A systematic review of risks associated with environmental change on refractive and non-refractive ocular health: Special focus on Africa
Bibliographic record
Abstract
Background Over the past two decades, the African continent has faced numerous environmental shifts that affect population health. Climate change, rapid urbanization, and air pollution contribute significant risks to human health. The impact of these shifts on refractive and non-refractive ocular health in Africa remains largely empirically undocumented. Objectives A systematic review of the risks associated with environmental factors on refractive and non-refractive ocular health, with a specific emphasis on the African context. Methods This systematic review over the recent decade, conforming to PRISMA guidelines, spanned two primary databases, PubMed, and Google Scholar. It included a combination of keywords related to ocular health, environmental change, air pollution, climate change, water quality, and other related concepts. In total, it captured 77 articles from 2013 to July 2023 that met review quality guidelines. Results The review comprised varied study designs with a notable inclusion of cross-sectional (25·9%), cohort (14·3%), and review articles (36·4%). Findings indicated a significant correlation between air pollutants like PM 2·5 and NO x with ocular diseases such as dry eye and ocular surface disorders (16·8%), conjunctival disorders (7·8%), and myopia (5·2%). Climate change exacerbated by rising temperatures and UV radiation was implicated in 39% of studies, with a specific focus on its relation to cataracts (5·2%) and retinal-related disorders (10·4%). Additionally, indoor air pollution disproportionately affected women and children in rural settings of Africa. Conclusions The data indicate the need for urgent continental and regional policies against air pollution and climate change to safeguard ocular health, especially among vulnerable African populations. The review underscores the need for interdisciplinary policy to address challenges. The documentation of the relationship between environmental factors and ocular health intersects with Sustainable Development Goals that emphasize the need for improved preventive eye care and intervention, particularly among vulnerable populations and rural inhabitants.
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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 | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.016 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".