Purpose To determine the relationship between fine particulate matter (PM 2.5 ) and ocular outcomes such as visual impairment and age-related eye disease. Methods Baseline data were used from the Canadian Longitudinal Study on Aging. The Comprehensive Cohort consisted of 30,097 adults ages 45 to 85 years. Annual mean PM 2.5 levels (µg/m 3 ) for each participant's postal code were estimated from satellite data. Ozone, sulfur dioxide, and nitrogen dioxide levels were also estimated. Binocular presenting visual acuity was measured using a visual acuity chart. Intraocular pressure (IOP) was measured in millimeters of mercury using the Reichart Ocular Response Analyzer. Participants were asked about a diagnosis of glaucoma, macular degeneration, or cataract. Logistic and linear regression models were used. Results The overall mean PM 2.5 level was 6.5 µg/m 3 (SD = 1.8). In the single pollutant models, increased PM 2.5 levels (per interquartile range) were associated with visual impairment (odds ratio [OR] = 1.12; 95% confidence interval [CI], 1.02–1.24), glaucoma (OR = 1.14; 95% CI, 1.01–1.29), and visually impairing age-related macular degeneration (OR = 1.52; 95% CI, 1.10–2.09) after adjustment for sociodemographics and disease. PM 2.5 had a borderline adjusted association with cataract (OR = 1.06; 95% CI, 0.99–1.14). In the multi-pollutant models, increased PM 2.5 was associated with glaucoma and IOP only after adjustment for sociodemographics and disease (OR = 1.24; 95% CI, 1.05–1.46 and β = 0.24; 95% CI, 0.12–0.37). Conclusions Increased PM 2.5 is associated with glaucoma and IOP. These associations should be confirmed using longitudinal data and potential mechanisms should be explored. If confirmed, this work may have relevance for revision of World Health Organization thresholds to protect human health.
Purpose: Our goal was to explore the longitudinal association between vision-related variables and incident depressive symptoms in a community-dwelling sample of older adults and to examine whether sex, education, or hearing loss act as effect modifiers. Methods: A 3-year prospective cohort study was performed using data from the Canadian Longitudinal Study on Aging consisting of 30,097 individuals aged 45-85 years. Visual acuity was evaluated with habitual distance correction using an illuminated Early Treatment of Diabetic Retinopathy Study chart. Visual impairment was defined as binocular presenting visual acuity worse than 20/40. Incident depressive symptoms was defined using a cutoff score of 10 or greater on the Center for Epidemiologic Studies Depression scale. Participants were asked if they had ever had a physician diagnosis of age-related macular degeneration (AMD), glaucoma, or cataract. Multivariable Poisson regression was used. Results: Of 22,558 participants without depressive symptoms at baseline, 7.7% developed depressive symptoms within 3 years. Cataract was associated with incident depressive symptoms (relative risk = 1.20, 95% confidence interval 1.05, 1.37) after adjusting for age, sex, income, education, partner status, smoking, level of comorbidity, hearing loss, and province. Visual impairment, AMD, and glaucoma were not associated with incident depressive symptoms. No effect modification was detected. Conclusions: Our longitudinal data confirm that the risk of depressive symptoms is higher in those who report ever having a cataract. Further research should confirm this and interventions should be considered.
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Purpose: Confounding is an important problem in observational research. Improper modeling of the confounder will lead to residual confounding that may distort results and impact inferences. An example of this will be presented from research on age-related macular degeneration and depression. Methods: A 3-year prospective cohort study was performed using data from the Canadian Longitudinal Study on Aging consisting of 30,097 individuals aged 45-85 years. Incident depression was assessed using the Center for Epidemiologic Studies Depression scale. Participants were asked if they had ever had a physician diagnosis of age-related macular degeneration (AMD). Multivariable Poisson regression was used. Age was modeled in four ways including as a linear term, as a 4-category variable, as a spline, and as a polynomial. Models were compared using the Akaike's Information Criteria (AIC) with lower scores indicating better performance. Results: The point estimates and inferences differed depending on how age was modeled. Age had a J-shape relationship with the incidence of depression. The model with the lowest AIC was when age was entered as a categorical variable. When age was modeled in this way, AMD was not significantly associated with the incidence of depression (relative risk (RR) = 1.21, 95% Confidence Interval (CI) 0.97, 1.53). By contrast, when age was modeled as a linear term, AMD was significantly associated with the incidence of depression (RR = 1.28, 95% CI 1.02, 1.61). Conclusions: Researchers should clearly report their adjustment strategies and should be cautious when modeling the relationship between age and depression in order to minimize residual confounding.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.