Systematic Review and Meta-Analysis of Interventions to Improve Access and Coverage of Adolescent Immunizations
Bibliographic record
Abstract
Vaccination strategies are among the most successful and cost-effective public health strategies for preventing disease and death. Until recently, most of the existing immunization programs targeted infants and children younger than 5 years which have successfully resulted in reducing global infant and child mortality. Adolescent immunization has been relatively neglected, leaving a quarter of world's population underimmunized and hence vulnerable to a number of preventable diseases. In recent years, a large number of programs have been launched to increase the uptake of different vaccines in adolescents; however, the recommended vaccination coverage among the adolescent population overall remains very low, especially in low- and middle-income countries. Adolescent vaccination has received significantly more attention since the advent of the human papillomavirus (HPV) vaccine in 2006. However, only half of the adolescent girls in the United States received a single dose of HPV vaccine while merely 43% and 33% received two and three doses, respectively. We systematically reviewed literature published up to December 2014 and included 23 studies on the effectiveness of interventions to improve immunization coverage among adolescents. Moderate-quality evidence suggested an overall increase in vaccination coverage by 78% (relative risk: 1.78; 95% confidence interval: 1.41-2.23). Review findings suggest that interventions including implementing vaccination requirement in school, sending reminders, and national permissive recommendation for adolescent vaccination have the potential to improve immunization uptake. Strategies to improve coverage for HPV vaccines resulted in a significant decrease in the prevalence of HPV by 44% and genital warts by 33%; however, the quality of evidence was low. Analysis from single studies with low- or very low-quality evidence suggested significant decrease in varicella deaths, measles incidence, rubella susceptibility, and incidence of pertussis while the impact was nonsignificant for incidence of mumps with their respective vaccines. Further rigorous evidence is needed to evaluate the effectiveness of strategies to improve immunization uptake among adolescents from low- and middle-income countries.
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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 | 0.013 | 0.056 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.022 | 0.033 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".