Literature DB >> 32279024

An analysis of the relationship between chronic obstructive pulmonary disease, smoking and depression in an integrated healthcare system.

Natalie B Riblet1, Daniel J Gottlieb2, Jessica E Hoyt3, Bradley V Watts4, Brian Shiner5.   

Abstract

OBJECTIVE: Chronic Obstructive Pulmonary Disease (COPD) and smoking are highly associated with depression and hypoxia. There is limited knowledge about whether hypoxic conditions interact to cause depression.
METHOD: A population-based cohort study was conducted using the Veterans Affairs (VA) Corporate Data Warehouse. Patients must have accessed any healthcare at a VA facility between 2004 and 2014 and had a negative depression screen (Patient Health Questionnaire-2 (PHQ-2) score ≤ 2). Patients with COPD or a positive depression screen (PHQ-2 score: 3+) during or prior to the year with a negative depression screen were excluded. Logistic regression with annual observations was used to evaluate depression incidence based on COPD and smoking status. Models were adjusted for demographics and other comorbid conditions. A probability scale was used to examine interactions between COPD and smoking.
RESULTS: A total of 3,284,496 patients were included. Patients with COPD and current smokers were at increased risk for developing depression. There were minimal interaction effects between COPD and smoking. The odds of developing depression in a year varied from 1.4% among never smokers without COPD to 2.9.% among current smokers with COPD.
CONCLUSION: Smoking and COPD are independent risk factors for depression and interact to cause depression. Further research is needed to confirm whether hypoxia contributes to this association. Published by Elsevier Inc.

Entities:  

Keywords:  Chronic obstructive pulmonary disease; Depression; Smoking

Mesh:

Year:  2020        PMID: 32279024     DOI: 10.1016/j.genhosppsych.2020.03.007

Source DB:  PubMed          Journal:  Gen Hosp Psychiatry        ISSN: 0163-8343            Impact factor:   3.238


  4 in total

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3.  Using machine learning to predict suicide in the 30 days after discharge from psychiatric hospital in Denmark.

Authors:  Tammy Jiang; Anthony J Rosellini; Erzsébet Horváth-Puhó; Brian Shiner; Amy E Street; Timothy L Lash; Henrik T Sørensen; Jaimie L Gradus
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  4 in total

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