Glaucoma Control Strategies in Sub-Saharan Africa: A Review of the Clinical and Health Economic Evidence
Bibliographic record
Abstract
Purpose: A review of the effectiveness, costs, and cost-effectiveness of detection and treatment strategies for glaucoma control in Sub-Saharan Africa (SSA) was conducted.Methods: Detailed searches were performed using the Ovid Medline, Ovid Embase, The Cochrane Library, Web of Science, Scopus, and LILACS databases up to September 2016. The key Medical Subject Heading search terms used included glaucoma, diagnosis, treatment, effectiveness, costs, cost-effectiveness, and Sub-Saharan Africa. Effectiveness was measured as the proportion of study participants with an intra-ocular pressure less than or equal to 22 mmHg.Results: A total of 5658 records were examined with 48 papers identified. The sensitivity and specificity of portable instruments or smartphone technologies to detect glaucomatous changes ranged from 58.3% to 93.8% and from 82.4% to 96.8%, respectively. The overall effect size for various glaucoma interventions was: 0.39 (95% confidence interval (CI) 0.27–0.54, I2 = 64.85, p = 0.036) for laser trabeculoplasty; 0.56 (95% CI 0.23–0.84, I2 = 85.74, p = 0.001) for drainage implant devices; 0.66 (95% CI 0.61–0.71, I2 = 0.00, p = 0.402) for medical management; and 0.73 (95% CI 0.65–0.80, I2 = 93.25, p = 0.000) for all other non-drainage tube surgical interventions, including trabeculectomy surgery and the use of anti-metabolites. The mean annual cost of anti-glaucoma drugs across SSA was USD 394, with a mean direct non-medication cost per year of USD 54, and a mean surgical cost per year of USD 283.Conclusions: While effective glaucoma control interventions exist, their widespread use and diffusion across SSA remain challenging principally due to low per capita income levels and high glaucoma treatment costs.
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 | 0.013 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".