Baseline Attitudes About Prostate Cancer Screening Moderate the Impact of Decision Aids on Screening Rates.

Ann Behav Med

Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, 3300 Whitehaven Street, NW, Suite 4100, Washington, DC, 20007, USA.

Published: October 2015

Background: The impact of decision aids on prostate cancer screening outcomes has been inconsistent.

Purpose: We assessed whether pre-existing attitudes moderated the impact of decision aids on screening.

Methods: Men aged 45-70 (56.2% Caucasian, 39.9% African-American) were randomly assigned to a print decision aid (N = 630), a web decision aid (N = 631), or usual care (N = 632). Telephone interviews assessed pro/con screening attitudes and screening behaviors at baseline, 1-month and 13-months post-randomization.

Results: Logistic regression analyses revealed significant arm by attitude interactions: Higher baseline cons scores predicted lower screening in the print (OR = 0.60 (95% CI: 0.40, 0.92)) and web (OR = 0.61 (95% CI: 0.40, 0.91)) arms but not in usual care (OR = 1.34 (95% CI: 0.90, 2.00)).

Conclusions: The decision aids amplified the impact of men's baseline attitudes about limitations of screening: Compared to the usual care arm, men in both decision aid arms were less likely to be screened when they perceived more limitations of screening.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4959888PMC
http://dx.doi.org/10.1007/s12160-015-9692-5DOI Listing

Publication Analysis

Top Keywords

decision aids
16
impact decision
12
decision aid
12
usual care
12
baseline attitudes
8
prostate cancer
8
screening
8
cancer screening
8
95% 040
8
limitations screening
8

Similar Publications

Background: Endometriosis is a chronic disease characterized by endometrial-like tissue outside the uterus. Superficial endometriosis (SE) is the most prevalent form, yet it remains underdiagnosed due to subtle clinical and imaging presentations. Traditionally, diagnosis relies on laparoscopy, which is relatively invasive and often contributes to diagnostic delay.

View Article and Find Full Text PDF

Developing a decision support tool to predict delayed discharge from hospitals using machine learning.

BMC Health Serv Res

January 2025

Department of Industrial Engineering, Dalhousie University, PO Box 15000, Halifax, B3H 4R2, NS, Canada.

Background: The growing demand for healthcare services challenges patient flow management in health systems. Alternative Level of Care (ALC) patients who no longer need acute care yet face discharge barriers contribute to prolonged stays and hospital overcrowding. Predicting these patients at admission allows for better resource planning, reducing bottlenecks, and improving flow.

View Article and Find Full Text PDF

Background: New indicators of potential human immunodeficiency virus (HIV) transmission are being actively explored. We aim to categorical testing of the viral load (VL) of persons living with HIV (PLWH) in order to explore new indicators to measure the intensity of the epidemic and the effectiveness of the response in the community.

Methods: A dynamic cohort study was conducted in Yining to monitor the VL of all persons living with HIV from 2017 to 2019.

View Article and Find Full Text PDF

Knee osteoarthritis (KOA) represents a progressive degenerative disorder characterized by the gradual erosion of articular cartilage. This study aimed to develop and validate biomarker-based predictive models for KOA diagnosis using machine learning techniques. Clinical data from 2594 samples were obtained and stratified into training and validation datasets in a 7:3 ratio.

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!