Bipolar Disorder Related Hospitalizations - a Descriptive Nationwide Study Using a Big Data Approach

Data de publicação: Data Ahead of Print:

Autores da FMUP

  • Manuel António Gonçalves De Pinho

    Autor

  • José Alberto Da Silva Freitas

    Autor

Participantes de fora da FMUP

  • von Doellinger, O
  • Ribeiro, JP

Unidades de investigação

Abstract

Bipolar Disorder (BD) is a mental disorder which frequently requires long hospitalizations and need for acute psychiatric care. The aim of this study was to describe a nationwide perspective of BD related hospitalizations and to use a BigData based approach in mental health research. We performed a retrospective observational study using a nationwide hospitalization database containing all hospitalizations registered in Portuguese public hospitals from 2008-2015. Hospitalizations with a primary diagnosis of BD were selected based on International Classification of Diseases version 9, Clinical Modification (ICD-9-CM) codes of diagnosis 296.xx (excluding 296.2x; 296.3x and 296.9x). From 20,807 hospitalizations belonging to 13,300 patients, around 33.4% occurred in male patients with a median length of stay of 16.0 days and a mean age of 47.9 years. The most common hospitalization diagnosis in BD has the code 296.4x (manic episode) representing 34.3% of all hospitalizations, followed by the code 296.5x (depressed episode) with 21.4%. The mean estimated hospitalization charge was 3,508.5(sic) per episode, with a total charge of 73M(sic) in the 8-year period of this study.This is a nationwide study giving a broad perspective of the BD hospitalization panorama at a national level. We found important differences in hospitalization characteristics by sex, age and primary diagnosis.

Dados da publicação

ISSN/ISSNe:
0033-2720, 1573-6709

Psychiatric Quarterly  Kluwer Academic/Human Sciences Press Inc.

Tipo:
Article
Páginas:
325-333
Link para outro recurso:
www.scopus.com

Citações Recebidas na Web of Science: 7

Citações Recebidas na Scopus: 10

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Keywords

  • Bipolar disorder; Hospitalization; Big data; Health services

Financiamento

Proyectos asociados

A machine learning-based approach to support the assessment of clinical coded data quality in the context of Diagnosis-Related Groups classification systems

Investigador Principal: José Alberto da Silva Freitas

Estudo Clínico Académico . 2020

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