Prevalence, risk factors and burden of diabetic retinopathy in China: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Diabetic retinopathy (DR), the primary retinal vascular complication of diabetes mellitus (DM), is a leading cause of vision impairment and blindness in working-age population globally. Despite mounting concerns about the emergence of DM as a major public health problem in the largest developing country, China, much remains to be understood about the epidemiology of DR. We aimed to investigate the prevalence of and risk factors for DR, and estimate the burden of DR in China in 2010. METHODS: China National Knowledge Infrastructure (CNKI), Wanfang, Chinese Biomedicine Literature Database (CBM-SinoMed), PubMed, Embase and Medline were searched for studies that reported the prevalence of and risk factors for DR in Chinese population between 1990 and 2017. A random-effects meta-analysis model was adopted to pool the overall prevalence of DR. Variations in the prevalence of DR in different age groups, DM duration groups and settings were assessed by subgroup meta-analysis and meta-regression. Odds ratios (ORs) of major risk factors were pooled using random-effects meta-analysis. The number of people with DR in 2010 was estimated by multiplying the age-specific prevalence of DR in people with DM with the corresponding number of people with DM in China. Finally, the national number of people with DR was distributed into six geographic regions using a risk factor-based model. RESULTS: A total of 31 studies provided information on the prevalence of DR and 21 explored potential risk factors for DR. The pooled prevalence of any DR, nonproliferative DR (NPDR) and proliferative DR (PDR) was 1.14% (95% CI = 0.80-1.52), 0.90% (95% CI = 0.56-1.31) and 0.07% (95% CI = 0.02-0.14) in general population; In people with DM, the pooled prevalence rates were 18.45% (95% CI = 14.77-22.43), 15.06% (95% CI = 11.59-18.88) and 0.99% (95% CI = 0.40-1.80) for any DR, NPDR and PDR, respectively. The prevalence of any DR in DM patients peaked between 60 and 69 years of age, and increased steeply with the duration of DM. DM patients residing in rural China were at a higher risk to have DR than those in urban areas. In addition, insulin treatment, elevated FBG level and higher HbA1c concentration were confirmed to be associated with a higher prevalence of DR in people with DM, with meta-ORs of 1.99 (95% CI = 1.34-2.95), 1.33 (95% CI = 1.12-1.59) and 1.15 (95% CI = 1.09-1.20) respectively. In 2010, a total of 13.16 million (95% CI = 8.95-18.00) Chinese aged 45 years and above were living with DR, among whom the most were in South Central China and the least were in Northwest China. CONCLUSIONS: DR has become a serious public health problem in China. Optimal screening of and interventions on DR should be implemented. Improved epidemiological studies on DR are still required.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.034 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".