Predictors of Inpatient Utilization among Veterans with Dementia.

Curr Gerontol Geriatr Res

Houston VA HSR&D Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey VA Medical Center, (MEDVAMC 152), 2002 Holcombe Boulevard, Houston, TX 77030, USA ; Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA ; VA South Central Mental Illness Research, Education and Clinical Center (A Virtual Center), USA.

Published: July 2014

Dementia is prevalent and costly, yet the predictors of inpatient hospitalization are not well understood. Logistic and negative binomial regressions were used to identify predictors of inpatient hospital utilization and the frequency of inpatient hospital utilization, respectively, among veterans. Variables significant at the P < 0.15 level were subsequently analyzed in a multivariate regression. This study of veterans with a diagnosis of dementia (n = 296) and their caregivers found marital status to predict hospitalization in the multivariate logistic model (B = 0.493, P = 0.029) and personal-care dependency to predict hospitalization and readmission in the multivariate logistic model and the multivariate negative binomial model (B = 1.048, P = 0.007, B = 0.040, and P = 0.035, resp.). Persons with dementia with personal-care dependency and spousal caregivers have more inpatient admissions; appropriate care environments should receive special care to reduce hospitalization. This study was part of a larger clinical trial; this trial is registered with ClinicalTrials.gov NCT00291161.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4058849PMC
http://dx.doi.org/10.1155/2014/861613DOI Listing

Publication Analysis

Top Keywords

predictors inpatient
12
utilization veterans
8
negative binomial
8
inpatient hospital
8
hospital utilization
8
predict hospitalization
8
multivariate logistic
8
logistic model
8
personal-care dependency
8
inpatient utilization
4

Similar Publications

A new model-based approach for estimating rural hospital markets.

J Rural Health

January 2025

North Carolina Rural Health Research Program, Cecil G. Sheps Center for Health Services Research, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.

Purpose: To provide a new approach for defining rural hospital markets.

Methods: First, we estimated models of hospital choice. We defined hospitals in the choice set using nationwide hospital data from the Healthcare Cost Report Information System (HCRIS).

View Article and Find Full Text PDF

Background: Pelvic ring and acetabular fractures are among the most complicated and severe injury patterns in orthopaedic trauma surgery. Inpatient treatment is not only costly but also very time-consuming. The aim of this study is to identify predictors leading to a prolonged length of hospital stay.

View Article and Find Full Text PDF

Background: Long-COVID research to date focuses on outcomes in non-hospitalised vs. hospitalised survivors. However Emergency Department attendees (post-ED) presenting with acute COVID-19 may experience less supported recovery compared to people admitted and discharged from hospital (post-hospitalised group, PH).

View Article and Find Full Text PDF

Heart failure (HF), a chronic and progressive disease, is increasing in prevalence worldwide and is associated with increased hospitalizations and death. Despite notable improvements in medical therapy for HF, patients are still at risk of future negative outcomes. Current guidelines recommend four classes of medication for treating patients with HF, deemed guideline-directed medical therapy (GDMT).

View Article and Find Full Text PDF

Background: Coronary Artery Bypass Grafting (CABG) is a high-risk surgery. Cardiovascular diseases are strongly associated with comorbidities. This study aimed to assess the prediction of in-hospital mortality by comorbidities in patients who underwent CABG.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!