Background: Depression is a common comorbidity among people with chronic obstructive pulmonary disease (COPD), but the health effects of depression in this group of patients remain poorly understood. The purpose of the present study was to investigate the association between COPD and depression, and the effects of comorbid COPD and depression on health care utilization.
Methods: Our study sample included 10,180 Korean adults (4,437 men and 5,743 women; all aged ≥ 45 years) who participated in the cross-sectional Korean Longitudinal Study of Aging (KLoSA). The participants were required to self-report any previous diagnosis of COPD. Depression was assessed with the 10-item Center for Epidemiologic Studies Depression Scale (CES-D10). Health care utilization was defined as multiple physician visits (≥6) and multiple hospital admissions (≥2) in the previous year.
Results: Participants with COPD had a higher prevalence of depression than those without COPD (16.8% vs. 38.1%, respectively; P < 0.001). After adjustment for covariates, participants with COPD had a significantly higher likelihood of multiple physician visits (odds ratio [OR], 95% confidence interval [CI], 1.80 [1.26-2.58]) and multiple hospital admissions (OR [95% CI], 1.62 [1.04-3.51]), while those with COPD plus depression had a higher likelihood of multiple hospital admissions (OR [95% CI], 2.71 [2.34-5.48]).
Conclusions: We found a positive association between COPD and depression. Depression in patients with COPD is associated with an increased likelihood of multiple hospital admissions.
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http://dx.doi.org/10.1111/crj.13384 | DOI Listing |
Eye (Lond)
January 2025
Rothschild Foundation Hospital, Institut Français de Myopie, Paris, France.
Background: To assess associations between mortality and major ocular parameters and diseases.
Methods: The population-based Ural Eye and Medical Study (UEMS) and Ural Very Old Study (UVOS) included 5899 individuals (age: 40+ years) and 1526 individuals (age: 85+ years), respectively. Cause-specific mortality was determined using the government regional information and analytical system.
JBI Evid Synth
January 2025
RISE-Health, Nursing School of Porto, Porto, Portugal.
Objective: The objective of this review is to evaluate the effectiveness of combined physical and psychological interventions on anxiety and depression symptoms in adult patients with chronic obstructive pulmonary disease (COPD).
Introduction: By 2030, COPD is expected to be the third-leading cause of death and the seventh in terms of overall health impact, measured in disability-adjusted life years. As with other comorbidities, anxiety and depression disorders influence the prognosis.
J Cardiothorac Surg
January 2025
Department of Cardiac Rehabilitation, Zhejiang Hospital, Hangzhou, Zhejiang Province, 310007, China.
Objective: the study aimed to analyze the therapeutic effects of neuromuscular electrical stimulation (NMES) combined with respiratory muscle training (RMT) on patients with moderate-to-severe chronic obstructive pulmonary disease (COPD).
Methods: 135 patients with moderate/severe chronic obstructive pulmonary disease were selected as the research object and randomly selected. 72 cases were divided into rehabilitation group and 63 cases in control group.
Sci Rep
January 2025
School of Medicine, Alborz University of Medical Science, Karaj, Iran.
The COVID-19 pandemic has resulted in many survivors experiencing post-acute COVID-19 syndrome (PCS) with symptoms including fatigue, breathlessness, and cognitive complaints. E-cigarette use has already been associated with increased susceptibility to COVID-19 because of its effects on ACE2 receptor expression and inflammation, raising concern that it might worsen the long-term outcomes of COVID-19, including PCS. While traditional smoking is associated with a higher risk of PCS, the role of e-cigarettes remains unclear due to conflicting evidence.
View Article and Find Full Text PDFJMIR Form Res
January 2025
1, Department of Health Administration, College of Software and Digital Healthcare Convergence, Yonsei University, Changjogwan, Yonseidae-gil 1, Wonju, 26493, Republic of Korea, +82 (0) 33-760-2257.
Background: Diabetes is prevalent in older adults, and machine learning algorithms could help predict diabetes in this population.
Objective: This study determined diabetes risk factors among older adults aged ≥60 years using machine learning algorithms and selected an optimized prediction model.
Methods: This cross-sectional study was conducted on 3084 older adults aged ≥60 years in Seoul from January to November 2023.
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