Modifiable risk factors for diphtheria: A systematic review and meta-analysis
Bibliographic record
Abstract
Objective: To identify modifiable risk factors for diphtheria and assess their strengths of association with the disease. Methods: This review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement. Electronic databases and grey literature were searched from inception until January 2023. Studies had to report on diphtheria cases and estimates of association for at least one potential risk factor or sufficient data to calculate these. The quality of non-ecological studies was assessed using the Newcastle-Ottawa Scale (NOS), while the quality of evidence was evaluated using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) criteria. Results: The search yielded 37,705 papers, of which 29 were ultimately included. All the non-ecological studies were of moderate to high quality. Meta-analysis of 20 studies identified three factors increasing the risk of diphtheria: incomplete vaccination (<3 doses) (pooled odds ratio (POR) = 2.2, 95% confidence interval (CI) = 1.4-3.4); contact with a person with skin lesions (POR = 4.8, 95% CI = 2.1-10.9); and low knowledge of diphtheria (POR = 2.4, 95% CI = 1.2-4.7). Contact with a case of diphtheria; sharing a bed or bedroom; sharing utensils, cups, and glasses; infrequent bathing; and low parental education were associated with diphtheria in multiple studies. Evidence for other factors was inconclusive. The quality of evidence was low or very low for all the risk factors. Conclusions: Findings from the review suggest that countries seeking to control diphtheria need to strengthen surveillance, improve vaccination coverage, and increase people's knowledge of the disease. Future research should focus on understudied or inconclusive risk factors.
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 | 0.022 | 0.049 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.049 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".