.

METHODS:
We linked participants of the Scottish Health Survey (SHS) to historical and prospective hospital admission data. We combined information from the SHS baseline interview and historical hospitalisations to define a range of exposure variables. The main outcomes of interest were: (1) first time admission for MDD occurring after the SHS interview; and (2) readmission for MDD. We used Cox regression to determine the association between each predictor and each outcome, after adjusting for age, gender and deprivation quintile.

RESULTS:
52,990 adult SHS participants were included. During a median follow-up of 4.5 years per participant, we observed 530 first-time admissions for MDD. A relatively wide range of factors - encompassing social, individual health status, and lifestyle-related exposures - were associated with this outcome (p<0.05). Among the 530 participants exhibiting a de novo admission for MDD during follow-up, 118 were later re-admitted. Only older age (over 70) and a prior non-depression related psychiatric admission were associated with readmission for MDD.

LIMTATIONS:
MDD was defined using records of International Classification of Disease hospital discharge codes rather than formal diagnostic assessments.

CONCLUSION:
These findings have implications for mental health service organisation and delivery and should stimulate future research on predictive factors for admission and readmission in MDD.
Original languageEnglish
Pages (from-to)10-14
Number of pages5
Journaljournal of affective disorders
Volume183
DOIs
Publication statusPublished - 1 Sep 2015

Keywords

  • depression
  • predictors

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