Increasing pneumococcal vaccine uptake in older adults: a scoping review of interventions in high-income countries
Bibliographic record
Abstract
BACKGROUND: There is low uptake of the pneumococcal vaccination in eligible older adults, even in high-income countries that offer routine and universal vaccination programs. OBJECTIVE: To systematically characterize interventions aimed at improving pneumococcal vaccine uptake in older adults. DESIGN: We conducted a scoping review following PRISMA-SCr guidelines of five interdisciplinary databases: Medline-Ovid, Embase, CINAHL, PsychInfo, and Cochrane Library. Databases were searched from January 2015 until April 2020. The interventions were summarized into three pillars according to the European Union Conceptional Framework for Action: information campaigns, prioritization of vaccination schemes, and primary care interventions. RESULTS: Our scoping review included 39 studies that summarized interventions related to pneumococcal vaccine uptake for older adults, encompassing 2,481,887 study participants (945 healthcare providers and 2,480,942 older adults) across seven countries. Examples of interventions that were associated with increased pneumococcal vaccination rate included periodic health examinations, reminders and decision-making tools built into electronic medical records, inpatient vaccination protocols, preventative health checklists, and multimodal educational interventions. When comparing the three pillars, prioiritization of vaccination schemes had the highest evidence for improved rates of vaccination (n = 14 studies), followed by primary care interventions (n = 8 studies), then information campaigns (n = 5 studies). CONCLUSION: Several promising interventions were associated with improved outcomes related to vaccine uptake, although controlled study designs are needed to determine which interventions are most effective.
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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 | 0.022 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".