Dataset Information

Association of Environmental Factors with Age-Related Macular Degeneration using the Intelligent Research in Sight Registry.

ABSTRACT:

Purpose

Investigate associations of natural environmental exposures with exudative and nonexudative age-related macular degeneration (AMD) across the United States.

Design

Database study.

Participants

Patients aged ≥ 55 years who were active in the IRIS Registry from 2016 to 2018 were analyzed. Patients were categorized as nonexudative, inactive exudative, and active exudative AMD by International Classification of Diseases 10^th Revision and Current Procedural Terminology (CPT) codes. Patients without provider-level ZIP codes matching any ZIP code tabulation area were excluded.

Methods

Environmental data were obtained from public sources including the US Geological Survey, National Renewable Energy Laboratory, National Oceanic and Atmospheric Administration, and Environmental Protection Agency. Multiple variable, mixed effects logistic regression models with random intercepts per ZIP code tabulation area quantified the association of each environmental variable with any AMD versus non-AMD patients, any exudative AMD versus nonexudative AMD, and active exudative AMD versus inactive exudative and nonexudative AMD using 3 separate models, while adjusting for age, sex, race, insurance type, smoking history, and phakic status.

Main outcome measure

Odds ratios for environmental factors.

Results

A total of 9 884 527 patients were included. Elevation, latitude, solar irradiance measured in global horizontal irradiance (GHI) and direct normal irradiance (DNI), temperature and precipitation variables, and pollution variables were included in our models. Statistically significant associations with active exudative AMD were GHI (odds ratio [OR], 3.848; 95% confidence interval [CI] with Bonferroni correction, 1.316-11.250), DNI (OR, 0.581; 95% CI, 0.370-0.913), latitude (OR, 1.110; 95% CI, 1.046-1.178), ozone (OR, 1.014; 95% CI, 1.004-1.025), and nitrogen dioxide (OR, 1.005; 95% CI, 1.000-1.010). The only significant environmental associations with any AMD were inches of snow in the winter (OR, 1.005; 95% CI, 1.001-1.009) and ozone (OR, 1.011; 95% CI, 1.003-1.019).

Conclusions

The strongest environmental associations differed between AMD subgroups. The solar variables GHI, DNI, and latitude were significantly associated with active exudative AMD. Two pollutant variables, ozone and nitrogen dioxide, also showed positive associations with AMD. Further studies are warranted to investigate the clinical relevance of these associations. Our curated environmental dataset has been made publicly available at https://github.com/uw-biomedical-ml/AMD_environmental_dataset.

SUBMITTER: Hunt MS

PROVIDER: S-EPMC9754968 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Association of Environmental Factors with Age-Related Macular Degeneration using the Intelligent Research in Sight Registry.

Hunt Matthew S MS Chee Yewlin E YE Saraf Steven S SS Chew Emily Y EY Lee Cecilia S CS Lee Aaron Y AY Manookin Michael B MB

Ophthalmology science 20220712 4

<h4>Purpose</h4>Investigate associations of natural environmental exposures with exudative and nonexudative age-related macular degeneration (AMD) across the United States.<h4>Design</h4>Database study.<h4>Participants</h4>Patients aged ≥ 55 years who were active in the IRIS Registry from 2016 to 2018 were analyzed. Patients were categorized as nonexudative, inactive exudative, and active exudative AMD by International Classification of Diseases 10<sup>th</sup> Revision and Current Procedural Te ...[more]

PMID: 36531573

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Dataset Information

Association of Environmental Factors with Age-Related Macular Degeneration using the Intelligent Research in Sight Registry.

Purpose

Design

Participants

Methods

Main outcome measure

Results

Conclusions

Publications

Association of Environmental Factors with Age-Related Macular Degeneration using the Intelligent Research in Sight Registry.

Similar Datasets

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