The Impact of Vision Impairment on Self-Reported Falls Among Older US Adults: Cross-Sectional and Longitudinal Study
Bibliographic record
Abstract
Background: Falls are the leading cause of injury among older adults, with vision impairment recognized as a significant risk factor. However, many existing studies have been limited by small sample sizes, retrospective designs, or insufficient adjustment for confounding factors. To overcome these limitations, we used data from the University of Michigan's Health and Retirement Study (HRS) to analyze the association between self-reported vision and fall risk among older adults in a large, nationally representative sample. Objective: The objective of this study was to investigate the association between vision impairment and falls and assess whether subjective vision impairment predicts future falls in older adults. Methods: This cross-sectional and longitudinal analysis used data from the HRS (1996-2020) to assess the relationship between self-reported vision, glaucoma history, and falls among US adults aged 65 years and older. HRS uses a biennial, multistage area probability sample survey design, collecting data with community-dwelling individuals followed up every 2 years until death, tracking health, economic, and social outcomes. Multivariate logistic regression was used to analyze associations between self-reported vision and self-reported falls in the past 2 years. Results: A total of 38,835 respondents contributed 117,834 observations. The weighted proportion of participants reporting falls was 37.9% (95% CI 37.7%-40.1%). Significant risk factors for falls included overall eyesight impairment (adjusted odds ratio [aOR] 1.36, 95% CI 1.20-1.56), distance vision impairment (aOR 1.37, 95% CI 1.32-1.42), near vision impairment (aOR 1.33, 95% CI 1.27-1.37), and glaucoma (aOR 1.15, 95% CI 1.07-1.24). A similar association was observed for serious falls, where overall eyesight impairment (aOR 1.20, 95% CI 1.03-1.44), distance vision impairment (aOR 1.14, 95% CI 1.07-1.22), near vision impairment (aOR 1.12, 95% CI 1.05-1.18), and glaucoma (aOR 1.15, 95% CI 1.05-1.26) were significant. In longitudinal analyses, overall vision impairment (aOR 1.23, 95% CI 1.16-1.29), distance vision impairment (aOR 1.27, 95% CI 1.20-1.38), near vision impairment (aOR 1.23, 95% CI 1.19-1.32), and glaucoma (aOR 1.25, 95% CI 1.13-1.37) increased the risk of future falls. Reported overall vision was significantly associated with the number of falls in both the same (P<.001) and subsequent (P<.001) survey cycles. Conclusions: Both distance and near vision impairment, as well as glaucoma, are associated with a higher risk of falls in older adults and present possible areas for intervention and prevention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".