ABSTRACT
OBJECTIVES: The purpose of this study was to examine the factors associated with vision impairment (VI), age-related eye disease (ARED), and frequency of eye examinations among older adults. METHODS: A cross-sectional study (N = 166) was designed to identify barriers in vision and eye care services among adults 50 years and older in four counties in Appalachian Tennessee. Surveys were administered in March 2023. Simple and multiple logistic regression analyses were used to determine the risk factors of VI and ARED and the frequency of eye examinations. RESULTS: In two out of the three regression models, predictors such as traveling >10 mi to an eye care provider, barriers to eye care, and a lack of exposure to eye health information emerged as significant factors. Individuals who traveled >10 mi to an eye care provider were more than twice as likely than individuals who traveled shorter distances to have VI and not maintain routine eye care (adjusted odds ratio [AOR] 2.69, 95% confidence interval [CI] 1.08-6.75; AOR 2.82, 95% CI 1.05-7.55, respectively). Reporting barriers to eye care doubled the odds of ARED (AOR 2.33, 95% CI 1.02-5.34) and substantially increased the odds of reporting a 3-year or longer interval since the last eye examination (AOR 7.45, 95% CI 1.85-29.96) compared with having no barriers to eye care. Moreover, limited exposure to eye health information tripled the odds of VI (AOR 3.26, 95% CI 1.15-9.24) and not maintaining routine eye care (AOR 3.07, 95% CI 0.97-9.70) compared with more exposure to eye health information. Other predictors also were uncovered in the analysis. CONCLUSIONS: This study contributes to the scarce literature on risk factors associated with vision health among older adults in Appalachia.
Subject(s)
Vision Disorders , Humans , Tennessee/epidemiology , Male , Female , Aged , Cross-Sectional Studies , Middle Aged , Vision Disorders/epidemiology , Risk Factors , Health Services Accessibility/statistics & numerical data , Aged, 80 and over , Eye Diseases/epidemiology , Surveys and QuestionnairesABSTRACT
Subtle blood-brain barrier (BBB) disruption is involved in numerous neurological conditions. This disruption is found diffusely in the brain and requires quantitative methods for assessment. We propose a statistical method to identify individual voxels where the BBB is disrupted using T1-weighted MRI. We used models of severe and focal vs. mild and generalized disruption of the BBB to show proof of principle with the cold injury model, hypoxia, and a model of inflammation using low- and high-dose lipopolysaccharide (LPS) treatment. Using voxel-based analysis, we found that mild hypoxia resulted in diffuse disruption of the BBB, whereas more severe hypoxia and high-dose LPS treatment resulted in prominent leakage, particularly in the periventricular area, suggestive of blood-cerebrospinal fluid (CSF) barrier disruption. Our data suggest that the periventricular area may be compromised first in conditions of inflammation and hypoxia. Voxel-based analysis could be used in future studies assessing subtle blood-CSF or BBB disruption.
Subject(s)
Blood-Brain Barrier/metabolism , Brain/metabolism , Cerebrospinal Fluid/metabolism , Cold Injury/metabolism , Hypoxia/metabolism , Inflammation/metabolism , Animals , Blood-Brain Barrier/diagnostic imaging , Blood-Brain Barrier/drug effects , Blood-Brain Barrier/pathology , Brain/drug effects , Brain/pathology , Cerebrospinal Fluid/diagnostic imaging , Cerebrospinal Fluid/drug effects , Cold Injury/diagnostic imaging , Cold Injury/pathology , Contrast Media , Disease Models, Animal , Gadolinium , Hypoxia/diagnostic imaging , Hypoxia/pathology , Inflammation/chemically induced , Inflammation/diagnostic imaging , Inflammation/pathology , Lipopolysaccharides/pharmacology , Magnetic Resonance Imaging , Male , Rats , Rats, WistarABSTRACT
Decision analysis has become an increasingly popular decision-making tool with a multitude of clinical applications. Incorporating patient and expert preferences with available literature, it allows users to apply evidence-based medicine to make informed decisions when confronted with difficult clinical scenarios. A decision tree depicts potential alternatives and outcomes involved with a given decision. Probabilities and utilities are used to quantify the various options and help determine the best course of action. Sensitivity analysis allows users to explore the uncertainty of data on expected clinical outcomes. The decision maker can thereafter establish a preferred method of treatment and explore variables which influence the final clinical outcome. The present paper reviews the technique of decision analysis with particular focus on its application to clinical decision making.